Environmental Change and Tradeoffs in Freshwater Ecosystem Services

ENVIRONMENTAL CHANGE AND TRADEOFFS IN FRESHWATER ECOSYSTEM SERVICES:

NILE TILAPIA (OREOCHROMIS NILOTICUS) INTRODUCTION TO THE KAFUE RIVER, ZAMBIA

A Dissertation

Submitted to the Graduate School

of the University of Notre Dame

in Partial Fulfillment of the Requirements

for the Degree of

Doctor of Philosophy

Andrew M. Deines

David M. Lodge, Director

Graduate Program in Biological Sciences

Notre Dame, Indiana

April 2013

ENVIRONMENTAL CHANGE AND TRADEOFFS IN FRESHWATER ECOSYSTEM SERVICES:

NILE TILAPIA (OREOCHROMIS NILOTICUS) INTRODUCTION TO THE KAFUE RIVER, ZAMBIA

Abstract

Andrew M. Deines

Global environmental change is putting increasing demands on freshwater resources. Three of the greatest threats to freshwater ecosystems are invasive species, over exploitation, and flow modification. These threats are manifestations of the human use of freshwater ecosystem services that illustrate the tradeoffs in services which will become more common. These drivers of environmental change all provide some set of ecosystem services while also reducing the provision of other services, making them excellent examples from which to draw guidance for future management.

The introduction of the freshwater fish Nile tilapia (Oreochromis niloticus) to the

Kafue River, Zambia provides the motivation for the four studies presented here; each explores a particular tradeoff in ecosystem services that results from species invasions, overexploitation, and flow modification. A review of global tilapia introductions demonstrates that the ecological effects of tilapia invasion are ubiquitous, but Andrew M. Deines perceptions of whether tilapia positively or negatively affect social well-being is dependent on socioeconomic background. I also show the introduction of Nile tilapia to the Kafue River has decreased genetic diversity and threatens the long-term production of both aquaculture and capture fisheries. I evaluate the value of the Kafue River fishery by modeling the tradeoff between fisheries production and hydropower generation, as imposed by dam-induced flow modification, and find that annual fishery production is

$USD 7 million per year, but this production is not affected by flow modification.

Finally, I consider how the harvest of invasive species may contribute to conservation goals, and demonstrate that market forces alone are unlikely to substantially reduced invasive populations because the value of harvest may supplant the value of other ecosystem services.

Overall, I break new ground on the extent and impacts of global tilapia introductions, the relationship between flow modification and fisheries production in a novel context, and the ongoing social and economic adaptation to species invasions.

This research provides managers, policy makers and stakeholders new analyses and tools with which to better inform future decisions about tradeoffs in ecosystem services that accompany major global environmental changes.

For Jill, my family, and friends who have led me to water

CONTENTS

Figures ...... vi

Tables ...... xi

Chapter 1: Introduction ...... 1 1.1 Drivers of change in freshwater ecosystem services ...... 3 1.1.1 Fisheries harvest ...... 4 1.1.2 Flow modification ...... 7 1.1.3 Invasive species ...... 10 1.2 Dissertation outline ...... 12 1.2.1 Tradeoffs among ecosystem services associated with global tilapia introductions ...... 13 1.2.2 Hybridization of native Oreochromis species (Cichlidae) and the introduced Nile tilapia (O. niloticus) in the Kafue River, Zambia. . 14 1.2.3 The potential tradeoff between artisanal fisheries production and hydroelectricity generation on the Kafue River, Zambia ...... 16 1.2.4 Can Market-Driven Harvest of Invasive Species Contribute to Conservation Goals? ...... 17 1.2.5 Conclusion ...... 18 1.3 References ...... 19

Chapter 2: Tradeoffs among ecosystem services associated with global tilapia introductions ...... 28 2.1 Abstract ...... 28 2.2 Introduction ...... 29 2.3 Methods ...... 35 2.3.1 Literature review ...... 35 2.3.2 Analyses ...... 40 2.4 Results ...... 44 2.4.1 Proportion of introductions associated with ecological impacts .... 48 2.4.2 Presence or absence of ecological effects as a function of effect type, species, region, and study design ...... 50

iii

2.4.3 Perceptions of tradeoffs among ecosystem services resulting from tilapia ...... 51 2.5 Discussion ...... 59 2.6 Conclusion ...... 63 2.7 Acknowledgments ...... 65 2.8 References ...... 65

Chapter 3: Hybridization of native Oreochromis species (Cichlidae) and the introduced Nile tilapia (O. niloticus) in the Kafue River, Zambia...... 72 3.1 Abstract ...... 72 3.2 Introduction ...... 73 3.3 Methods ...... 76 3.3.1 Site description ...... 76 3.3.2 Sampling ...... 79 3.3.3 Analysis ...... 81 3.4 Results ...... 86 3.4.1 Microsatellite data quality ...... 87 3.4.2 Evidence for hybridization ...... 89 3.5 Discussion ...... 98 3.6 Conclusion ...... 104 3.7 Acknowledgments ...... 106 3.8 References ...... 106

Chapter 4: The potential trade-off between artisanal fisheries production and hydroelectricity generation on the Kafue River, Zambia ...... 112 4.1 Abstract ...... 112 4.2 Introduction ...... 113 4.3 Methods ...... 117 4.3.1 Site description ...... 117 4.3.2 Data compilation...... 119 4.3.3 Modeling the impact of flood regime on fishery production ...... 124 4.3.4 Sensitivity analysis ...... 127 4.3.5 Simulating flood regime and hydroelectric generating capacity ... 128 4.3.6 Harvest Revenue ...... 129 4.4 Results ...... 130 4.4.1 The Impact of Flood Regime on Fishery Production ...... 131 4.4.2 The Effect of Flood Regime on Hydroelectric Generating Capacity140 4.5 Discussion ...... 140 4.6 Conclusion ...... 148 4.7 Acknowledgments ...... 150 4.8 References ...... 150

Chapter 5: Can Market-Driven Harvest of Invasive Species Contribute to Conservation Goals? ...... 156 iv

5.1 Abstract ...... 156 5.2 Introduction ...... 157 5.3 A bioeconomic framework ...... 161 5.3.1 Step 1: Market driven reduction in populations ...... 163 5.3.2 Step 2: Reduction of impacts to ecosystem services ...... 166 5.3.3 Step 3: Increasing demand for invasive species ...... 169 5.3.4 Alternative management scenarios ...... 172 5.4 Discussion ...... 186 5.4.1 A research agenda and tools for managers ...... 186 5.5 Conclusion ...... 191 5.6 Acknowledgments ...... 193 5.7 References ...... 193

Chapter 6: Conclusion ...... 198 6.2 Tradeoffs among ecosystem services associated with global tilapia introductions ...... 200 6.3 Hybridization of native Oreochromis species (Cichlidae) and the introduced Nile tilapia (O. niloticus) in the Kafue River, Zambia...... 201 6.4 The potential tradeoff between artisanal fisheries production and hydroelectricity generation on the Kafue River, Zambia ...... 202 6.5 Can Market-Driven Harvest of Invasive Species Contribute to Conservation Goals? ...... 203 6.6 Concluding remarks ...... 204 6.7 References ...... 206

Appendix A: Supplement to Chapter 2:Tradeoffs among ecosystem services associated with global tilapia introductions ...... 209

Appendix B: Supplement to Chapter 4: The potential trade-off between artisanal fisheries production and hydroelectricity generation on the Kafue River, Zambia283

Appendix C: Supplement to Chapter 5: Can Market-Driven Harvest of Invasive Species Contribute to Conservation Goals? model description ...... 318

FIGURES

Figure 1.1. Conceptual diagram of (left) major services provided by freshwater ecosystems, (right) major drivers of global environmental change in freshwaters and (middle) abbreviated titles of chapters addressing tradeoffs in ecosystem services which arise from their interactions considered herein. These tradeoffs are used to quantify the ecological and economic affect these drivers have on the provisions of ecosystem services and well-being...... 3

Figure 2.1 (A) Map of the global distribution of reports on the ecological effects of tilapia introduction by focal species in each publication. Locations were mapped to specific waterbodies when possible, or placed in the geographic center of the country or state/province where the study took place and offset to limit overlap.46

Figure 2.1. (B) Map of reported ecological effects (solid symbols) and mechanisms (hollow symbols) by region. Numbers within symbols indicate the total number of studies reporting that effect or mechanism, while the number in parenthesis indicates the number of studies which report that effect and use quantitative data, and controls or reference treatments...... 47

Figure 2.2. The proportion of countries reporting ecological effects of tilapia introduction across all effect types by all species combined and by important species arranged in order of increasing power of inference in attributing ecological impacts to tilapia (see Methods for complete description of the bins, which represent increasing research effort). The numbers above the bars are the number of countries reporting ecological impacts of tilapia...... 49

Figure 2.3. The number of papers categorized by perception of tilapia impact on ecosystem services for (a) particular species of tilapia and region; (b) different study designs (all reports compared to reports with quantitative data and controls); (c) and year of publication. Significant effects are indicated by (*) the best or (**) the second best ordinal regression model. O.aur=O. aureus, O.moss=O. mossambicus, O.nil=O. niloticus, Oreo= All other Oreochromis species, T.mar=T. mariae, T.S.=All other Tilapia of Sarotherodon species, T.zil=T. zilli, Afr=Africa, Eur=Europe, NeoTro=Neotropics, Ocn=Oceania, SrLka= Sri Lanka.53

Figure 2.4. The number of papers categorized by perception of tilapia impact on ecosystem services and the reported occurrence of ecological impacts in the same papers (including all impact types, species and regions) (a) for all papers and (b) only for papers that consider quantitative data and include a control. .. 56

Figure 2.5. Tilapia production as percent GDP of countries for the year when tilapia research was published, arranged in order of increasing power of inference (i.e., increasing bin number) and indicating the number of papers in each bin...... 57

Figure 2.6. Papers categorized by the original authors’ perception of the impact of tilapia on ecosystem services plotted as a function of the percentage of each country’s GDP that was derived from tilapia production at the time of publication of the original papers...... 58

Figure 3.1. The Kafue Floodplain fishery region in Zambia, Southern Africa (inset). (a) sampling sites from left to right: S1c=Itezhi-tezhi, the control site; S2= Mutukuzhi, S3=Namwala, S4=Chunga Lagoon, S5=Mazabuka (site of original introduciton), S6=Chinyanya, S7=Kasaka. (b) Total collection composition with putative field species identifications from gillnets and fishermen for O.nil=O. niloticus, O. mac= O. macrochir, O. ander= O. andersonii and putative Hybrids and (c) the % catch per unit effort of these species from gillnet sampling in number meter-1 night-1...... 78

Figure 3.2. Large allele dropout tested via excess small-allele homozygotes. No significant negative trend was found by linear regression for (o, solid line) min, (Δ, dash line) median, (+, dot line) max, or (x, dash-dot line) range of allele sizes in base pairs for all loci...... 92

Figure 3.3. Probability of population assignment of putative parental-types collected outside the hybrid zone. The top bar shows the a priori species identifications where O.and & O. mac= morphologically pure native O. andersonii and O. macrochir, respectively, collected from outside area of potential hybridization, and nil1-nil4 are the four replicates of the putative parental O. niloticus sample representing genotypes of introduced Nile tilapia into the Kafue River. Vertical bars are the mean posterior probability of cluster assignment from 10 replicate model runs. The color of posterior assignments correspond to Figure 3.1. (a) Naïve population assignment and (b) assignment specifying prior population based on sampling locations...... 94

morphologically pure native O. andersonii and O. macrochir, respectively, collected from outside the area of potential hybridization; rO.nil = replicates of O. niloticus representing genotypes of introduced Nile tilapia into the Kafue River; and S3-7 are the putative hybrids ordered geographically by sample sites progressing east to west as in Figure 3.1...... 96

Figure 3.5. NEWHYBRIDS identification of hybrid individuals by probability of classification into the specified genotype classes where F1= first generation hybrid, F2=offspring of two F1 parents, and Bx1= backcross of an F1 hybrid to either O. andersonni or O. macrochir and Bx2= backcross to O. niloticus.. (a) O. andersonii and O. niloticus parental populations used to identify hybrid individuals and (b) O. macrochir and O. niloticus parental populations used to identify putative hybrid individuals. The top bars show the a priori species identifications as in Figure 3.4 except O.nil = putative parental O. niloticus. S3-S7 = putative hybrid populations arranged from sampling sites east to west as in Figure 3.1...... 97

Figure 4.1. Map of the Kafue River, Zambia, in southern Africa (insert) focusing on the locations used to describe the fishery and hydrological regime from the upstream Hook Bridge and Itezhi-tezhi dam, through the Kafue floodplain fishery area (grey hatched), and to the downstream Kafue Gorge dam and power station. Fishery sampling locations (▲) included in this analysis are from west to east: Namwala, Maala, Chunga Lagoon, Nyimba, Mazabuka, and Chinyanya. .. 118

Figure 4.3. Hydrograph modeling results. (A) Simulated “no-dam” mean monthly discharge in cubic metres per second at Itezhi-tezhi and (B) the canonical correlation analysis scores which represent a flow index which best discriminates hydrographs before and after dam construction (●) and the simulated “no-dam” CCA scores (■). The first vertical dashed line indicates the date of closure of Kafue Gorge dam (downstream), while the second indicates the closure of Itezhi- tezhi dam (upstream)...... 131

Figure 4.5. Averaged simulated seasonal hydrographs at Kafue Gorge with (light grey) and without (black) Itezhi-tezhi dam. Dashed lines represent the dry-season generating capacity in each scenario, corresponding to 254 m3/s (888 MW) and 203 m3/s (770 MW) with and without Itezhi-tezhi, respectively. Hashed areas represent the differences in turbine flow during the low water season...... 141

rate (vertical dotted lines determined in A), and are translated in (B) into ecosystem service values for curve ii (horizontal dotted lines, arrows, in B). Population and market parameters used are: a=10.55, b=13500, r= 1.2, K=58200, c=2.13, q=2.9e-4...... 164

Figure 5.2. Equilibrium population biomass as a function of demand for invasive species under different market scenarios. Demand is increased by manipulating the maximum quantity of harvest demanded (parameter b) from 0% to 370% of the estimated demand for the hypothetical crayfishery. All other parameters as in Figure 5.1...... 171

Figure 5.3. Percent of ecosystem service recovery that results from increasing maximum quantity demanded (parameter b) via market-driven harvests in (a-d) Open- access, (e-h) monopoly and (i-l) pure competition scenarios...... 172

Figure 5.4. Mean equilibrium population size in the amenity value scenario with increasing demand by both maximum price (parameter a) and maximum demand quantity (parameter b). The maximum value of recovered ecosystem services, v, increases down the rows (illustrative values 0.01x, 1x, and 10x relative to the maximum value crayfish harvest, a*K). The four density-impact curves (Figure 5.1B) are presented across columns...... 181

Figure 5.5. Percent recovery of ecosystem service values in the amenity-value scenario with increasing demand by both maximum price (a) and maximum quantity(b). The maximum value of recovered ecosystem services, v, increases down the rows (illustrative values 0.01x, 1x, and 10x relative to the maximum value crayfish harvest, a*K). The four density-impact curves (Figure 5.1B) are presented across columns...... 183

Figure 5.6. Net social welfare (i.e., the difference between the value of restored ecosystem services and the subsidy required to pay for additional harvests not supported by sales of catch) with increasing demand by both maximum price (a) and maximum quantity (b). The maximum value of recovered ecosystem services, v, increases down the rows (illustrative values 0.01x, 1x, and 10x relative to the maximum value crayfish harvest, a*K). The four density-impact curves (Figure 5.1B) are presented across columns. A zero-isocline marks the value of ecosystem service restoration that could completely pay for harvests at that level...... 185

Figure 6.1. Conceptual diagram of (left) major services provided by freshwater ecosystems and (right) major drivers of global environmental change in freshwaters and (middle) abbreviated titles of chapters addressing tradeoffs in ecosystem services which arise from their interactions considered herein. The

boxes bordered by red dashes illustrate the services and drivers considered throughout this dissertation...... 199

Figure A. 1. The frequency of reviewed publications in each year...... 247

Figure C. 1. Intersection of the growth (dashed, Eq. 14) and harvest (solid, Eq. 13) functions, and equilibrium population biomass (dotted, Eq. 15). The parameters used to construct this figure are as in Figure 5.1...... 323

Figure C. 2.Examples of Density-Recovery curves. The same parameters are used here as in (Yokomizo et al. 2009) Fig 1. i) u=0,b=0.1; ii) u=0.5,b=0.1; iii) u=1,b=1; iv) u=1,b=0.1. The ecosystem service value on the y-axis is purely arbitrary in this figure...... 325

Figure C. 3. The Open Access equilibrium population biomass at varying levels of demand both in max price willing to pay (“a”, increasing over the panels) and by total amount that could be cleared (“b”, the x axis)...... 332

Figure C. 4. Long-term Monopoly population biomass equilibriums over varying levels of demand by “a”-( over the panels) and “b”, the x-axis...... 333

Figure C. 5. The equilibrium population biomass of pure competition (price-taker) harvest over a range of fixed prices determined by sifting the demand curve by a (by panels) and b (x-axis). The equilibrium population size is the MEY...... 334

TABLES

Table 2.1. The best ordinal regression models of the perceived benefit of tilapia introduction for all ecological effect categories combined...... 55

Table 3.1 Details of microsatellite loci...... 82

Table 3.2 Summary statistics for sampling and genetic variability at microsatellite loci for populations sampled...... 90

Table 3.3. Summary of ΔK for each of the describes STRUCTURE analyses...... 93

Table 4.1. Results of model selection and parameter estimates ...... 133

Table 5.1. Potential effects of harvesting invasive species on the invasion process .... 160

Table A. 1 List of Literature dervied data and references included in Chapter 2: Tradeoffs among ecosystem services associated with global tilapia introductions ...... 210

Table A. 2. The logistic regression models of ecological effects of tilapia introduction for all ecological effect categories combined Model selection, control and year parameters ...... 280

Table A. 3. The logistic regression models result of ecological effects of tilapia introduction for all ecological effect categories combined Species results ...... 281

Table A. 4 The logistic regression models result of ecological effects of tilapia introduction for all ecological effect categories combined Region results ...... 282

Table B. 1 Mesh sizes and sampling locations on the Kafue River, Zambia ...... 294

Table B. 2. Fish species reported in each dataset compiled ...... 307

Table B. 3. Full modeling results. Refer to text and Supplementary Information text for description ...... 315

Table C 1. Parameters, their example values drawn from crayfish, and the source of those values ...... 321 xi

CHAPTER 1:

INTRODUCTION

Global environmental change is altering how people obtain goods, services, and well-being from nature (Millennium Ecosystem Assessment 2005). At the same time, increasing populations and consumption place growing demands on the services and resources provided by ecosystems (Guo et al. 2010; Foley et al. 2011; Allendorf and

Allendorf 2012), particularly freshwaters (Jackson et al. 2001a). Current declining trends and future predictions of biodiversity (Butchart et al. 2010; Rands et al. 2010) -a fundamental building block of environmental goods and services (Hooper et al. 2012)- make it clear that not all demands for ecosystem services can be met. Management of conflicting demands for ecosystem services to maximize wellbeing under these conditions requires informed tradeoffs among services (Redpath et al. 2013).

A major question in modern ecology is how drivers of global environmental change affect the production of ecosystem services (Tilman 1999; Isbell et al. 2011;

Hooper et al. 2012; Naeem et al. 2012). The goal of this dissertation is to examine tradeoffs in freshwater ecosystem services associated with global change from the interdisciplinary perspectives of ecology and economics to better enable the management of natural resources that provide for wellbeing. Significant biotic and 1

hydrologic drivers of environmental changes in freshwater ecosystems and the services they provide are overexploitation, invasive species, habitat degradation, pollution, and flow modification (Figure 1.1; Sala et al. 2000, 2005; Allan et al. 2005; Dudgeon et al.

2005; Poff and Zimmerman 2010; Simberloff et al. 2012). Each of these drivers provide some set of ecosystems services while also reducing the provision of the same or other services, both now and in the future. Thus, these threats are manifestations of the human use of ecosystem services, which illustrates that making use of nature in one way often entails substantial opportunity cost with respect to using nature in other ways.

I focus on invasive species, overexploitation and flow modification as themes to bring together concepts in ecology (Lodge 1993), bioeconomics (Abson and Termansen

2011) and the sustainable management of ecosystem services (Lodge et al. 2006) to address how global drivers of environmental change affect the production of ecosystem services. First, I summarize these three drivers of environmental change in terms of the ecosystem services they provide and the services they place at risk. I then introduce four chapters that explore the tradeoffs among ecosystem services provided by introduced species and the services lost when those species become invasive.

1.1 Drivers of change in freshwater ecosystem services

Ecosystem services are the goods and other benefits produced by the environment and contribute to wellbeing through direct consumption, supporting the functioning of ecosystems, or through other cultural or non-market means (Millennium

Ecosystem Assessment 2005). Freshwater ecosystem services include, for example, commodities such as drinking water, food, recreation, as well as cultural values such as aesthetic or religious uses (Figure 1.1). These and many other goods and services are further supported by other hydrologic and biotic processes (Brauman et al. 2007); for 3

example interactions with local climates, evapotranspiration, soil development and stabilization, and environmental filtration. While the concept of ecosystem services allows tradeoffs in services to be assessed based on a wide definition of value (Brauman et al. 2007), monetary values of some of the services associated with freshwaters can be quantified (Chee 2004). Estimates of the values of individual freshwaters in the United

States range from on the order of tens of thousands to hundreds of thousands, millions and even billions of dollars for services such as water quality, recreation, and fishing

(Wilson and Carpenter 1999).

1.1.1 Fisheries harvest

Fisheries are both a service provided by ecosystems in that they provide for food security, employment, and recreation but they are also a major driver of global environmental change as a result of the negative ecological effects of overexploitation and destructive harvest methods. I consider capture fisheries and then aquaculture, which may interact with the introduction of potentially harmful non-native species.

Harvest is a cross-cutting driver of environmental change. Over-harvesting trades fisheries production now for fisheries production in the future. Harvest interacts with species introductions by prompting the importation of non-native species for capture and culture, and with dam construction, which may both provide and reduce fishing opportunities. The next three sections review the ecosystem services associated with fisheries harvests, flow modification and species introductions.

1.1.1.1 Freshwater fisheries

Inland capture fisheries are a major producer of social and economic well-being around the world, producing 11.5 million tons in 2011 (FAO 2012) and directly employing more than 56 million people (Welcomme et al. 2010). These are most likely large underestimates owing to the difficulties of accurate and consistent reporting of small-scale, unregulated and remote fishing activities (De Graaf et al. 2012); Inland fisheries catch could be as large as 93 million tons (Welcomme 2011). Recreational fishing is not well included in these figures, but represent a significant source of fishing effort in temperate higher-income regions of the world (De Graaf et al. 2012). In 2011, almost 29 million people in the United States participated in recreational freshwater fishing, spending $36 billion (U.S. Fish and Wildlife Service 2012).

The changes in the function of freshwater ecosystems as a result of fishing include habitat destruction, incidental mortality, and fishing-induced evolution (Pikitch et al. 2004). Across Asia, Africa and much of the tropics, growing numbers of fishers and strong decreasing trends in the size of fish caught indicate that many important freshwater fisheries have been overexploited, altering the communities and ecosystems which ultimately support well-being now and in the future (Welcomme et al. 2010). Yet, total harvests from inland waters are still growing (Allan et al. 2005; FAO 2012), whereas marine capture fisheries have been largely static for at least the last decade (FAO 2012).

In the context of marine fisheries the combination of overexploitation and other

anthropogenic effects have contributed to recent collapses in important fish stocks and ecosystems around the world (Jackson et al. 2001b; Worm et al. 2009).

1.1.1.2 Freshwater aquaculture

Aquaculture is poised to meet increasing demand for fish that is being left unfilled by capture fisheries (Cressey 2009; Carpenter et al. 2011). Inland aquaculture accounts for more than 60% of global aquaculture and produced more than 44 million tons in 2011, increasing more than 40% from 2006 and employing more than 16 million people (FAO 2012). Thus, the per capita tons of production in aquaculture is about 13 times greater than the per capita production of inland capture fisheries. Almost 90% of global aquaculture production comes from Asia, but rapid expansion is occurring in many parts of the world, particularly in Africa (Bostock et al. 2010). While cyprinids dominate species production, cichlids, particularly the tilapia Oreochromis niloticus, are the fastest growing large-scale (>2million tons year-1) aquacultural fishery.

The direct consumption of ecosystem services by aquaculture and the direct effects of aquaculture practices on biodiversity and ecosystems are important tradeoffs in the global fisheries market. Environmental change as a result of aquaculture stems directly from the opportunity costs of the inputs they consume -namely feed, energy, land, water, and seed stock- and the production waste output –nutrient wastes, chemotheraputants, disease, and escaped organisms (Beveridge et al. 1994; Diana 2009;

Bostock et al. 2010; Sutherland et al. 2012).

Aquaculture effects on wild fisheries, allocation of energy and land as well as the nutrient wastes received by freshwater ecosystems can be compared in the context of global environmental change by expressing these impacts in terms of carbon dioxide equivalents (C02eq; Hall et al. 2011). Aquaculture directly contributes approximately 1%

(385 Mt C02eq ) of all total global C02 emissions or about 7% of all agricultural emissions.

The global carbon footprint of aquaculture is estimated at 3-15kg C02eq /kg of fish production, about equivalent to other protein producers such as pork, mutton and dairy

(Nijdam et al. 2012).

The most pressing issue in aquaculture is the use of non-native species and the impacts these species on receiving ecosystems (Diana 2009). The total production of introduced species outside of their native range can be comparable to production in their native ranges, despite negative impacts to ecosystem services (Lodge et al. 2012a).

The issue of tradeoffs in ecosystem services as a result of species introduction is a cross- cutting issue, and is considered in detail below.

1.1.2 Flow modification

The availability of freshwater to support ecosystem services will decrease in the coming century, with increasing demand exacerbated by the effects of climate change and increasing variability of the hydrological cycle (Jackson et al. 2001a). The combined ecological and physical processes of the hydrological cycle control the quantity, quality, location and timing of freshwater flows which provide water for consumptive use (e.g.

drinking water), in situ use (e.g. fisheries), aesthetic uses and a wide assortment of other supporting ecosystem services (Brauman et al. 2007).

The clearest anthropogenic alteration of the hydrological cycle directly affecting these ecosystem processes is flow modification through impoundment by dams and channelization by levees (Carpenter et al. 2011). In the United States, almost all water discharge is impacted by flow alterations (Jackson et al. 2001a). Globally, more than half of all the world’s large river systems have been fragmented by dam construction

(Nilsson et al. 2005), while water withdrawals have decreased the global discharge of freshwater almost 3% (Doll et al. 2009) and climate change is expected to have an even stronger effect (Doll and Zhang 2010). There is increasing recognition that the ecosystem services that are negatively impacted by fragmentation and flow modification could provide significant valuable services that were overlooked when hydrologic modification projects were developed (Baron et al. 2002).

Fragmentation of rivers by the construction of dams is an obvious inhibition to the dispersal of migratory fish species; more than half of all studies of riverine barriers and flow alternations have involved anadromous Salmonids in North America (Murchie et al. 2008; Poff and Zimmerman 2010), while Ziv et. al. (2012) has recently showed substantial impacts on migratory fish are probable as a result of planned hydropower development in the Mekong Basin. There has been considerable interest in removing dams (e.g. Service 2011) to restore connectivity, though the process of dam removal possess additional ecological challenges and tradeoffs (Stanley and Doyle 2003).

In addition to dam-induced fragmentation, changes in downstream flow regime as a result of dam operation significantly impact biodiversity and fish communities

(Dudgeon et al. 2005). In a recent review of the effects of flow modification, the diversity and abundance of fish was found to decline due to both increases and decreases in the magnitude of flow in addition to a variety of impacts on ecosystem functions (Poff and Zimmerman 2010). The use of “environmental flows” or managed releases of water to more closely mimic the natural flow regime have been proposed and implemented as a way to restore some of these ecosystem services (Arthington et al. 2010; King and Brown 2010; Poff et al. 2010).

The management of freshwater flows is clearly one of competing interests and demands for the ecosystem services provided by flows of freshwaters, and tradeoffs are inevitable (Redpath et al. 2013). Recently rapid gains have been made in the understanding the relationships between flowing water and the provisions of ecosystem services (Brauman et al. 2007), but gaps remain in understanding the nature and magnitude of interactions of multiple drivers of global environmental change on these services over much of the range where impacts may occur. Many large dams exist and further dam projects or underway in Africa (International Rivers Africa Program 2010).

While the freshwater fisheries in this region contribute 10% to global production (FAO

2012), only 2% of the studies on effects of flow modification have been done in Africa

(Poff and Zimmerman 2010). This pattern is further evident in Asia: producing 43.1% of

inland fish production (FAO 2012) but with only 1% of studies reviewed (Murchie et al.

2008).

1.1.3 Invasive species

The introduction of harmful nonindigenous species is one of the most important drivers of global environmental change (Millennium Ecosystem Assessment 2005). The process of biological invasion defines invasive species as those species removed from their native range and introduced into novel ecosystems in which they establish reproductive populations and spread to other locations while negatively affecting ecosystems services, health and/or economies (Lodge et al. 2006). The ecological impacts of species introduction have been well described, ranging from contributing to the extinction of native species, altering nutrient cycling and hydrology, inflicting damage and disease on crops and other natural resources, and seriously foiling industrial operations (Mack et al. 2000). Estimates of the costs of invasion amount to

$120 billion in the United States alone (Pimentel et al. 2005).

The ecological, economic and social forces which propel and resist the impacts of species introductions are an active area of research (Simberloff et al. 2012). Two thing are now clear however: that the eradication of species once established is all but impossible under most circumstances (Pluess et al. 2012), and the most effective way to manage the impacts of invasive species is to prevent their introduction (Leung et al.

2002; Keller et al. 2007). A wide variety of preventative management tactics have been developed, e.g., policy guidance and instruments that limit the purposeful or accidental

importation of potentially harmful species (Lodge et al. 2006; Fowler et al. 2007; Peters and Lodge 2009); risk assessments that accurately screen harmful from benign species

(Pheloung et al. 1999; Gordon and Gantz 2011; Keller et al. 2011b); description of invasion pathways (Keller et al. 2011a); and sensitive screening and early detection technologies (Jerde et al. 2011; Lodge et al. 2012b). These efforts could be improved by a better understanding of global patterns of the social and economic motivations for some species introductions.

The last step of the invasion process is the most familiar to managers, researchers and the public, when the extent of ecological impacts of biological invasion is clear and the only recourse is to adapt and bear the environmental, social and economic costs of forgone ecosystem services (Lodge et al. 2006). The continued costs of invasive lamprey (Petromyzon marinus) control in the Great Lakes is a well known example of these perpetual costs borne in order to maintain recreational fishery values

(Lupi et al. 2003)

The impacts of species invasion are difficult if not impossible to reverse (Norton

2009). Thus, when prevention has failed, management may seek to optimize remaining ecosystem services, including services provided by the invasive species. While invasive species may cause considerable damage to historical ecosystem services, these species may also provide services that should not be overlooked in the adaptation stage of invasion (Davis et al. 2011; Schlaepfer et al. 2011). For example, food (Nuñez et al.

2012), biofuels (Glaser and Glick 2012), habitat (Gleditsch and Carlo 2011) and aesthetic values (Nuñez and Simberloff 2005).

1.2 Dissertation outline

Major threats to freshwater ecosystem services are increasing at the same time, and in part because of, demand for those services. Freshwater fisheries, aquaculture, flow modification, and invasive species are directly competing uses of ecosystem resources, while at the same time are producers of ecosystem services. The relative effects of these drivers of environmental change can be measured by observing where they interact and place conflicting demands on ecosystem services. Resolving the nature and magnitude of tradeoffs between these ecosystem services comprise major challenges and opportunities for sustainable resource use and continued wellbeing.

The introduction of the freshwater fish Nile tilapia (Oreochromis niloticus) to the

Kafue River, Zambia provides the impetus for the chapters which follow. Each of these chapters explores a particular tradeoff in ecosystem services that results from competing drivers of environmental change (Figure 1.1). Chapter 2 provides a global review of the ecological effects of tilapia introduction and the possible benefits of tilapia aquaculture and fisheries production. Chapter 3 performs field sampling throughout the Kafue region and a spatial genetic analysis of hybridization between native and introduced tilapia species. Chapter 4 conducts a long-term analysis of the ecological and monetary tradeoff between fisheries and hydropower on the Kafue. Finally Chapter 5 conducts an exploratory bioeconomic evaluation of harvesting invasive species as an 12

tool optimizing adaptation to species invasions. Chapters 2,3,and 5 focus on the tradeoff between ecosystem services provided by introduced and native species globally, regionally and theoretically, while Chapter 4 explores a regional trade off between flow modification and fisheries production.

1.2.1 Tradeoffs among ecosystem services associated with global tilapia introductions

The Nile tilapia, and related species in the cichlid tribe, Tilapiini (principally of genera Oreochromis, Sarotherodon and Tilapia) have filled the growing market for freshwater aquaculture because of their fast growth, tolerance of high densities, low trophic position , hearty nature , and good flavor(Lowe-McConnell 2000; Ross 2000).

Three volumes over the last 30 years have reviewed the biology and aquaculture of tilapias (Fryer and Iles 1972; Pullin and Lowe-McConnell 1982; Beveridge and McAndrew

2000), detailing the physiological, behavioral and genetic characteristics that have made tilapias an aquacultural fish of choice. The same characteristics which have made tilapias a commercial success are, however, remarkably consistent with traits that have been described for invasive freshwater fish species (Bruton and Van As 1986; Lorenzen

2000; Copp et al. 2005). Thus, there has been intense controversy regarding the extent of beneficial ecosystem services of tilapia introduction for aquaculture and fisheries relative to the negative impacts of tilapia invasion (Canonico et al. 2005; Gozlan 2008;

De Silva et al. 2009; Vitule et al. 2009). Recent reviews of the impact of tilapia introduction yield polar opposite interpretations of the ecological harm, or lack thereof, of introduction (Canonico et al. 2005; Gozlan 2008).

In this chapter, I ask how the ecosystem services provided by the introduction of tilapia species, particularly O. niloticus, are traded-off against the loss of ecosystem services that result when tilapia become invasive on a global scale (Figure 1.1). I conduct the most comprehensive literature review yet to 1) provide an up-to-date estimate of the global scale of tilapia introduction and the occurrence of impacts to ecosystem services; 2) assess whether the presence or absence of changes to ecosystem services as a result of tilapia introduction differ among species of tilapia, global region of the introduction, and the type of ecological effect reported; and 3) determine how perceptions of the harms or benefits of tilapia introduction are related to the occurrence of ecological effects and/or socioeconomic indicators.

This chapter lays a foundation for subsequent chapters by establishing that the ecological effects of tilapia introduction are unequivocal, but whether those effects increase or decrease the benefits of ecosystem services depends on the local socioeconomic context. Thus, the magnitude of ecosystem service tradeoffs as a result of tilapia introduction remain largely unmeasured because they may be case specific. In the next two chapters, I make case-specific measurements of ecosystem services for the

Kafue River, Zambia.

1.2.2 Hybridization of native Oreochromis species (Cichlidae) and the introduced Nile tilapia (O. niloticus) in the Kafue River, Zambia.

Hybridization of tilapia species when species are brought into secondary contact is not uncommon (Agnese 1998), threatening the basic genetic diversity available to

tilapia aquaculture (Allendorf et al. 2001). The high productivity of O. niloticus aquaculture in Africa is predicated on the existence of genetic diversity originally gleaned from wild populations, but this diversity is already becoming impoverished within African aquaculture (Brummett and Ponzoni 2009). Thus, the continued and future productivity of tilapia aquaculture will rely on continued inputs of genetic variation from wild stocks. Meanwhile, the impact of hybridization on tilapia capture fisheries is uncertain. In most hybrid theory, hybrids are expected to be less fit

(Harrison 1993; Coyne and Orr 2004). Empirically however, hybrid vigor appears in some Oreochromis hybrids (Moralee et al. 2000; Wang and Xia 2002), but reduced fitness is expected in others (Gregg et al. 1998; D’Amato et al. 2007). The outcome of hybridization and subsequent evolution will determine the magnitude of ecosystem services tradeoffs on the Kafue River.

The Nile tilapia, Oreochromis niloticus, was introduced to the Kafue region of

Zambia in the 1980s for aquaculture production (Audenaerde 1994) and soon escaped and spread in the Kafue River (Schwanck 1995). In this chapter, I test the hypotheses that 1) Nile tilapia have now spread throughout the Kafue region; and 2) that hybridization between Nile tilapia and two native Oreochromis species has, after 30 years, yielded a highly mixed population, or “hybrid swarm”. These hypothesis set up the potential for losses in Oreochromis genetic diversity on the Kafue River that may impact aquaculture and fisheries production and represent a tradeoff between aquaculture and capture fisheries of tilapia species.

I show that hybridization is extensive on the Kafue River, adding another example of this ecological effect to the global review of tilapia introduction in Chapter 2.

Missing from this evaluation of ecosystem service tradeoffs is a measure of the value of the ecosystem services under consideration. In the next chapter I revisit the Kafue River to do just that- provide a more explicit valuation of the ecosystem services on the Kafue river by comparing tradeoffs between the capture fishery and the generation of hydropower.

1.2.3 The potential tradeoff between artisanal fisheries production and hydroelectricity generation on the Kafue River, Zambia

The livelihoods of people in Zambia along the Kafue River are tightly linked to diverse water related ecosystem services (Cliggett et al. 2010). The Kafue Flats region covers 7,500 km2, more than 80% of Zambia’s total surface water area at high flood, and once supported some of the wealthiest indigenous groups on the continent because they hunted abundant wildlife, harvested at least 35 native fish species, and seasonally grazed vast cattle herds on the extensive floodplain (Schelle and Pittock 2005; Smardon

2009; Haller and Chabwela 2009). Since the completion of the Kafue Gorge dam in 1973 and the Itezhi-Tezhi dam in 1977, a new ecosystem service—hydropower—has been harvested from the Kafue, but with concomitant tradeoffs: a flattened hydrograph in the Kafue fishery which dramatically decreased the surface area of the floodplain, the productivity of the associated wetlands (Munyati 2000), livestock, wildlife and possibly fish (Chipungu 1981; Mumba and Thompson 2005; Schelle and Pittock 2005).

In this chapter, I hypothesize that the changes in the flood regime on the Kafue

River that resulted from the construction of the Itezhi-tezhi dam upstream of major fisheries areas reduced fisheries production at the same time as providing for increased hydropower generation at the downstream Kafue Gorge dam. By finding the relationships among flood regime, fisheries, and hydropower production I estimate the monetary value of fisheries and hydropower both with and without the presence of the

Itezhi-tezhi dam. The results of fisheries and hydrological modeling demonstrate that the fishery has been much reduced over the last 60 years, but more by overfishing than damming (Figure 1.1). In the next chapter, I extend the valuation of ecosystem service tradeoffs by revisiting emerging issues in species introductions, by exploring the bioeconomics of harvesting invasive species.

1.2.4 Can Market-Driven Harvest of Invasive Species Contribute to Conservation Goals?

There has recently been interest in programs which promote eating, or otherwise harvesting invasive species as a conservation solution to reduce invasive species populations and the negative impacts those populations have on ecosystem services. Considering the human history of overharvesting once abundant and valuable species (e.g. Jackson et al. 2001b), the consumption of pest species as a control strategy may warrant consideration. These programs are popular with the public, the media, and elected officials- with little or no rigorous consideration to whether they may actually provide net benefits to conservation or economic goals. Meanwhile, serious

potential risks include the creation of incentives to propagate and spread otherwise harmful species rather than eradicate them (Nuñez et al. 2012).

In this chapter I examine tradeoffs between the value of ecosystem services provided by invasive species and the ecosystem services lost, and search for conditions under which these values are equal. I combine simple bioeconomic scenarios of invasive species populations, harvests, and markets to model how populations of invasive species may be reduced when increasing the demand for invasive species products and connect these populations to values of ecosystem services. In this way, I consider the tradeoffs between both the direct and indirect market value of invasive species and ask under what circumstances harvests contribute to conservation goals. I show that under many circumstances, populations are unlikely to be reduced enough to substantially contribute to conservation without substantial subsidies or unless the value of ecosystem service recovery is very large compared to the value of the harvest.

There may remain, however, indirect non-market benefits of harvesting invasive species for increasing the awareness and support of the public and policy makers for more comprehensive invasive species management.

1.2.5 Conclusion

What are the tradeoffs among ecosystem services provided by introduced species and the services lost when those species become invasive? In the final chapter I assess how each chapter explored in detail tradeoffs in ecosystem services, and outline questions to guide future research.

1.3 References

Abson, D.J. and Termansen, M. (2011) Valuing ecosystem services in terms of ecological risks and returns. Conservation biology: the journal of the Society for Conservation Biology 25, 250–8.

Agnese, J.-F. (1998) Natural Hybridization in tilapias. In: Genetics and Aquaculture in Africa. (ed J.-F. Agnese). Orstrum, Paris, pp 95–103.

Allan, J.D., Abell, R., Hogan, Z., Revenga, C., Taylor, B.W., Robin L. Welcomme and Winemiller, K. (2005) Overfishing of Inland Waters. Bioscience 55, 1041–1051.

Allendorf, F.W., Leary, R.F., Spruell, P. and K.Wenburg, J. (2001) The problems with hybrids: setting conservation guidelines. TRENDS in Ecology & Evolution 16, 613– 622.

Allendorf, T.D. and Allendorf, K. (2012) What Every Conservation Biologist Should Know about Human Population. Conservation Biology 26, 953–955.

Arthington, A.H., Naiman, R.J., Mcclain, M.E. and Nilsson, C. (2010) Preserving the biodiversity and ecological services of rivers: new challenges and research opportunities. Freshwater Biology 55, 1–16.

Audenaerde, D.F.E.T. van den (1994) Introduction of aquatic species into Zambian waters, and their importance for aquaculture and fisheries. 33.

Baron, J.S., Poff, N.L., Angermeier, P.L., et al. (2002) Meeting ecological and societal needs for freshwater. Ecological Applications 12, 1247–1260.

Beveridge, M., Ross, L.G. and Kelly, L.A. (1994) Aquaculture and Biodiversity. Ambio 23, 497–502.

Beveridge, M.C.M. and McAndrew, B.J. (2000) Tilapias: Biology and Exploitation. Fish and Fisheries Series 25, 505.

Bostock, J., McAndrew, B., Richards, R., et al. (2010) Aquaculture: global status and trends. Philosophical transactions of the Royal Society B 365, 2897–2912.

Brauman, K. a., Daily, G.C., Duarte, T.K. and Mooney, H. a. (2007) The Nature and Value of Ecosystem Services: An Overview Highlighting Hydrologic Services. Annual Review of Environment and Resources 32, 67–98.

Brummett, R.E. and Ponzoni, R.W. (2009) Concepts, alternatives, and envrionmental considerations in the development and use of improved strains of tilapia in African aquaculture. Reviews in Fisheries Science 17, 70–77.

Bruton, M.N. and Van As, J. (1986) Faunal invasions of aquatic ecosystem in southern Africa, with suggestions for their management. In: Proceedings of the national Synthesis Symposium on the Ecology of Biological Invasions. Oxford University Press, Stellenbosch, South Africa, pp 47–61.

Butchart, S.H.M., Walpole, M., Collen, B., et al. (2010) Global Biodiversity: Indicators of Recent Declines. Science 328, 1164–1168.

Canonico, G.C., Arthington, A., McCrary, J.K. and Thieme, M.L. (2005) The effects of introduced tilapias on native biodiversity. Aquatic Conservation: Marine And Freshwater Ecosystems 15, 463–483.

Carpenter, S.R., Stanley, E.H. and Vander Zanden, M.J. (2011) State of the World’s Freshwater Ecosystems: Physical, Chemical, and Biological Changes. Annual Review of Environment and Resources 36, 75–99.

Chee, Y.E. (2004) An ecological perspective on the valuation of ecosystem services. Biological Conservation 120, 549–565.

Chipungu, P.M. (1981) The Impact of the Kafue Gorge Dam on the Kafue floodplain fishery (Zambia). 120–129.

Cliggett, L., Colson, E., Hay, R., Scudder, T. and J.Unruh. (2010) Adaptive Responses to Environmental and Sociopolitical Change in Southern Zambia. In: Human Ecology: Contemporary Research and Practice. (eds D. Bates and J. Tucker). Springer, New York, pp 225–236.

Copp, G.H., Garthwaite, R. and Gozlan, R.E. (2005) Risk identification and assessment of non-native freshwater fishes: concepts and perspectives on protocols for the UK. Sci. Ser. Tech Rep. Cefas Lowestoft.

Coyne, J.A. and Orr, H.A. (2004) Speciation. Sinauer, Sunderland, MA.

Cressey, D. (2009) Future Fish. Nature 458, 398–400.

D’Amato, M.E., Esterhuyse, M.M., Waal, B.C.W. van der, Brink, D. and Volckaert, F.A.M. (2007) Hybridization and phylogeography of the Mozambique tilapia Oreochromis mossambicus in southern Africa evidenced by mitochondrial and microsatellite DNA genotyping. Conservation Genetics 8, 475–488.

Davis, M.A., Chew, M.K., Hobbs, R.J., et al. (2011) Don’t judge species on their origins. Nature 474, 153–154.

Diana, J.S. (2009) Aquaculture Production and Biodiversity Conservation. BioScience 59, 27–38.

Doll, P., Fiedler, K. and Zhang, J. (2009) Global-scale analysis of river flow alterations due to water withdrawals and reservoirs. Hydrology and Earth System Sciences 13, 1432–2413.

Doll, P. and Zhang, J. (2010) Impact of climate change on freshwater ecosystems: a global-scale analysis of ecologically relevant river flow alterations. Hydrology and Earth System Sciences 14, 783–799.

Dudgeon, D., Arthington, A.H., Gessner, M.O., et al. (2005) Freshwater biodiversity: importance, threats, status and conservation challenge. Biological Review 81, 163–182.

FAO (2012) State of the World Fisheries and Aquaculture 2012. Food and Agriculture Organization of the United Nations, Rome.

Foley, J. a, Ramankutty, N., Brauman, K. a, et al. (2011) Solutions for a cultivated planet. Nature 478, 337–42.

Fowler, A.J., Lodge, D.M. and Hsia, J.F. (2007) Failure of the Lacey Act to protect US ecosystems against animal invasions. Fronteirs in Ecology and the Environment 5, 353–359.

Fryer, G. and Iles, T.D. (1972) The Cichlid Fishes of the Great Lakes of Africa. Oliver & Boyd, Edinburgh.

Glaser, A. and Glick, P. (2012) Growing Risk.

Gleditsch, J.M. and Carlo, T.A. (2011) Fruit quantity of invasive shrubs predicts the abundance of common native avian frugivores in central Pennsylvania. Diversity and Distributions 17, 244–253.

Gordon, D.R. and Gantz, C.A. (2011) Risk assessment for invasiveness differs for aquatic and terrestrial plant species. Biological Invasions.

Gozlan, R.E. (2008) Introduction of non-native freshwater fish: is it all bad? Fish and Fisheries 9, 106–115.

De Graaf, G., Bartley, D., Jorgensen, J. and Marmulla, G. (2012) The scale of inland fisheries, can we do better? Alternative approaches for assessment. Fisheries Management and Ecology, no–no.

Gregg, R.E., Howard, J.H. and Shonhiwa, F. (1998) Introgressive Hybridization of tilapia in Zimbabwe. Journal of Fish Biology 52, 1–10.

Guo, Z., Zhang, L. and Li, Y. (2010) Increased dependence of humans on ecosystem services and biodiversity. PloS one 5.

Hall, S.J., Delaporte, A., Phillips, M.J., Beveridge, M. and O’Keefe, M. (2011) Blue Frontiers: Managing the environmental costs of aquaculture. Penang, Malaysia.

Haller, T. and Chabwela, H.N. (2009) Managing common pool resources in the Kafue Flats, Zambia: from common property to open access and privatisation. Development Southern Africa 26, 555–567.

Harrison, R.G. (1993) Hybrid Zones and the Evolutionary Process.

Hooper, D.U., Adair, E.C., Cardinale, B.J., et al. (2012) A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–8.

International Rivers Africa Program (2010) Africa Dams Briefing 2010. 1–67 pp. Berkeley, USA.

Isbell, F., Calcagno, V., Hector, A., et al. (2011) High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202.

Jackson, R.B., Carpenter, S.R., Dahm, C.N., Mcknight, D.M., Naiman, R.J., Postel, S.L. and Running, S.W. (2001a) Water in a changing world. Ecological Applications 11, 1027–1045.

Jackson, J.B.C., Kirby, M.X., Berger, W.H., et al. (2001b) Historical Overfishing and the Recent Collapse of Coastal Ecosystems. Science 293, 629–638.

Jerde, C.L., Mahon, A.R., Chadderton, W.L. and Lodge, D.M. (2011) “Sight-unseen” detection of rare aquatic species using environmental DNA. Conservation Letters 4, 150–157.

Keller, R.P., Drake, J.M., Drew, M.B. and Lodge, D.M. (2011a) Linking environmental conditions and ship movements to estimate invasive species transport across the global shipping network. Diversity and Distributions 17, 93–102.

Keller, R.P., Kocev, D. and Dzeroski, S. (2011b) Trait-based risk assessment for invasive species: high performance across diverse taxonomic groups, geographic ranges and machine learning/statistical tools. Diversity and Distributions 2011.

Keller, R.P., Lodge, D.M. and Finnoff, D.C. (2007) Risk assessment for invasive species produces net bioeconomic benefits. Proceedings of the National Academy of Sciences 104, 203–207.

King, J. and Brown, C. (2010) Integrated basin flow assessments: concepts and method development in Africa and South-east Asia. Freshwater Biology 55, 127–146.

Leung, B., Lodge, D.M., Finnoff, D., Shogren, J.F., Lewis, M.A. and Lamberti, G. (2002) An ounce of prevention or a pound of cure: bioeconomic risk analysis of invasive species. Proceedings of the Royal Society B 269, 2407–2413.

Lodge, D.M. (1993) Biological invasions- lessons for ecology. TRENDS in Ecology & Evolution 8, 133–137.

Lodge, D.M., Deines, A.M., Gherardi, F., et al. (2012a) Global Introductions of Crayfishes: Evaluating the Impact of Species Invasions on Ecosystem Services. Annual Reviews in Ecology Evolution and Systematics 43, 449–472.

Lodge, D.M., Turner, C.R., Jerde, C.L., et al. (2012b) Conservation in a cup of water: estimating biodiversity and population abundance from environmental DNA. Molecular ecology 21, 2555–8.

Lodge, D.M., Williams, S., Macisaac, H.J., et al. (2006) Biological Invasions: Recommendations for U.S. Policy and Management. Ecological Applications 16, 2035–2054.

Lorenzen, K. (2000) Population dynamics and management. In: Tilapias: Biology and Exploitation. (eds M.C.M. Beverage and B.J. McAndrew). Kluwer Academic Publishers, Boston, pp 163–225.

Lowe-McConnell, R.H. (2000) The roles of tilapias in ecosystems. In: Tilapias: Biology and exploitation. (eds M.C.M. Beveridge and B.J. McAndrew). Kluwer Academic Press, Great Britain, pp 129–162.

Lupi, F., Hoehn, J.P. and Christie, G.C. (2003) Using an Economic Model of Recreational Fishing to Evaluate the Benefits of Sea Lamprey (Petromyzon marinus) Control on the St. Marys River. Journal of Great Lakes Research 29, 742–754.

Mack, R.N., Simberloff, D., Lonsdale, W.M., Evans, H., Clout, M. and Bazzaz, F.A. (2000) Biotic Invasions: Causes, Epidemiology, Global Consequences, And Control. Ecological Applications 10, 689–710.

Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-being: Synthesis. Island Press, Washington, D.C.

Moralee, R.D., Bank, F.H. van der and Waal, B.C.W. van der (2000) Biochemical genetic markers to identify hybrids between the endemic Oreochromis mossambicus and the alien species, O. niloticus (Pisces: Cichlidae). Water SA 26, 263–268.

Mumba, M. and Thompson, J.R. (2005) Hydrological and ecological impacts of dams on the Kafue Flats floodplain system, southern Zambia. Physics and Chemistry of the Earth 30, 442–447.

Munyati, C. (2000) Wetland change detection on the Kafue Flats, Zambia, by classification of a multitemporal remote sensing image dataset. Int. J. Remote Sensing 21, 1787–1806.

Murchie, K.J., Hair, K.P.E., Pullen, C.E., Redpath, T.D., Stephens, H.R. and Cooke, S.J. (2008) Fish Response To Modified Flow Regimes In Regulated Rivers: Research Methods, Effects And Opportunities. River Research And Applications 24, 197– 217.

Naeem, S., Duffy, J.E. and Zavaleta, E. (2012) The functions of biological diversity in an age of extinction. Science (New York, N.Y.) 336, 1401–6.

Nijdam, D., Rood, T. and Westhoek, H. (2012) The price of protein: Review of land use and carbon footprints from life cycle assessments of animal food products and their substitutes. Food Policy 37, 760–770.

Nilsson, C., Reidy, C.A., Dynesius, M. and Revenga, C. (2005) Fragmentation and Flow Regulation of the World’s Large River Systems. Science 308, 405–408.

Norton, D.A. (2009) Species invasions and the limits to restoration: Learning from the New Zealand Experience. Science 325, 569–571.

Nuñez, M. a., Kuebbing, S., Dimarco, R.D. and Simberloff, D. (2012) Invasive Species: To eat or not to eat, that is the question. Conservation Letters, 5, 334-341.

Nuñez, M.A. and Simberloff, D. (2005) Invasive Species and the Cultural Keystone Species Concept. Ecology and Society 10, 4–7.

Peters, J.A. and Lodge, D.M. (2009) Multiple Weak Links Problem. Fisheries 34, 373–380.

Pheloung, P.C., Williams, P.A. and Halloy, S.R. (1999) A weed risk assessment model for use as a biosecurity tool evaluating plant introductions. Journal of Environmental Management 57, 239–251.

Pikitch, E.K., Santora, C., Babcock, E.A., et al. (2004) Ecosystem-based fishery management. Science 305, 346–347.

Pimentel, D., Zuniga, R. and Morrison, D. (2005) Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics 52, 273–288.

Pluess, T., Cannon, R., Jarošík, V., Pergl, J., Pyšek, P. and Bacher, S. (2012) When are eradication campaigns successful? A test of common assumptions. Biological Invasions.

Poff, N.L., Richter, B.D., Arthington, A.H., et al. (2010) The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshwater Biology 55, 147–170.

Poff, N.L. and Zimmerman, J.K.H. (2010) Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshwater Biology 55, 194–205.

Pullin, R.S. V and Lowe-McConnell, R.H. (1982) The Biology and Culture of Tilapias. ICLARM Conference Proceedings 7.

Rands, M.R.W., Adams, W.M., Bennun, L., et al. (2010) Biodiversity Conservation: Challenges Beyond 2010. Science 329.

Redpath, S.M., Young, J., Evely, A., et al. (2013) Understanding and managing conservation conflicts. Trends in ecology & evolution 28, 100–9.

Ross, L.G. (2000) Environmental physiology and energetics. In: Tilapias: Biology and Exploitation. (eds M.C.M. Beveridge and B.J. McAndrew). Kluwer AcademicPublishers, Boston, pp 89–128.

Sala, O.E., Chapin, F.S.I., Armesto, J.J., et al. (2000) Global Biodiversity Scenarios for the Year 2100. Science 287, 1770–1774.

Sala, O.E., Vuuren, D. van, Pereira, H. miguel, et al. (2005) Biodiversity across Scenarios. In: Ecosystems and Human Well-being: Scenarios, Volume 2. (eds S.R. Carpenter, P.L. Pingali, E.M. Bennett and M.B. Zurek). Island Press, Washington, pp 377– 408.

Schelle, P. and Pittock, J. (2005) Restoring the Kafue Flats: A partnership approach to environmental flows in Zambia. 10.

Schlaepfer, M.A., Sax, D.F. and Olden, J.D. (2011) The Potential Conservation Value of Non-Native Species. Conservation Biology.

Schwanck, E.J. (1995) The introduced Oreochromis niloticus is spreading on the kafue floodplain, Zambia. Hydrobiologia 315, 143–147.

Service, R.F. (2011) Will Busting Dams Boost Salmon ? Science 334, 888–892.

De Silva, S.S., Nguyen, T.T.T., Turchini, G.M., Amarasinghe, U.S. and Abery, N.W. (2009) Alien species in aquaculture and biodiversity: A paradox in food production. Ambio 38, 24–28.

Simberloff, D., Martin, J.-L., Genovesi, P., et al. (2012) Impacts of biological invasions: what’s what and the way forward. Trends in ecology & evolution 28, 58–66.

Smardon, R.C. (2009) Sustaining the World’s Wetlands: Setting Policy and Resolving Conflicts. Springer-Verlag.

Stanley, E.H. and Doyle, M.W. (2003) Trading off : the ecological effects of dam removal. Frontiers in Ecology and the Environment 1, 15–22.

Sutherland, W.J., Bardsley, S., Clout, M., et al. (2012) A horizon scan of global conservation issues for 2013. Trends in ecology & evolution 28, 16–22.

Tilman, D. (1999) The Ecological Consequences of Changes in Biodiversity: a search for general principals. Ecology 80, 1455–1474.

U.S. Fish and Wildlife Service (2012) 2011 National Survey of Fishing , Hunting , and Wildlife-Associated Recreation. Preliminary Findings. U.S. Department of Interior.

Vitule, J.R.S., Freire, carolina A. and Simberloff, D. (2009) Introduction of non-native freshwater fish can certainly be bad. Fish and Fisheries 10, 98–108.

Wang, J. and Xia, D. (2002) Studies on fish heterosis with DNA fingerprinting. Aquaculture Research 33, 941–947.

Welcomme, R.L. (2011) An overview of global catch statistics for inland fish. ICES Journal of Marine Science 68, 1751–1756.

Welcomme, R.L., Cowx, I.G., Coates, D., Béné, C., Funge-Smith, S., Halls, A. and Lorenzen, K. (2010) Inland Capture Fisheries. Philosophical transactions of the Royal Society B 356, 2881–2896. 26

Wilson, M.A. and Carpenter, S.R. (1999) Economic Valuation of Freshwater Ecosystem services in the United States: 1971-1997. Ecological Applications 9, 772–783.

Worm, B., Hilborn, R., Baum, J.K., et al. (2009) Rebuilding Global Fisheries. Science 325, 578–585.

Ziv, G., Baran, E., Nam, S., Rodríguez-Iturbe, I. and Levin, S. a (2012) Trading-off fish biodiversity, food security, and hydropower in the Mekong River Basin. Proceedings of the National Academy of Sciences of the United States of America 109, 5609–14.

CHAPTER 2:

TRADEOFFS AMONG ECOSYSTEM SERVICES ASSOCIATED WITH GLOBAL TILAPIA

INTRODUCTIONS

2.1 Abstract

Previous publications have reached different conclusions about the balance between the socioeconomic benefits of tilapia introduction for aquaculture and capture fisheries, and the potential negative impacts of these species on the ecosystem services provided by native fishes and communities such as the provisioning of food, habitat and water quality. The goals of this review are to 1) provide an up-to-date estimate of the global scale of tilapia introduction and the occurrence of impacts to ecosystem services;

2) assess whether changes to ecosystem services differ among species, regions and type of ecological effect reported; and 3) determine how perceptions of the effects of tilapia introduction are related to the occurrence of ecological effects and/or the contribution of tilapia production to countries’ gross domestic product. We find that the scale of global introductions is more than a third larger than previously acknowledged, with feral populations existing in at least 114 countries. The majority of research is consistent with the occurrence of ecological change as a result of tilapia introduction, regardless of species or region of study. Negative perceptions of tilapia introductions are positively

associated with the occurrence of ecological effects, but there are important regional influences including the economic contribution of tilapia. There is an increasing recognition that introductions provide both significant benefits and considerable but not fully documented harm to ecosystem services. We therefore highlight the need by managers, policy makers and other stakeholders to consider both ecological effects and socio-economic context, such as reviewed here, in making informed decisions about the tradeoffs in ecosystem services that accompany introduction of tilapia. A fuller set of information on both benefits and harms is especially needed in the many countries to which tilapia have not yet been introduced.

2.2 Introduction

The dissemination of aquatic species as a result of globalization has often had unexpected and harmful impacts on receiving ecosystems, human communities and economies (Lodge et al. 2006; Keller et al. 2009), though introduced species are not universally harmful and in some cases may provide significant benefits (Davis et al.

2011; Gleditsch and Carlo 2011; Schlaepfer et al. 2011). Global databases of introductions indicate relatively few negative impacts for many freshwater fishes species particularly relative to the economic value of their production (Gozlan 2008).

Different authors have, however, reached different conclusions about the extent of undesirable impacts on ecosystems relative to the benefits of aquaculture and fisheries, particularly those based tilapia (Leveque 2002; Canonico et al. 2005; Gozlan 2008;

Brummett and Ponzoni 2009; De Silva et al. 2009; Vitule et al. 2009). Since 2002 tilapia 29

(Cichlidae: primarily of genera Oreochromis, Tilapia, and Serranochromis; In the remainder of this paper, we use “tilapia” to refer to these species collectively) have fueled one of the fastest growing global aquaculture sectors (Bostock et al. 2010), expanding twice as fast as fisheries based on salmonids or carps (FAO 2011). Given the rapid expansion of aquaculture, many decisions are being made to introduce fishes into new watersheds, and decisions about tilapia, in particular, will have far-reaching effects.

Ecosystem services include the goods and other resources that are consumed by people and contribute to welfare directly through markets or other non-market values

(Millennium Ecosystem Assessment 2005). Changes in the biotic components of an ecosystem, including changes caused by species introduction, often increase some services while simultaneously decreasing others (Hooper et al. 2005; Lodge et al. 2012).

Specifically, increasing tilapia production may cause a decrease in populations of harvestable native species or a decrease in recreational or cultural ecosystem services linked to the native species harmed by tilapia (Canonico et al. 2005). Differing opinions on the type of ecosystem services provided or affected by tilapia prevents a comprehensive policy framework for the management of this species (Redpath et al.

2013). The comprehensive review we provide here may reduce areas of disagreement and allow more informed future decisions.

Global tilapia production in 2010 exceeded 4 million tons, 3.5 million of which came from aquaculture and was valued at $5.7 billion USD. From capture fisheries, at least 30% of global tilapia production comes from feral tilapia populations from six main

countries: Mexico (62935t), Philippines (44896t), Thailand (38300t), Indonesia (31960t),

Sri Lanka (28250t), and Brazil (25246t). In some countries the majority of freshwater capture production derives from feral tilapia: Cuba and Panama (100%), Nicaragua

(84%), Mexico (58%), and Sri Lanka (54%) (FAO Fisheries and Aquaculture Department

2012). The losses of ecosystem goods or services caused by the introduction and harvest of tilapia are less well quantified and monetized than the benefits.

The ecosystem service tradeoffs potentially caused by tilapia production have been the subject of previous reviews that documented undesirable ecological effects

(Pullin et al. 1997; Canonico et al. 2005). For example, tilapia are considered to be the proximate cause of declines in many native fishes: desert pupfish (Cyprinodon macularius) in the South West United States (Black 1980; Varela-Romero et al. 2000),

Sinarapan (Mistichthys luzonensis) from Lake Buhi in the Philippines (Gindelberger

1981), and various native cichlids that once comprised important components of fisheries harvests in Lake Nicaragua (McKaye et al. 1995) and Lake Victoria (Mkumbo and Ligtvoet 1992; Goudswaard et al. 2002). Tilapia have also harmed other fisheries such as milkfish aquaculture in Nauru (Ranoemihardjo 1981), and cyprinid harvests in

India (Sugunan 1995, 2000). Reductions in other ecosystem services caused by tilapia are associated with the loss of aquatic plants and the habitats they provide to native species (Crutchfield 1995), as well as undesirable biotic and abiotic changes associated with eutrophication (Figueredo and Giani 2005). In contrast, some tilapia fisheries may

act to protect native species by removing fishing pressure from natives (Arthur et al.

2010).

Because of the diversity of reported ecological effects and the lack of a quantitative review that considers the strength of inferences from different kinds of study designs, conflicting perceptions of the ecological impacts of tilapia have emerged.

Assessments of tilapia impacts range from no negative impacts to extremely harmful

(Canonico et al. 2005; Gozlan 2008). This range of perceptions is perpetuated because areas where tilapia have been introduced for culture and capture fisheries are concentrated where resources to support ecological research are limited (Canonico et al. 2005; UNESCO 2007; De Silva et al. 2009; Josupeit 2010; Skelton and Swartz 2011;

Amano and Sutherland 2013). It is likely that the absence of evidence for ecological effects is substituted in management and policy decisions for evidence of the absence of ecological effects. Under those circumstances, the monetized value of tilapia production may drive perceptions and decisions about tilapia introduction (Lövei et al.

2012). Our goal is to provide a comprehensive review to assess the effects of tilapia introductions on ecosystem services, motivated by a desire to provide the most up-to- date information to those making decisions about the introduction and management of tilapia.

To accomplish our general goal, we address three specific questions:1) What proportion of tilapia introductions are associated with ecological effects? 2) Does the presence or absence of ecological effects vary by the type of ecological effect

considered, the particular tilapia species, the time and place where the introduction has occurred, or the study design used to assess effects? 3) Do perceptions of the relative harm or benefit differ by species, region, or the presence of ecological and socioeconomic effects?

Our first question address the number of countries in which tilapia introductions have caused ecological effects. A review of global freshwater fish introductions (Gozlan

2008) reported that 5 - 20% of cichlid introductions (of which tilapia are the major constituent (Welcomme 2011)), have resulted in negative ecological impacts. Due to the contrast of these results and the experience of tilapia researchers in particular cases, these results have been treated with skepticism (Vitule et al. 2009). With our comprehensive literature review, we provide a new count for the number of countries that have received tilapia introductions, and derive an improved estimate for the proportion of countries for which tilapia introductions have been associated with ecological effects. We define these effects as demonstrable changes in ecosystem function and/or the provisioning of ecosystem goods and services.

In our second question we ask if the presence or absence of ecological effects vary by the tilapia species, type of effect, the year and place of the study, or the study design. The life-history traits of tilapia that have made them an aquacultural success

(e.g. rapid somatic and population growth, wide environmental tolerances) are the same traits often associated with invasive species (Bruton and Van As 1986; Lorenzen

2000; Canonico et al. 2005). These and other traits differ between common species of

tilapia, such that different species might be expected to have different ecological effects. For example, while most tilapia are opportunistic omnivores, species of the genus Tilapia are typically macrophyte feeders while Oreochromis are typically microphagous (Beveridge and Baird 2000). As a result, Tilapia have previously been used to control aquatic weeds (Hauser et al. 1976), suggesting these species may impact plant habitats relatively frequently. Similarly, O. mossambicus is the only cichlid listed as among the world’s worst 100 hundred invasive species (Lowe et al. 2000), raising the expectation that O. mossambicus should be more associated with undesirable ecosystem impacts than other tilapia.

The occurrence and magnitude of the ecological effects from a species introduction is often dependent on the characteristics of the community and ecosystem that receives them (Kolar and Lodge 2001; Bruno et al. 2005; Zenni and Nuñez 2013).

Tilapia are native to Africa and a small portion of the middle east, but are now found throughout the world, particularly in tropical regions. Thus, we predict that hybridization and competition between introduced and native species of tilapia would be greatest in Africa because these basic theory expects these interactions to be strongest among similar species though recent work provides some evidence to the contrary in fish (Abrams 1983; Perry et al. 2002; Mouchet et al. 2013). To answer our second question, we used mixed-model logistic regression to estimate the importance of study design, species and regions to the occurrence of ecological impacts.

Finally, our third question examines whether perceptions of the relative harm or benefit of tilapia introduction differ by species, region, or the presence of ecological and socioeconomic effects. For each of the research papers reviewed, we recorded the authors’ conclusion, if any, about whether the effect on ecosystem services of the introduced tilapia studied was positive (increase in ecosystem services), negative

(decrease in ecosystem services), or both (positive and negative changes). We used mixed-model ordinal regression to model the contribution of ecological effects, species of tilapia, region, study design, and the relative monetary value of introduced tilapia fisheries to a country’s economy in explaining the observed perception of the impact on ecosystem services. We predicted that reports of ecological effects would be negatively correlated with the perception of tilapia introduction, but that the contribution of tilapia production to a country’s economy would be positively correlated with perception as well as the amount of research put towards tilapia ecology.

2.3 Methods

2.3.1 Literature review

Original published reports of the ecological impacts of tilapia introduction were sought from all sources, dates and locations, including peer reviewed publications, books, government and non-governmental organization reports, conference proceedings, and theses. A comprehensive keyword search using the following Boolean search string, “TS=(tilapi* OR oreochromis OR sarotherodon) AND TS=(impact OR

invasiv* OR effect OR introduc* OR compet* OR pred* OR hybrid* OR exotic OR biodiversity OR interact* OR nonnative OR non-native OR nonindigenous OR nonindigenous OR genetic* OR establish* OR habitat OR feral OR consequence* OR naturaliz* OR threat)” was used in the following online databases: Academic Search

Premier, Applied Science and Technology Index, Biological Abstracts, General Science

Abstracts, and Web of Science. All relevant search results were retrieved. Only original work in each report was reviewed; cited works were retrieved and reviewed separately whenever possible. All reports containing original records of introduction and/or ecological effects were subsequently forward-searched using Web of Science. Identified reports were deemed unavailable if they could not be located in the WorldCat database

(http://www.worldcat.org/) or retrieved through Inter-Library Loan at the University of

Notre Dame Libraries. Reports in languages other than English (7% of total) were not included.

For each paper, we recorded the focal tilapia species and the country or geographic region of introduction. Relying on the original authors’ interpretations, we recorded reported ecological effects attributed to tilapia introductions in four categories: effects on resident fish; effects on non-fish biota; effects on abiotic factors; and no effects considered. Effects on resident fish and non-fish biota were defined as changes in at least one demographic parameter (e.g., population size, somatic growth rates) of the resident species population(s) attributed to the introduced tilapia species.

Changes in these parameters were recorded as positive, negative or complex, the later

representing multiple parameter interactions. The resident fish category included native fish species and naturalized introduced species. Effects on non-biotic habitat components consisted of common water quality measurements (e.g. dissolved oxygen, pH, chlorophyll) typically as an indicator of eutrophication. The “no effects considered” category consisted of studies on introduced tilapia populations for which none of the first three categories of ecological effects were considered. These studies were typically focused narrowly on tilapia ecology, for example, on a comparison of somatic growth of tilapia under different conditions with no explicit consideration of the reciprocal effects of tilapia on the ecosystem.

In addition to ecological impacts, we also categorized the ecological mechanisms to which any reported effects were attributed by the original authors: competition, disease, and interspecific hybridization. The competition category consisted of any type of competition for food or space, and included interference and exploitative competition. Disease consisted of instances when tilapia served as a vector for nonindigenous pathogens or parasites, and/or provided a reservoir which maintained or propagated the incidence of an existing pathogen or parasite. The hybridization category consisted of interbreeding with native or previously introduced tilapia species.

We acknowledge that different study designs allow different strengths of inference about cause and effect (Diamond 1986), e.g., about whether a given impact on ecosystem services is caused by tilapia introduction or some other (potentially unmeasured) cause. Thus, we examined whether authors’ conclusions about impacts

were contingent on the type of study design employed. We created four categories to record characteristics of study designs used by the original authors to make inferences about ecological effects and mechanisms: quantitative measurement; time-series; observations or experiments with replicates; and observations or experiments with control or reference treatments (i.e., lacking tilapia). Quantitative measurement was defined as the numerical measurement of the parameters implicated in the effect or mechanism categories; purely anecdotal reports, presence-absence reports, and species lists were not included as quantitative measures. Time series was defined as the use of data collected either before and after tilapia introduction, or collected at least three data points across a ten years post tilapia introduction (Walpole et al. 2009).

Replication was defined as the presence of at least two replicates (i.e. n≥2) for at least one tilapia introduction treatment or where the populations of introduced tilapia can be reasonably considered to be distinct from other populations. In most cases this meant multiple isolated water bodies where tilapia had been introduced. Distant populations in a large lake or river, or experimental ponds completely drained between replicates were also included. Finally, we defined a control or reference treatment as a similar waterbody where tilapia were not introduced. For the hybridization mechanism, quantitative and control data were defined more specifically. Quantitative data was defined as the determination of hybridization using morphometric or meristic methods, genetic techniques, or experimental crosses. A hybridization study was recorded as

having control or reference treatments when the study reported high-order genetic mixing (e.g., via the detection of backcrosses and/or the fertility of hybrids).

Time-series, replication, and controls were not necessarily quantitative, e.g., anecdotes of tilapia impacts may still have a time-series component. Thus study design categories were independent, and for each paper, we extracted a fully crossed matrix of impacts and mechanisms times study design (i.e., a 7 X 4 matrix), in which was recorded the presence or absence of each study design feature for each impact and each mechanism.

The original authors’ description of the socioeconomic impact of tilapia was categorized as positive, negative, both, or not considered. We made determinations of

“positive” or “negative” perceptions only when the original paper included clear and explicit statements. Examples of phrases that led to a score of “negative” included: “We therefore consider that O. niloticus poses an unacceptable risk to its congenerics in the

Limpopo River system” (Zengeya et al. 2012), and “The species have become widely established, with a range of negative consequences for the rich natural fish fauna of this

Central American country” (McCrary et al. 2007). Statements scored as “ positive” included: “The general importance of tilapias to the Asian region is evident from the fact that the first ever, multination, selective breeding programme on a cultured finfish species in Asia was on O. niloticus” (De Silva et al. 2006) and “Use of these non-native tilapia and carp species in fisheries enhancement in mainland SE Asia supported substantial increases in harvestable biomass while having only mild impacts on native

fish communities” (Arthur et al. 2010). In contrast, where authors acknowledged both benefits and harms, “both” was assigned.. The default category was “not-considered”.

2.3.2 Analyses

2.3.2.1 Proportion of introductions associated with ecological impacts

We divide the number of countries from which ecological impacts have been attributed to tilapia by the total number of countries to which tilapia had been introduced and in which a specified level of research effort had been directed toward detecting ecological impacts. We categorized effort into six study design bins (see below). The bins represent a gradient of increasing strength of inference as a result of increasing sophistication of the research effort. Thus we provided a range of answers to our first research question, contingent on which bin or effort was used as a denominator. This approach recognized that detecting impacts of tilapia is analogous to ecological sampling for the presence of cryptic, rare or endangered species where non- detection may be a result of insufficient sampling effort. This metric does not indicate the overall scientific quality of the papers reviewed, because for many papers, the information relevant to our purpose was not the main focus of the original paper.

Study design bins comprise the number of countries which meet the following criteria. Bin 1 is the total number of countries in which tilapia species have been introduced. Bin 2 comprises the number of countries in which established, feral populations of tilapia exist. We based bins 1 and 2 on fishbase.org (Froese and Pauly.

2012) and the FAO Database on Introduced Aquatic Organisms (DIAS: FAO 2012). The literature review described above also provided data for bins 1 and 2, and was the sole source of data for bins 3-6. Bin 3 contained reports without quantitative data, while bin

4 reports were based on only quantitative data no other data types. Bin 5 reports contained quantitative data and control data, and bin 6 reports contained quantitative data, control data, and replicate or time-series data. Replicate and time-series data were considered equivalent here because of the common logistical tradeoff made in ecological studies between replication in time and space. We conservatively assumed that “no effect considered” papers represented absence of ecological effects, acknowledging that in many cases the original author may not have intended to address the ecological effects of tilapia. The justification for this assumption is that it is possible that the reason ecological effects were not considered was because effects did not exist.

This assumption makes it more difficult to reject the null hypothesis that ecological effects are uncommon as reported in Gozlan (2008).

Moving from bin 1 to bin 6 brings increasing strength of inference, increasing confidence that the reported presence or absence of an ecological effect represents the true state of the system. For example, an effect reported from a particular paper making use of quantitative data, replication, and controls (bin 6) is considered more likely to represent a real ecological effect than a paper reporting that an impact occurs but does not use controls (bin 4). Thus, we expect that the global probability of impacts calculated from higher bins are better representations of the true probability of impact.

A plateau in the detection of ecological effects with increasing bin number would suggest that the estimate is approaching the true value of the prevalence of ecological impacts.

2.3.2.2 Presence or absence of ecological effects as a function of effect type, species, region, and study design

We used mixed-model logistic regression to test whether the presence or absence of ecological effects was influenced by year of publication, tilapia species, region, effect type and study design. Only reports from the literature review (bins 3-6 ) were included in this analysis, and on the basis of results for Question 1 (see Results) study design was coded simply as the inclusion, or not, of controls or reference treatments. The data set was also limited to reports that explicitly considered the ecological impacts of tilapia, excluding “no effect considered” papers because doing so assured that relevant controls and references were applicable specifically to the ecological effects of tilapia. We set study design and publication year as fixed effect covariates, and nested these according to species or regions as random effects. Logistic regression models were fit for all possible combinations of including or excluding each fixed or random effect, yielding 16 basic models. These models were run for all ecological effects combined, and separately for effects on resident fish, the biotic community, and habitat. The best models were selected using Akaike’s Information

Criterion (AIC). Analyses were carried out in R (vers 2.15.2; R Core Team 2012), using the lmer function in the lme4 package for the logistic regression with mixed effects. 42

2.3.2.3 Perceptions of tradeoffs among ecosystem services resulting from tilapia

We used three approaches to test how the perception of tilapia is related to the relative magnitude of the socioeconomic benefit of tilapia. First, we used an ordinal regression employing a cumulative link mixed model to test for a relationship between perception of tilapia introduction outcomes (i.e. positive, negative, both) and the presence or absence of ecological effects. In this model, as in question 2, we also included fixed effects for publication year, study design, tilapia species, and region, while including the presence or absence of ecological effects as a fixed effect. These ordinal models were estimated using the clmm function in the ordinal package in R.

Model selection was performed in the same manner as described for question 2.

Second, we repeated the ordinal regression modeling described above, but included a socioeconomic term as a fixed effect, which was the percentage of country specific gross domestic product (GDP) contributed by tilapia fisheries. Model selection was carried as described above, and these ordinal regression models including GDP where then compared to those described above without GDP. We estimated percentage GDP for each country for which we had a literature-derived result for impact of tilapia. Countries’ GDP was retrieved from the World Bank DataBank

(http://databank.worldbank.org; accessed Dec. 13, 2012). We calculated the total value of tilapia harvest in each country (FAO 2012) by estimating the value per ton of tilapia aquaculture production and linearly extrapolating this to the countries’ total tilapia production from aquaculture and wild harvest. We made this estimation for every year

data was available from 1950 to 2010 using 2010 dollars ($USD). The percent of GDP from tilapia production was then matched to the year closest to the publication year within +/- 5 years.

Finally, we tested whether research effort or the perception of tilapia was related to the proportion of GDP contributed by tilapia. We used two one-way ANOVAs to test whether percentage GDP differed across countries with different levels of research effort (indexed by our study design, i.e., bin 3 to bin 6) or for perception of tilapia ecosystem service impact (i.e. negative, ambivalent, positive).

2.4 Results

The literature search conducted on May 25, 2010 identified 290 relevant publications for review. References within these publications and from forward literature searches identified a further 532 relevant publications for a total of 822 articles screened. Of these, 144 were unavailable. Of the 678 publications reviewed in detail, 416 included information on ecological interactions or fisheries involving tilapia, and 352 specifically addressed ecological effects of tilapia (see full list of publications in

Appendix A).

The rate of tilapia publications increased from the 1950s through the 1970s and plateaued in the early 1980s at about 25 publications annually (Appendix A). There were 140 countries into which at least one tilapia species had been introduced. Figure

2.1A maps the global distribution of tilapia ecology or fishery publications by focal species in each study, and summarizes the presence or absence of ecological effects 44

across major regions for both all reporting publications and for only studies which use controls. The United States and Sri Lanka were the two largest contributors to the tilapia literature with 93 and 30 reviewed publications, respectively, followed by India

(n=21), Mexico (20), Australia (17), Brazil (13), Kenya (11), Bangladesh (8), Israel (7),

Philippines (6), South Africa (5), and Madagascar (4; Figure 2.1A ). Due to the large discrepancy in sample size between the United States and Sri Lanka, and all other countries, countries other than the United States and Sri Lanka were pooled into five regions for analysis (Figure 2.1B ): Africa (31 countries), Asia (25), Neo-tropics (21), Lake

Victoria (18), and Oceania (14).

Publications from 37 countries reported ecological effects attributed to tilapia

(Figure 2.1). The Neotropics was the only region to report the presence of the full suite of ecological effects considered. In publications that explicitly considered the presence or absence of ecological effects, 152 reported effects while 22 reported that effects did not occur. An additional 53 publications reported ecological effects were reported both to occur and not occur for different types of effects or mechanisms.

2.4.1 Proportion of introductions associated with ecological impacts

Tilapia species have been introduced into 140 countries around the world.

Previous studies have estimated this closer to 90 to 100 countries (Pullin et al. 1997;

Coward and Little 2001). At present, 114 countries (55% of all countries in the world) have reported populations of non-native tilapia species established outside of aquaculture. At the country scale at least 26% of all known tilapia introductions were associated with ecological impacts (Figure 2.2, bin 1). From bin 1 to bin 6, the proportion of countries with impacts increased. For bin 4 and above, the proportion of countries which report effects is greater than 80%, and reaches 100% for some species.

Thus in almost all countries where even a modest level of ecological research effort has been targeted at tilapia, ecological impacts have been observed.

By bin 5, the increase in proportion of countries reporting ecological effects of tilapia has reached near the maximum relative to bin 4 and bin 6 (Figure 2.2). Based on this result, subsequent analyses (for the logistic regression addressing question 2, and the ordinal regression addressing question 3) use the presence or absence of controls or references is used as a threshold of inferential power.

2.4.2 Presence or absence of ecological effects as a function of effect type, species, region, and study design

Logistic regression provided no evidence that the year a study was conducted, the particular species, or the regions where the study occurred influenced detection of ecological effects (Appendix A). The best logistic regression model (lowest AIC) for the presence or absence of all effects combined was the simplest, intercept only model, implying no influence of any of the tested covariates on the overall presence or absence of ecological effects. In all 16 base models, dAIC was less than 7.7, suggesting negligible discrimination between these models and providing no support for the use of controls as a threshold indicator of study design, year, species or region on the presence or absence of ecological effects.

We expected different species, particularly O. mossambicus to be more strongly associated with ecological effects, but this was not the case. O. mossambicus may simply be considered the “worst” invasive fish (Lowe et al. 2000), more due to its global distribution rather than the presence of ecological effects. Both Oreochromis and

Tilapia species were implicated in the occurrence of ecological effects despite

substantive differences between these genera in life-history, such as parental care and diet (Beveridge and Baird 2000; Klett and Meyer 2001). As expected hybridization was reported more often in Africa (Error! Reference source not found.B) but was also reported among feral introduced tilapia in all regions considered and regional effects were not significant.

When considering different ecological effects and mechanisms separately by logistic regression, only impacts on resident fish and biotic community have sufficient representation across species and regions for using our logistic regression model (Error!

Reference source not found.). In both cases, the best selected model was the intercept only model. For impacts on resident fish, the top seven models where all within dAIC =

2 of the best model. For impacts on biotic community the top four model were all within dAIC=2, each with the addition of a second parameter (year, region or species, respectively) indicating little ability to distinguish between no influence and the influence of these parameters.

2.4.3 Perceptions of tradeoffs among ecosystem services resulting from tilapia

For all ecological effect types combined, the best ordinal regression included terms for the presence of region and species (Figure 2.3A), use of controls (Figure 2.3B), and publication year (Figure 2.3C). The second best model additionally included species groupings, but no species significantly deviated from the mean species effect. The two top models (dAIC <1.6) also supported significant effects of year of publication, with older publications more likely to report positive outcomes (Error! Reference source not

found.C). These two top models also indicated a significant effect of region, with

Oceania dominated by negative perceptions and Sri Lanka by positive perceptions

(Figure 2.3A).

The presence of ecological effects was the most influential determinant of perceptions of tilapia introduction in the ordinal regression model, increasing the probability of negative perceptions of tilapia introduction (Table 2.1). As expected, authors’ perceptions about tilapia impact on ecosystem services were consistent with measures of ecological effects (Figure 2.4). For studies in which impacts were documented, over four times as many papers concluded that the impacts of tilapia were negative than concluded that impact was positive, with an intermediate number of papers having both positive and negative components. In contrast, for papers reporting no effects of tilapia, the opposite trend suggests positive perceptions are more associated with lack of ecological effects (Figure 2.4A). The largest single category of publications reported both ecological impacts and a negative perception of tilapia introduction (Figure 2.4A). For papers that included a control or reference treatment, the pattern was similar but less pronounced (Figure 2.4B). The inclusion of increasing research effort decreased the probability of reporting negative perceptions such that the occurrence of both impacts and controls made ambivalent outcomes more likely.

As the proportional contribution of tilapia fisheries to country GDP increased, we observed the expected trend of increased research effort (i.e. bin assignment), but this trend was not statistically significant (Figure 2.5; F= 1.16, p= 0.33). As the proportional contribution of tilapia fisheries to country GDP increased, there was a significant trend towards increased positive perception about the ecosystem service impact of tilapia (F=

4.135 , p= 0.016; Figure 2.6). Including GDP in the ordinal regression required a loss in sample size due to missing GDP data, reducing from 191 publications in the overall model to 129. The best model included GDP as well as control treatment, impact, and region effects (Table 2.1). The second and third best models, however, were also highly supported (dAIC<2) but neither included GDP. The second model was more parsimonious than the first, and the third model was identical to the best model from the previous ordinal regression which excluded GDP. These results suggest that while

GDP and perception were related, percent contribution of tilapia to GDP was not a sufficient predictor of perception of the ecosystem tradeoffs.

TABLE 2.1.

THE BEST ORDINAL REGRESSION MODELS OF THE PERCEIVED BENEFIT OF TILAPIA

INTRODUCTION FOR ALL ECOLOGICAL EFFECT CATEGORIES COMBINED.

Random

Fixed effects coefficient effects

Ctrl

GDP

dAIC

Effects Region

Model -LL AIC Species

All impact categories (n=191)

Ctrl+Yr+Impacts+(Reg) 167.7 347.3 0.0 1.1 -0.03 -2.5 na 0.3

Ctrl+Yr +Impacts+(Sp)+(Reg) 167.5 348.9 1.6 1.1 -0.03 -2.4 na -1.3 0.3 Ctrl+Impacts+(Reg) 171.1 352.1 4.8 1.0 -2.4 na 0.3

Ctrl+Impacts+(Sp)+(Reg) 171.0 353.9 6.6 1.0 -2.3 na -1.4 0.3

Including GDP (n=129)

Ctrl+Impacts+GDP+(Reg) 114.4 240.8 0.0 1.2 -2.5 -0.02 -0.1

Ctrl+Impacts+(Reg) 115.4 240.9 0.1 1.1 -2.5 0.0

Ctrl+ Yr +Impacts+(Reg) 114.8 241.6 0.8 1.1 -0.02 -2.5 -0.1

Ctrl+ Yr +Impacts+GDP+(Reg) 114.4 242.8 2.0 1.2 -4e-3 -2.5 -0.02 -0.1 Summary model selection statistics, fixed, and random effects coefficients for species and regions. Items in parentheses indicate random effects. –LL= negative log likelihood, AIC= Akiake’s information criteria, dAIC = difference in Akaike’s information criteria to best model, Ctrl= presence or absence of control treatments, Yr= year of publication, Effects= presence or absence of ecological effects, GDP=Percent contribution of tilapia to GDP. and ‘na’ indicates that GDP was not included in the model selection. Bold indicates statistically significant effects p<0.05.

2.5 Discussion

This study represents the most comprehensive review of tilapia literature to date and reveals that previous studies have underestimated the ecological effects of tilapia at the global scale. In this analysis, the lowest proportion of countries that have received introductions and reported ecological effects (26%, Figure 2.2) is much higher than the average 5% and even the most extreme 20% previously reported (Gozlan 2008). The ecological effects of tilapia introduction are reported almost everywhere tilapia introductions are studied with no significant effects detected by species or region.

Indeed, the geographic extent in our analysis include all continents and most major freshwater habitat types, except polar and montane freshwater (Abell et al. 2008) and includes all major tilapia species. We interpret the widespread occurrence of ecological effects across species and regions as suggesting that the occurrence of ecological effects may be expected wherever established feral tilapia populations exist in similar habitats.

The perception that tilapia negatively affect ecosystem services was significantly associated with documented ecological effects on ecosystem services, but with some strong regional differences. Trends in tilapia contributions to a county’s GDP are consistent with the interpretation that these regional differences may be explained by the socioeconomic benefits of tilapia introduction. However, contribution to GDP is insufficient to predict whether tilapia introduction are viewed as positive or negative forces on ecosystem services because increasing research effort associated with higher contributions to GDP also tends to increase the recognition of both positive and

negative perspectives. As we expected, these results quantitatively demonstrate that studies with higher research effort were more likely to temper claims of negative ecological impacts with appeals to socioeconomic benefits, or equivalently, to temper claims of positive socioeconomic impact with appeals to ecological impacts.

We addressed the divergence of conclusions about tilapia impacts apparent in the literature by separately addressing the ecological effects of tilapia introduction, which are rarely monetized, from the more easily monetized ecosystem service provided by tilapia production and harvest. Geographical and socioeconomic contexts of tilapia introduction, as well as ecological effects, play a significant role in the perspective of benefits or harm. While documented ecological changes were strongly associated with negative perspectives, this relationship was also dependent on the socio-economic context. These results are in agreement with previous work emphasizing that concepts of invasion are context specific and mutable (Lodge and

Shrader-Frechette 2003; Nuñez and Simberloff 2005), that complex perceptions are typical for introduced species that provide some ecosystem services while harming others (Davis et al. 2011; Gleditsch and Carlo 2011; Schlaepfer et al. 2011). Below we discuss some general considerations and potential analyses to further inform decision and policy making regarding tilapia introductions.

One of the main results of this review was the discovery that regional context partly determines the perception of ecosystem services provided by, or lost to, tilapia introduction. We used very coarse country and regional groupings in this analysis to

construct reasonable sample sizes of publications for analysis. Regional patterns may therefore be partly due to the grouping of publications and introductions across regions considered. This effect may be particularly true for the United States and Sri Lanka, the two largest contributors of tilapia research by a considerable margin. Together, these two countries represent 35% of global tilapia research publications, but only 2% of countries to which tilapia have established feral populations. Moreover, reports from these countries had generally opposing perspectives towards the ecosystem service outcome of tilapia introduction. Approximately 80% of US publications emphasized negative impacts on ecosystem services, while in Sri Lanka, 75% of the publications emphasized positive outcomes. The results from these countries should, however, be viewed with caution because neither the United States nor Sri Lanka represent typical biogeography of tilapia introductions. The United States exists at the very edge of the environmental niche tolerances of tilapia (Zambrano et al. 2006) and winter die-offs occur even in Texas and other southern states (Germany and Noble 1977). Meanwhile

Sri Lanka is environmentally well suited for tilapia but there are few if any native freshwater fish species (De Silva 1989) to resist or be affected by introductions (Moyle and Marchetti 2006). Thus the ecological effects and resulting tradeoffs reported in the

US and Sri Lankan habitats may not be comparable to other regions or countries, despite the fact that these countries are the largest contributors to tilapia ecology publications.

There are very few reports of tilapia in Europe, and this presents an opportunity for decision makers. On the one hand, introductions of tilapia in Europe appear relatively rare (Figure 2.1 A) but feral populations have recently been reported in Italy

(Bianco and Turin 2010) and Turkey (Akin et al. 2005). The fate of these populations as well as future introductions still lie in the hand of decision makers. Meanwhile, much of

Oceania has already experienced tilapia introductions and a fairly large number of the publications reviewed herein (30) are from this region. Our analyses suggest that

Oceania is associated with negative outcomes significantly more than any other region.

Yet, Oceania represents probably the largest and most geographically dispersed region included in the study. There are doubtless undocumented islands to which tilapia have become established, and others to which it has not, with obvious potential for a large scale natural experiment.

Tilapia introductions often occur simultaneously with other major drivers of ecological change in freshwaters such as habitat degradation, heavy fishing pressure, flow modifications, and pollution (Dudgeon et al. 2005; De Silva et al. 2009). But, almost none of the studies reviewed herein comprehensively address these possibly confounding factors with rigorous study designs. Few of the publications were experimental; most were observational studies of natural experiments and fishery- derived data. Replicates and controls were rare and their association to treatments often tenuous. As with other invasive species, assigning causation for observed changes under these circumstances is difficult (Lodge and Shrader-Frechette 2003).

We suggest our analysis across bins of increasing inferential ability partly overcomes this problem by increasing our confidence that publications are reporting tilapia as the true cause of observed ecological effects. Nonetheless, our analysis suffers the weaknesses of a “vote-counting” approach (Gurevitch and Hedges 2001) within each bin. One of these weaknesses is the reliance on the statistical power within each study, which is often severely limited by small sample sizes and insufficient control. Another limitation is inability to estimate effect magnitude, i.e., the size (not just the occurrence) of the ecological effects on ecosystem services. An extensive set of tools have been developed for meta-analysis of ecological literature that estimates and compares these magnitudes in terms of effect sizes (Osenberg et al. 1999; Cadotte et al. 2012). Such an analysis could probably be conducted on the small subset of reports used herein that used both controls and replicates.

2.6 Conclusion

This review quantitatively demonstrates that tilapia introductions often represent a tradeoff between ecosystem services provided by tilapia and services which are negatively affected by tilapia. Being able to consider this tradeoff explicitly in the decision making process is important to adapting to ongoing environmental change and resolving the conflicting attitudes pervasive in the global tilapia literature. We demonstrated on a global scale that increasing research effort increases the probability of detecting ecological impacts of tilapia introduction. More than 80% of published ecological research on tilapia reported changes in ecosystem services. The occurrence 63

of ecological effects was not a product of different species of tilapia, different global regions, the type of data used, or study-design. It is unequivocal that tilapia are frequently associated with, and a demonstrated cause of, undesirable ecological changes in many areas.

Our results illustrate that increasing research efforts leads to increasingly ambivalent perspectives about the net socioeconomic value of tilapia introductions, as undesirable ecological impacts become as apparent as the socioeconomic benefits of tilapia production. In some cases, perspectives are regionally determined. There is not, nor do we think there is likely ever to be, a global consensus on the socioeconomic merits of tilapia introduction. Rather, we recommend that decisions be informed by comparisons of the regional and local economic benefits to the regional and local ecological costs. While the ecological effects may be similar over much of the introduced range of tilapia, as our results demonstrate, there is no reason to expect uniform socioeconomic benefits. The work of managers, decision and policy makers and other stakeholders is therefore made all the more relevant in the careful consideration of local context in decisions about future tilapia introductions. And there will be ample future opportunities for informed decisions.

Only about half of all tropical countries have at least one established, feral, tilapia population. This number is probably an underestimate due to underreporting in global datasets (Welcomme 2011a) and limited resources for research in areas where tilapia are prominent (UNESCO 2007; Josupeit 2010; Skelton and Swartz 2011).

Nonetheless, an important opportunity clearly exists for careful decisions in the many countries that are suitable for tilapia production, but are as yet uncolonized by tilapia.

2.7 Acknowledgments

My coauthors J.M. Deines, M.E. Wittmann, and D.M. Lodge. B. Althouse, A. K.

Baldridge, M. A. Barnes, N. Dorn, Z. Feiner, C. Gantz, D. Hayes, L. Sargent, and R. Wright contributed to the design and implementation of the data collection. The Lodge Lab contributed useful comments on analyses and early versions of this manuscript.

Funding for this research has been provided by GLOBES (NSF DGE-0504495), NSF DDEP award #1046682, US EPA, NOAA and the Center for Sponsored Coastal and Ocean

Research (CSCOR Award # NA09NOS4780192, NA10NOS4780218),and the

Environmental Change Initiative at the University of Notre Dame. In particular, the staff of the inter-library loan office at the University of Notre Dame Libraries made this literature review possible.

2.8 References

Abell, R., Thieme, M.L., Revenga, C., et al. (2008) Freshwater Ecoregions of the World: A New Map of Biogeographic Units for Freshwater Biodiversity Conservation. Bioscience 58, 403–414.

Abrams, P. (1983) The Theory Of Limiting Similarity. Annual Review of Ecology and Systematics 14, 359–376.

Akin, S., Buhan, E., Winemiller, K.O. and Yilmaz, H. (2005) Fish assemblage structure of Koycegiz Lagoon-Estuary, Turkey: Spatial and temporal distribution patterns in relation to environmental variation. Estuarine, Coastal and Shelf Science 64, 671– 684.

Amano, T. and Sutherland, W.J. (2013) Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security. Proceedings of the Royal Society B 280, 20122649.

Arthur, R.I., Lorenzen, K., Homekingkeo, P., Sidavong, K., Sengvilaikham, B. and Garaway, C.J. (2010) Assessing impacts of introduced aquaculture species on native fish communities: Nile tilapia and major carps in SE Asian freshwaters. Aquaculture 299, 81–88.

Beveridge, M.C.M. and Baird, D.J. (2000) Diet, feeding and digestive physiology. In: Tilapias: Biology and Exploitation. (eds M.C.M. Beverage and B.J. McAndrew). Kluwer Academic Publishers, Boston, pp 59–87.

Bianco, P.G. and Turin, P. (2010) Record of two established populations of Nile tilapia, Oreochromis niloticus, in freshwaters of northern Italy. Journal of Applied Ichthyology 26, 140–142.

Black, G.F. (1980) Status of the desert pupfish, Cyprinodon macularius ( Baird and Girard), in California. 42.

Bostock, J., McAndrew, B., Richards, R., et al. (2010) Aquaculture: global status and trends. Philosophical transactions of the Royal Society B 365, 2897–2912.

Bruno, J.F., Fridley, J.D., Bromberg, K.D. and Bertness, M.D. (2005) Insights into biotic interactions from studies of species invasions. In: Species Invasions: Insights in ecology, evolution, and biogeography. (eds D.F. Sax, J.J. Stachowicz and S.D. Gaines). Sinauer Associates, Inc, Sunderland, MA, p 495.

Cadotte, M.W., Mehrkens, L.R. and Menge, D.N.L. (2012) Gauging the impact of meta- analysis on ecology. Evolutionary Ecology 26, 1153–1167.

Canonico, G.C., Arthington, A., McCrary, J.K. and Thieme, M.L. (2005) The effects of introduced tilapias on native biodiversity. Aquatic Conservation: Marine And Freshwater Ecosystems 15, 463–483.

Coward, K. and Little, D.C. (2001) Culture of the “aquatic chicken”: present concerns and future prospects. Biologist 48, 12–16.

Crutchfield, J.U.J. (1995) Establishment and Expansion of Redbelly Tilapia and Blue Tilapia in a Power Plant Cooling Reservoir. American Fisheries Society Symposium 15, 452–461.

Davis, M.A., Chew, M.K., Hobbs, R.J., et al. (2011) Don’t judge species on their origins. Nature 474, 153–154.

Dudgeon, D., Arthington, A.H., Gessner, M.O., et al. (2005) Freshwater biodiversity: importance, threats, status and conservation challenge. Biological Review 81, 163–182.

FAO (2012) Database of Introduced Aquatic Species. Available at: www.fao.org/fishery/dias/en [Accessed September 2, 2012].

FAO Fisheries and Aquaculture Department (2012) Fisheries Global Information System (FAO-FIGIS)- Web Site. Available at: http://www.fao.org/fishery/figis/en [Accessed December 13, 2012].

Figueredo, C.C. and Giani, A. (2005) Ecological interactions between Nile tilapia (Oreochromis niloticus, L.) and the phytoplanktonic community of the Furnas Reservoir (Brazil). Freshwater Biology 50, 1391–1403.

Food and Agriculture Organization of the United Nations (2011) Fishery and Aquaculture Statistics 2009. FAO, ROME.

Froese, R. and Pauly., D. (2012) FishBase.

Germany, R.D. and Noble, R.L. (1977) Population Dynamics of Blue tilapia in Trinidad Lake, Texas . Proc. 31st Ann. Conf. S.E. assoc. fish and wildl. Agencies 31, 412– 417.

Gindelberger, B. (1981) Why Sinarapan almost Disappered from Lake Buhi. Naga 4, 3–5.

Gleditsch, J.M. and Carlo, T.A. (2011) Fruit quantity of invasive shrubs predicts the abundance of common native avian frugivores in central Pennsylvania. Diversity and Distributions 17, 244–253.

Goudswaard, P.C., Witte, F. and Katunzi, E.F.B. (2002) The tilapiine fish stock of Lake Victoria before and after the Nile perch upsurge. Journal of Fish Biology 60, 838– 856.

Gozlan, R.E. (2008) Introduction of non-native freshwater fish: is it all bad? Fish and Fisheries 9, 106–115.

Gurevitch, J. and Hedges, L.. (2001) Meta-analysis: combining the results of independent experiments. In: Design and Analysis of Ecological Experiments. (eds S.M. Scheiner and J. Gurevitch). Oxford University Press, New York, pp 347–369.

Hauser, W.J., Legner, E.F., Medved, R.A. and Platt, S. (1976) Tilapia- A management tool. Fisheries 1, 24.

Hooper, D.U., Chapin, F.S., Ewel, J.J., et al. (2005) Effects of Biodiversity on Ecosystem Functioning: A Consensus of Current Knowledge. Ecological Monographs 75, 3– 35.

Josupeit, H. (2010) World supply and demand of tilapia 2010. In: INFOFISH Tilapia Conference. FAO GlobeFish, Kuala Lumpur, p 6.

Keller, R.P., Lodge, D.M., Lewis, M.A. and Shogren, J.F. (2009) Bioeconomics of invasive species: integrating ecology, economics, and management. Oxford University Press

Klett, V. and Meyer, A. (2001) What, if Anything, is a Tilapia?—Mitochondrial ND2 Phylogeny of Tilapiines and the Evolution of Parental Care Systems in the African Cichlid Fishes. Molecular Biology and Evolution 19, 865–883.

Kolar, C.S. and Lodge, D.M. (2001) Progress in invasion biology: predicting invaders. TRENDS in Ecology & Evolution 16, 199–204.

Leveque, C. (2002) Out of Africa: the success story of tilapias. Environmental Biology of Fishes 64, 461–464.

Lodge, D.M., Deines, A.M., Gherardi, F., et al. (2012) Global Introductions of Crayfishes: Evaluating the Impact of Species Invasions on Ecosystem Services. Annual Reviews in Ecology Evolution and Systematics 43, 449–472.

Lodge, D.M. and Shrader-Frechette, K. (2003) Nonindigenous Species: Ecological Explaination, Environmental Ethics, and Public Policy. Conservation Biology 17, 31–37.

Lodge, D.M., Williams, S., Macisaac, H.J., et al. (2006) Biological Invasions: Recommendations for U.S. Policy and Management. Ecological Applications 16, 2035–2054.

Lorenzen, K. (2000) Population dynamics and management. In: Tilapias: Biology and Exploitation. (eds M.C.M. Beverage and B.J. McAndrew). Kluwer Academic Publishers, Boston, pp 163–225.

Lövei, G.L., Lewinsohn, T.M. and The Biological Invasions in Megadiverse Regions Network (2012) Megadiverse developing countries face huge risks from invasives. Trends in ecology & evolution 27, 2–3.

Lowe, S., Browne, M., Boudjelas, S. and M, D.P. (2000) 100 of the World’s Worst Invasive Alien Species A selection from the Global Invasive Species Database. The Invasive Species Specialist Group (ISSG) a specialist group of the Species Survival Commission (SSC) of the World Conservation Union (IUCN),.

McCrary, J.K., Murphy, B.R., Jay R. Stauffer, J. and Hendrix, S.S. (2007) Tilapia (Teleostei: Cichlidae) status in Nicaraguan natural waters. Environmental Biology of Fishes 78, 107–114.

McKaye, K.R., Ryan, J.D., Jay R. Stauffer, J., Perez, L.J.L., Vega, G.I. and Berghe, E.P. van den (1995) African Tilapia in Lake Nicaragua. Bioscience 45, 406–411.

Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-being: Synthesis. Island Press, Washington, D.C.

Mkumbo, O.C. and Ligtvoet, W. (1992) Changes in the diet of Nile perch, lates niloticus (L), in the Mwanza Gulf, Lake Victoria. Hydrobiologia 232, 79–83.

Mouchet, M. a., Burns, M.D.M., Garcia, A.M., Vieira, J.P. and Mouillot, D. (2013) Invariant scaling relationship between functional dissimilarity and co-occurrence in fish assemblages of the Patos Lagoon estuary (Brazil): environmental filtering consistently overshadows competitive exclusion. Oikos 122, 247–257.

Moyle, P.B. and Marchetti, M.P. (2006) Predicting Invasion Success: Freshwater Fishes in California as a Model. BioScience 56, 515.

Nuñez, M.A. and Simberloff, D. (2005) Invasive Species and the Cultural Keystone Species Concept. Ecology and Society 10, 4–7.

Osenberg, C.W., Sarnelle, O., Cooper, S.D. and Holt, R.D. (1999) Resolving Ecological Questions through Meta-Analysis: Goals, Metrics, and Models. Ecology 80, 1105– 1117.

Perry, W.L., Lodge, D.M. and Feder, J.L. (2002) Importance of Hybridization between Indigenous and Nonindigenous Freshwater Species : An Overlooked Threat to North American Biodiversity. Society of Systematic Biologists 51, 255–275.

Pullin, R.S. V, Palomares, M.L., Casal, C. V, Dey, M.M. and Pauly, D. (1997) Environmental Impacts of Tilapias. In: Proceedings from the Fourth International Symposium on Tilapia in Aquaculture, Coronado Springs Resort, Walt Disney World. International Center for Living Aquatic Resources management (ICLARM), Orlando, Florida, pp 553–570.

R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Ranoemihardjo, B.S. (1981) Nauru: Eradication of Tilapia from fresh- and brackishwater lagoons and ponds with a view to promoting milkfish culture. 24.

Schlaepfer, M.A., Sax, D.F. and Olden, J.D. (2011) The Potential Conservation Value of Non-Native Species. Conservation Biology.

De Silva, S.S. (1989) Status of Introduced species in Sri lanka. In: Exotic Aquatic Orgnaisms in Asia. Proceedings of the workshop on Introduction of Exotic Aquatic Organisms in Asia. , Vol. Special P. (ed S.S. De Silava). Asian Fishery Soceity, Manila, Philippines, p 99.

De Silva, S.S., Nguyen, T.T.T., Turchini, G.M., Amarasinghe, U.S. and Abery, N.W. (2009) Alien species in aquaculture and biodiversity: A paradox in food production. Ambio 38, 24–28.

De Silva, S.S., Nguyen, T.T.T., Wabery, N. and Amarasinghe, U.S. (2006) An evaluation of the role and impacts of alien finfish in Asian inland aquaculture. Aquaculture Research 37, 1–17.

Skelton, P.H. and Swartz, E.R. (2011) Walking the tightrope: trends in African freshwater systematic ichthyology. Journal of fish biology 79, 1413–35.

Sugunan, V. V (2000) Ecology and fishery management of reservoirs in India. Hydrobiologia 430, 121–147.

Sugunan, V. V (1995) Reservoir fisheries of India. FAO Fisheries Technical Paper no 345, 423p.

UNESCO (2007) A global perspective on research and development. UNESCO Institute for Statistics Factr Sheet 5, 1–8.

Varela-Romero, A., Ruiz-Campos, G., Yepiz-Velazquez, L.M. and Alaniz-Garcia, J. (2000) Distribution, habitat and conservation status of desert pupfish (Cyprinodon macularius) in the Lower Colorado River Basin, Mexico. Reviews In Fish Biology And Fisheries, 12, 157–165.

Vitule, J.R.S., Freire, carolina A. and Simberloff, D. (2009) Introduction of non-native freshwater fish can certainly be bad. Fish and Fisheries 10, 98–108.

Walpole, M., Almond, R.E.A., Besançon, C., et al. (2009) Tracking progress toward the 2010 Biodiversity Target and beyond. Science 325, 1503–1504.

Welcomme, R.L. (2011a) An overview of global catch statistics for inland fish. ICES Journal of Marine Science 68, 1751–1756.

Welcomme, R.L. (2011b) Review Of The State Of The World Fishery Resources:, (Vol. 942). FAO, ROME.

Zambrano, L., Martinez-Meyer, E., Menezes, N. and Peterson, A.T. (2006) Invasive potential of Common carp (Cyprinus carpo) and Nile tilapia (Oreochromis niloticus) in American freshwater systems. Can. J. Fish. Aquat. Sci. 63, 1903– 1910.

Zengeya, T. a., Robertson, M.P., Booth, A.J. and Chimimba, C.T. (2012) A qualitative ecological risk assessment of the invasive Nile tilapia, Oreochromis niloticus in a sub-tropical African river system (Limpopo River, South Africa). Aquatic Conservation: Marine and Freshwater Ecosystems, 23, 51-64.

Zenni, R.D. and Nuñez, M. a. (2013) The elephant in the room: the role of failed invasions in understanding invasion biology. Oikos, in press.

CHAPTER 3:

HYBRIDIZATION OF NATIVE OREOCHROMIS SPECIES (CICHLIDAE) AND THE INTRODUCED

NILE TILAPIA (O. NILOTICUS) IN THE KAFUE RIVER, ZAMBIA.

3.1 Abstract

The freshwater Nile tilapia, Oreochromis niloticus, is native to northern Africa but has been introduced around the world for aquaculture, including within Africa but outside its native range. Hybridization between escaped O. niloticus and native

Oreochromis species is of serious concern due to its potential negative effects on wild genetic resources for conservation, improved aquaculture, and the capture fishery of both native and Nile tilapia. Nile tilapia were introduced into Zambia for commercial aquaculture in the early 1980’s, and soon escaped and spread in the Kafue River. At the time the Kafue was home to two native Oreochromis species, O. andersonii and O. macrochir. Here, we document the current extent of O. niloticus spread, test for hybridization with the two native species through genetic analysis of eight microsatellite loci, and evaluate the potential for losses in genetic diversity that may impact aquaculture and fisheries production. We genotyped O. niloticus (n = 6) obtained from the Zambian Department of Fisheries and O. andersonii (n = 22) and O. macrochir (n =

28) from Lake Itezhi-tezhi (upriver of a dam that Nile tilapia has not yet breached) to

provide genetic baselines for the introduced and native fish populations, respectively.

Comparisons of these genotypes were then made to 33 fish collected from the Kafue river fishery area that possessed phenotypic features suggesting they were hybrids.

STRUCTURE analysis implied that the majority of the fishery area specimens were of hybrid ancestry, representing an admixture of O. niloticus, O. andersonii, and O. macrochir. Only one individual was identified as being a likely recent hybrid by the program NEWHYBRIDS. Thus, past introgression appears to have produced many later generation hybrids of mixed ancestry among the Oreochromis species. The state of tilapiine biodiversity resources on the Kafue River suggest that tilapia introduction may affect the genetic resources available for aquaculture and fisheries, and managers should carefully account for these risks when considering further introductions to regions where non-native tilapia have not yet established.

3.2 Introduction

The conservation of ecosystem services that enhance food production are essential to future sustainable development (Foley et al. 2011), and aquaculture is an increasingly important source of dietary protein (FAO 2010; Nijdam et al. 2012).

Aquaculture is expected to provide needed increases and stability in food supplies while reducing the environmental impacts of fishery overexploitation. However, evidence suggests that these benefits have not been fully realized (Naylor et al. 2000; FAO 2012).

There are several undesirable impacts of aquaculture on ecosystem services, including consumption of space, water, and food resources, release of production wastes, and the 73

introduction of species into habitats outside their native range (Beveridge et al. 1994).

In the latter instance, introductions of non-native fishes raise the specter of hybridization and introgression with native populations when close relatives exist outside of culture. Because wild genetic diversity is the ultimate source of desirable traits for culture (Brummer et al. 2011), hybridization between introduced and closely related native fish species represents a serious threat to the conservation and management of important genetic resources (Rhymer and Simberloff 1996; Allendorf et al. 2001; Perry et al. 2002). Rather than a panacea of productivity, aquaculture may often present mangers with a tradeoff between the clear and immediate market value of production versus the more difficult to value supporting and regulating ecosystem services resulting from genetic biodiversity.

Tilapia fishes in Africa provide model cases for evaluating the potential benefits and risks of aquaculture, particularly in regards to the tradeoffs in aquaculture productivity versus the risk of hybridization and loss of biodiversity. The common name

“tilapia” has been widely applied to many cichlid species, most commonly in the genera

Tilapia, Oreochromis, and Sarotherodon which are native to Africa and represent a sister clade to the well-studied haplochromine cichlids (Fryer and Iles 1972; Trewavas 1983).

Of the many tilapia genera, Oreochromis is the most prominent in global aquaculture

(Bostock et al. 2010). In particular, the Nile tilapia, O. niloticus, is the most commonly reared species, accounting for about 75% of global tilapia production (Josupeit 2010).

Indeed, almost the entirety of tilapia aquaculture production in Africa is O. niloticus

(Muir et al. 2005), representing over 430,000 tons in 2008 (Josupeit 2010). Oreochromis niloticus has now been introduced to at least 15 African countries outside of its native range (Deines et al. Ch.2; FAO 2012). The large growth of tilapia aquaculture within

Africa (Jamu and Brummett 2004) presents an increasing risk of releases (both unintentional and intentional) that may harm valuable native fish species.

Evidence indicates that hybridization is common between Oreochromis species

(Agnese 1998). Hybridization of O. niloticus has been documented with the native O. mossambicus in South Africa (Gregg et al. 1998; Moralee et al. 2000; D’Amato et al.

2007) and with the native O. esculentus in Lake Victoria (Welcomme 1966; Mwanja and

Kaufman 1995; Angienda et al. 2010). Though the prevalence of hybridization where it does occur can vary (Angienda et al. 2010), in every case where it has been studied hybridization has been detected between co-occurring tilapia species (Deines et al.

Ch.2). Thus, there is little doubt that O. niloticus spread by aquaculture can endanger the genetic diversity of native fish species when and wherever they come into contact

(Deines et al. Ch.2; Lind et al. 2012). Moreover, hybrid fish may have relatively poor long-term fitness, reducing the total productivity of the capture fishing industry

(Amarasinghe and De Silva 1996).

Here we 1) document the spread of O. niloticus in the fishery area of the Kafue

River in Zambia (Figure 3.1A); 2) test for hybridization of O. niloticus with two native

Oreochromis species, O. macrochir and O. andersonii; and 3) evaluate the potential impact of interspecific hybridization on native Oreochromis genetic diversity. Nile tilapia

were introduced into Zambia for aquaculture and subsequently escaped in the 1980’s

(Audenaerde 1994; Schwanck 1995). Since then the opportunity has existed for extensive hybridization and introgression to occur between the escaped non-native and native fishes. To investigate this hybridization hypothesis, we compare multilocus genotypes for eight microsatellite loci among putatively pure native O. andersonii and O. macrochir, a sample of current aquaculture stock of O. niloticus introduced into Zambia, and putatively hybrid fish (identified on the basis of morphology) collected from the fishing region of the Kafue River. We report evidence for hybridization among these fish and discuss its implications for the conservation of genetic diversity in native fish and the potential effects hybridization may have for the aquaculture and capture fisheries in

Zambia.

3.3 Methods

3.3.1 Site description

The Nile tilapia was first introduced into Zambia for aquaculture in the early

1980’s from at least four strains kept in Scotland, Israel, Germany and Kenya by the

Zambian Department of Fisheries (DOF; Audenaerde 1994). The DoF distributed O. niloticus throughout the country for aquaculture development, including the Northern

Provence that spans the Congo and Luangua drainages, Lake Kariba on the Zambezi

River, and the Kafue River, a major tributary of the Zambezi River.

The Kafue River is characterized by a large flat floodplain in the Southern

Province of Zambia (Figure 3.1A) that makes up the main fishing region. The rainy season begins here in November with flood waters historically peaking in April and May, inundating more than 6,000 square kilometres, an area roughly one third the size of

Lake Ontario of the Laurentian Great Lakes. Since 1977, flooding has been controlled by the upstream Itezhi-tezhi dam and to a lesser extent by the downstream dam at Kafue

Gorge (Deines et al. 2013). Nevertheless, flooding in the mid-1980s allowed the Nile tilapia to escape from aquaculture into the Kafue River near the town of Mazabuka (Site

S5, Figure 3.1). Subsequently, Nile tilapia populations were well-established and reported spreading by 1994 (Schwanck 1995). DoF fishery-independent gillnet surveys conducted up to 2005 reported “unidentified tilapia” throughout the main fishery region of the Kafue River (DoF, unpublished data) thought to be O. niloticus or their hybrids with O. andersonii (R. Nkahta, pers. com.). The upstream dispersal of Nile tilapia from the original introduction appears to have been blocked by the Itezhi-tezhi dam, as no O. niloticus has been yet been reported from Lake Itezhi-tezhi (Site S1c, Figure 3.1).

Oreochromis niloticus is, however, present in aquaculture in the upper Kafue watershed above Itezhi-tezhi and is strongly suspected to be feral in the upper reaches of the Kafue

(outside of the area sampled for this study). The Kafue fishery has remained artisanal since its inception in the 1950s. Fishers typically use dugout canoes or fiberglass

“banana boats” and multifilament gillnets to catch fish. Illegal harvesting methods are

also commonly used: large (> 100m,< 1mm mesh) hand drawn seines, monofilament gillnets, gillnets of mesh less than 50 mm, and beating the water to drive fish into gillnets. Oreochromis species are one of the main large-bodied cichlids harvested. Other harvested species include cichlids of the genera Tilapia, Sagrochromis and Serranochromis, as well as Clarius catfish, the Kafue pike, Hepsetus odeo, many small Cyprinid species, and to a lesser extent, Mormyrids and Synodontids. All sizes of most species are harvested and marketed. There has been a dramatic decline in fish abundance over the last 50 years, corresponding to large increases in fishing effort

(Deines et al. 2013). Despite this, the total annual harvest appears to be steady at 5000 tons and is valued at USD $7 million per year (Deines et al. 2013).

3.3.2 Sampling

In August through September 2008 and 2010, we collected fish by gillnetting with the DoF at six locations in the Kafue floodplain fishery area and one location on

Lake Itezhi-tezhi (Figure 3.1). We used a multifilament gillnet fleet with mesh ranging from 2.5cm to 11.4cm (stretched) in increments of 1.3cm and hung to yield a mesh twice as tall as wide as per standard DoF procedures. Most nets were 90m2, but additional nets of the mid-range mesh were employed to target tilapias. The total area of the standard net fleet was 783m2. Nets were surface set at around 1700hrs and fished around 0600 the next morning. Sites were fished for between one and four consecutive nights (mode=2 nights) and total catch per unit effort was calculated as the

total number per meter-night. For genetic analysis, gillnet catches were supplemented from local fishers.

Parental O. niloticus specimens (n = 6) of the GIFT strain were obtained from the

DoF’s Mwekera aquaculture research station, and parental O. andersonii (n = 22) and O. macrochir (n = 28) from our sampling of Lake Itezhi-tezhi. A total of 457 individual

Oreochromis specimens where collected in the Kafue fishing region. Species were morphologically identified following Skelton (2001). Potential hybrids between O. andersonii and O. niloticus were distinguished based on their possession of two species specific traits: (1) vertically striped caudal fins unique to O. niloticus; and (2) three large black spots on the flanks near the lateral line typical of O. andersonii (Trewavas 1983). A total of 33 of the 457 collected fish (7%) were considered to be putative hybrids based on these two morphological traits. Samples taken from Lake Itezhi-tezhi and Mwekera were assumed to represent pure parental fish; the morphologies of these fish were consistent with this assumption. Photographs of all specimens are available from the corresponding author.

A tissue sample from each fish was clipped from its pectoral or caudal fin and preserved in 95% ethanol. Tissue samples were shipped to the University of Notre

Dame (Indiana, USA) for further genetic analysis. Whole DNA was extracted from fin tissue using the QIAGEN DNeasy blood and tissue spin-column kit according to the manufacturer’s protocol (QIAGEN, Germantown, MD). Three tetra-nucleotide repeat and five di-nucleotide repeat microsatellites developed for O. niloticus and O.

mossambicus (Table 3.1; Lee and Kocher 1996; D’Amato et al. 2007; Saju et al. 2010) were used for genetic analysis and are designated Om02, Om03, Om05, UNH104,

UNH111, UNH172, UNH208, and UNH216. Genomic DNA isolated from individual fish were PCR amplified using the eight pairs of Oreochromis microsatellite primers, with the forward oligonucleotide fluorescently labeled with one of three different dyes (D2, D3, or D4) designated in Table 3.1 (Well-Red, Sigma-Aldrich) for subsequent manual scoring of allele fragment length on a Beckman-Coulter CEQ 8000 machine (Brea, CA). Reaction conditions followed those of D’Amato et al. (2007). Negative controls with no template

DNA were included on every plate; all reactions with any positive control were discarded. Each microsatellite was PCR amplified separately for each specimen. The resulting PCR products were pooled in triples for genotyping after we initially determined the size ranges for loci and matched amplified loci with non-overlapping fragment sizes within the three different fluorescently labeled dyes used in PCR.

3.3.3 Analysis

3.3.3.1 Microsatellite data quality

We employed a three stage process to test for microsatellite genotyping error.

First, 38 randomly selected individuals were re-amplified and re-genotyped for the three markers UNH104, UNH172,UNH208. The resulting data set was compared to the original genotype calls, allowing us to estimate PCR error rates by measuring differences in estimated allele fragment lengths between runs where discrepancies result from one

TABLE 3.1

DETAILS OF MICROSATELLITE LOCI.

Source and Locus ID Label Primer sequences (5’-3’) oC Motif Genebank Accession F:TGTGAATTTGACAACTTCCTTTC (Saju et al. 2010) Om02 D2 55 ATCT R:ATCCTTGCAATAAGGTTACAG GU391021 F:CTTTTTAATGAGCAACTTTTAAGTC (Saju et al. 2010) Om03 D3 55 GATA R:TGTGAATTTGACAACTTCCTTTC GU391022 F:GTAAAGTTTGGAACAGAAATGCT (Saju et al. 2010) Om05 D4 55 ATCT R:GATCACTTTTGGACAGACTGG GU391024 (D’Amato et al. F:GCAGTTATTTGTGGTCACTA UNH 104 D3 58 CA 2007) R: GGTATATGTCTAACTGAAATCC G12257 (Lee and Kocher F:TGCTGTTCTTATTTTCGC UNH 111 D3 58 AC 1996) R:ATAAGAGTGTATGCATTACTGG G12264 (Lee and Kocher F:AATGCCTTTAAATGCCTTCA UNH 172 D4 58 CA 1996) R: CTTTTATAGTCGCCCTTTGTTA G12324 (Lee and Kocher F:CTTCTTGGCCTACAATTT UNH 208 D2 55 CA 1996) R:CAGATGGGTGATAGCAA G12359 (Lee and Kocher F:GGGAAACTAAAGCTGAAATA UNH 216 D2 53 AC 1996) R:TGCAAGGAATATCAGCA G12367

of the two alleles in a given run being shifted to a much larger size in the other run outside the normal size range for the microsatellite. We tested for significant differences in allele fragment length between runs for each locus using a one-way

ANOVA. Second, we tested for the possible presence of null-alleles (heterozygotes mis- scored as homozygotes due to genotyping error or mutations in the primer binding

sites) by analyzing genotype frequencies for the eight microsatellites for conformity to

Hardy-Weinberg equilibrium (HWE) using Arlequin 3.5 (Excoffier and Lischer 2010) under the assumption that mis-scored loci possessing null alleles would result in a significant excess of homozygous genotypes. We then used the program Microchecker

(Van Oosterhout et al. 2004) to test all loci in each population for possible null alleles, employing the Brookfield-1 equation (Brookfield 1996). Third, we evaluated the data set for large-allele dropout, as described by Dewoody et al. (2006). Large allele dropout is the mis-scoring phenomenon where alleles significantly larger than the general size range for a locus - perhaps due to an insertion - are missed in genotype scoring due to amplification errors, PCR artifacts, or because the fragment occurred in an unexpected range and may have been considered to represent a different microsatellite. As a result, markers having smaller mean fragment length sizes should show increased homozygosity, while larger sized microsatellites should show increased heterozygosity.

We tested for an excess of small allele homozygotes by calculating FIS for each microsatellite using Fstat (Goudet 2001) and then regressing FIS against the minimum, median, maximum, and range of allele fragment lengths for the loci using a standard

OLS linear regression model.

3.3.3.2 Evidence for hybridization

To test for hybridization, we first analyzed the microsatellite data using the program STRUCTURE (Pritchard et al. 2000), which assesses the number of separate populations, K, that make up a sample by searching for subdivisions within a sample 83

which each best satisfy HWE. We then tested for recent genetic hybrids using the program NEWHYBRIDS (Anderson and Thompson 2002) which assigns individuals into predefined parental, F1, F2, and backcross categories based on their multilocus genotypes.

Our STRUCTURE analysis consisted of two steps. First, we determined whether the two parental native fish samples of O. andersonii and O. macrochir collected outside the putative area of hybridization along with the parental O. niloticus obtained from DoF could be genetically partitioned into different populations. The rationale was that if these three parental types alone could be genetically distinguished then this would provide a baseline for investigating whether the 33 specimens collected from the fishery region and tentatively identified as hybrids based on morphology represented fish of mixed genetic ancestry. Hybrid status would then be indicated by these putative hybrids being classified as belonging to both the O. niloticus and native fish populations when these specimens were added to the STRUCTURE analysis.

We performed this first STRUCTURE analysis of parental Nile tilapia and the two native fish species both with and without considering prior information of parental types or sampling locations, testing for the best fit of population structures based on

K=1 to K=6 different populations. Analysis for each K value was based on ten random- start replicates each with a 20,000 iteration burn-in period followed by 20,000 iterations for parameter estimation. We used the program default parameters except for specifying parental priors, and selected the best fitting number of distinct populations

using the ΔK method of Evanno et al. (2005). Because of differences in sample size between O. niloticus and the two native species, which could force the fewer number of

Nile tilapia individuals into one or the other alternative native populations to better conform to HWE, we quadrupled the six O. niloticus multilocus genotypes to generate a test population of 24 fish, roughly equal to the sample sizes for parental O. andersonii (n

= 22) and O. macrochir (n = 28) specimens collected from Lake Itezhi-tezhi. After establishing the K value which was best supported by STRUCTURE, we then added the

33 putative hybrids from the fishery region to the data set and repeated the analysis to determine the posterior assignment probabilities of these fish to the introduced and native fish populations.

In addition to STRUCTURE we also evaluated hybridization using the program

NEWHYBRIDS (Anderson and Thompson 2002). Two aspects of the NEWHYBRIDS program restricted the utility of the algorithm for our particular problem. First,

NEWHYBRIDS is designed to test for hybrids between only a pair of two parental populations, and in our case we have three possible hybridizing taxa. Second,

NEWHYBRIDS is most effective when active and at least modest numbers of F1 and second generation hybrids are being formed each generation. In the case of Nile Tilapia, the initial escape occurred about 30 years ago, so extensive introgression may have occurred relatively long ago and established a well-mixed hybrid population with only low levels of active F1 and second generation hybrid formation occurring at the present time. We therefore ran just two different scenarios in NEWHYBRIDS for the two pairwise

combinations of introduced and native species that could be hybridizing (O. andersonii x

O. niloticus, and O. macrochir x O. niloticus) together with the potential hybrid fish. The

O. niloticus data set was not duplicated in this analysis because NEWHYBRIDS uses estimates of allele frequencies to predict posterior probabilities for genotype category assignments and, thus, the smaller sample size for O. niloticus was not relevant. We used the default specification of NEWHYBRIDS of estimating six genotype classes: the two parental classes, F1, F2 and backcrosses, running ten random-start replicates to confirm that initial conditions did not strongly influence estimation, and then initiated each analysis with a 10,000 iteration burn-in and then collected MCMC averages for between 25,000 and 50,000 iterations.

3.4 Results

We collected a total of 457 Oreochromis specimens from the Kafue River region

(Figure 3.1), 403 of which were collected from the putative hybrid zone and only 53 of which were captured in our gillnet sampling; the remainder we obtained from fishers employing a variety of harvesting methods. The combined collections from both gillnetting and fishers likely represent a reasonable cross-section of the diversity of

Oreochromis specimens in the Kafue River (Baker 2008). From our collections at

Mutukuzhi (Site S2) near the Itezhi-Tezhi dam we show that phenotypic O. niloticus have spread from the site of original introduction near Mazabuka (Site S5, Schwanck 1995) throughout the Kafue River fishery area (Figure 3.1B). From the gillnet collection we can compare catch per unit effort as an index of abundance (Figure 3.1C). Near the site of 86

the original introduction, the composition was almost entirely O. niloticus. Moving upstream from the original point of introduction the composition of Oreochromis became phenotypically more native-like, including putative hybrids at Chunga lagoon

(site S4). Moving downstream, we caught no Oreochromis in gillnets, likely due to heavy fishing pressure in the area. These field results provide compelling evidence for the ongoing spread of O. niloticus on the Kafue River.

3.4.1 Microsatellite data quality

Quality control tests on the data provided little evidence for miscalled alleles due to variation in fragment lengths, null alleles, or large allele-dropout. After removing failed and ambiguous scores from the microsatellite data set, about 25% of all genotype scores could not be reliably determined for individual fish. Nonetheless, we included all reliable genotypes in the SRUCTURE and NEWHYBRIDS analyses as the Bayesian framework of these programs are designed to account for missing genotypes at loci.

To further explore the accuracy of our genotyping, we rescored a subset of 38 specimens (O. andersonii=6, O. macrochir= 17, O. niloticus= 2, putative hybrids= 13).

Overall, there was little disagreement in the length of allele fragments called between genotyping runs. There was no significant difference in the fragment length difference between reads, as determined by one-way ANOVA (F=0.1619, p= 0.8507). The mean difference in estimated fragment lengths was 0.06BP, 0.05BP, and 0.06BP for UNH104,

UNH172, and UNH208, respectively; much less than the 2 bp size of the di-nucleotide repeat separating different alleles. We found disagreement between the two runs for

five of the 111 genotypes rescored (4.5%), with three instances involving the locus

UNH172, one UNH104, and one UNH208. For only one of these five genotypes was the estimated allele size greater than or equal to half the difference in repeat length (i.e., >

1 bp). The exception was UNH104, where alleles were shifted 1 and 3 BP between runs, probably as a result of PCR stutter, and this locus for this individual was removed from further analysis. For the few cases discussed involving a large shift in allele size between replicate runs, it was apparent that the larger shifted fragment represented the aberrant allele call.

There was little support for null alleles or large allele drop-out in the data set. Of the 24 total possible tests for deviation from HWE in the parental populations (O. andersonii, O. macrochir, and O. niloticus), significant heterozygote deficiencies were detected for only Om05 (p = 0.018) and UNH208 (p = 0.03) in the O. macrochir population (Table 3.2). Microchecker also identified the possible presence of a null- allele for only UNH208 in the O. macrochir population. Indeed, no O. macrochir amplified at locus UNH208. Nonetheless, neither of these two loci were significant on a table-wide basis after correcting for multiple tests. It is possible that the two departures from HWE could be due to the presence of null alleles in O. macrochir, but it is also possible that they may represent a degree of genetic subdivision in this native population or, we think most likely, an artifact of the large amount of missing data.

There was also no evidence for large allele dropout as indicated by the lack of a significant decrease in FIS with increasing allele size in the data set when regressed

against minimum (p= 0.987), median (p= 0.588), maximum (p=0.672) or the range (p=

0.672) of allele size (Figure 3.2), as would be predicted for large allele dropout. Thus, apart from a large percentage of missing data most likely due to failed PCR, there was little evidence for significant genotyping error in the study.

3.4.2 Evidence for hybridization

The first STRUCTURE analysis of the three parental species clearly identified K = 3 as the most supported number of genetically differentiated populations in the microsatellite data set when using the replicated O. niloticus data (Table 3.3).

Oreochromis macrochir was assigned unambiguously in both the naïve case without specifying parental priors based on sampling location and when these priors where included (Figure 3.3). All specimens of each of O. andersonii and O. niloticus parental types were assigned to their proper species category with posterior probabilities greater than 70% for the naïve population assignment and greater than 95% for population assignment including parental priors. This confirmation of three genetically distinguishable species was important for two reasons. First, it provided a baseline for subsequent SRUCTURE comparisons of the 33 specimens collected from the fishery region to test for possible hybrid genotypes. Second, it gave little indication of mixture between the native species O. andersonii and O. macrochir, at least in Lake Itezhi-tezhi.

Thus, genetically distinguishable forms (genotypic clusters sensu Mallet 1995) of these fish co-occur in sympatry, supporting their species status.

TABLE 3.2

SUMMARY STATISTICS FOR SAMPLING AND GENETIC VARIABILITY AT MICROSATELLITE

LOCI FOR POPULATIONS SAMPLED.

locus statistic O.ander O.mac O.nil Hybrids N 26 28 6 Ng 21 22 4 23 All Na 95 76 38 74 Pa 37 26 11 25 FIS 0.033 0.016 0.213 0.279

N g 25 27 5 23 Na 11 16 6 6 Pa 2 7 1 1 Om02 Ho 0.8 1 0.8 0.739 He 0.82 0.93 0.889 0.741 FIS 0.031 -0.081 0.111 0.003 Phwe 0.69 0.61 0.150 0.030

N g 25 25 5 27 Na 11 15 7 9 Pa 2 4 3 2 Om03 Ho 0.76 0.92 1 0.742 He 0.81 0.92 0.911 0.790 FIS 0.068 0.003 -0.111 0.063 Phwe 0.51 0.34 0.500 0.001

N g 25 27 5 28 Na 14 2 6 14 Pa 6 1 1 4 Om05 Ho 0.76 0 0.6 0.643 He 0.82 0.07 0.889 0.777 FIS 0.07 1 0.351 0.176 Phwe 0.07 0.018 0.147 0.010

N g 21 28 5 31 Na 18 15 5 14 Pa 5 3 1 4 UNH104 Ho 0.9 0.86 0.6 0.677 He 0.94 0.78 0.822 0.784 FIS 0.037 -0.102 0.294 0.138 Phwe 0.49 0.052 0.0878 0 90

TABLE 3.2. CON’T

locus statistic O.ander O.mac O.nil Hybrids Ng 6 9 2 2 Na 4 6 3 3 Pa 2 3 1 1 UNH111 Ho 1 1 0.5 0.5 He 0.74 0.82 0.833 0.833 FIS -0.395 -0.241 0.5 0.5 Phwe 0.35 0.53 0.335 0.337

N g 21 17 4 28 Na 2 4 4 11 Pa 1 3 2 8 UNH172 Ho 0.14 0.53 0.75 0.643 He 0.14 0.63 0.75 0.746 FIS -0.053 0.163 0 0.141 Phwe 1 0.68 1 0.0001

N g 22 17 5 31 Na 19 2 4 10 Pa 14 0 1 2 UNH208 Ho 0.77 0 0.8 0.516 He 0.91 0.11 0.73333 0.683 FIS 0.153 1 -0.103 0.248 Phwe 0.27 0.03 1 0.0001

N g 25 27 2 16 Na 16 16 3 7 Pa 5 5 1 3 UNH216 Ho 0.72 0.78 0.5 0.063 He 0.89 0.91 0.833 0.720 FIS 0.199 0.148 0.5 0.916 Phwe 0.13 0.2 0.331 0

N= total sample size, Ng= number of effective genotypes, Na=number of alleles, Pa=number of private alleles, Ho=observed heterozygosity, He=expected heterozygosity, PHWE= probability of Hardy-Weinberg equilibrium.

TABLE 3.3.

SUMMARY OF ΔK FOR EACH OF THE DESCRIBES STRUCTURE ANALYSES.

Expected ΔK Analysis K K=2 K=3 K=4 K=5 Parental clustering K=3 1.49 44.94 * 0.23 1.15 Hybrid identification K=2 0.64 0.62* 0.27 0.34 *Best chosen population clustering model

The addition of the putative hybrid specimens to the STRUCTURE analysis implied that the vast majority of these genotypes (31/33 = 94%) were of mixed ancestry

(Figure 3.4) consistent with the hybridization hypothesis. Population selection equally indicated K=2 and K=3 (Table 3.3) populations, and we proceed assuming K=3 as it is most consistent with previous results. Most putative hybrid fish had a non-trivial probability of belonging to all three potential parental populations, O. niloticus, O. andersonii, and O. macrochir. This result was striking because it implies that a relatively high degree of introgression has occurred involving all three species in the fishery area to create the morphologically mixed phenotypes observed. Moreover, the individuals least likely to be of mixed origin all came from Chinyanya (Site S6; Figure 3.1A), the site closest to the original introduction and with the highest composition of phenotypic O. niloticus. This geographic pattern of hybridization -where the most genetically native- like individuals are nearest the center of the invasion - is the opposite of what we would expect given the initial morphological identifications.

In comparison to the SRUCTURE analysis, the program NEWHYBRIDS revealed little evidence for F1 or backcross hybrids. Only one of the 33 fishery area specimens had a high probability of being a F2 hybrid in the O. andersonii x O. niloticus analysis (

Figure 3.5A), and none for O. andersonii x O. niloticus (Figure 3.5B). In most cases, the morphologically hybrid-like fish were categorized as O. niloticus, with a few having a high probability of being the native species in the analysis. Both HEWHYBRIDS results for O. andersonni and O. macrochir are consistent with the surprising geographical

Figure 3.4. STRUCTURE analysis of the three parental species populations including the 33 putative hybrid individuals from the Kafue fishing region. Vertical bars are the mean posterior probability of cluster assignment from 10 replicate model runs, while the color of posterior assignments correspond to Figure 3.3. The top bar shows the a priori species identifications where O.and and O. mac = morphologically pure native O. andersonii and O. macrochir, respectively, collected from outside the area of potential hybridization; rO.nil = replicates of O. niloticus representing genotypes of introduced Nile tilapia into the Kafue River; and S3-7 are the putative hybrids ordered geographically by sample sites progressing east to west as in Figure 3.1.

pattern observed in STRUCTURE where the most native-like individuals were from Sites

S5 and Site S6, near the original site of introduction of O. niloticus.

Genetic variation was not elevated in the putative hybrid population, as indicated by the number of alleles and private allele variants in the mixed population being generally at or below levels found in the two native species (Table 3.2). In addition, FIS was generally higher for the mixed population (Table 3.2) and seven of the eight microsatellites were not in HWE (Table 3.2). These FIS scores indicate fish of hybrid ancestry are relatively heterozygote deficient and may therefore not constitute a randomly mating population, but may also be breeding with individuals that are genetically more O. andersonii, O. macrochir or O. niloticus-like but not included in this analysis. Thus, hybridization and introgression do not appear to have infused the mixed population with great stores of standing variation as expected for hybrid invasions.

3.5 Discussion

Our results imply that since their escape in the 1980s non-native Nile tilapia have spread throughout the Kafue river fishery area and hybridized with the native species O. andersonii and O. macrochir. The finding that escaped O. niloticus fish are hybridizing with native Oreochromis is not surprising and our study adds one more case to a growing list of such instances (Deines et al. Ch.2). However, three aspects of hybridization in the Kafue River are surprising. First, our data implicate hybridization and introgression of both native species with O. niloticus, creating a complex mixed population containing genes from all three fish. Second, we find an unexpected 98

geographical pattern of hybridization, where the genetically most native-like individuals are found nearest the center of invasion. Finally, the genetic diversity of the mixed population appears lower than that of the parental type, also counter to hybrid theory.

The presence of fish that appear to be composites of O. niloticus, O. andersonii and O. macrochir is intriguing because until now, researchers, managers and fishers had not expected admixture of O. macrochir with O. niloticus on the Kafue River. The latter two native species do not frequently hybridize in sympatry in Lake Itezhi Itezhi where O. niloticus has not yet been introduced. The presence of Nile tilapia may be facilitating a general melding of both the native species gene pools into a new conglomerate form.

A widespread conglomeration of these species is further supported by the finding of only one genotype that had a high probability of being a hybrid between O. andersonii and O. niloticus in the NEWHYBRIDS analysis, despite significant admixture detected by STRUCTURE. It is a reasonable possibility that this F2 classification is a statistical artifact. F2 is the most well-mixed genotypic class considered by

NEWHYBRIDS, and given the substantial mixing observed in the STRUCTURE analysis, it seems likely one in 33 could be randomly classified as an F2. Most interbreeding does not seem to be occurring between genetically ‘pure’ O. niloticus and the native taxa at the current time. Rather, fish of mixed ancestry may be mating with each other and with O. andersonii and O. macrochir to create a complex hybrid population (a ‘hybrid swarm’) selected to retain only a subset of parental alleles (Harrison 1990).

The geographic distribution of genetic hybrids across the Kafue fishing area is also surprising. The most native-like genotypes are found closest to original point of introduction, opposite to the trend in the phenotypic identifications (Figure 3.1), and opposite to what would be expected of a spreading hybrid zone contained linearly such as in a river (Barton and Gale 1990). That a genetic cline could exist after perhaps 30 years of hybridization is unlikely for invasive species (Rice and Sax 2005). One possible explanation is that a spreading front of hybridization has passed this nearby location more so than other sites, with extensive introgression from native Oreochromis into O. niloticus (Hall et al. 2006), leaving behind only positively selected O. niloticus alleles which could not be detected by the neutral microsatellites used here. Due to the small sample sizes of individual fish and the course resolution provided by eight loci (Scribner et al. 2001) this hypothesis was not resolved in the current study.

The genetic diversity in the mixed population is lower than the native parental populations, despite the addition of O. niloticus, which is unexpected by invasion and hybrid theory (Lavergne and Molofsky 2007; Hegarty 2012). This result is supported by the reduced number of total and private alleles in the hybrid fish, and the observed deviations in HWE observed for seven of the eight microsatellites for specimens from the Kafue fishery region (Table 3.2). Below we consider alternative explanations for these patterns in diversity, specifically that we have not sampled all of the mixed population itself, that we may not have well captured the genetic composition of the

100

originally introduced strain of O. niloticus, and the possibility of fishing-induced selection.

Our results provide a lower bound for the proportion of Oreochromis of hybrid ancestry in the Kafue. We say lower bound because the STRUCTURE analysis implied that many higher order backcrosses exist in the fishery region which would not be expected to show obvious signs of admixture based the morphological characters we focused on in the study (Hulata et al. 1995). Thus, our current genotyped sample size of only 7% of the total was likely not a random sample of the entire mixed ‘hybrid swarm’ of Oreochromis which may exist in the Kafue River. Failing to include all the genotypes necessary may explain the observed absences of HWE.

Some uncertainty exists about the source of the O. niloticus stock introduced for aquaculture to Zambia 30 years ago, and the differences between the unknown original strain(s) and the strain tested herein may account for some of the genetic surprises reported. Records suggest that the original introduction derived from O. niloticus procured from the University of Stirling, UK (Audenaerde 1994). The Mwekera O. niloticus that we genotyped here is reported to be from the GIFT strain developed in the

Philippines. This difference could account for the presence of certain private alleles found in the mixed population but not in any of the native parental populations we surveyed. It is always also possible that O. andersonii and/or O. macrochir genes were mixed with the sampled stock of O. niloticus before or after its arrival at the Mwekera research station. There is some support for this later possibility from the naïve

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STRUCTURE analysis of parental types (Figure 3.3A). The issue of the source of the original escaped Nile tilapia in the Kafue River remains to be resolved; doing so would provide us with the most accurate baseline for quantitatively assessing the degree of hybridization and introgression that have occurred.

Finally, the observed departures from HWE in the Kafue Oreochromis may be due to fishing pressure. The Kafue River has experienced exponential growth in fishing effort over the last 60 years, increasing by about 50% since the introduction of O. niloticus in the mid 1980s while fish populations have decreased by at least two orders of magnitude (Deines et al. 2013). Intense harvesting pressure is a strong source of genetic change (Law 2000) and large declines in populations may represent a bottleneck increasing heterozygote deficiency (Nei et al. 1975). Multiple factors may be playing a role in the observed discrepancies in the molecular data in addition to effects of hybridization.

Nonetheless, our data support widespread genetic change in Oreochromis populations in the Kafue river fishery. More comprehensive studies of the entire diversity of Oreochromis specimens in the Kafue fishery region, as well as exhaustive surveys of the entire genome (e.g., by Restriction enzyme Amplified DNA marker sequencing (RAD-tags); Baird et al. 2008) are needed to more accurately determine the degree of hybridization and introgression occurring and the cause(s) for departures from HWE observed herein.

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There is growing evidence from throughout Africa of hybridization between native and introduced tilapia species and the ecological effects of Nile tilapia introduction (Deines et al. Ch.2). The potential for hybridization demonstrated here combined with the effects of fishing-induced selection for slower growth and smaller size (Law 2000) raises the possibility of undesirable impacts on capture fishery production if hybrids are less productive and/or less desirable in the market than pure types. Meanwhile, the genetic composition of O. niloticus in African aquaculture is already heavily depressed (Brummett and Ponzoni 2009), and additional loss of diversity in wild stocks forgoes future aquacultural improvement (Lind et al. 2012). Additionally, the documented occurrence of ecological impacts of Oreochromis introductions around the world (Deines et al. Ch.2) suggest that changes in ecosystem services, in addition to reduction in wild genetic resources, are also likely to be occurring in the Kafue as a result of the introduction. Indeed, our sampling shows that fish morphologically classified as O. niloticus can now be found throughout the Kafue River fisheries area and are at least or more common than the native Oreochromis (Figure 3.1).

Despite the negative impacts of Nile tilapia, it is still conceivable that the introduction of O. niloticus into the Kafue River had some positive effects on ecosystem services by boosting the total fishery harvest above what it would otherwise be.

Current harvest levels containing O. niloticus and hybrid fish may not have been possible if they consisted entirely of native species in the absence of the introduction. The value of the aquaculture production of O. niloticus in Zambia is also great; almost USD$14

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million in 2010 (FAO Fisheries and Aquaculture Department 2012), twice the average

Kafue river production value. Thus, it could be argued that the introduction of O. niloticus both in aquaculture and in the capture fishery may actually be helping conservation goals by alleviating harvesting pressure on native species (Arthur et al.

2010), provided the threat of hybridization and introgression could be mitigated.

3.6 Conclusion

There is on-going debate over the net socio-economic benefit of tilapia introduction, with managers under increasing pressure to choose between supporting or banning the use of O. niloticus. Given that tilapia introductions will harm wild genetic resources to some extent and because the ability to scientifically evaluate the ecological and evolutionary risks of introductions is limited in Africa where tilapia hybridization is most relevant (UNESCO 2007; Josupeit 2010; Skelton and Swartz 2011), our results suggest that decision makers in the public and private sectors should carefully consider both the benefits and harms caused by tilapia introductions. Economic and social considerations often seem to trump the risk of harmful impacts of tilapia introductions

(Deines et al.Ch.2), with the default outcome often being passive acceptance of introduction (Lövei et al. 2012).

We have provided a baseline for considering the tradeoffs of ecosystem services on the Kafue between aquaculture production of O. niloticus and the risk of undesirable genetic admixture on wild tilapia diversity. The Kafue River system has implications for other nearby waterbodies (e.g. Lake Itezhi-tezhi) to help more fully inform decision 104

markers concerning future O. niloticus introductions before they occur in Zambia, other countries in Africa, and on other continents. Additional study is needed to fully quantify these tradeoffs, including measuring direct effects on the ecosystem and economic values, to devise context specific policy and procedures to maximize human wellbeing.

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3.7 Acknowledgments

My coauthors I. Bbole, C. Katongo, T. H.Q. Powell, J.L. Feder, and D.M. Lodge. P.

Ngalande, L. Njobvu Chilufya, R. Nkhata, and many other at The Zambian Department of

Fisheries and the World Fish Center provided support expertise. M.A. Barnes, S.P. Egan,

G. Hood, and W. Perry provided conceptual and technical support. Funding for this research has been provided by GLOBES (NSF DGE-0504495), NSF DDEP award #1046682, the Notre Dame Environmental Change Initiative, and the Kellogg Institute at the

University of Notre Dame.

3.8 References

Agnese, J.-F. (1998) Genetics and Aquaculture in Africa: Actes du collogue Genetique et aquaculture en Afrique. Abidijan, Avril 1997 .

Allendorf, F.W., Leary, R.F., Spruell, P. and K.Wenburg, J. (2001) The problems with hybrids: setting conservation guidelines. TRENDS in Ecology & Evolution 16, 613– 622.

Amarasinghe, U.S. and De Silva, S.S. (1996) Impact of Oreochromis mossambicus x O. niloticus (Pisces: Cichlidae) hybridization on population reproductive potential and long-term influence on a reservoir fishery. Fisheries Management and Ecology 3, 239–249.

Anderson, E.C. and Thompson, E.A. (2002) A Model-Based Method for Identifying Species Hybrids Using Multilocus Genetic Data. Genetics 160, 1217–1229.

Angienda, P.O., Lee, H.J., Elmer, K.R., Abila, R., Waindi, E.N. and Meyer, A. (2010) Genetic structure and gene flow in an endangered native tilapia fish (Oreochromis esculentus) compared to invasive Nile tilapia (Oreochromis niloticus) in Yala swamp, East Africa. Conservation Genetics 12, 243–255.

Arthur, R.I., Lorenzen, K., Homekingkeo, P., Sidavong, K., Sengvilaikham, B. and Garaway, C.J. (2010) Assessing impacts of introduced aquaculture species on

106

native fish communities: Nile tilapia and major carps in SE Asian freshwaters. Aquaculture 299, 81–88.

Audenaerde, D.F.E.T. van den (1994) Introduction of aquatic species into Zambian waters, and their importance for aquaculture and fisheries. 33.

Baird, N.A., Etter, P.D., Atwood, T.S., et al. (2008) Rapid SNP Discovery and Genetic Mapping Using Sequenced RAD Markers. PLOS One 3, e3376.

Baker, C.S. (2008) A truer measure of the market: the molecular ecology of fisheries and wildlife trade. Molecular ecology 17, 3985–98.

Barton, N.H. and Gale, K.S. (1990) Genetic Analysis of Hybrid Zones. In: Hybrid Zones and the Evolutionary Process. (ed R.G. Harrison). Oxford University Press, Ney York, pp 13–45.

Beveridge, M., Ross, L.G. and Kelly, L.A. (1994) Aquaculture and Biodiversity. Ambio 23, 497–502.

Bostock, J., McAndrew, B., Richards, R., et al. (2010) Aquaculture: global status and trends. Philosophical transactions of the Royal Society B 365, 2897–2912.

Brookfield, J.F.Y. (1996) A simple new method for estimating null allele frequency from heterozygote deficiency. Molecular Ecology 5, 453–455.

Brummer, E.C., Barber, W.T., Collier, S.M., et al. (2011) Plant breeding for harmony between agriculture and the environment. Frontiers in Ecology and the Environment 9, 561–568.

Brummett, R.E. and Ponzoni, R.W. (2009) Concepts, alternatives, and environmental considerations in the development and use of improved strains of tilapia in African aquaculture. Reviews in Fisheries Science 17, 70–77.

Deines, A.M., Adam Bee, C., Katongo, C., Jensen, R. and Lodge, D.M. (2013) The potential trade-off between artisanal fisheries production and hydroelectricity generation on the Kafue River, Zambia. Freshwater Biology, no–no.

Deines, A.M., Wittmann, M.E., Deines, J.M. and Lodge, D.M. Tradeoffs among ecosystem services associated with global tilapia introductions. in prep.

107

Dewoody, J., Nason, J.D. and Hipkins, V.D. (2006) Mitigating scoring errors in microsatellite data from wild populations. Molecular Ecology Notes 6, 951–957.

Evanno, G., Regnaut, S. and Goudet, J. (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular ecology 14, 2611–20.

Excoffier, L. and Lischer, H.E.L. (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular ecology resources 10, 564–7.

FAO (2012) Database of Introduced Aquatic Species. Available at: www.fao.org/fishery/dias/en [Accessed September 2, 2012].

FAO (2012) State of the World Fisheries and Aquaculture 2012. Food and Agriculture Organization of the United Nations, Rome.

FAO (2010) The state of world fisheries and aquaculture 2010. FAO, Rome.

FAO Fisheries and Aquaculture Department (2012) Fisheries Global Information System (FAO-FIGIS)- Web Site. Available at: http://www.fao.org/fishery/figis/en [Accessed December 13, 2012].

Foley, J. a, Ramankutty, N., Brauman, K. a, et al. (2011) Solutions for a cultivated planet. Nature 478, 337–42.

Fryer, G. and Iles, T.D. (1972) The Cichlid Fishes of the Great Lakes of Africa. Oliver & Boyd, Edinburgh.

Goudet, J. (2001) FSTAT, a program to estimate and test gene diversities and Fixation indices (version 2.9.3). Available from http://www2.unil.ch/popgen/softwares/fstat.htm.

Gregg, R.E., Howard, J.H. and Shonhiwa, F. (1998) Introgressive Hybridization of tilapia in Zimbabwe. Journal of Fish Biology 52, 1–10.

Hall, R.J., Hastings, A. and Ayres, D.R. (2006) Explaining the explosion: modelling hybrid invasions. Proceedings of the Royal Society B 273, 1385–1389.

Harrison, R.G. (1990) Hybrid zones: windows on evolutionary process. Oxford Surveys in Evoultionary Biology 7, 69–128.

Hegarty, M.J. (2012) Invasion of the hybrids. Molecular ecology 21, 4669–71.

108

Hulata, G., Karplus, I. and Harpaz, S. (1995) Evaluation of some red tilapia strains for aquacutlture: growth and colour segregation in hybrid progeny. Aquaculture Research1 26, 765–771.

Jamu, D. and Brummett, R. (2004) Opportunities and Challenges for African Aquaculture. In: Use of Genetically Improved and Alien Species for Aquaculture and Conservation of Aquatic Biodiversity in Africa. WorldFish Center Conference Proceedings 68,. (eds M. V Gupta, D.M. Bartley and B.O. Acosta). WorldFish Center, Penang, Malaysia, pp 1–9.

Josupeit, H. (2010) World supply and demand of tilapia 2010. In: INFOFISH Tilapia Conference. FAO GlobeFish, Kuala Lumpur, p 6.

Lavergne, S. and Molofsky, J. (2007) Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proceedings of the National Academy of Sciences of the United States of America 104, 3883–8.

Law, R. (2000) Fishing, selection, and phenotypic evolution. ICES Journal of Marine Science 57, 659–668.

Lee, W.-J. and Kocher, T.D. (1996) Microsattellite DNA markers for genetic mapping in Oreochromis niloticus. Journal of Fish Biology 49, 169–171.

Lind, C.E., Brummett, R.E. and Ponzoni, R.W. (2012) Exploitation and conservation of fish genetic resources in Africa: issues and priorities for aquaculture development and research. Reviews in Aquaculture 4, 125–141.

Mallet, J. (1995) A species definition for the modern synthesis. Trends in ecology & evolution 10, 294–9.

Muir, J.F., Gitonga, N., Omar, I., Pouomogne, V. and Radwan, I. (2005) Hidden Harvests: Unlocking the Potential of Aquaculture in Africa. In: NEPAD- Fish For All Summit. The World Fish Center, Abuja, Nigeria, p 56.

109

Mwanja, W. and Kaufman, L. (1995) A note on recent advances in the genetic characterization of Tilapia stocks in Lake Victoria region. African Journal of Tropical Hydrobiology and Fisheries 6, 51–53.

Naylor, R.L., Goldburg, R.J., Primavera, J.H., et al. (2000) Effect of aquaculture on world fish supplies. Nature 405, 1017–1024.

Nei, M., Maruyama, T. and Chakraborty, R. (1975) The Bottleneck Effect and Genetic Variability in Populations. Evolution 29, 1–10.

Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M. and Shipley, P. (2004) Micro- Checker: Software for Identifying and Correcting Genotyping Errors in Microsatellite Data. Molecular Ecology Notes 4, 535–538.

Perry, W.L., Lodge, D.M. and Feder, J.L. (2002) Importance of Hybridization between Indigenous and Nonindigenous Freshwater Species: An Overlooked Threat to North American Biodiversity. Society of Systematic Biologists 51, 255–275.

Pritchard, J.K., Stephens, M. and Donnelly, P. (2000) Inference of Population Structure Using Multilocus Genotype Data. Genetics 155, 945–959.

Rhymer, J.M. and Simberloff, D. (1996) Extinction by Hybridization and Introgression. Annual Review of Ecology and Systematics 27, 83–109.

Rice, W.R. and Sax, D.F. (2005) Testing fundamental evolutionary questions at large spatial and demographic scales: Species invasions as an underappreciated tool. In: Species Invasions: Insights in ecology, evolution, and biogeography. (eds D.F. Sax, J.J. Stachowicz and S.D. Gaines). Sinauer Associates, Inc, Sunderland, MA, pp 291–313.

Roman, J. and Darling, J.A. (2007) Paradox lost: genetic diversity and the success of aquatic invasions. TRENDS in Ecology & Evolution 22, 454–464.

Saju, J.M., Lee, W.-J. and Orban, L. (2010) Characterization of nine novel microsatellites isolated from Mozambique tilapia, Oreochromis mossambicus. Conservation Genetics Resources 2, 287–385.

Schwanck, E.J. (1995) The introduced Oreochromis niloticus is spreading on the Kafue floodplain, Zambia. Hydrobiologia 315, 143–147.

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Scribner, K.T., Page, Ke.S. and Bartron, M.L. (2001) Hybridization in freshwater fishes: a review of case studies and cytonuclear methods of biological inference. Reviews in Fish Biology and Fisheries 10, 293–323.

Skelton, P. (2001) A Complete Guide to Freshwater Fishes of Southern Africa, 2 revised. Struik Publishers, Cape Town.

Skelton, P.H. and Swartz, E.R. (2011) Walking the tightrope: trends in African freshwater systematic ichthyology. Journal of fish biology 79, 1413–35.

Trewavas, E. (1983) Tilapiine Fishes of the genara Sarotherodon, Oreochromis and Danakilia. Cornell University Press, Ithaca, New York.

UNESCO (2007) A global perspective on research and development. UNESCO Institute for Statistics Fact Sheet 5, 1–8.

Welcomme, R.L. (1966) Recent changes in the stocks of Tilapia in Lake Victoria. Nature 212, 52–54.

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CHAPTER 4:

THE POTENTIAL TRADE-OFF BETWEEN ARTISANAL FISHERIES PRODUCTION AND

HYDROELECTRICITY GENERATION ON THE KAFUE RIVER, ZAMBIA

4.1 Abstract

Freshwater resource managers are increasingly obligated to consider the impacts of large river engineering projects on ecosystem services. We evaluated the effect of altered water regime from the operation of a large dam on the production of the downstream tropical floodplain fishery of the Kafue River, Zambia. We compared the benefits of increased hydropower relative to potentially lost fishery production. We compiled a long-term data set consisting of experimental gillnet catches, artisanal harvesting effort and monthly river flows for 25 years prior to and 29 years after the

1977 completion of the upstream Itezhi-tezhi dam. As a metric of the flood regime we calculated a canonical correlation score for each hydrological year before and after dam closure. For the period following dam construction, we used the Muskingum method of flood routing to estimate “no-dam” flows through the fishery area and downstream hydroelectric turbines in the Kafue Gorge dam. We compared 16 alternative models of catch per unit effort with and without an effect of water regime on fish population growth rate. Using the two best fitting models, we estimated the total observed fishery

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harvest and simulated “no-dam” fisheries harvest and found no significant effect of altered water regime on fishery production. We estimate that the large up-stream dam increases downstream hydropower production by about $17 million USD per annum.

The reduction in fishery production caused by the altered water regime is not significantly different than zero, though the average amounts to about $2.3 million annually. The total estimated value of harvest ranges from $1.3 million to $56 million annually. Large observed declines in fish abundance over the 54 year study period are attributed primarily with similarly large increases in total fishing effort in this mostly open-access artisanal fishery. These results contrast with other examples of the effects of flow alteration on fish, probably because levels of fisheries exploitation on the Kafue

River are very high relative to better studied regions on other continents; our focus on the whole fish community; and the unprecedented length of the time series we considered. If the goal is to sustain fishery production, investments in altering flow regime are likely to be less effective than investments to decrease fishing effort.

4.2 Introduction

Maximizing the production of ecosystem services is a desirable outcome for resource management, particularly when increasing the provision of one service decreases the provision of another. In these instances, it is important to estimate the value of ecosystem services--and the tradeoffs between potentially competing services-- to efficiently use resources. We estimate two major ecosystem services provided by the

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Kafue River, Zambia, hydroelectricity generation and fisheries production, and discuss management implications of the potential tradeoff between them.

Globally, there is increasing scientific and policy interest in “environmental flows,” i.e., flows that more closely mimic natural hydrologic patterns, as a tool to sustain ecosystem services and human well-being and better balance potential tradeoffs in flow alteration and other ecosystem services. This interest derives from the fact that large, impounded rivers around the world are facing competing uses of water resources as seasonal flood regimes are altered for hydropower or irrigation by storing water from high-flow seasons for use in dryer periods. Changes in water flow can impact river biodiversity by altering the physical channel structure, disrupting organisms’ life history patterns, severing connectivity, and by encouraging species invasions (Dudgeon et al,.

2005). These water regime changes also impact fisheries production and related ecological services (Welcomme, 2008; Poff & Zimmerman, 2010).

The task of assessing tradeoffs in ecosystem services as a result of flow alteration is especially relevant in Africa where there are 20 large dams now under construction or advanced planning, 42 undergoing expansions or rehabilitation, and 83 proposed new dams (International Rivers Africa Program, 2010). Little knowledge exists for similar rivers on the effect of changes in flow regime on ecosystem services. Most work on environmental flows has occurred in the Northern hemisphere in mostly small streams and rivers, has focused on particular species (e.g. Chinnok salmon, Oncorhynchus tshawytscha; Service, 2011) or been conducted in the absence of significant fisheries.

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The responses of ecosystems, particularly fisheries, to changes in hydrologic regime, thus remain largely unstudied in Africa (Poff & Zimmerman, 2010). This general paucity of data regarding ecological responses to hydrological change has led to the development of the comprehensive Integrated Basin Flow Assessment (IBFA; King &

Brown, 2010) approach over the last 15 years which integrates expert ecological knowledge and social-economic factors to inform river management decisions and observe hydrological effects on ecosystems as they are implemented. The IBFA process may yield data and effective management outcomes in the future, but it is still too soon to see the long-term effectiveness. A retrospective analysis of previous hydrological changes and fisheries response could provide immediate guidance for river resource managers. The Kafue River, Zambia presents an opportunity to examine the importance of water regime in a highly relevant social, economic and environmental context: heavily exploited artisanal fisheries.

The Kafue River provides many ecosystem services including irrigation, cattle grazing, and wildlife and bird habitat in national parks and Ramsar wetland sites

(Mumba & Thompson, 2005; Lagler, Kapetsky & Stewart, 1971). Hydropower generation and capture fisheries are however the most direct and obvious uses of this water resource. They are important to the public and policy makers, the most likely to be in direct conflict for water, and for which the most complete records are available.

Pre-dam fisheries studies conducted on the Kafue in the mid-1970s found annual fisheries harvest to be significantly correlated to flood regime (Chapman et al., 1971;

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Lagler et al., 1971; Dudley, 1974; Kapetsky & Illies, 1974; Welcomme, 1975; Muncy,

1977). These relationships between hydrological regime and fishery yield are complicated, however, because these studies focused on total harvest which overlooks the reciprocal relationships between fish abundance and fishery effort. Moreover, no studies have evaluated the impact of change in water regime on Kafue fisheries since dam construction was completed, though negative impacts on fisheries are often assumed (Chipungu, 1981; Schelle & Pittock, 2005). Changes in other ecosystem properties such as decreased extent of wetland habitat (Munyati, 2000; Mumba &

Thompson, 2005) and water chemistry (Obrdlik, Mumeka & Kasonde, 1989) have also been reported. In response to these observations environmental flows have been advocated by some stakeholders (Schelle & Pittock, 2005).

We hypothesized that the upstream construction of Itezhi-tezhi (ITT) dam altered the Kafue River water regime and reduced downstream fish abundance. To determine the relationship between water regime and fishery production, we compiled catch per unit effort from scientific surveys, artisanal fishing effort, total harvest, and monthly-mean discharge hydrographs from the Kafue River for the years 1954-2010 and developed state-space population growth models to test the effects of flood regime on multi-species fish community population growth rate. Using flow data from above ITT reservoir we simulated the water regime on the Kafue River for the post-ITT dam period as if the dam had not been constructed. Using this “no-dam” simulated flow we used the best fitting population growth models to estimate fisheries production and

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hydroelectric generating capacity downstream of ITT. Finally, we compared the revenue derived from these ecosystem services with and with-out water regulation by ITT dam.

4.3 Methods

4.3.1 Site description

The Kafue Flats are a large, flat floodplain of the Kafue River in Zambia (Figure

4.1). Historically, after the onset of the rainy season in November, flood waters began to rise from a dry season low of about 30m3s-1, and peaked in April or May at more than

1500m3s-1. More than 6,000 square kilometres were underwater during typical flood stage; for comparison this is an area more than 10 times that of Lake Constanz

(Germany, Austria, Switzerland) and roughly one third the size of Lake Ontario of the

Laurentian Great Lakes. The fishery has remained primarily artisanal since its inception in the 1950s. Fishers typically use dugout canoes or fiberglass “banana boats” and multifilament gillnets. Though illegal, large (>100m) hand drawn seines of <1mm mesh are also common, as are monofilament gillnets, and gillnets of mesh less than 50mm mesh. Fish-driving is also practiced, by beating the water to drive fish into these gillnets.

The main large-bodied fish species harvested are cichlids, principally species of the genra Oreochromis, Tilapia, Sagrochromis and Serranochromis, as well as Clarius catfish and the Kafue pike, Hepsetus odeo. Most species of all sizes are also harvested and marketed, including many Cyprinid species and to a lesser extent, Mormyrids and

Synodontids.

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The main fishery area is bracketed by two dams (Figure 4.1). The downstream Kafue Gorge dam (KG), completed in 1972, was originally installed with 600 megawatt (MW) of generating capacity, but was later expanded to 900 MW (Smardon, 2009). The estimated maximum capacity of the reservoir at KG is 800 million m3 (van der Knaap, 1994) and without additional water regulation during the dry season, the dam would only have enough water available for 207 MW of power output. To increase hydropower capacity, the ITT dam was built at the upstream end of the Kafue Flats in 1977. With a much larger reservoir holding 4,950 million m3, ITT provides steady flow

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downstream during the dry season, reducing the risk of insufficient water for maximum power generation at KG (Smardon, 2009).

4.3.2 Data compilation

Experimental gillnet fisheries data from scientific surveys (Error! Reference source not found.A) were compiled from published literature and unpublished data from the Zambian Department of Fisheries (DoF) for the years 1954-2010, and were assumed to be an index of total fish abundance as catch per unit effort (CPUE) calculated in mass per meter of net per night (Williams, 1960; CSO, 1970; 1978, 1984;

Everett, 1971, 1974; Kapetsky 1974). In this analysis we included only those mesh sizes and sampling locations (Appendix B) as those reported in the pre-dam data to produce a data string that is comparable across all years. Misidentification and changing nomenclature of taxa complicated comparisons of catches across time. We unified species to current nomenclature to the genus or species level (Mortimer, 1965; Skelton,

2001; Froese, & Pauly, 2012) and made sure representatives of each taxa at genus level were represented whenever species specific data was available for specific collections

(Appendix B). Fishing effort data on the Kafue was available as the number of gillnets and/or the fleet size as the number of boats in use in each year (Error! Reference source not found.B) (Mortimer, 1965; Kapetsky & Illies, 1974; CSO, 1978; Muyanga & Chipungu,

1978; Chipungu, 1981; CSO, 1984; Lupikisha, 1992; DoF, 1993; Lupikisha, 1993; DoF &

CSO, 2007). Total harvest (Error! Reference source not found.C) estimates were as

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reported for 1954-1996 by Nyimbili (2006), but comparable total harvest data after

1996 were not available.

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Figure 4.2. Fishery and hydrologic data for the Kafue River fishery, Zambia. (A) Box-plots of annual experimental gillnet catch

per unit effort showing the mean, quartile range, approximate 95% confidence interval, and outliers outside this interval. (B) Annual

artisanal effort in metres of gillnet (O) with the last data point slightly offset on the x-axis for visibility, and number of boats (●). (C)

Annual total reported harvest, and (D) mean monthly discharge from Itezhi-tezhi dam. The first vertical dashed line indicates the

date of closure of Kafue Gorge dam (downstream), while the second indicates the closure of Itezhi-tezhi dam (upstream).

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Monthly mean discharge (m3s-1) at the ITT dam was obtained from the Food and

Agriculture Organization (FAO, 1968) for the years 1953 through 1963, the Zambian

Ministry of Energy and Water Development for 1960 through 1991 (unpublished data), and Nyimbili (2006) from 1980 through 2005 (Error! Reference source not found.D).

Using the before and after damming mean monthly hydrographs we preformed canonical correlation analysis (CCA) with the CCA package (González et al, 2008) in R

(vers 2.14.0; R Development Core Team, 2011), to simiplify the hydropgraph into one varible for each year for use in population modeling. CCA is similar to principal component analysis (PCA) in that it assists description of multivariate data by describing new axes which are linear combinations of the original data that better describe patterns of sample variance. Whereas PCA simplifies patterns of correlations of multivariate data measured among individual samples, CCA identifies patterns between mutually exclusive groups of samples. Specifically, PCA finds the eigenvalues and eigenvectors of the sample variance-covariance matrix, while CCA is constrained to maximize the ratio of within group and between group variance-covariance matrixes. In this way, the first canonical variate represents a new orthogonal axis which most effectively discriminates the sample groups under consideration, in this case, hydrographs before and after damming (Zelditch et al., 2004). Thus, the correlation score associated with this axis in each year was used as a flood regime metric in the fisheries model described below.

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4.3.3 Modeling the impact of flood regime on fishery production

We used a multivariate auto-regressive state-space (MARSS) model to fit time series of experimental CPUE, fisheries effort and water regime to population growth models using maximum likelihood estimation. This state-space approach allowed the simultaneous estimation of the unobserved state process of fish abundance (CPUE) and fisheries effort (meters of gillnet) with observation error and including the effect of water level as a covariate to the CPUE process.

The MARSS model assumes Gompertz population growth expressed as a linear model by taking the natural log of CPUE and effort (See supporting information).

Gompertz growth is similar to logistic growth (commonly known as the Ricker model in fisheries) but implies growth is density dependent on the natural log of the population, meaning the growth rate varies exponentially with population size rather than linearly as with logistic. This formulation has considerable computational and statistical advantages and performs well in density-dependent populations (Dennis & Taper 1994).

In multivariate state-space, state and observation processes are arranged into a system of equations expressing population growth in matrix form including covariates (Holmes

& Ward, 2012).

 xv  Bv Bcv  xv   uv   Qv 0     w ,w ~ MVN0,  (1a)  cv    cv   cv  t t   cv  x t  0 1 x t1 u    0 Q 

 yv  Zv 0  xv   av   Rv 0     v , v ~ MVN0,  (1b)  cv   cv  cv   cv  t t   cv  y t  0 Z x t a    0 R 

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Where xi are state vectors at time t defined by a state process equation

(Equation 1a) with i superscripts representing the estimated variates (v) CPUE and effort, or the covariates (cv), the CCA score representing water regime. Bi are parameter matrices to be estimated. Process error, wt, is modeled as a multivariate normal distribution with mean zero and variance-covariance matrix of process

(environmental) stochasticity Q. Vectors of observed data yi are related to the process states through the observation process equation (Equation 1b). Z are identity matrices that associate one or more observations to unobserved state processes, with a parameters that linearly scale multiple observations of the same state, and multivariate normal observation error vt with R variance-covariance matrixes.

In total we specified 3 state processes: CPUE, effort, and water. Following standard practice we demeaned and standardized all data and used the resulting z- scores for estimation. We make the simplifying assumption that processes do not co- vary, that is for example, the variance of CPUE and effort do not scale with water level.

The alternative, including all possible co-varying effects greatly increases computation and decreases power (Bolker, 2008). We also fixed CCA score variance at unity to give the process model the flexibility to exactly equal the true covariate values; thus the covariate processes are not modeled but exactly specified (Holmes & Ward, 2011).

Initial results indicated process errors less than 1e-15 in all cases, leading to instability in the estimation algorithm. We therefore fixed process error for CPUE and effort at a trivially small value, 1e-5. Fixing process error in this way assumes that most of the 125

variation present in the data stems from how the data was observed and reported, not environmental variation in the populations themselves. This assumption is consistent with the inherent difficulties of measuring fish stocks and compiling data from multiple studies.

We specified two observation vectors for the effort process, meters of gillnet and boat counts. Gillnets are the more accurate measure of actual fishing effort and therefore boat counts are linearly scaled to gillnets by estimating the number of gillnet metres per boat in vector a(v). Assuming this scaling is constant for all years with boat data is reasonable given that the common-type canoe has changed little from early accounts and even pictures of the fishery (Lagler et al. 1971), and that these boats likely always carried their full capacity of nets, which are limited to only a few by the size of the boats and yet are cheap in terms of investment relative to the boat itself.

We created 16 base models that variously included or excluded all combinations of the effect of density dependence for CPUE and effort, of CPUEt-1 on effort, and watert-

1 on CPUE. Models were estimated using the MARSS package (vers 2.8; Holmes & Ward,

2012) in R, and ranked by Akaike’s Information Criteria (corrected, AICc). For the two best models we estimated the 95% bootstrap confidence intervals (CI) for the parameters and the CPUE and effort states. To perform the bootstrap we resampled the mean CPUE data for each year and parametrically simulated 500 bootstrap replicates of gillnets and boats counts based on the error estimated in each model.

Using the original model estimate for initial conditions, these bootstrap data were used

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to re-estimate parameters and states for each model and estimate the 95% CI for the observations of CPUE, gillnets, and boats.

4.3.4 Sensitivity analysis

The treatment of observation error in state-space models is critical to estimating environmental effects on population dynamics (Linden & Knape, 2009) and the key motivation for sensitivity analysis. Ives et al (2003) recommended against directly fitting observation error from data without independent estimates, and suggested instead to fit the model with rough estimates of observation variance and then test the sensitivity of the model to these estimates. We therefore fit models while estimating observation error, fixing observation error with rough estimates, and fixing observation error at unity. We fixed rough estimates of observation error as follows. For CPUE, we used the mean variance from 10000 bootstrap samples of each year with >= 4 sampling periods

(= 0.069). Gillnet observation variance derives from the variance observed in gillnets used in several areas over a 12 month period in 1972 (=0.321, CSO, 1978). To roughly estimate observation error in boat counts, we assumed that fishermen and boats where censused with the same error (counts of fishermen roughly coincide with boats counts; data not shown), we therefore conservatively use the maximum variance in any year between multiple counts of boats and/or counts of fishermen, which occurred in 2006

(=0.085). This yielded a total of 48 models for comparison.

We also tested the ability of the MARSS and AICc model selection procedure to select the correct model by re-running the selection for each model replacing the data

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with the estimated state vectors. That is, for each of the 16 base models the state vectors represented data simulated from a known model with known parameters, and we tested if running model selection on that simulated data would recapture the model from which it was generated.

4.3.5 Simulating flood regime and hydroelectric generating capacity

The ITT dam is intended to provide a more consistent supply of water during the dry season and thereby keep the turbines at KG dam capable of running at full capacity year-round. We apply the widely used Muskingum method of flood routing, or predicting downstream flows based on known upstream flows (McCarthy, 1938) to predict actual and “no-dam” flows at KG based on flows at ITT. Detailed methods are provided in the supporting information and summarized below.

First, we estimated simulated “no-dam” discharge for ITT using daily discharge at

Hook Bridge monitoring station up-stream from ITT reservoir (Figure 4.1, Appendix B) to estimate the inflows into the Muskingum model and discharge at ITT to represent outflows. Similarly, to model hydropower production at the downstream KG dam under both actual and simulated “no-dam” flows we used the actual and this estimated “no- dam” simulated daily discharge at the ITT dam to represent inflows to the Muskingum model, and flows at KG dam to represent outflows. To calculate the “no-dam” scenario water regime CCA metric we multiplied the CCA loadings calculated from the observed

ITT hydrograph by the “no-dam” simulated mean monthly flow at ITT.

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To estimate hydropower production under alternate observed and “no-dam” flow regimes, we took the annual average for each simulated Kafue Gorge hydrograph as an expected difference in flow attributed to the Itezhi-tezhi dam’s influence on the

Kafue River‘s flood regime. Reduced discharges into the reservoir at KG do not necessarily imply reduced generating capacity as the KG generating station can still operate until the reservoir is emptied, and even then it can use the reduced inflow directly. Therefore we conservatively compute the minimum reduction of power output in the “no-dam” scenario relative to that which uses the full capacity of the reservoir.

To estimate the value of this reduction in generation we use the replacement cost of importing the foregone electricity, which is estimated at about $31 per MWh (PB Power,

2006). Using the forgone difference makes the reasonable assumption that all the electricty that can be generated is readily consumed, and that imported electricity is available and would be consumed at this price.

4.3.6 Harvest Revenue

Using the actual and “no-dam” CCA water regime metrics and the parameter estimates of the best models, we calculated harvest for each year after the dam up to

2006 for each of 500 bootstrap CPUE replicates by rescaling the state process estimates and taking the product of the CPUE (kg m-1 night-1), effort (m), and activity rate of fishers

(proportion of nights per year spent fishing), for which a range of estimates exist. We multiplied the upper 95% CI of CPUE * effort by an activity rate = 1 (365 days of fishing per year) to retrieve maximum total harvest per year assuming that all nets were

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deployed every night of the year. For an intermediate estimate of annual harvest, we multiplied the median of the product by the “standard” activity rate reported by the

DoF = 0.65 (237.25 days) (DoF, 1993). For a minimum harvest estimate we used activity rate = 0.4 (146 days), the minimum reported in the compiled data (Lupikisha, 1993).

There is no known record of the price of fish in Zambia for any years in the time series analyzed. We can provide only a point estimate of the value of harvest in 2006, the last year of the time series analyzed, based on a survey at 6 markets in Lusaka of the retail price of fresh tilapia (species of the genus Oreochromis) in 2008: about 15,000

Kwacha per kg (B. Klco and A.M. Deines, unpublished data). We assume that the real price of fish was relatively constant between 2006 and 2008, adjust for inflation, and convert the nominal 2008 price in kwacha into real 2006 dollars and calculate the harvest revenue, D, in US dollars as

D2006= H2006 * P2008 * (CPI2006/CPI2008) * z2006 (2)

Where H is the harvest described above and P is the market price of tilapia in

2008, CPI is the consumer price index (CSO 2008) and z is the 2006 exchange rate for

Zambian Kwacha to USD, equal to 2.9e-4 (http://www.xe.com, accessed 3-18-12).

4.4 Results

The simulated “no-dam” flow at ITT estimated from Hook Bridge flows is shown in Figure 4.3A. The CCA flood regime metric (Figure 4.3B) captured 95% of the variance

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between the pre-and post- dam mean monthly hydrographs, with 67% of the CCA loading assigned to the historical low water months of September and October. These

CCA loadings and the “no-dam” flows were used to simulate what the CCA metric would have been had the ITT dam not existed (Figure 4.3B), and appear consistent with the pre-dam regime.

4.4.1 The Impact of Flood Regime on Fishery Production

We excluded from consideration models that were non-stationary and models that estimated negative intrinsic population growth (uCPUE). Non-stationary models have a variance distribution that increases over time which can cause the model estimates to diverge and the estimation algorithm to become unstable, indicating the particular

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model is structurally a poor representation of the system (Holmes & Ward, 2012).

Similarly, models which estimate negative intrinsic population growth are not biologically feasible representations of the fishery, which suggests the particular parameterization of these models does not adequately represent the system. We also excluded models with difference in AICc (dAICc) greater than 10 from the best model.

Full modeling results for all models are presented in Appendix B.

The difference in dAICc between the three best models (dAIC=0.06) provides very little support for choosing any over the others, though there is some support from dAICc that these three models are better fits to the data than the other models

(dAIC≥2.25) (Table 4.1). All three models included density dependence (b1-1) for CPUE and negative effects of effort on CPUE (b3), consistent with biological intuition.

Contrary to our expectation, two of the top three models, including the best model, did not include an effect of water regime on CPUE. The best model included a small positive effect of CPUE on effort (b2). The second-best model included a small negative impact of water regime on CPUE and implies a strictly linear increase in effort over time. The third model also did not include a water regime effect nor an effect of CPUE on effort, but did include weak density dependence on effort.

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TABLE 4.1.

RESULTS OF MODEL SELECTION AND PARAMETER ESTIMATES

Effect of CPUE Effect of Effect of Effort Water density CPUE on effort on Density Regime

depend. effort CPUE Depend. on CPUE 133 -LL dAICc UCPUEe Ueffort (b1-1) (b2) (b3) (b4-1) (b5) RCPUE Rgillnet Rboats -1 -1 -1 -2 -2 -3 1.4e 2.2e 2.4e 7.4e 8.0e -0.2* 5.5e -0.1* -1 -2 -1 1 -93.21 0 -4 -2 -2 -2 -2 -2 ind ind 1.3e – 8.4e – 8.8e – -1.5e –1.7 6.2e –9.6e 0.7–0.9 -2.4e –2.7e -0.2– -7.4e -1 -1 -1 2.5e 3.5e 4.3e -1 -1 -1 -2 -2 -2 1.4e 2.2e 2.4e 6.3e 7.9e -0.2* -0.1* -2.0e -1 -2 -2 2 -93.24 0.06 -2 -1 -2 -2 ind -2 ind -2 1.3e – 9.8e – 8.8e – 2.7e –1.4e 6.4e –9.3e 0.6–0.9 -0.2– -8.8e -0.1–4.2e -1 -1 -1 2.4e 3.5e 4.1e 3 -93.24 0.06 7.17e-2 8.31e-2 -0.22 ind -0.14 -3.21e-3 ind 1.4e-1 2.2e-1 2.4e-1 4 -93.12 2.25 8.00e-2 8.04e-2 -0.26 7.18e-3 -0.14 ind -2.9e-2 1.4e-1 2.2e-1 2.4e-1 5 -93.17 2.35 7.58e-2 8.41e-2 -0.25 ind -0.14 -4.01e-3 -2.5e-2 1.4e-1 2.2e-1 2.4e-1 6 -99.14 2.53 6.18e-2 7.89e-2 -0.21 ind -0.13 ind ind 1.9e-1 3.2e-1 8.6e-1 7 -99.10 4.73 6.32e-2 7.89e-2 -0.23 ind -0.13 ind -2.0e-2 1.9e-1 3.2e-1 8.6e-1 8 -99.12 4.77 6.93e-2 7.94e-2 -0.22 3.46e-3 -0.14 ind ind 1.9e-1 3.2e-1 8.6e-1

TABLE 4.1. CON”T

Effect of CPUE Effect of Effect of Effort Water density CPUE on effort on Density Regime

depend. effort CPUE Depend. on CPUE -LL dAICc UCPUEe Ueffort (b1-1) (b2) (b3) (b4-1) (b5) RCPUE Rgillnet Rboats 9 -99.13 4.80 6.58e-2 8.04e-2 -0.21 ind -0.13 -1.3e-3 ind 1.9e-1 3.2e-1 8.6e-1 10 -96.97 5.13 1.38 7.90e-2 -1.89 ind -1.42 ind ind 1.7e-1 2.2e-1 2.4e-1 11 -99.07 6.97 7.46e-2 7.95e-2 -0.25 5.00e-3 -0.14 ind -2.5e-2 1.9e-1 3.2e-1 8.6e-1 12 -99.09 7.02 6.96e-2 8.15e-2 -0.24 ind -0.14 -2.2e-3 -2.2e-2 1.9e-1 3.2e-1 8.6e-1 134 -1 -1 -1 -1 -1 13 -100.8 10.35 2.25e 1.13 -0.37 -1.28 -0.27 -9.98e ind 1.9e 3.2e 8.6e NOTE: ind= independent (not included), *significant, only evaluated for models 1&2. Estimates in bold indicate parameters that were fixed a priori as part of the model specification. -LL= negative log likelihood, dAIC= delta Akaike’s Information Criterion, UI= growth parameters for catch per unit effort (CPUE) and effort, respectively, Ri= Observation variance parameters for CPUE, gillents and boats, respectively. See text for details.

The sensitivity analysis showed that all but one model was successfully recaptured, the exception being a non-stationary model that was removed from the analysis. In all remaining models except one, the estimated observation error was smaller than our conservative fixed estimates. This excepted model was however removed from consideration because it also estimated negative population growth.

Whether observation error was fixed using rough estimates, fixed at large variance

(unity), or estimated within the model, the results of model selection were very similar.

In all cases, three of the top four models corresponded to the top three models in Table

4.1, demonstrating reasonable model selection over a wide range of observation variance. The exception to this pattern was model 6 (Table 4.1), which was estimated as the best model when observation error was fixed at rough estimates or at unity. Model

6 with estimated observation error (as in Table 4.1), however, still fits better than either the roughly fixed model or the fixed at unity model.

We used model 1 and model 2 to estimate the current- or status quo- fishery harvest, and model 2 to estimate the fishery using simulated “no-dam” discharge from

ITT (Figure 4.4). Similarity in model likelihoods suggests there was no significant difference in CPUE estimated between these best two models. The 95% CI around parameters for models 1 and 2 (Table 4.1) demonstrated no significant effect of CPUE on effort levels or water regime on CPUE. Moreover, the CI on the estimate of CPUE

(Figure 4.4A) demonstrated no significant difference between the observed CPUE

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estimated from model 1 and model 2, or between those models and the “no-dam” water regime simulation.

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Figure 4.4. The first and second best model estimates of the Kafue River fisheries modeling results shown in log scale to

highlight low abundances and mean observed data. In each panel black symbols are the mean observations, and solid and dashed

lines are median model estimates and 95% confidence intervals, respectively. Black and red lines are model 1 and model 2

estimates under the observed water regime, respectively, and blue lines are model 2 estimates under the simulated “no-dam” water

regime. (A) Catch per unit effort (CPUE) with additional available data shown for the year 2008 and 2010 which were not included in

the model. (B) Effort in metres of gillnet (О) and boats (●) transformed into gillnets units. (C) The reported and estimated total 137 harvest from the Kafue River. The substantial over-plotting of the fitted lines highlights the general result that hydrologic regime has

little effect on the fishery.

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Effort increased exponentially in both model 1 and model 2 (Figure 4.4B). In model 1, the effect of CPUE on effort was non-significant, while model 2 did not include any covariant or density dependent effects of effort (Table 4.1). In both models the intrinsic growth rate of effort was greater than that for the fish population indexed by

CPUE (Table 4.1). This difference in growth rate was only significant in model 2 where the 95% CI of CPUE and effort do not overlap, whereas the CPUE growth rate in model 1 was itself not significant as the 95% CI includes zero, and overlaps with the effort CI

(Table 4.1).

The median total harvest (Figure 4.4C) corresponded well, within about an order of magnitude, to the reported DoF harvest data though the total harvest was not directly included in the model. There was no significant difference in harvest between the observed status quo models and the no-dam simulation though the no-dam scenario suggests a slightly larger harvest. We estimated that revenue derived from total harvest in 2006 was approximately USD $7 million, while under the “no-dam” scenario it was approximately $9 million, but could range from approximately $1.3 million up to about

$56 million per year as result of the large range of the harvest confidence intervals and fishing activity rates. While not statistically different, the difference in median harvest between the status-quo and no-dam scenarios is about 900 metric tons, equivalent to about $2.3 million.

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4.4.2 The Effect of Flood Regime on Hydroelectric Generating Capacity

The analysis of the impact of ITT dam on the value of hydropower generated at KG dam suggests that with ITT dam in place the KG turbines can keep 254 m3s-1 of constant flow during the dry season, which corresponds to 888 MW, since each cubic metre per second generates 3.501 MW (estimated by OLS regression of daily power output on discharge through turbines; R2=0.961, N=2161, Std. Err.=0.015) (Figure 4.5). The installed generating capacity is 900 MW (corresponding to 256 m3s-1); therefore the KG power generation is on average unconstrained. Without ITT dam the KG turbines could keep only 203 m3s-1 of constant flow during the dry season, corresponding to 713 MW. This implies a total power deficit of (888 – 713) MW * 136 d * 24 hrs*d-1 = 571,200 MWh. The cost of importing electricity to Zambia is about $31 per MWh (PB Power, 2006); thus we estimate the total annual replacement cost as about $17.7 million if the ITT dam were not in place.

4.5 Discussion

We showed that the construction of the Itezhi-tezhi dam had substantial impacts on the water regime in the Kafue Flats. This hydrological manipulation has allowed gains in hydroelectric generating capacity of about $18 million per year at Kafue Gorge dam, estimated by the replacement cost method (i.e., the cost of purchasing the same amount of electricity from another source at the current price). A more accurate measure of gains would be lost total surplus, which accounts for losses to consumers who must pay higher prices for electricity and to taxpayers who must make up lowered revenues. There is considerable evidence that the Zambian power authority does not price its electricity according to market conditions, however, so it is impossible to estimate lost total surplus by using observed price data (IPA Energy Consulting, 2007). Thus, our estimate is likely an underestimate of the true value of the hydroelectric production benefit of ITT.

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Our fishery modeling sensitivity analysis indicated that our modeling methods were able to select appropriate models. The best models, however, did not indicate a significant impact of the dam-altered water regime on the fisheries production of the

Kafue River. Our estimates are the first published for the monetary value of this fishery, which is as large as $56 million annually: potentially more than three times as great as the replacement value of hydropower generated as a result of the construction of ITT

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dam. Our model selection and simulations suggest, however, that under current fishery practices no tradeoff or at most a small tradeoff of about $2.3 million exists between hydropower and fisheries production. The price of tilapia, a preferred species used here to represent prices for all fish species, may overestimate the total value of the fishery, however, the large uncertainty around the total value of the fishery due to unknown fishing activity rates probably overwhelms those for fish price. Moreover, if our price estimate using tilapia overestimates fishery values, and our hydropower analysis undervalues electricity, this further supports a general conclusion of only small, if any, tradeoffs between fisheries and hydropower.

Total harvests calculated from our model were consistent with the independently reported DoF statistics. Inland fisheries harvests are notoriously underreported in general (Welcomme 2011), but considering that harvest was not included in the model and that the CPUE data were apparently not used in constructing the DoF harvest estimates, the similarity of these independent estimates of the Kafue

River fishery lends confidence both to the models presented here and to the long term harvest records reported by the DoF. Differences in these harvest estimates are however apparent particularly in the early years where the models predict dramatic declines in harvest, while the DoF data reports increasing harvests. It is likely that during the early years of development the activity rate was lower than even the low estimate (146 days per year) considered here due to large changes in social structures as populations transformed from largely agro-pastoralist with seasonal fishing to largely

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fishing based (Haller & Merten, 2008). Low overall activity rates implied by only seasonal fishing which would significantly reduce the estimated total harvest during that time to be more in line with DoF records. The generally close agreement of the model and DoF estimates suggests that in the future reported harvest should be explicitly incorporated into the model estimation.

In apparent contrast to the results reported here, previous studies have found significant relationships between total harvest and various aspects of pre-dam water regimes on the Kafue river (Chapman et al,. 1971; Lagler et al., 1971; Dudley, 1974;

Kapetsky & Illies, 1974; Welcomme, 1975; Muncy, 1977). Two studies examined the effect of changed flood regime on fisheries and these both occurred after the construction of KG dam downstream, but before ITT dam was built upstream. These studies are largely consistent with our results, finding no detectable influence on the growth rate of two important tilapia species (Dudley, 1979), or on CPUE for most species

(Dudley & Scully, 1980).

It would be incorrect to interpret our results as indicating that there is no relationship between hydrology and fishery production on the Kafue River. Rather our results are more specific, indicating only that there is no relationship between total fish abundance and the hydrological changes most influenced by the dam, as indexed by the

CCA. Selection of appropriate biologically relevant water metrics for comparing average yearly CPUE to the monthly mean flows is a multivariate reduction problem seeking a complex balance between accounting for as much hydrologic variation as possible while

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minimizing co-variance between indices (Olden & Poff, 2003). Hundreds of metrics have been published for this purpose (Poff et al,. 2010). The task of selecting metrics was simplified in this case because we only needed to find the differences in the hydrograph along one dimension, before and after the ITT dam, rather than search for metrics with biological relevance. The ability of the CCA to clearly discriminate the multivariate differences in hydrological regime before and after dam construction is a novel and critical strength of our analysis. This approach could also be applied predictively to the many regions where dams are being constructed or are proposed given expectations about future dam-induced alteration of flows.

The results of this study also apparently contrast with previous studies of the effects of damming on fish in many streams and rivers around the world (Poff &

Zimmerman, 2010). We suggest four reasons that these conflicting results may arise.

First, we considered the whole fish community in both experimental gillnets and total harvest while previous work has dealt with particular specialist or sensitive taxa (Poff &

Zimmerman, 2010). It is possible that by considering the total fish community, negative impacts on sensitive or target taxa may be obscured by positive effects on other species, and vice-versa, due to CPUE aggregating effects (Kleiber & Maunder, 2008) and/or the portfolio effect (Hooper et al., 2005). The fishery also appears to have few strong species preferences; all species of all sizes are harvested and successfully marketed.

Species specific contributions to the fishery markets may therefore also be obscured by

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the portfolio effect. A species-specific analysis to hydrological change in the Kafue River would be a logical next step.

Species-specific data are relatively sparse in the early data set (Appendix B) and the selectivity of gillnets data further obscure such analysis, but some examples are informative. For instance, the spiny eel, Mastacembelidae, and eastern bottlenose,

Mormyrus longirostris, have not been reported for almost 30 years, since just after dam construction. Conversely, the Nile tilapia Oreochromis niloticus was introduced to the

Kafue River in the 1980s (Schwanck, 1995) and our gillnetting efforts in 2008 and 2010 reveal that this species is now as common as the native O. andersonii, and is caught throughout the Kafue River between ITT and KG. It seems unlikely that species losses or additions would, however, obscure the general result reported. Species losses would only strengthen the signal of negative hydrological effects. Nile tilapia, while increasingly common, make up only a small portion of the total fish caught in experimental gillnets: 0.2% of all fish and 0.4% of the biomass in the whole post-dam era and a maximum of 0.7% of all fish and 5% of the biomass in 2010, placing their particular contribution well within the CPUE 95% confidence intervals. It also seems unlikely that changes in species specific fisher preferences were significant, lists of important fisheries from the early fishery (e.g. Williams 1960) are largely similar to more recent observations. These factors together suggest that the whole fishery production, by biomass and value, are the most appropriate metrics for our purposes of comparing fishing and hydropower production.

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A second reason our results seemingly conflict with previous studies may be due to the exceptionally long and highly resolved data set from both before and after flow alteration. The relevant fish-related papers reviewed in Poff & Zimmerman (2010) contain a maximum of 10 years of pre-dam data and 45 years of post-dam data, though the average and median post-dam data set is only 6 and 2 years respectively (n= 33).

We provide 25 years of pre-dam observations, and 29 years of post dam observations.

It seems plausible that long-term compensatory changes in the fishery could swamp short-term effects of damming detected in other studies.

Third, many problems that are well known to exist with the quality of long-term inland fisheries data may apply less strongly to the Kafue River data. Globally, underreporting of inland fisheries statistics is a serious problem, particularly in small subsistence fisheries with diffuse landing sites and local consumption which hampers collection of harvest records and ultimately the total production reported (Welcomme,

2011). The Kafue CPUE data considered here avoids these common complications because data are derived directly from research sampling and are not dependent on accurate accrual of harvest data from fishers. In many systems, effort in terms of fish collection gears and protocols have been inconsistent and difficult to estimate accurately (De Graaf et al., 2012), but this also appears less so in the Kafue fishery where boats and gillnets have changed little over time. On the other hand, we know catches with seine nets and other prohibited methods in the Kafue were underreported and therefore poorly represented in the model. We also did not include the possibility 146

of changes in fisher behavior as a direct result of hydrological regime, though it is possible that dam construction drew new participants to the fishery. Overall, however, the consistency of the independent CPUE, effort, and total harvest observations over the full length of the data and compiled across multiple studies leads us to believe that the Kafue has fared very well in comparison to other systems in terms of the quality of data collection. Indeed, the Kafue was considered the best-documented African floodplain fishery before damming (Welcomme, 1975).

Finally, we reconcile the differences in the Kafue River and other studies of the relationship between hydrology and fish abundance as the result of harvesting effort in the Kafue River over the period of study that is likely more intense (and increasing) than in most if not all other studied ecosystems. The impact of harvest on fish abundance probably overwhelms the effect from hydrological manipulation. The z-scored data used in the population modeling allows direct comparison of the relative magnitudes of the effects (and uncertainty) of effort and CCA scores (Table 4.1). In models 1 and 2, the effects of fishing were not significantly different but in model 2, the best model that included an effect of water regime, the effect of water regime on fish abundance was an order of magnitude lower than the effect of effort. Moreover, the growth rate of fishing effort is significantly larger than the growth rate of the fish. Meanwhile, the observation error variance for both gillnets and boats in models 1 and 2 is about twice that for CPUE and an order of magnitude larger than the effect of water regime,

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indicating the relatively small effects of water regime are easily lost in the noise that surrounds effort in the system.

4.6 Conclusion

Dam construction does not seem to have had significant impacts on the Kafue

Flats fishery. The overall trends in CPUE, effort, and harvest in the Kafue fishery are largely consistent with overfishing, particularly the concept of fishing-down in open access fisheries (Allan et al., 2005). Our data and analysis of the effort on the Kafue

River demonstrate that fishing effort has been continually and exponentially increasing over time mostly independent from the abundance of the fish and fishers already present, suggesting little internal control over effort and the primacy of increased fishing effort on the observed declines fish abundance. These results do not, however, rule out an interaction between fishing effort and hydrologic regime such that a reduction in fishing effort could prompt a response to changes in hydrology, or magnify a currently unobserved response into the realm of detectability.

The implication of these results is that effort reduction will be the most effective way to increase fisheries output, and thereby improve the livelihoods of fishermen.

Management tactics which aim to directly increase fish abundance without addressing effort, such as habitat improvements via environmental flows, direct stocking or species introductions, are likely to be ineffectual or short-term solutions. These issues closely mirror general observations of global fisheries, where a wide-assortment of context- 148

specific management strategies are seeing some success at rebuilding important fish stocks by decreasing exploitation rates (Worm et al., 2009). In Zambia, national policy safeguards the right to fish for all Zambians, limiting the power of both the state and traditional institutions to regulate access in the Kafue fishery (Haller & Merten, 2008).

Yet, indirectly limiting effort through closed fishing seasons, closed areas and gear restrictions are the main regulatory instruments available to the DoF. Devolution of management to local levels with participatory management schemes have had varying levels of success in Lake Kariba and the Mweru-Luapula complex in northern Zambia, at least in terms of stakeholder acceptance (Kapasa, 2004, AMD Pers. Obs.). These and future management strategies will need to explore and accommodate broader socioeconomic forces which we show as external drivers of the Kafue fishery in terms of effort and ultimately fish abundance and harvests. We have provided a foundation for future, more comprehensive analyses of alternative management scenarios by estimating linkages to the wider economy in terms of the monetary value of the Kafue fishery and hydropower production. We have not considered other river-related ecosystem services that are potentially modified by damming, such as the provisioning of pasturing for livestock and habitat for wildlife, not because we think these values are less significant, only less tenable. Given the foundation provided here, changes in management practices in the future could probably increase total ecosystem services from the Kafue River.

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4.7 Acknowledgments

My coauthors C.A. Bee, R. Jensen, and D.M. Lodge. P. Ngalande, L. Njobvu Chilufya, R. Nkhata, I. Bbole, and many others from the Zambian Department of Fisheries contributed data and other assistance. C. Katongo, H.G. Mudenda, B. Klco, J. Deines, and the World Fish Center assisted with much on the ground support. The Zambian Department of Water Affairs and J. Kolding contributed data and J. McLachlan made helpful suggestions on the fisheries model. M. Whitman and the Lodge Lab and two anonymous reviewers provided comments on earlier versions of the manuscript. Funding was provided by NSF awards #0504495 and #1046682, and The Kellogg Institute at the University of Notre Dame.

4.8 References

Allan J.D., Abell R., Hogan Z., Revenga C., Taylor B.W., Robin L. Welcomme, et al. (2005) Overfishing of inland waters. Bioscience, 55, 1041-1051.

Bolker, B. M. (2008). Ecological Models and Data in R (p. 396). Princeton University Press.

CSO (1970) Fisheries Statistics (Natural waters) 1969. Central Statistical Office, Zambia

CSO (1978) Fisheries Statistics 1972 (Natural Waters): Volume I. Central Statistical Office, Zambia

CSO (1984) Fisheries Statistics 1973 (Natural Waters): Volume 1. Central Statistical Office, Zambia

CSO (2008) Consumer Price Index (CPI) – September 2008. Central Statistical Office, Zambia

Chapman D.W., Miller W.H., Dudley R.G. & Scully R.J. (1971) Ecology of fishes in the Kafue River. Food and Agriculture Organization of the United Nations. FI: SF/ZAM 11. Rome.

Chipungu P.M. (1981) The Impact of the Kafue Gorge Dam on the Kafue floodplain fishery (Zambia). In: Seminar on river basin management and cevelopment in

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Africa, Blantyre, Malawi, 8-10 December 1980 (Ed. Kapetsky, J.M.). CIFA Technical Paper no. 8: 120-129

De Graaf G., Bartley D., Jorgensen J. & Marmulla G. (2012) The scale of inland fisheries, can we do better? Alternative approaches for assessment. Fisheries Management and Ecology, DOI:10.1111/j.1365-2400.2011.00844.x

Dennis, B., & Taper, M. L. (1994). Density dependence in time series observations of natural populations: estimation and testing. Ecological Monographs, 64, 205– 224.

DoF (1993) 1993 Fisheries Statistics Report. Zambian Department of Fisheries. Chilanga, Zambia.

DoF & CSO (2007) Report of Kafue Floodplain Frame Survey - 2006. Zambian Department of Fisheries and Central Statistical Office, Zambia.

Dudgeon D., Arthington A.H., Gessner M.O., Kawabata Z.-I., Knowler D.J., Leveque C., et al. (2005) Freshwater biodiversity: importance, threats, status and conservation challenge. Biological Review, 81, 163-182.

Dudley R.G. (1979) Changes in growth and size distribution of Saroherodon macrochir and Sarotherodon andersoni from the Kafue Floodplain, Zambia, since the construction of the Gafue Gorge Dam. Journal of Fish Biology, 14, 205-223.

Dudley R.G. (1974) Growth of Tilapia of the Kafue Floodplain, Zambia: predicted effects of the Kafue Gorge Dam. Transactions of the American Fisheries Society, 103, 281-291.

Dudley R.G. & Scully R.J. (1980) Changes in experimental gillnet catches from the Kafue Floodplain, Zambia, since construction of the Kafue Gorge Dam. Journal of Fish Biology, 16, 521-537.

Everett, G. V. (1971). Sampling fish stocks in the Kafue River. Fish. Research Bulletin Zambia, 5, 297–304.

Everett, G. V. (1974). An Analysis of the 1970 commercial fish catch in three areas of the Kafue Floodplain. The African Journal of Tropical Hydrobiology and Fisheries, 3, 147–159.

FAO (1968) Multipurpose Survey of the Kafue River Basin, Zambia. Vol III Climatology and Hydrology. United Nations Development Program Food and Agriculture Organization of the United Nations, Rome.

151

Froese, R., & Pauly., D. (2012). FishBase. Version 4/2012. www.fishbase.org. Accessed 13 June 2012.

Gonzalez, I., Dejean, S., Martin, P. G. P., & Baccini, A. (2008). CCA: An R package to extend canonical correlation analysis. Journal of Statistical Software, 23, 1–14.

Haller T. & Merten S. (2008) “We are Zambians—Don’t tell us how to fish!” Institutional change, power relations and conflicts in the Kafue Flats fisheries in Zambia. Human Ecology, 36, 699-715.

Holmes E.E. & Ward E.J. (2012) Analysis of multivariate timeseries using the MARSS package, version 2.8. Mathematical Biology Program Northwest Fisheries Science Center, Seattle, WA.

Hooper D.U., Chapin F.S., Ewel J.J., Hector A., Inchausti P., Lavorel S., et al. (2005) Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecological Monographs, 75, 3-35.

IPA Energy Consulting (2007) Revised ZESCO’s cost of service. Capability and capacity strengthening of the Energy Regulation Board. IPA Energy Consulting, Edinburgh.

International Rivers Africa Program (2010) Africa Dams Briefing 2010. Berkeley, USA.

Ives A., Dennis B., Cottingham K. & Carpenter S.R. (2003) Estimating community stability and ecological interactions from time-series data. Ecological Monographs, 73, 301-330.

Kapasa C.K. (2004) From government management to collaborative management: A case study of co-management process and perceptions of stakeholders on key supportive institutions of Mweru Luapula fishery, Northern Zambia. The Worldfish Center, Lusaka. Zambia Report no. 1.

Kapetsky J.M. (1974) Growth, Mortality, and Production of Five Fish Species of the Kafue River Floodplain, Zambia. PhD Thesis, The University of Michigan.

Kapetsky J.M. & Illies J. (1974) The Kafue River Floodplain: and example of pre- impoundment potential for fish production. In: Lake Kariba: A man-made tropical ecosystem in central Africa. (Eds E.K. Balon & A.G. Coche), pp. 497-523. Dr. W. Junk Publishers, The Hague.

King J. & Brown C. (2010) Integrated basin flow assessments: concepts and method development in Africa and South-east Asia. Freshwater Biology, 55, 127-146.

152

Kleiber, P., & Maunder, M. N. (2006). Inherent bias in using aggregate CPUE to characterize abundance of fish species assemblages. Fisheries Research, 93, 140– 145.

Lagler K.F., Kapetsky J.M. & Stewart D.J. (1971) The Fisheries of the Kafue Flats, Zambia, in Relation to the Kafue Gorge Dam. FAO SF/Zam 11-2/FI

Linden A. & Knape J. (2009) Estimating envrionmental effects on population dynamics: consequences of observation error. Oikos, 118, 675-680.

Lupikisha J.M.C. (1992) Department of Fisheries 1991 Fisheries Statistics. Zambian Department of Fisheries. Chilanga, Zambia.

Lupikisha J.M.C. (1993) 1992 Fisheries Statistics. Zambian Department of Fisheries. Chilanga, Zambia.

McCarthy G.T. (1938) The unit hydrograph and flood routing. In: Conference of the North Atlantic Division, US Corps of Engineers. US Corps of Engineers, New London, CT.

Mortimer M.A.E. (1965) Natural Resources Handbook, The fish and fisheries of Zambia. Falcon Press Inc., Indola, Zambia.

Mumba M. & Thompson J.R. (2005) Hydrological and ecological impacts of dams on the Kafue Flats floodplain system, southern Zambia. Physics and Chemistry of the Earth, 30, 442-447.

Muncy R.J. (1977) An Evaluation of the Zambian Kafue River floodplain fishery. In: Symposium on River and Floodplain Fisheries in Africa. pp 153-164. Bujumbura, Burundi, 21 November-23 November 1977. FAO, Rome.

Munyati C. (2000) Wetland change detection on the Kafue Flats, Zambia, by classification of a multitemporal remote sensing image dataset. International Journal of Remote Sensing, 21, 1787-1806.

Muyanga E.D. & Chipungu P.M. (1978) A short review of the Kafue Flats fishery from 1968 to 1978. In: National Seminar on Environment and Change, the Consequences of Hydroelectric power Development on the utilization of the Kafue Flats. p. 9. Kafue Basin Research Committee, Lusaka, Zambia.

Nyimbili B. (2006) An evaluation of fish population changes in the Kafue Flats floodplain fishery of Zambia from 1980 to 2005. Ms Thesis, University of Bergen, Norway.

Obrdlik P., Mumeka A. & Kasonde J.M. (1989) Regulated rivers in Zambia- The case study of the Kafue River. Regulated Rivers: Research & Management, 3, 371-380.

153

Olden, J. D., & Poff, N. L. (2003). Redundancy and the choice of hydrologic indices for characterizing stream flow regimes. River Research and Applications, 19, 101– 121.

PB Power (2006) Zambian energy regulation board capacity strengthening. Generation Planning. Least Cost generation Plan Report. PB Power. Johannesburg, South Africa.

Poff N.L., Richter B.D., Arthington A.H., Bunn S.E., Naiman R.J., Kendy E., et al. (2010) The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshwater Biology, 55, 147- 170.

Poff N.L. & Zimmerman J.K.H. (2010) Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshwater Biology, 55, 194-205.

R Development Core Team (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Schelle P. & Pittock J. (2005) Restoring the Kafue Flats: A partnership approach to environmental flows in Zambia. World Wildlife Fund International.

Schwanck E.J. (1995) The introduced Oreochromis niloticus is spreading on the kafue floodplain, Zambia. Hydrobiologia, 315, 143-147.

Service R.F. (2011) Will Busting Dams Boost Salmon? Science, 334, 888-892.

Skelton, P. (2001). A Complete Guide to Freshwater Fishes of Southern Africa. Struik, Cape Town.

Smardon R.C. (2009) Sustaining the World’s Wetlands: Setting Policy and Resolving Conflicts. Springer-Verlag, New York. van der Knaap (1994) Status of fish stocks and fisheries of thirteen medium-sized African reservoirs. CIFA Technical Paper no. 26. FAO, Rome.

Welcomme R. L. (1975) The fisheries ecology of African floodplains. CIFA Technical Paper no. 3. FAO, Rome.

Welcomme R. L. (2008) World prospects for floodplain fisheries. Ecohydrology & Hydrobiology, 8, 169-182.

Welcomme, R. L. (2011). Review of the state of the world fishery resources: FAO Fisheries and Aquaculture Circular, 942, rev. 2. 154

Williams N.V. (1960) A Reveiw of the Kafue River Fishery. Rhodesia Agricultural Journal, 57, 86-92.

Worm, B., Hilborn, R., Baum, J. K., Branch, T. A., Collie, J. S., Costello, C., Fogarty, M. J., et al. (2009). Rebuilding Global Fisheries. Science, 325, 578–585.

Zelditch, M. L., Swiderski, D. L., Sheets, H. D., & Fink, W. L. (2004). Geometric Morphometrics for Biologists (p. 443). San Deigo: Elsevier Academic Press.

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CHAPTER 5:

CAN MARKET-DRIVEN HARVEST OF INVASIVE SPECIES CONTRIBUTE TO CONSERVATION

GOALS?

5.1 Abstract

Eating invasive populations has garnered attention as a way to reduce the impact of invasive species. We propose a bioeconomic framework to assess under what conditions market-driven harvest is likely to contribute to conservation goals. This framework consists of three steps: 1) modeling the size of harvested populations given market demand; 2) translating those population sizes into ecosystem services value; and

3) simulating how increases in demand for invasive species may reduce populations and restore ecosystem services. We compare four simplified market and management scenarios for harvest and find few conditions where markets supply high levels of conservation without large subsidies and increased demand for products derived from the invasive species. Further species-specific ecological and economic research on population response to harvest, the relationship between population density and economic impact, and demand in the marketplace would allow the implementation of this framework to better inform policy and management of invasive species. In addition, better understanding of how harvesting invasive species may interact with

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other management strategies could also inform the more cost effective management of rusty crayfish, Asian carp, lionfish, Burmese pythons, and many other harmful species for which harvesting has been proposed

5.2 Introduction

The spread of nonindigenous invasive species has occurred at considerable costs to biodiversity (Sala 2000), public and private budgets (Keller et al. 2009), and human and wildlife health (Daszak et al. 2000). One adaptation strategy for managing invasive species has recently garnered attention: eating them. The consumption of pest species as a control strategy is intuitively appealing in the sense that two wrongs -invasive species introduction and overharvesting- make a right: the reduction of the impacts of invasive species. Potential benefits and risks of eating invasive species have been described qualitatively (Nuñez et al. 2012), though gastronomic control of invasive species is only a taste of the motivations driving invasive species harvests (Barnes et al. in press). We focus here on any market-driven harvest, whether it be to provide food for people or animals or to produce other commercial goods.

Can market-driven harvests of invasive species contribute to conservation? The hypothesis proffered by the media is that, like so many historical populations that have been reduced by harvests, the populations of invasive species could be so reduced by market-driven harvesting that harmful ecological impacts of the species will be reduced.

Some precedent for this strategy exists, and large-scale “experiments” in harvesting invasive species are already underway. In Australia and New Zealand, commercial 157

harvest of feral pigs, deer, and other introduced mammals have reduced populations and contributed to other conservation goals (Parkes et al. 1996; Choquenot et al. 1998), and fishing in Lake Victoria has reduced stocks of the invasive Nile perch (Lates niloticus), allowing the resurgence of some native species (Spinney 2010). Markets for invasive species may also recoup a portion of the costs incurred following invasion

(Nuñez et al. 2012). For example, feral goat harvests in Australia yield about $15 million annually (Forsyth et al. 2009). Ongoing “experiments” in promoting invasive harvest are occurring in response to the Indo-Pacific lionfish (Pterois volitans) rapidly invading the southeastern United States Atlantic coast and the Caribbean. The “Eat Lionfish” campaign was launched by the US National Oceanic and Atmospheric Administration

(NOAA). In the US Midwest, commercial harvesting is touted as a control strategy for invasive silver (Hypophthalmichthys molitrix) and bighead (H. nobilis) carps (collectively referred to as Asian carps) (Charlebois et al. 2010; The Asian Carp Regional Coordinating

Committee 2012).

On the other hand, serious concern exists among ecologists and natural resource managers that attaching market or cultural values to invasive species may, in fact, waste resources and exacerbate invasions. They worry that market-driven harvests might incentivize the maintenance or even spread of the species to maximize yields rather than meet conservation goals (Table 5.1). Promoting harvest of some invasive species risks sending mixed messages about invasive species in general that may hinder prevention efforts for other invasive species (Lodge & Shrader-Frechette 2003; Nuñez et

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al. 2012). Moreover, high-profile harvests of invasive species, such as Asian Carp,

Lionfish, or Burmese Pythons (Python molurus bivittatus) in the Everglades (Dell’Amore

& Andries 2013) may divert financial and political capital from other, possibly more effective, invasive species management programs.

The efficacy of harvesting invasive species to achieve conservation goals is a problem that links biology and economics, with clear connections to the long history of the economic study of natural resource exploitation. In this paper we apply well- established, simple, bioeconomic principals to address how increasing market demand for invasive species may affect the populations of invasive species and the potential recovery of ecosystem service values. Our goal is to demonstrate a general conceptual framework for harvesting invasive species that could inform management via more detailed applications to particular species.

An open-access scenario is the simplest and most obvious starting place to describe the bioeconomics of harvesting invasive species, because the long-term effects of open-access harvest on natural resource stocks is readily apparent to the casual observer (the “tragedy of the commons”; Hardin 1968) and because the theoretical biological and economic components of open access fisheries are well developed

(Gordon 1954; Clark 2010). First, we demonstrate the development of the bioeconomic framework assuming an open-access market scenario, but then expand this to three other possible market scenarios.

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TABLE 5.1.

POTENTIAL EFFECTS OF HARVESTING INVASIVE SPECIES ON THE INVASION PROCESS

Invasion Stage Effects on population Effects on management of other of target species invasive species Entrainment +Harvest may increase awareness of into pathways invasive species, thus promoting prevention efforts for many species −Harvest of one invasive species may confuse efforts to prevent invasion of another Species +Harvesters may contribute to introduction −Harvest may motivate surveillance for other potential intentional introductions invaders. of target species − Live trade of harvest may increase propagule pressure Population +Harvest may contribute establishment to conservation goals through population reduction −Populations may maintained at high levels Spread +Harvest-induced declines in population may decrease propagule pressure into neighboring habitats −May encourage complacency about management NOTE: For each stage of the invasion process (left column; Lodge et al. 2006), the potential impacts of harvesting on the population of the target invasive species (middle column), and on management of other invasive species. Plus signs (+) indicate potential benefits of harvesting invasive species; and minus signs (−) indicate potential harm.

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Second, we consider a monopoly scenario, essentially the opposite of open- access, where a single harvester has control over harvesting effort and can therefore control the price of invasive species and the size of the population to maximize their own long-term profit without regard to the effects on other ecosystem services. This scenario might apply to government sponsored harvest of some species that include strict restrictions on harvester entry. Third, we consider a pure competition scenario, where the price of invasive species is outside the control of the harvester, and the harvester is able to maintain control only of effort and therefore the population size of the target species in a way that maximizes their own long-term profit. This pure competition scenario seems the most likely scenario to develop for harvests of invasive species in a free market. Fourth, we incorporate the value of ecosystem services directly in the market with an amenity-value scenario that maximizes the sum of harvester profits and recovery of ecosystem services. In this last case, we also estimate the direct revenue and costs to harvesters to estimate the size of subsidy (if any) to pay for the effort required to catch the amenity-maximizing harvest. Finally, we compare the size of this subsidy to the total value of recovered ecosystem services to describe the conditions under which ecosystem recovery is “free” as a result of market-driven harvests of invasive species.

5.3 A bioeconomic framework

Commerce propels biological invasions by increasing trade and transportation connections among locations (Levine and D’antonio 2003, Lin et al. 2007), but to what 161

extent commerce in invasive species could reduce or offset the environmental and social costs of invasive species has not been considered. We develop a conceptual framework for evaluating the feasibility of reducing the impact of invasive populations through market-driven harvest in three steps. The first step is modeling market-driven reductions in invasive populations based on the demand for invasive species. Second, the population size of invasive species is translated into ecosystem services values.

Finally, we apply these linked population and ecosystem service models over increasing levels of demand to ask how much increasing demand contributes to the recovery of ecosystem services.

The bioeconomic relationships among markets, populations and harvesting effort has most often been described in reference to fisheries, which we focus on here because the theoretical biological and economic underpinnings are well developed and widely available, but also because aquatic invasive species (e.g. crayfishes, Asian carp, lionfish) are currently important targets for harvesting in the U.S. Here, we describe these models using a hypothetical fishery which draws biological and market parameters from research on rusty crayfish (Orconectes rusticus) invasions in the upper midwest U.S. (Peters 2010), and from the signal crayfish (Pacifastacus leniusculus) invasion in Lake Tahoe as a simplified but realistic example. The bioeconomic models described primarily follow Clark (2010), with detailed descriptions provided in Appendix

C. This simplified example is suitable for introducing our bioeconomic approach, but we later address the implications of more realistic models.

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5.3.1 Step 1: Market driven reduction in populations

We begin by describing the growth of a hypothetical invasive population. We make the common simplifying assumption that the harvested population grows according to the logistic growth model, i.e. the Gordon (1954) model in fisheries literature.

dxb  xb  Eq. 1  rxb 1  dt  K 

Which describes the change in population biomass, xb, over time as a function of the intrinsic growth rate of the population, r, and the carrying capacity, K. Because the model includes density dependence, population growth is hump-shaped (Figure 5.1A).

At low abundance the population grows slowly, but as abundance increases the growth rate also increases, up to a maximum above which the population growth rate declines until the population growth is zero at the carrying capacity.

Now we imagine the development of a market for this species, and specify a demand curve which describes the price per unit, P, consumers are willing to pay for invasive species as a function of the quantity harvested, xh.

x Eq. 2 P  a(1 h )

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Figure 5.1. (A) Logistic population growth (green line), and harvest (magenta line), with dots indicating population densities at which growth and harvest are at equilibrium; (B) Value of the ecosystem services as a function of population density of a hypothetical invasive crayfish (density-impact curves). For (B), curve ii is emphasized in text. access population equilibriums, also known as sustainable yields, exist where harvest rates are equal to the population growth rate (vertical dotted lines determined in A), and are translated in (B) into ecosystem service values for curve ii (horizontal dotted lines, arrows, in B). Population and market parameters used are: a=10.55, b=13500, r= 1.2, K=58200, c=2.13, q=2.9e-4.

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The demand curve we have assumed is a linear model with the maximum willingness to pay for a unit of harvest, a, and the maximum quantity consumers are willing to buy under any circumstances, b. This demand curve implies, like most demand curves, that price declines with increasing harvest and the rate of this decline

(the price elasticity) is reduced by increasing the total quantity of harvest that can be sold.

We also specify the per unit cost of harvest, C, which depends on the cost of harvesting effort, ce, the size of the population, xb, and the proportion of the population that is harvested with each unit of effort, q.

c Eq. 3 C  e qxb

The cost function incorporates the observation that it is cheaper to harvest a given quantity from a large population than from a small population.

Using these relationships (Eqs. 1-3), we first consider the open-access scenario.

Under open-access, new harvesters will continue to enter the fishery until their total costs of harvesting are equal to the total revenue received by selling their harvests,

P*xh=C*xh. Thus, by setting Eq. 2 equal to Eq. 3 and solving for xh, it is possible to determine the bioeconomic equilibrium (*) level of harvest.

Eq. 4 *  ce  xh  b1   aqxb 

The goal of this first step in the bioeconomic framework is to find the population size which remains given this equilibrium harvest. At equilibrium, the growth rate of the 165

population is equal to the rate the population is being harvested, and this population size can be found by equating Eq. 4 to Eq. 1 and solving for xb (Figure 5.1A, Appendix C ).

For the growth and demand model described here, three equilibrium populations may exist where the cost of harvesting equals the price received for the harvest for the given demand curve (dots on Figure 5.1A). In the next section we use these equilibrium population sizes to calculate reductions in impacts to ecosystem services.

5.3.2 Step 2: Reduction of impacts to ecosystem services

The second part of our bioeconomic framework addresses how a reduction in invasive species population due to market driven harvest can affect the value of benefits derived from invaded ecosystems. Density-impact functions (Figure 5.1B) relate the population density of an invasive species to impacts on ecosystem services (Yokomizo et al. 2009). Under ideal circumstances, mechanistic density-impact functions could capture the whole of the impacts to ecosystems, and express these impacts in monetary terms (Abson & Termansen 2011) that could be easily related to social well-being. A range of density-impact functions might be derived from theoretical approaches and have empirical support in a variety of functional responses such as predator-prey models (Choquenot and Parkes 2001) and impacts of invasive plants (Thiele et al. 2009).

The mechanistic functions of whole ecosystem processes however are complex and incompletely understood even for simple systems. Moreover, the process of recovery and restoration of ecosystem services is difficult- recovery is not simply the reverse of the impact (Norton 2009). For the purpose of demonstrating the

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bioeconomic framework for harvesting invasive species, we explore a range of plausible density-impact responses from Yokomizo et al. (2009) (Figure 5.1B), which could be replaced by an empirically derived function in a particular application. We use these functions to translate equilibrium population size into a value of ecosystem services that would be recovered via the reduction in population of an invasive species.

I  v  vD1/ 1 exp  x / K  u /   Z Eq. 5 xb     b    where

D  1 exp 1 u/  /1 Z1 exp 1 u/   Z 1/1 expu /  

Where I, is taken as the monetary value of ecosystem services, and D and Z are accessory functions which simplify the printing of Eq. 5. This density impact equation is a function of the invasive species population size which scales from the maximum value of ecosystem services that could be recovered if the invasion population was eradicated, v, to a value of zero when the invasive population is at carrying capacity. By adjusting the shape parameters u and β, we derived four density-impact curves (Figure 5.1B).

Alternative density-impact parameterizations imply varying levels of success at achieving conservation goals from a given level of population reduction (Yokomizo et al.

2009).

The general shape of the density impact curves used here (Figure 5.1B) are supported empirically. For example, the loss of ewes in Australia increases rapidly with initially increasing numbers of feral pigs, but asymptotes at higher pig densities

(Choquenot and Parkes 2001), similar to curve i in Error! Reference source not found.B.

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The lake-wide reduction of native snails by introduced rusty crayfish (Orconectes rusticus) occurs mostly at high crayfish density, similar to curve iv (Lodge et al. 1998).

Density dependent browse damage on trees by brush-tail possum (Trichosurus vulpecula), however, varies depending on tree species (Duncan et al. 2011). In general, we think that most density-impact curves asymptote at high invasive species density

(curves i,ii,iv) but we include a linear form (curve iii) because two data points --no or low abundance and “invaded”- may often be all that are available.

By solving the density-impact equation (Eq. 5) for the market-driven population biomass equilibriums identified above (Figure 5.1A) for a given set of market and population parameters, ecosystem service recovery value(s), I, are obtained as a result of harvests of invasive species. In Figure 5.1B, the equilibrium populations are extended down into Figure 1B to intersect the density-impact curves. The intersection of those equilibrium population levels with the density impact curves identify the level of recovery of ecosystem services (arrows in Figure 5.1B) for one particular parameter set drawn from the hypothetical crayfishery (curve ii in Figure 5.1B).

In the open-access case, three equilibriums populations are possible that satisfy the open-access definition that total revenue is equal to total costs. The right-hand side equilibrium represents cheap per unit harvests from a large population, resulting in sales of a large catch for a relatively low price; however, there is little ecosystem service benefit from this harvest because the population of invasive species remains large. It is well understood from both theory and practice (Gordon 1954; Roughgarden & Smith

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1996; Grafton et al. 2007; Clark 2010) that, for a variety of reasons not modeled explicitly here, open access fisheries often lead to overexploitation and collapse of natural resource stocks towards low population equilibriums (on the left side of the growth curve). This, of course, is the conservation goal of harvesting invasive species.

We illustrate below, however, that demand must be very high to achieve collapse of the harvested population.

5.3.3 Step 3: Increasing demand for invasive species

The final component of our bioeconomic framework for harvesting invasive species is evaluating the effect of increasing demand on the population size and consequently on the recovery of ecosystem services. Based on our hypothetical crayfishery, we modeled the effect of demand for crayfish by increasing the maximum quantity that could be sold (parameter b, Eq. 2) by up to 370%. We also increased the maximum price (parameter a, Eq. 2) by up to 600% of the default value, but found that population biomass was relatively insensitive to maximum price (Error! Reference source not found.); therefore we initially focus below on the response of biomass to maximum demand.

In general, population sizes decrease with increasing demand (Figure 5.2). Yet, under open-access conditions, to unambiguously harvest this hypothetical population to a low level would require a very large increase in demand. For a large range of demand there exist multiple population equilibria for a given level of demand (indicated by “Z” shape of the open access curve) (Figure 5.2), which correspond to the multiple

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equilibrium (dots) in Figure 5.1. For demand levels where multiple population equilibria exist, the particular equilibrium biomass that results depends on the starting conditions, and no small amount of environmental stochasticity (Scheffer & Carpenter 2003), which are beyond the scope of this paper. We expect that invasive populations will most likely occur at the highest of these possible equilibriums based on the difficulties typically encountered in eradication programs (Pluess et al. 2012).

The potential recovery of ecosystem services that correspond to the populations levels achieved by increasing demand under open access (Figure 5.2) are typically either very high or very low (Figure 5.3A). Increasing demand for invasive species products does generally recover ecosystem services. For density impact curve iv, for example, ecosystem service values improve rapidly with increasing demand. For most density impact curves, however, the attainment of conservation goals will require large increases in demand. For this hypothetical crayfish example, increasing demand by at least 200% would be required to see modest returns in ecosystem service values for equilibria on the right side of the population curve (Figure 5.1), and up to 400%, at least initially, to bump populations towards “collapse” equilibria on the left of the growth cruve.

Effectively open-access fishing has collapsed many global fisheries (Worm et al.

2009). While this truism lends intuitive credence to the potential of harvesting invasive species to reduce their population, it overlooks the fact that getting these stocks to this position has not, actually, been easy.

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Figure 5.3. Percent of ecosystem service recovery that results from increasing maximum quantity demanded (parameter b) via market- driven harvests in (a-d) Open-access, (e-h) monopoly and (i-l) pure competition scenarios.

For some stocks, collapse has been the result of decades, even millennia, of fishing effort, fueled by high demand (Jackson et al. 2001; O’Connor et al. 2011).

5.3.4 Alternative management scenarios

We introduced our bioeconomic framework by describing an open-access scenario. We now apply the three steps described above to find the harvest levels that maximize the long term value of production and/or ecosystem services. Specifically we

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consider three market scenarios: monopoly control of price and production; pure competition; and amenity-value, where the value of ecosystem service recovery is incorporated explicitly into the equilibrium solution. We do not consider the open- access scenario in this section because it is by definition a zero profit scenario: there is nothing to maximize over time and the static solution described in the previous section is sufficient. While static solutions to the remaining scenarios are possible (Error!

Reference source not found.), here we use the more general long term solution (Clark

2010) because it more appropriately addresses the central question of social welfare:

 Eq. 6 t maximizexh(t) e Pxh Cxh dt 0

Where Pxh is the total revenue a harvester receives for a harvest and Cxh is the total cost of that harvest, as in the derivation of Eq. 4. The integral and the term e-δt, indicate that we want to maximize the discounted present value (given the discount rate, δ) of the all the future harvests (Error! Reference source not found.). Ideally, the optimal equilibriums are associated with optimal approaches to those equilibriums; however, we focus on only the long-term equilibrium populations. In the following sections we adapt Eq. 6 for each of the alternative market and management scenarios considered.

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5.3.4.1 Monopoly

By maintaining control of harvest rates a monopolist harvester controls both the price of invasive species and the population level of the target species, and thereby maximizes their own long term profit.

 Eq. 7 t   x  c  Monopoly maximizex (t) e a1 h x  e x dt h   b h qx h  0    b 

Where P in Eq. 6 is replaced by the price function (Eq. 2) and C is replaced by the cost function (Eq. 3). Note how a monopolist is able to manipulate the price by controlling the total harvest. For example, by harvesting only a few individuals from a large population with relatively little effort and selling these small harvests only to the consumers willing to pay the highest prices.

Equilibrium population sizes under the monopoly scenario are higher than the equilibrium population(s) under open-access (Figure 5.2). As in the open access scenario, increasing demand across the maximum price (a) had little effect on monopoly equilibrium populations (Error! Reference source not found.), while increasing demand across maximum quantity (b) decreases the total population (Figure 5.2) and approaches maximum sustainable yield at half of carrying capacity. The generally larger population sizes provide little scope for the recovery of ecosystem services across a wide range of demand and density-impact functions (Figure 5.3B). The exception to this pattern is density-impact curve iv, though even an 50% recovery of ecosystem services in this case requires an almost 300% increase in demand (Figure 5.3).

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From the perspective of a harvester, monopoly profits are a considerable improvement over open access where profits are, by definition, zero. This scenario begins to demonstrate that a profit motive provides substantial incentive to control access and harvest rates of target invasive species, and therefore to maintain populations at levels that are far from ideal in regards to the recovery of ecosystem services.

Monopolistic control however is untenable in reality for a variety of reasons.

Invasive species are typically geographically dispersed, posing significant enforcement issues for limiting harvest levels to the optimum for profit maximization. It is also unlikely that price control could be adequately maintained in the face of many reasonable substitute commodities. The hypothetical invasive crayfish in our example are nested within a larger market for all crayfish, and also within the larger seafood market. The demand for, and hence the price, of crayfish (or any invasive species) is set within the context of their substitute products and therefore outside the control of the harvesters of invasive species. Monopolies are also illegal in many jurisdictions. In the next section we describe a pure competition scenario which better describes the situation likely to be faced by harvesters of invasive species.

5.3.4.2 Pure competition

The key distinction between a pure competition scenario and the monopoly scenario is that here we imagine a market for invasive species that is relatively large and dispersed, such that the production of any particular harvester or population does not 175

affect the price per unit of the invasive species. In this way individual harvesters do not influence the price, rather they are “price-takers” who harvest so as to maximize profit at a price taken from outside their realm of control. Thus our model has a fixed price.

 Eq. 8 t   z  c  Pure competition maximizex (t) e a1 x  e x dt h   b h qx h  0    b 

The parameter z is a constant place-holder for harvest in Eq. 2, such that the shifts in demand explored in the third step of the bioeconomic framework can be implemented in the same way as for the monopoly scenario above by changing the values of parameters a and b. This maximization, unlike open-access but similar to the monopoly scenario, assumes that harvesters are able to control the level of harvest, and hence the population biomass. Thus, this scenario could be thought of as invasive species “ranching”.

There is no effect of pure competition market harvests on the population biomass at low levels of demand (Figure 5.2). That is, it costs more money to harvest any individuals than can be made by their sale and the population persists at carrying capacity. Once this price threshold is surpassed by increases in demand, the equilibrium biomass quickly approaches the maximum economic yield of the population. A 25% increase in demand from this price threshold has the about the same effect on equilibrium population biomass as a 600% increase under the open-access scenario, and only under extreme demand in the monopoly scenario. The recovery of ecosystem

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services under pure competition ranges widely, depending on the density-impact function being considered (Figure 5.3C).

Of all the scenarios we consider, this scenario provides the most realistic, strong incentives for intentionally establishing new populations for harvest. Specifically, a harvester would be motivated by the desire to avoid the zero-profits of open-access by creating populations to which other harvesters do not have access and where production levels could be controlled. Removing open-access in these new populations could be achieved by, for example, establishment of populations on private property, lobbying government for exclusive rights in the public arena, creating harvester cartels, or creating cultural attachment (Nuñez and Simberloff 2005). Perhaps the most troublesome possibility is clandestine –even illegal- spread into new habitats. These populations could contribute to additional harm to ecosystem services that would not have existed without the market, and provide a source of propagules for further invasion (Deines et al. 2005).

Other factors may reduce or mitigate the perverse incentive to spread invasive species, particularly if these translocations are illegal, which would be the case for at least some important invasive species. First, the risk of additional spread will be dependent on the likely availability of other existing populations not currently being harvested, which would serve as additional source without further introduction. A less obvious factor is the persistent problem of open access. Without legal access restrictions, such as private property rights, a harvester that creates a new population

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will only enjoy harvest control and profits as long as the new population remains unknown to other harvesters, at which point open-access drives revenue to zero.

However, for harvesters more interested in short-term gain (large discount rates, δ) and species with rapid growth (r) to harvestable population sizes (>c/qp), these considerations may not substantially influence present introduction decisions. This bioeconomic framework could be used to explore the influence of discount rates and management time frames, and could be expanded to include multiple populations, but such a model is outside the scope of the present work. In the next scenario we explicitly include the benefits of ecosystem recovery to maximize social welfare and more directly compare the value of ecosystem benefits and the costs of harvests.

5.3.4.3 Amenity Values

In the open-access, monopoly, and pure competition scenarios, the value of ecosystem services recovered as a result of decreased populations of the target invasive species was evaluated as an externality of the bioeconomic equilibriums. In this final scenario we seek to internalize the value of recovered ecosystem services (Abson &

Termansen 2011) and maximize the total value of invasive species harvest plus the value of ecosystem services impacted by invasive populations. We achieve this by making the simplifying assumption that the monetary value (amenity) of the ecosystem at a given population level, i.e, the relationship incorporated in the density-impact function I(xb)

(Eq. 5), is added to the revenue of harvesters in the pure competition scenario (Eq. 8)

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 Amenity values   z  c  Eq. 9 maximizex (t) et a1 x  e x  I dt h   b h qx h xb  0    b 

This assumption is unrealistic in that the value of ecosystem services are often not monetized, but their value could be conceived as payments or other subsidies given to harvesters, which we estimate below. We explore the effect of both the size of the ecosystem value and of increasing demand on the magnitude of the reduction in population size of the invasive species, and the resulting level of ecosystem service recovery.

Unlike the previous scenarios where equilibrium populations could be expressed as a function of demand while holding other parameters constant, the addition of the density-impact curve to the revenue maximization equation complicates this scenario.

The density impact curve is a function of the maximum ecosystem service value attainable if the invasive species population were eradicated, and this must be included in step 3 of the bioeconomic framework. Moreover, while the demand parameter a, the maximum price, did not substantially influence the equilibrium populations, a does have an effect in this scenario. We therefore analyze the combined effect of both increasing ecosystem values and increasing both demand parameters a and b on population using a numeric approach (Error! Reference source not found.). Results are presented as heat maps of the response variable (equilibrium population size, ecosystem recovery, and social welfare in Figures 4, 5, and 6, respectively) calculated for 100,000 randomly drawn demand parameter pairs across a range of maximum ecosystem service values.

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We base the maximum ecosystem service value at the maximum price times the carrying capacity (a*K) and explore a range of ecosystem service values, v, from a*K*0.01 (nearly equivalent to the pure-competition scenario) to a*K*20. This otherwise arbitrary range covers the full response spectrum of recovery of ecosystem service from no recovery to 100% recovery within the limits of the hypothetical crayfishery parameters.

Over much of the range of increasing demand, equilibrium population sizes are found near half of the carrying capacity (Figure 5.4). This result is consistent with the pure-competition scenario approach to maximum economic yield as demand increases, particularly for the relatively low values of ecosystem services (a*K*0.01). The amenity equilibrium population in Figure 5.2 corresponds to Figure 5.4F.

The most striking feature of this analysis is that under most conditions, increased demand increases the expected equilibrium population size of harvested invasive species. This counter-intuitive result stems from the fact that at low levels of demand, the most social welfare is derived from dramatically reducing populations, thus regaining the ecosystem service values. As demand increases however, substantial value may also be made through harvests, the maximum long-term value of which occurs at populations levels near the MEY near half carrying capacity. Increasing the ecosystem value (moving down columns in Figure 5.4) increases the range of demand for which ecosystem values are worth more than harvest values, and population equilibria approach zero.

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The application of alternative density-impact curves heavily influences populations (Figure 5.4) and ecosystem recovery (Figure 5.5) outcomes. The faster a given density impact-curve restores ecosystem services as populations decline (i.e., generally moving to the left in Figures 5.4, 5.5, and 5.6), the larger is the equilibrium

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population (e.g., Figure 5.4I-L). This result is also counter intuitive, and arises because if ecosystem service values can be recovered by small reductions in populations (e.g. curve iv), than maximizing welfare is best accomplished from having the best of both: some ecosystem recovery and some harvest value. Indeed, almost complete recovery of ecosystem services occurs for most conditions, except under low maximum ecosystem system service value (top row, Figure 5.5) and where the density-impact curve implies slow recovery of ecosystem services (i.e. curve i; Figure 5.5).

A key feature that distinguishes the amenity value scenario from other market scenarios is that the full costs of harvesting are not paid for by sales at equilibrium. In the amenity scenario, some mechanism is assumed to exist to transfer the value of ecosystem services to harvesters. That is, the market for harvesting invasive species may not pay the entirety of the cost of harvests. To achieve the welfare-maximizing populations (Figure 5.4), the negative difference between the revenue received for sale of the harvest (P*xh) and the cost of harvests (C*xh) must be paid in some form of subsidy to harvesters. We calculated the total required cost of this subsidy across demand, ecosystem values and density impact curves (Error! Reference source not found.). Then, we found the difference between this subsidy and the recovered values of ecosystem services to determine under what circumstances (if any) there is a net gain in social welfare from harvesting invasive species.

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Net social welfare decreases with increasing demand for invasive species across density impact curves, and increases with increasing ecosystem service values (Figure

5.6). The cost of harvesting invasive species to populations low enough to provide recovery of ecosystem services is greater than the value of those ecosystem services over a wide range of conditions, while higher values of recovered ecosystem services clearly increase net social welfare (Figure 5.6). With minor exception, nearly complete recovery of ecosystem services (e.g. Figure 5.5I) can be obtained at net positive social welfare only when the value of ecosystem services is at least 10 times the maximum market value of the invasive species population (a*K) (bottom row, Figure 5.6), and when the density-impact relationship approaches curve i (Figure 5.6). At high values of ecosystem service, the value of restored ecosystem services is “free”- equal to the subsidy that must be paid at that equilibrium. While density impact curve i implies the slowest recovery of ecosystem services as populations decline (Figure 5.1B), the amenity equilibrium populations supply positive net welfare across a broader range of demand than for other density-impact curves for the same ecosystem values (Figure 5.5). Under no conditions in the amenity scenario did harvest profits entirely cover the cost of ecosystem recovery.

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The increasing costs of harvests from declining populations makes reaching maximum amenity-value recovery of ecosystem services an expensive prospect, above what the market alone would supply. Ecosystem service values must be considerable relative to the value of harvests to maximize welfare without paying substantial subsidies to harvesters from outside the market.

5.4 Discussion

5.4.1 A research agenda and tools for managers

Here we have demonstrated a bioeconomic method for evaluating the potential for market-driven harvests to contribute to conservation goals under a variety of simple market and management scenarios. For a detailed application to a specific invasion, managers and researchers are faced with the task of specifying and parameterizing this simple framework, or more complex extensions, by quantifying how target species respond to harvests, determining the shape and magnitude of the density-impact curve, and measuring the market for invasive species goods. We outline some available resources and tools for this undertaking and discuss the role of harvesting invasive species in the context of the general invasion process and how harvest may interact with other management options.

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5.4.1.1 Modeling responses of target species to harvest

The bioeconomic results here suggest that market driven harvests often result in higher equilibrium populations than those preferred under conservation goals. In reality, however, these equilibria are unlikely to be ecologically stable, typically resulting in populations lower than would be estimated with the Gordon model (Roughgarden and Smith 1996). This is especially true in the open-access scenario where multiple alternative states exist, and much recent work has increased our understanding of transitions between these states (Scheffer & Carpenter 2003; Scheffer et al. 2012).

We have not included many realistic complexities in demonstration of our bioeconomic framework, including environmental stochasiticity, spatial distributions of populations, or alternative population growth models. Furthermore, harvested populations and the people that harvest them constitute linked dynamic systems sensitive to stochastic environmental processes and large-scale economic fluctuations for which more sophisticated models than the logistic Gordon model used here are more appropriate and well studied (Hilborn & Walters 1992; Worm et al. 2009; Clark

2010). Finally, the equilibria modeled here do not incorporate the fact that if harvesting stops, invasive populations and ecological impacts, will again increase.

Much relevant guidance from fisheries, forestry and wildlife management is already available, but must be re-focused on the goal of deliberate population reductions rather than the more usual goal of long-term sustainable harvest. In a recent example, the effort required to cause recruitment overfishing of invasive lionfish

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(Pterois volitans) in the Caribbean was estimated (Barbour et al. 2011). There is also an extensive literature on invasive species eradication, but this is typically feasible only when the extent of the population is very restricted geographically (Pluess et al. 2012).

For harvesting aimed at controlling invasive species, models of natural resource exploitation with explicit consideration of economic costs and benefits will be more useful analogs. We hope that others test our hypothesis that the general result derived here—that market driven harvests alone are unlikely to achieve conservation goals --will be robust to applications that include more realistic elements of population growth and harvest like those listed above.

5.4.1.2 Estimating density-impact curves

Although some kinds of density-impact curves, e.g., predator-prey relationships, are well grounded in theory and occasionally documented empirically (Choquenot and

Parkes 2001), in practice they are rarely estimated because both manipulative and natural experiments require substantial time and resources and are even then subject to considerable time- and place-specific environmental variability (Yokomizo et al. 2009).

Density-impact curves that can be directly translated into monetary units, like the market value of ewes lost to feral pigs (Choquenot and Parkes 2001) are most useful because they can be easily compared to costs and revenues from harvest and integrated into supply and demand curves. Other direct valuation methods may be possible is some cases, for example, by substituting the value of lost agricultural harvest or the costs of mitigation control. These methods however probably underestimate total 188

damages because impacts of invasive species often are associated with losses of multiple ecosystem services (Pejchar and Mooney 2009) and sometimes entail benefits as well as damages (Schlaepfer et al. 2011).

Many ecosystem services are the results of interactions of multiple species and their values are not well captured in markets (Abson & Termansen 2011). A wide variety of indirect market and non-market techniques are available for the valuation of complex suites of ecosystem services including hedonic pricing, contingent valuation, and the travel-cost method (Chee 2004). An additional promising approach for complex bioeconomic problems is structured expert judgment, which has been used to predict long-term damages caused by ship-borne invasive species in the Laurentian Great Lakes

(Rothlisberger et al. 2012). Valuing ecosystem services in these ways can integrate over regulating and cultural services in addition to the more easily estimated provisioning services (Pejchar and Mooney 2009) and can more directly inform policy (Keller et al.

2009). However, even when we have estimates of damage at one level of invasive species abundance (which is rare enough) what is needed for evaluating the usefulness of harvesting is damages over a range of abundances —a dose-response relationship which we have called the density-impact curve here— and the above tools could be turned to this purpose. For many invasive populations, original experimental research will be required to effectively estimate these density-impact relationships. Thus, the needs of invasive species management should prompt biologists to redouble their efforts to gather such information.

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5.4.1.3 Predicting invasive species markets

The success of a harvest strategy in reducing populations is tied to consumers’ demand for products derived from invasive species relative to the cost of the effort of producing those products. The Asian Carp Marketing Summit (Charlebois et al. 2010) and the Regional Lionfish Strategy Workshop (Gomez 2010) have provided some guidance for accomplishing this shift through market research, rebranding, policy instruments, investment in infrastructure, and cooperation among stakeholders. These may serve as models for marketing other nuisance species. We also see a significant role for projects that enhance invasive species awareness in general and deliberately tie the demand for invasive species products to the harms their populations cause. We are concerned, however, that the conservation value of harvesting is often asserted in the absence of data necessary to conduct the kinds of analyses we have outlined here. As we have illustrated (Figure 5.6), both high price and high demand are prerequisites for market-driven harvesting to achieve conservation goals; and many invasive species are neither in demand nor can they command a high price.

5.4.1.4 Harvesting up the invasion process

Consideration of harvesting invasive species should be done in the context of the larger process of invasion and the other management actions that could occur at each step (Table 5.1) (Lodge et al. 2006). Invasive species management takes two forms.

First, preventative management seeks to reduce the ecological and social impacts of invasive species by preventing the introduction of harmful species. Prevention is well 190

known as the most cost effective strategy for reducing the impacts of species invasion

(Leung et al. 2002; Simberloff et al. 2012). Second, should prevention fail, management seeks to mitigate and adapt to the impacts of invasive populations. Prevention activities occur at the top of the invasion process (Table 5.1). Adaptation and mitigation, such as harvesting invasive species, occur at the bottom (Table 5.1).

While harvesting invasive species is clearly a mitigation and adaption strategy for populations already established, we propose that indirect, social effects of promoting the harvest of invasive species may be propagated “up” the invasion process, causing both positive and negative interactions with other management objectives at other stages (Table 5.1). For example, eating wildlife promotes environmental awareness

(Lamberton 1994), and it follows that consuming invasive species may provide educational opportunities regarding biological invasions of many species (Nuñez et al.

2012). Such influences could increase the political will to take appropriate pre- introduction prevention action (Lodge et al. 2006). Alternatively, developing economic and social attachment to harvested invasive species may seriously inhibit their management (Nuñez & Simberloff 2005). These indirect risks and benefits could prove more beneficial, or more cumbersome, farther up the invasion process, where effects of managements are more widespread (Lodge et al. 2006; Simberloff et al. 2012).

5.5 Conclusion

Strategies for control of invasive species that include harvesting for food or other purposes have both potential benefits and pitfalls. The most likely scenario of invasive 191

species market development discussed here, pure competition, may facilitate the recovery of some ecosystem services. At the same time, however, a market characterized by pure competition provides a perverse incentive to maintain higher population sizes or even create new populations of invasive species.

Additional research and discussion among experts and stakeholders is needed to identify species-specific biological and economic circumstances in which market-driven harvesting will provide net social benefits. These discussions are urgent because harvesting invasive species is likely to become more common as ecosystems and the services they provide continue to change rapidly as a result of the continued increase in invasions. By considering which species respond in more desirable ways to harvest, have favorable density-impact curves, are marketable, and have low risk of additional spread, researchers will be more able to identify species-market combinations that would contribute to overall conservation goals and be worthy of management or policy investment. Our analysis of the amenity market scenario suggests that the greatest social benefit of investments will be in species characterized by a density-impact relationship like curve i (Figure 5.1B), high market price and high demand, and in an environment with high value ecosystem services (Figure 5.6). It is likely that a small percentage of species meet those criteria.

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5.6 Acknowledgments

My coauthors M.A. Barnes, C.A Bee, and D.M. Lodge. We thank S. Sim, members of the Lodge Lab, and several anonymous reviewers for helpful comments on an earlier draft of this manuscript. This work was supported by the GLOBES IGERT (National

Science Foundation Grant #DGE-0504495 “Global Linkages of Biology, the Environment, and Society” awarded to the University of Notre Dame), NSF DDEP #1046682 (to AD), the NOAA CSCOR program, and the EPA Great Lakes Restoration Initiative.

5.7 References

Abson, D.J. and Termansen, M. (2011) Valuing ecosystem services in terms of ecological risks and returns. Conservation biology: the journal of the Society for Conservation Biology 25, 250–8.

Barbour, A.B., Allen, M.S., Frazer, T.K. and Sherman, K.D. (2011) Evaluating the Potential Efficacy of Invasive Lionfish (Pterois volitans) Removals. PLOS One 6, e19666.

Barnes, M.A., Deines, A.M., Gentile, R.M. and Grieneisen, L.E. Adapting to invasions in a changing world: invasive species as an economic resource. Invasive Species and Climate Change (working title), Ziska, L.H. and J.S. Dukes (eds.), CABI Publishing.

Charlebois, P., Parks, S., TePas, K. and Peterson., M. (2010) The Asian Carp Marketing Summit. In: The Asian Carp Marketing Summit. Illinois-Indiana Sea Grant , Lewis and Clark Community College, Grafton, Illinois, p 27.

Chee, Y.E. (2004) An ecological perspective on the valuation of ecosystem services. Biological Conservation 120, 549–565.

Choquenot, D., Caughley, J. and McLeod, S. (1998) Scientific, Economic and Social Issues of Commercial Use of Wild Animals in Australia. Australian Government Publishing Service, Canberra.

Choquenot, D. and Parkes, J. (2001) Setting thresholds for pest control: how does pest density affect resource viability? Biological Conservation 99, 29–46.

193

Clark, C.W. (2010) Mathematical Bioeconomics: The Mathematics of Conservation, Third Edit. John Wiley & Sons, New York.

Daszak, P., Cunningham, A.A. and Hyatt, A.D. (2000) Emerging Infectious Diseases of Wildlife-Threats to Biodiversity and Human Health. Science 287, 443–449.

Deines, A.M., Chen, V.C. and Landis, W.G. (2005) Modeling the risks of nonindigenous species introductions using a patch-dynamics approach incorporating contaminant effects as a disturbance. Risk analysis: an official publication of the Society for Risk Analysis 25, 1637–51.

Dell’Amore, C. and Andries, K. (2013) Florida Python Hunt Captures 68 Invasive Snakes. National Geographic News.

Duncan, R.P., Holland, E.P., Pech, R.P., Barron, M., Nugent, G. and Parkes, J.P. (2011) The relationship between possum density and browse damage on kamahi in New Zealand forests. Austral Ecology 36, 858–869.

Forsyth, D.M., Parkes, J.P., Woolnough, A.P., Pickles, G., Collins, M. and Gordon, I. (2009) Environmental and economic factors determine the number of feral goats commercially harvested in Western Australia. Journal of Applied Ecology 46, 101–109.

Gomez, R. (2010) Regional Lionfish Strategy Workshop, Cancun, Mexico August 27-28, 2010.International Coral Reef Initiative, Cancun, MX, p 20.

Gordon, H.S. (1954) The Economic Theory of a Common-Property Resource: The Fishery. Journal of Political Economy 62, 124–142.

Grafton, R.Q., Kompas, T. and Hilborn, R.W. (2007) Economics of overexploitation revisited. Science (New York, N.Y.) 318, 1601.

Hardin, G. (1968) The tragedy of the commons. Science 162, 1243–1248.

Hilborn, R. and Walters, C.J. (1992) Quantitative Fisheries Stock Assessment: Choice, Dynamics & Uncertainty. Chapman and Hall, New York.

Jackson, J.B.C., Kirby, M.X., Berger, W.H., et al. (2001) Historical Overfishing and the Recent Collapse of Coastal Ecosystems. Science 293, 629–638.

Keller, R.P., Lodge, D.M., Lewis, M.A. and Shogren, J.F. (2009) Bioeconomics of invasive species: integrating ecology, economics, and management. Oxford University Press, 2009.

194

Lamberton, K. (1994) Yucca! Teaching the essentials of a wild food feast. The American Biology Teacher 56, 230–232.

Levine, J.M. and D’antonio, C.M. (2003) Forecasting Biological Invasions with Increasing International Trade. Conservation Biology 17, 322–326.

Lin, W., Zhou, G., Cheng, X. and Xu, R. (2007) Fast Economic Development Acclerates Biological Invasions in China. PLOS One 2(11): e1208. doi:10.1371/journal.pone.0001208

Lodge, D.M. and Shrader-Frechette, K. (2003) Nonindigenous Species: Ecological Explanation, Environmental Ethics, and Public Policy. Conservation Biology 17, 31–37.

Lodge, D.M., Stein, R.A., Brown, K.M., Covich, A.P., Bronmark, C., Garvey, J.E. and Klosiewki, S.P. (1998) Predicting impact of freshwater exotic species on native biodiversity: Challenges in spatial scaling. Australian Journal of Ecology 23, 53– 67.

Lodge, D.M., Williams, S., Macisaac, H.J., et al. (2006) Biological Invasions: Recommendations for U.S. Policy and Management. Ecological Applications 16, 2035–2054.

Norton, D.A. (2009) Species invasions and the limits to restoration: Learning from the New Zealand Experience. Science 325, 569–571.

Nuñez, M. a., Kuebbing, S., Dimarco, R.D. and Simberloff, D. (2012) Invasive Species: To eat or not to eat, that is the question. Conservation Letters, 5, 334-341.

Nuñez, M.A. and Simberloff, D. (2005) Invasive Species and the Cultural Keystone Species Concept. Ecology and Society 10, 4–7.

O’Connor, S., Ono, R. and Clarkson, C. (2011) Pelagic Fishing at 42,000 Years Before the Present and the Maritime Skills of Modern Humans. Science 334, 1117–1121.

Parkes, J.P., Nugent, G. and Warburton, B. (1996) Commercial exploitation as a pest control tool for introduced mammals in New Zealand. Wildlife Biology 2, 171– 177.

195

Pejchar, L. and Mooney, H.A. (2009) Invasive species, ecosystem services and human well-being. TRENDS in Ecology & Evolution 24, 497–504.

Peters, B.W. (2010) Evaluating Strategies For Controlling Invasive Crayfish Using Human And Fish Predation. University of Notre Dame Department of Biological Science.

Pluess, T., Cannon, R., Jarošík, V., Pergl, J., Pyšek, P. and Bacher, S. (2012) When are eradication campaigns successful? A test of common assumptions. Biological Invasions. 14, 1365-1378.

Rothlisberger, J.D., Finnoff, D.C., Cooke, R.M. and Lodge, D.M. (2012) Ship-borne Nonindigenous Species Diminish Great Lakes Ecosystem Services. Ecosystems, 462–476.

Roughgarden, J. and Smith, F. (1996) Why fisheries collapse and what to do about it. Proceedings of the National Academy of Sciences 93, 5078–5083.

Sala, O.E. (2000) Global Biodiversity Scenarios for the Year 2100. Science 287, 1770– 1774.

Scheffer, M. and Carpenter, S.R. (2003) Catastrophic regime shifts in ecosystem: linking theory to observation. TRENDS in Ecology and Evolution 18, 648–656.

Scheffer, M., Carpenter, S.R., Lenton, T.M., et al. (2012) Anticipating Critical Transitions. Science 338, 344–348.

Schlaepfer, M.A., Sax, D.F. and Olden, J.D. (2011) The Potential Conservation Value of Non-Native Species. Conservation Biology. 25, 428-437.

Simberloff, D., Martin, J.-L., Genovesi, P., et al. (2012) Impacts of biological invasions: what’s what and the way forward. Trends in ecology & evolution 28, 58–66.

Spinney, L. (2010) Dreampond revisited. Nature 466, 175–175.

The Asian Carp Regional Coordinating Committee (2012) FY 2012 Asian Carp Control Strategy Framework.

Thiele, J., Kollmann, J., Markussen, B. and Otte, A. (2009) Impact assessment revisited: improving the theoretical basis for management of invasive alien species. Biological Invasions 12, 2025–2035.

Worm, B., Hilborn, R., Baum, J.K., et al. (2009) Rebuilding Global Fisheries. Science 325, 578–585.

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Yokomizo, H., Possingham, H., Thomas, M.B. and Buckley, Y.M. (2009) Managing the impact of invasive species: the value of knowing the density – impact curve. Ecological Applications 19, 376–386.

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CHAPTER 6:

CONCLUSION

6.1 Introductory remarks

The goal of this dissertation is to quantify tradeoffs in freshwater ecosystem services, particularly tradeoffs between services provided by introduced species and the services lost when those species become invasive and cause ecological harm. The global introduction of tilapia originates from desires to exploit the provisioning services provided by their harvest, but it is clear that these services come at a cost to other provisioning, cultural, and supporting services.

Tradeoffs among ecosystem services are becoming more acute as drivers of environmental change place increasing demands on these services (JACKSON et al.

2001; Guo et al. 2010; Foley et al. 2011; Allendorf and Allendorf 2012). The possible interactions of ecosystem services provided by freshwaters and the drivers of environmental change that influence these services are many (Figure 6.1; Dudgeon et al.

2005; Brauman et al. 2007). In the preceding chapters I have considered provisioning services provided by freshwaters, specifically food and fisheries (Chapters 2, 3, 4, & 5) and hydropower (Chapter 4), as well as supporting services, namely habitat (Chapter 2,

4) and genetic diversity (Chapter 3)

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Major drivers of environmental change in freshwaters include invasive species, overexploitation as well as chemical pollution, habitat degradation and flow modification (Figure 6.1; Dudgeon et al. 2005). Over-exploitation is revealed herein as a cross-cutting driver of environmental change. Over-exploitation not only trades fisheries production now for fisheries production in the future, but exploitation interacts with species introductions by prompting the importation of non-native species for capture and culture (Chapters 2 & 3) and by providing a new harvest (Chapter 5).

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Meanwhile, dam construction may both provide and reduce fishing opportunities

(Chapter 4). In the following final sections, I review these interactions of drivers of environmental change and ecosystem services described in each chapter.

6.2 Tradeoffs among ecosystem services associated with global tilapia introductions

Chapter 1 provides a foundation for the dissertation, by establishing the risks of ecological effects associated with tilapia introduction, including the commonality of hybridization between tilapia species globally and then explored in detail in Chapter 3 on the Kafue River, Zambia. It also sets the stage for capture fisheries in potential conflict with other ecosystem services explored in Chapter 4, and highlights the common perceptions of positive ecosystem services associated with the harvest of invasive species as modeled in Chapter 5.

Invasive species are one of the major drivers of global environmental change affecting the ecosystems services provided by freshwater (Figure 6.1; Sala et al. 2000;

Dudgeon et al. 2005; Millennium Ecosystem Assessment 2005). The global introduction of tilapia species exemplifies the threat that invasive pose, the challenges faced by conflicting demands on freshwaters, and opportunities to better understand and advise decisions about the global translocation of species. Feral populations of tilapia now exist in at least 114 countries with unequivocal effects on ecosystem services. Tilapia introduction changes provisioning of food from native fish populations and the availability of other biotic and abiotic community components such aquatic vegetative

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habitat, water quality, and the genetic resources that support native fish, aquaculture, and other aquatic species.

In some cases introduced tilapia populations make substantial contributions to fisheries production and are perceived positively. In other cases, the reduction of ecosystem services provided by native species are seen as a loss of services provided by ecosystems. I have quantitatively shown how this perception is dependent of local socio-economic context, making the work of managers and stakeholders all the more relevant in comparing the benefits and harms of tilapia introduction.

6.3 Hybridization of native Oreochromis species (Cichlidae) and the introduced Nile tilapia (O. niloticus) in the Kafue River, Zambia.

Hybridization and introgression as measured here suggest another significant tradeoff in ecosystem services (Figure 6.1): the loss of the raw genetic diversity that is the foundation of both capture fisheries and aquaculture production. The introduction of the Nile tilapia into the Kafue River resulted in extensive hybridization between the introduced and the native Oreochromis species while at the same time and counter to expectations, possibly lowering the overall genetic diversity of Oreochromis on the river.

This analysis adds another example of hybridization to the numerous examples uncovered in Chapter 2, while adding the possibility of eroding species distinctions between previously well-defined sympatric Oreochromis andersonii and O. macrochir.

Understanding the full outcome of tilapia introduction and hybridization on the

Kafue will require continued surveillance. High resolution genotyping across a wider 201

cross-section of samples may provide a clearer picture of the genetic diversity of Kafue

Oreochromis. Aligning these results with fitness traits, such as somatic growth rates, and behavioral traits, such as mate choice, will help to further describe the potential long-term evolutionary implications of this species introduction. The relevant evolutionary scenarios to consider will be much influenced by interactions with other drivers of environmental change. In Chapter 4, I conduct in-depth exploration of the interaction and tradeoffs between other drivers of environmental changes acting on the

Kafue, flow modification and over-exploitation.

6.4 The potential tradeoff between artisanal fisheries production and hydroelectricity generation on the Kafue River, Zambia

The damming of the Kafue provides an opportunity to examine the magnitude of tradeoffs in ecosystem services in both ecological and monetary terms. Such evaluation is essential to comprehensive consideration of ecosystem services in policy and management decisions (Brauman et al. 2007). Specifically, in this chapter I examined the value of the tradeoff between hydropower production and the resulting impacts of flow modification on the fisheries production (Figure 6.1). In contrast to expectations, quantitative modeling of the effect of flow modification on fisheries production demonstrated there was little or no impact of flood regime change on fisheries production. The most likely explanation for these results is that large increases in fishing overwhelm any signal or effect of flood regime. Heavy exploitation is a stronger, or at least prior, driver of environmental change than flow modification. 202

As is the case for tilapia introductions in Chapter 2, little research is available in

Africa concerning the effects of damming on downstream fisheries (King and Brown

2010; Poff and Zimmerman 2010) despite the continuing contribution of these systems to fisheries (Welcomme and Hagborg 1977; Welcomme 2008) and livelihoods (Haller and Merten 2008). Freshwater is increasingly limiting economic development (Lodge

2010), making these issues particularly urgent in the African context. In Chapters 2 and

3 the introduction of tilapia has been to some extent the result of unmet demand for fisheries production being filled by the introduction of non-native species. In this chapter, the installation of hydropower may similarly be seen as case of unmet monetary demand for freshwater ecosystem services, where hydropower services fill a deficiency left by fisheries over-exploitation.

6.5 Can Market-Driven Harvest of Invasive Species Contribute to Conservation Goals?

Harvesting invasive species formally connects concepts of conflicting uses of ecosystem services that result from species introductions (Chapters 2 and 3) with monetary valuation of ecosystems services (Chapter 4). Chapters 2 and 3 introduced and examined tradeoffs between the fisheries provided by introduced species and other forgone ecosystem services (Figure 6.1). Chapter 4 developed a monetary estimate of related tradeoffs in fisheries, but connected to flow modification. In this chapter I describe simple ways to model the populations of invasive species, the negative impact they have on native ecosystem services, and the monetary values that could be generated by harvesting those species given increasing demand for invasive products. I 203

show that under reasonable market scenarios it is unlikely that market demand alone will greatly reduce populations without unreasonable increases in demand.

These results are consistent with previous chapters in terms of how perceptions of ecosystems service values are linked with monetary values. In Chapter 2, the perception of tilapia introduction benefits from harvests were shown to be context dependent. Here, I demonstrate that the value of uninvaded ecosystem services is an important context to weigh in decisions about species introductions. Chapter 3 examines the genetic risks of hybridization of native and introduced tilapia which potentially threaten future aquaculture and capture fisheries. These results may serve to shift managers’ and stakeholder’s perceptions of ecosystem service values, potentially changing net value of ecosystem services relative to the value of species introduction.

6.6 Concluding remarks

A major challenge remaining for managers posed by tradeoffs in ecosystem services as documented herein, is the distribution of ecosystem services among stakeholders, economic development, and environmental goals (Lodge 2010). The analyses provided here do not specify to whom the benefits of tilapia flow, or losses of services accrue. Species introductions may be positively associated with economies, but the costs of invasion may not be borne by the same people who benefit, raising social and environmental justice concerns (DRAKE and KELLER 2004). That is, fish farmers may

204

benefit from tilapia introduction, while other members of the community, such as fishers of native species, may suffer (Pullin et al. 1993).

I provide some of the resources these managers may need to inform their decisions about the introduction of tilapia species and tradeoff in ecosystem services in ecological and monetary terms. Additional opportunities for further improvement exist.

Pan-tropical tilapia introduction over the last half century is a globally replicated natural experiment of ecosystem tradeoffs (Lodge 1993; Brown and Sax 2004). Management experiences and expertise could be pooled across geographic and socio-economic regions to further explore comparative tilapia ecosystem service tradeoffs in the same as has been done for tilapia biology, production and aquaculture (Pullin and Lowe-

McConnell 1982; Beveridge and McAndrew 2000).

Meanwhile, tilapia are an increasingly studied model organism (Sax et al. 2007), for example with dramatic recent advances in sequencing the tilapia genome (Yue

2013). Continued synthesis of tilapia studies could underwrite significant progress in ecology. For example, to test theories in community assembly, biodiversity and evolution (Sax et al. 2007; Mouchet et al. 2013). The result of Chapter 2 suggest that the ecological impacts of tilapia are consistent across regions on the global scale, which would support the hypothesis that species traits are more important than receiving ecosystem characteristics in structuring communities. The hybridization results of

Chapter 3 suggests substantial genetic change as a result of tilapia introductions on the

Kafue; global patterns of these types of genetic changes may yield clues about

205

evolutionary responses to changing environments and inform expectations and adaptations to global environmental change projected into the future. The work herein provides a baseline for continued analysis of the ecological processes contributing to the global “success” of tilapia introductions.

6.7 References

Allendorf, T.D. and Allendorf, K. (2012) What Every Conservation Biologist Should Know about Human Population. Conservation Biology 26, 953–955.

Beveridge, M.C.M. and McAndrew, B.J. (2000) Tilapias: Biology and Exploitation. Fish and Fisheries Series 25, 505.

Brown, J.H. and Sax, D.F. (2004) An Essay on Some Topics Concerning Invasive Species. Austral Ecology 29, 530–536.

Drake, J.M. and Keller, R.P. (2004) Environmental Justice Alert: Do Developing Nations Bear the Burden of Risk for Invasive Species?e. Bioscience Vol. 54, 718–719.

Dudgeon, D., Arthington, A.H., Gessner, M.O., et al. (2005) Freshwater biodiversity: importance, threats, status and conservation challenge. Biological Review 81, 163–182.

Foley, J. a, Ramankutty, N., Brauman, K. a, et al. (2011) Solutions for a cultivated planet. Nature 478, 337–42.

Guo, Z., Zhang, L. and Li, Y. (2010) Increased dependence of humans on ecosystem services and biodiversity. PloS one 5, e13113

Haller, T. and Merten, S. (2008) “We are Zambians—Don’t Tell Us How to Fish!” Institutional Change, Power Relations and Conflicts in the Kafue Flats Fisheries in Zambia. Hum Ecol 36, 699–715.

206

Jackson, R.B., Carpenter, S.R., Dahm, C.N., Mcknight, D.M., Naiman, R.J., Postel, S.L. and Running, S.W. (2001) Water in a changing world. Ecological Applications 11, 1027–1045.

King, J. and Brown, C. (2010) Integrated basin flow assessments: concepts and method development in Africa and South-east Asia. Freshwater Biology 55, 127–146.

Lodge, D.M. (1993) Biological invasions- lessons for ecology. TRENDS in Ecology & Evolution 8, 133–137.

Lodge, D.M. (2010) It ’ s the Water , Stupid ! Bioscience 60, 6–7.

Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-being: Synthesis. Island Press, Washington, D.C.

Pullin, R.S. V and Lowe-McConnell, R.H. (1982) The Biology and Culture of Tilapias. ICLARM Conference Proceedings 7.

Pullin, R.S. V, Rosenthal, H. and Maclean, J.L. (1993) Environment and Aquaculture in Developing Countries. ICLARM Conference Proceedings. 359.

Sala, O.E., Chapin, F.S.I., Armesto, J.J., et al. (2000) Global Biodiversity Scenarios for the Year 2100. Science 287, 1770–1774.

Sax, D.F., Stachowicz, J.J., Brown, J.H., et al. (2007) Ecological and evolutionary insights from species invasions. TRENDS in Ecology and Evolution 22, 465–471.

Welcomme, R. (2008) World prospects for floodplain fisheries. Ecohydrology & Hydrobiology 8, 169–182.

Welcomme, R.L. and Hagborg, D. (1977) trowards a model of a floodplain fish population and its fishery. Environmental Biology of Fishes 2, 7–24.

207

Yue, G.H. (2013) Recent advances of genome mapping and marker-assisted selection in aquaculture. Fish and Fisheries, in press.

208

APPENDIX A:

SUPPLEMENT TO CHAPTER 2:TRADEOFFS AMONG ECOSYSTEM SERVICES ASSOCIATED

WITH GLOBAL TILAPIA INTRODUCTIONS

209

TABLE A. 1

LIST OF LITERATURE DERVIED DATA AND REFERENCES INCLUDED IN CHAPTER 2: TRADEOFFS AMONG ECOSYSTEM SERVICES

ASSOCIATED WITH GLOBAL TILAPIA INTRODUCTIONS

Resident Tilapia

210 Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia

Cite Location Pct

Effect Effect Effect Effect Effect Effect

species Effect

Design Design Design Design Design Design Design O.leucostictu Kenya; Lake (1) s, O.s.niger, + Q - Q + 0 + Naivasha T.zil Puerto Rico, Oreochromis (2) Dominican - + Rv spps Republic (3) various various + Rv + Rv + + Rv + Rv + Rv - O.aur,O.horn QR QR QR (4) Puerto Rico + + + + QR ,O.nil C C C Uganda; Lake (5) O.nil + Rv + Rv + Rv + Rv B Via, Kyoga

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic Fish pop’n

Tilapia Cite Location Pct species Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect O.nil,O.lues, (6) Lake Vic + Rv + Rv + Rv + Rv B T. zil (7) O.nil,

Oreochromis Africa + Rv + Rv + Rv + Rv B

species (8) O.nil Lake Vic + 0 + 0 + 0 + 0 - 211 Oreochromis (9) Africa + Rv + Rv + Rv + Rv B Rved T. (10) zilli,O.nil,O.le Lake Vic 0 0 + 0 + 0 + 0 B ucosticta (11) T.zilli Lake Vic + Q + Q + Q B (12) O.moss, O.nil Sri Lanka - R + QR 0 0 + 0 + USA; Salton (13) O.moss + QR + Rv Rv + Rv Sea O.nil,T.rend, (14) global + Rv + Rv + Rv B T.zill,O.moss (15) O.moss Australia + Rv + Rv + - (16) O.mossxO.nil Sri Lanka + QR + QR + Rv -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect Kenya; L. (17) O.lues + + + QC Baringo O.lues, Kenya; Lake (18) + Q + Q O.spir.nig Naivasha (19) Oreochromis Lake Vic + 0 + 0 + QR - spps

212 T. (20) zilli,O.nil,O.le Lake Vic + Q + Q -

ucosticta (21) O.nil East Africa + QR + Rv South Africa; (22) O.nil + 0 + QR - Limpopo O.moss,T.ma (23) Australia + 0 + Q - r O. nigra, Kenya; Lake (24) + + 0 O.leucosticta Naivasha O.lues, T.zilli, (25) Lake Vic + Q + Q O.nil (11) O.nil, Lake Vic + QR + Q

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect O.leucosticta Southern (26) O.nil Africa; + 0 + 0 - Limpopo\ (27) QR

213 O.moss, O.nil Sri Lanka + 0 + +

Southern QR (28) O.nil Africa; 0 0 0 0 + - C Limpopo T.s.nigra, Kenya; Lake (29) O.leucosticti 0 Q 0 Q + QC Naivasha ca O.moss x QR (30) Sri Lanka 0 0 0 0 + O.nil C QR (31) O.nil x O.mac Africa 0 Rv 0 Rv + C Oreochromis USA; QR (32) + spps California C (33) O.moss,O.m Zimbabwe + Q -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect ort,O.mac (34) T.zill x T.mar USA; Florida + Q - Tanzania; (35) O.esc + QC Lake Kitangiri (36) O.moss Kenya + Q QR 214 (37) O.moss Malaysia + + C (38) O.nil Uganda + QC (39) vairous global + Rv O.nil, QR (40) Puerto Rico + O.aur,O.horn C QR (41) O.nil, O.moss Belgium + C (42) O.nil Fiji + QC B (43) O.nil, O.spil Kuwait + QR Kenya; lake (44) O.nil, O.aur + Q Rudolf (45) O.nil Lake Vic + QTC + QT + RC 0 0 B (46) O.aur, Mexico;Infier + R 0 QR +

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect T.rend, nillo Dam, O.moss Zicuiran Dam. (47) QT O.moss Sri Lanka + + R - 0 + R

215 Mexico; Lake

(48) O.nil, T.rend 0 0 + 0 - Q Pa´tzcuaro,

O.moss,O.au (49) Nicaragua + QR + QR + QR + Q + QR - r,O.nil (50) various Nicaragua + Q + Q + 0 + 0 - Mexico; Lake Catemaco, (51) O.aur, nil + 0 + 0 + QR - Santa Anita Lagoon Mexico;Yuca tan; Laguna (52) O. + Q + T + Q - Chichancana b (53) O.nil Mexico;Yuca 0 0 0 0 + 0

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect tan Kenya; Lake (54) S. a. grahami + Q + 0 - 0 + Nakuru (55) QR QR QR O.aur USA; Florida + + - - C C C 216 (56) O.moss, O.nil Sri Lanka - QR + QR - QR +

(57) O.moss Australia 0 + Rv - 0 - Australia;bris (58) O.moss bane;north 0 0 + 0 - Q - pine dam (59) O.moss Australia + Rv - Q - Australia;bris (60) O.moss bane;north 0 Q 0 Q - Q pine dam USA, (61) O.nil - QR - Mississippi O.nil x (62) Brazil - Q O.moss (63) O.moss, USA; Hawaii + 0 + 0 + 0 + 0 -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect S.melanothe ron QT (64) O.nil, T.rend Madagascar + + QT + R + 0 + R B R (65) Tilapia-

217

unspecified Philippines + 0 + 0 + 0 + 0 - prob O. moss Israel;L. QR QR QR QR (66) O. aur + + + + B Kinneret C C C C Mexico; (67) O.nil + Q + Q + Q - Mexico City Oreochromis QR QR (68) Israel + + + + spps C C Mexico; (69) O.nil + Q + Q + Q - Xochimilco USA; Texas; (70) O.aur + QT + QT + Rv B Lake Fairchld USA; Texas; (71) O.aur + QT + QT + Q - Lake Fairchld

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect India; Tamil (72) O.moss + TR + TR + 0 B Nadu (73) Zimbabwe; O.nil,O.mac + 0 + 0 + Q Lake Chivero

218 (74) O.moss USA; Arizona + + 0 +

QT QT (75) O.moss, O.nil India + + + 0 B R R India; Tamil Nadu; (76) O.moss, O.nil + Q + Q + Q B Kelavarpalli Reservoir Australia; (77) O.moss Western + QR + QR + Q - Australia (78) O.nil lake Vic + Q + Q + Q B Nicaragua,Co O.aur,moss, (79) sta Rica; Lake + QC + QC + 0 - nil Nicaragua (80) O.moss Mexico;Yuca + Q + Q + QR -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect tan; Laguna C Chichancana b (81) USA; Texas; O.aur + QR + QR + QC - Trinidad lake 219 O.aurea, USA; (82) O.moss, + R + R + 0 - california, T.zilli (83) O.aur USA; Florida + Q + QR + 0 B (84) T.mariae USA; Florida + 0 + R + 0 - (85) O.aur USA; Florida + Rv + Rv + Q - QR QR QR (86) O.aur USA; Texas + + + - C C C USA; Salton (87) T. zilli + Q + Q + Q - Sea Tilapia and (88) Madagascar + 0 + 0 + 0 - oreochromis Oreochromis QT (89) Cuba + QT + + + spps, T.rend R

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect (90) O.moss, O.nil Indonesia + QR + QR + 0 B India; Tamil (91) O.moss + R + R + 0 B Nadu (92) USA; Salton O.moss, T. zil + QR + QR + QR -

220 Sea

(93) O.moss Mexico + 0 + 0 + 0 -

USA; Salton (94) T.zill + 0 + 0 + 0 - Sea Mexico; Laja (95) O.moss + QR + QR + QR - river African (96) USA; Florida + 0 + 0 + 0 - Tilapias USA, Gulf of QR QR QR (97) O.nil + + + - Mexico C C C QR QR QR (98) O.nil NA + + + - C C C O.aur, (99) Pakistan + QR + QR + 0 - O.moss, O.nil (100) T. zill Mexico; Baja + 0 + 0 + 0 -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect tilapia, USA; (101) + 0 + 0 + 0 - unspecified California (102) S. Cote

221 melanothero D'Ivoire, Lake - Q + Q + Q

n Ayame (103) T. zilli Lake Vic 0 0 + 0 + 0 (104) O.moss Sri Lanka 0 QR + QR + QR + O.mac,T.ren (104) Lake Kivu 0 Q 0 Q + Q d QT QT (105) O.moss Australia + + - RC RC (106) O.moss, O.nil Sri Lanka + Q + Q USA; Texas; (107) O.aur + Q - Trinidad lake (108) O.moss Philippines + 0 - QR (109) T.mar USA; Florida + - C (110) O.moss Sri Lanka + QR B (111) O.nil Brazil + QR -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect C USA; Texas; (112) O.aur + QT + QT + 0 0 0 B Trinidad lake (113) QR QR QR QR O.nil Bangladesh - + 0 0 +

222 C C C C

QR QR (114) T.rend Botswana + 0 C C S. Cote (115) melanothero D'Ivoire, Lake + 0 + 0 0 0 - n, O.nil Ayame QR (116) O.aur USA; Florida + Q + QR 0 - C Israel; Lake (117) O.aur + QT + QT 0 Q - Kinneret (118) O.nil Peru + 0 + R 0 0 B (119) T.zil Mexico; Baja + 0 + 0 0 0 - (120) T.zil Mexico; Baja + 0 + 0 0 0 - (121) O.aur USA; Florida + Rv + Rv 0 Rv - (122) O.aur USA;Texas; - Q + 0 0 0 -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect San Felipe Creek (123) O.moss Sri Lanka 0 Q + Q 0 Q (124) O.lues, T.zilli,

223 Lake Vic + Q 0 0

O.nil

(125) O.moss Bahamas + 0 0 0 - Uganda; Lake (126) O.nil 0 QR Nabugabo (127) O.moss Sri Lanka 0 Q + QR (128) O.aur USA 0 C O. moss x (129) Costa Rica + QR + QR + QR hornorum QR QR QR QR (130) O.nil Bangladesh + + + + C C C C QR QR QR (131) O.nil Bangladesh + + + C C C QR QR (132) O.nil Brazil + + - C C

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect QR QR QR (133) O.nil Bangladesh + + + C C C (134) QR QR O.nil Bangladesh + + + C C

224

USA; Salton QR (135) O.moss + Sea C Brazil, Lago (136) O.nil, T.rend + QTC + QTC B Paranoa USA; (137) O.moss,T.zill + 0 + 0 - California Brazil; QR QR (138) T.rend, + - Paranoa Res. C C USA; North (139) T. zill, O.aur + QT + QT + Rv - Carolina (140) O.moss Sri Lanka + QT + QT + 0 + Brazil; (141) T.rend, O.nil 0 QC QC + QC 0 QC Paranoa Res. (142) O.nil Sri Lanka 0 QR + QR 0 B (143) T. rend, USA; - R + QR + R +

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect O.nil,O.moss Alabama USA; Salton (144) O.moss 0 QT + QT + QT Sea (145) QR QR O.nil Kenya 0 + + + C C

225

Bangladesh; QR QR

(146) O.nil + + Meghna R C C QR QR (147) O.nil Sudan + + C C QR (148) O.aur USA; Texas + C (149) O.moss Sri Lanka + Q (150) O.nil China? + QR Israel;L. QR (151) S. gal + Kinneret C USA; Salton QR (152) O.moss + Sea C QR (153) O.nil Brazil + C

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect QR (154) O.nil Brazil + C QR (155) O.nil Senegal + C (156) USA; Salton QR

226 O.moss + Sea C

QR (157) O.nil Thailand + C QT (158) O.nil China + + R QR (159) O.moss India? + C USA, QR (160) O. aur + Wisconsin C Rennell, (161) O.moss + 0 - Bellona (162) O.moss USA; Illinois + Q + Q + 0 B USA; (163) T.zil + QR + QR + California

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect QT (164) T.rend Zimbabwe + + QR + QR - R (165) USA; O.aur Nevada, + QR + 0 + 0 - 227 Muddy R.

T.rend, (166) Puerto Rico + 0 + R + 0 B O.moss (167) O.nil Oman + Q + T - QR QR (168) O.nil Bangladesh + + B C C USA; (169) O.moss + 0 + QT + California USA; (170) T.zil + R + QR + California USA; (171) T.zill,O.moss + 0 B California Tilapia- A pacific (172) + 0 - unspecified Atoll (173) T.rend, South Africa + Q -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect O.moss Guan,Saipan, QR (174) O.moss + - Pagan C (175) T.zil USA; Cali + QR + (176) USA; South QR QR

228 O.nil + -

Carolina C C

QT QT QT (177) O.moss India + + - B R R R Nicaragua,Co (178) O.nil sta Rica; Lake - Q + Q - 0 + Nicaragua (179) O.moss Sri Lanka + R - 0 + Kenya; Lake (180) O.lues, T.zilli + QT + 0 B Naivasha USA; Salton (181) O.moss 0 Q + Q + 0 Sea (182) O.moss Sri Lanka + QR Kiribati; (183) O.moss + 0 + 0 0 0 - Fanning Atoll

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect Tanzania; (184) O.esc,T.rend Nyumba ya + Q 0 0 Mungu (185) O.moss, O.nil Asia + Rv + Rv B (186) Tilapias Rved Asia + Rv + Rv +

229 QR QR

(187) O.nil Laos; - + B

C C O.moss, (188) Sri Lanka + QR + QR + T.rend, O.nil QT QT (189) O.moss, O.nil India 0 + B R R Tanzania; (190) O.esc,T.rend Nyumba ya 0 Q + Q Mungu QR QR (191) O.moss India 0 + C C QR QR (192) O.moss India 0 + C C (193) O.lues, T.zilli Kenya; Lake + QT +

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect Naivasha USA; Salton (194) O.moss + Q Sea (195) O.moss India + 0 (196) O.moss India + QR B India; West 230 (197) O.moss + T + T

Bengal USA; QR QT (198) O.moss, O.nil - + Alabama T R USA; (199) O.nil Arkansas; - 0 + Q + lake hogue (200) O.nil Indonesia 0 0 + 0 + (201) O.moss Sri Lanka + QR + USA; (202) O.aur + Q - Oklahoma (203) O.aur USA;Iowa + QR + USA; (204) O.nil 0 Q - Q - Mississippi

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect (205) O.moss India + 0 + QR - (206) O.aur USA; Florida + Q + Q (207) USA; Texas; O.aur + Q + Q B Trinidad lake

231 (208) O.nil Lake Vic + 0 + QT

Brazil; Sao Paulo; (209) O.nil + Q + Q + Billings Reservoir (210) various USA + Rv + Rv B (211) O.nil Rwanda + 0 + 0 - T.zill,O.moss, (212) Madagascar + T + T - T.rend USA; (213) O.moss,T.zill + QR + QR - California (214) tilapias Madagascar + 0 + 0 - (215) O.moss, Sri Lanka + 0 + QR - QT QT (216) O.moss India + + + R R

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect T.mar,T.zill,O (217) USA; Florida + Q + QR - .moss (218) O.moss x USA; Idaho + Q + Q - horn

232 Mexico; Lake

(219) Tilapias + QT + Q -

patzcuaro Tilapia- (220) unspecified Manila + 0 + 0 - prob O. moss (221) O.moss India + 0 + 0 - (222) T.rend South Africa + 0 + 0 (223) O.moss Indonesia + QR + QR + Philippines; (224) O.moss + 0 + 0 B Lake Buhi QR (225) T. galilaea Israel + + QC - C (226) O.moss South Africa + 0 + 0 - O. moss, O. USA; Texas; (227) + 0 + 0 - aur, T. zil San Antonio

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect R. (228) O.nil Columbia + QT + QT - USA; Florida; (229) T.mar, O.aur + 0 + 0 - Canals (230) O.moss India + Q + Q B USA; QR QR 233 (231) O.aur Nevada, + + - C C

Muddy R. (232) O.moss India + R + R B (233) O.moss Bahamas + 0 + 0 - Oreochromis (234) Mexico + 0 + 0 - spps USA; Salton (235) O.moss, T. zil + QR + QR - Sea India; Kerala; (236) O.moss + 0 + Q - R. Chalakudy QT QT (237) T.zil Mexico; Baja + + - R R (238) O.moss Curacao, + QR + QR -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect aruba, bonaire (239) Mexico; Baja; Lower T.zil + QR + QR -

234 Colorado

basin Oreochromis (240) Mexico + Q + QR B spps Oreochromis (241) South Pacific + Rv + Rv B spps, O.moss Brasil; (242) O.nil Gargalheiras + QT + QT - Reservoir QR QR (243) O.moss Fiji + + - C C (244) O.aur, T.zill Mexico + 0 + R - (245) O.moss India + R + R - USA; (246) O. aur - QT + QT B Alabama

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect (247) O.nil, O.moss Philippines - Rv + Rv + QR QR (248) O.nil Laos; - + + C C QR QR (249) O.nil Lao PDR - + B C C (250) QT O./moss Sri Lanka - 0 + + R 235 Aruba,

(251) O.moss Bonaire, - QR + QR B Curacao O.nil, (252) O.moss, Mekong - 0 + 0 + O.aur Asia ans (253) various + Rv + Rv + America (254) O.nil Mozambiqu 0 QT + QT - (255) O.nil Bangladesh 0 QT + QT T.zill, (256) Turkey 0 Q + Q O.nil,O.aur

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect Papua New (257) O.moss Guinea; 0 R + R - Laloki T.zilli, O.nil, (258) Lake Vic 0 Q + Q O. leucisticta (259) Vietnam; Ea O.nil 0 Q + Q + 236 Kao

Mekong (260) O.nil 0 R + R + Basin Papua New Guinea; (261) O.moss 0 Q + Q Sepik-Ramu Basin USA; Salton (262) O.moss 0 Q + Q + Sea Papua New Guinea; (263) O.moss 0 Q + Q B Sepik-Ramu Basin

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect (264) O.moss, O.nil Sri Lanka 0 QR + QR + (265) O.moss, O.nil Sri Lanka 0 R + QR + (266) O.nil Philippines 0 Q + Q (267) O.nil Thailand 0 QR + QR + (268) Papua New

237 Guinea;

O.moss 0 QR + QR Angabanga

R. (269) O.nil Lake Vic 0 Q + Q B Australia; T. mar, (270) Barron R., 0 QR + QR B O.moss Mitchell R. Uganda; Lake (271) O.nil 0 QR + QR Nabugabo (272) O.moss, O.nil Sri Lanka 0 0 + Q + (273) O.aur, T.mar USA; Florida 0 QR + QR Tilapia- (274) India 0 R + R unspecified (275) O.moss Asia 0 R + R +

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect (276) O.moss Sri Lanka 0 QR + QR + (277) O.nil Lao PDR 0 Q + Q (278) Papua New Guinea; O.moss 0 0 + 0 238 Sepik-Ramu

Basin QT QT (279) T.rend, O.nil Brazil 0 + B R R Brazil; Rio (280) T.rend Paraiba do 0 QR + QR Sul (281) O.nil Italy 0 QR + QR - Uganda; (282) O.nil Nyamusingiri 0 QR + QR , Kyasanduka (283) O.moss Puerto Rico 0 QR + QR Brazil; Rio de (284) T.rend Janeiro; Lajes 0 Q + Q Reservoir,

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect (285) O.aur USA; Texas 0 QR + QR USA;Alabam (286) O.moss, O.nil 0 QR + QR + a (287) O.moss, USA;Texas 0 0 + R - O.aur, T.zill

239 T.mariae,

(288) USA; Nevada + Q -

T.zilli O.aur, (289) S.melanothe USA; Florida + QR - ron, O.moss O.moss x USA; Salton (290) + Q horn Sea S. (291) melanothero USA; Florida + QR B n Mexico; (292) O.moss + QR Quinta roo (293) O.moss Sri Lanka + QR + (294) T.mar Australia + 0 -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect (295) T.mar USA; Nevada + 0 - (296) O.nil Thailand + QR T.mar, T.zill, (297) USA; Florida + Rv - O.moss (298) O.moss India + 0 - (299) O.aur, 240 USA;Texas + 0 -

O.moss Venezuela; (300) O.moss Lake + 0 Valencia Venezuela; (301) O.moss Lake + Q Valencia USA; Texas; (302) O.aur + Q - Trinidad lake Zimbabwe; (303) O.nil + 0 Lake Chivero Zimbabwe; (304) O.mac,O.nil + 0 Lake Chivero

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect O.moss, (305) Australia + Rv - T.mar (306) O.nil Lake Vic + QT B (307) T.mariae USA; Florida + 0 - (308) USA;

241

O.moss california, + Q - salton sea USA; (309) O.aur + 0 - Pennsylvania (310) O.moss Australia; + Rv - Australia;Bris (311) O.moss + QR bane Southern (312) O.mac Africa; + 0 - Limpopo USA;Mississi (313) O.nil + QR B ppi USA; (314) O.nil + QR - Mississippi

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect (315) O.nil Lake Vic + QT (316) O.moss Australia + QR - (317) USA; O. aur + 0 - Pennsylvania

242 (318) O.moss Namibia + 0 -

USA; Florida; (319) O.aur + Q + L Parker Papua New Guinea; (320) O.moss + 0 - Sepik-Ramu Basin O.moss, (321) S.melanothe USA; Florida + 0 - ron QT (322) O.moss, O.nil Indonesia + + R Haiti, Dom (323) O.moss + 0 + Reb (324) O.aur, T.mar, USA; Florida, + R -

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect O.moss, Hawaii T.melanothe ron (325) O.moss, T.melanoplur Sri Lanka + QR + 243 a, O.nil

(326) O.nil Sri Lanka + QR + S. (327) melanothero USA; Hawaii + R - n Sri Lanka; (328) O.moss + Q + Tissawewa Australia;bris (329) O.moss bane;north + Q B pine dam (330) O.nil Lake Vic + Q (331) O.moss Puerto Rico + 0 - Sri Lanka; (332) O.moss + Q + parakrama

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect sumudra (333) O.moss, T. zil Guam R + R (334) O.nil Italy + Q B (335) USA; Mississippi; O.nil + QR 244 Pascagoula

River Turkey; (336) O.nil + 0 - Nevsehir O.moss, Australia; (337) + QR - T.mar Cairns USA; O.aur, T.mar, Arizona, O.moss, T. California, (338) melanothero + R - Florida; n, T. zil, Colorado T.horn River O.aur, (339) USA; Florida + R - O.moss,

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect T.mar,S. melan (340) O.moss USA;Texas + 0 - (341) T. Heudeloit USA; Florida + Q B (T. mar)

245 USA;Californi (342) O.moss + R B a T. Heudeloit (343) USA;Florida + 0 (T. mar) O.moss, (344) Sri Lanka + QR + T.rend (345) T. quin Namibia + Q QR QR (346) O.nil Israel + 0 + C C (347) O. aur, O.nil South Africa 0 Rv - (348) O.moss Australia + QR - (349) O.aur, nil Israel + Q O.mac, O. Southern (350) R - moss Africa

TABLE A. 1. CON’T

Resident Tilapia Biotic Non-biotic Comp. Disease Genetic

Fish pop’n

Tilapia Cite Location Pct

species

Effect Design Effect Design Effect Design Effect Design Effect Design Ffect Design Design Effect NOTE: For ecological effects “+” = effects reported to occur, “-“ = effects reported not to occur, 0= effects unspecified, blank indicates effect not considered. Q=Quantitative data reported, T= Time data reported, R=replicate data reported, and C=control data recorded. The “Pct” column indicated the reported perception of tilapia introduction with “+” = positive perception, “-“ = negative perspective and “B” = both positive and negative perspectives reported.

246

Figure A. 1. The frequency of reviewed publications in each year.

247

A.1 References

1. A. Q. Siddiqui, Lake Naivasha (Kenya, East Africa) fishery and its management together with a note on the food habits of fishes, Biological Conservation 12, 217–228 (1977).

2. D. E. Alston, S. Wisovich, F. Richardson, M. Nicolas, in Tilapia aquaculture in the Americas, B. A. Costa-Pierce, J. E. Rakocy, Eds. (World Aquaculture Society, Baton Rouge, Louisiana, USA, 2000), vol. 2.

3. G. C. Canonico, A. Arthington, J. K. McCrary, M. L. Thieme, The effects of introduced tilapias on native biodiversity, AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS 15, 463–483 (2005).

4. A. S. McGinty, Efficacy of mixed-species communal rearing as a method for performace testing of tilapias, The Progressive Fish Culturist 49, 17–20 (1987).

5. R. Ogutu-Ohwayo, The decline of the native fishes of lakes Victoria and Kyoga (East Africa) and the impact of introduced species, especially the Nile perch, Lates niloticus, and the Nile tilapia, Oreochromis niloticus, Environmental Biology of Fishes 27, 81–96 (1990).

6. T. Twongo, in The Impacts of Species Changes in African Lakes, T. J. Pitcher, P. J. B. Hart, Eds. (Chapman & Hall, New York, 1995), pp. 45–55.

7. R. Ogutu-Ohwayo, R. E. Hecky, A. S. Cohen, L. Kaufman, Human impacts on the African Great Lakes, Environmental Biology of Fishes 50, 117–131 (1997).

8. R. Ogutu-Ohwayo, in Invasive species and biodiversity management, Sandlund, Schei, Viken, Eds. (Kluwer Academic Publishers, Dordrecht, The Netherlands, 1999), pp. 47–63.

9. R. Ogutu-Ohwayo, R. E. Hecky, Fish Introductions in Afica and Some of the Their Implications, Can. J. Fish. Aquat. Sci. 48, 8–12 (1991).

10. R. L. Welcomme, Recent changes in the stocks of Tilapia in Lake Victoria, Nature 212, 52–54 (1966).

248

11. R. L. Welcomme, Observations on the biology of introduced species of Tilapia in Lake Victoria, Revue de zoologie et de botanique africaines 76, 249–279 (1967).

12. U. S. Amarasinghe, S. S. De Silva, in Papers contributed to the workgroup on tilapia in capture and culture-enhanced fisheries in the indo-pacific fishery commission countries, E. A. Baluyut, Ed. (Pacific Rim Innovation and Management Exponents, Inc, Bogor, Indonesia, 1991), pp. 218–237.

13. B. A. Costa-Pierce, R. Riedel, in Tilapia Aquaculture in the Americas, B. A. Costa- Pierce, J. E. Rakocy, Eds. (The World Aquaculture Society, Baton Rouge, 2000), vol. 2, pp. 1–20.

14. R. L. Welcomme, in Distribution and management of exotic fishes, W. R. Courtenay, J. R. Stauffer Jr., Eds. (The Johns Hopkins University Press, Balitmore, MD, 1984), pp. 22–40.

15. A. H. Arthington, Ecological and Genetic Impacts of Introduced and Translocated Freshwater Fishes in Australia, Can. J. Fish. Aquat. Sci. 48, 33–43 (1991).

16. U. S. Amarasinghe, S. S. De Silva, Impact of Oreochromis mossambicus x O. niloticus (Pisces: Cichlidae) hybridization on population reproductive potential and long- term influence on a reservoir fishery, Fisheries Management and Ecology 3, 239– 249 (1996).

17. D. W. Nyingi, J.-F. Agnese, Recent introgressive hybridization revealed by exclusive mtDNA transfer from Oreochromis leucostictus (Trewavas, 1933) to Oreochromis niloticus (Linnaeus, 1758) in Lake Baringo, Kenya, Journal of Fish Biology 70, 148– 154 (2007).

18. A. Q. Siddiqui, Changes in fish species compositionin Lake Naivasha, Kenya, Hydrobiologia 64, 131–138 (1979).

19. W. Mwanja, L. Kaufman, A note on recent advances in the genetic characterization of Tilapia stocks in Lake Victoria region, African Journal of Tropical Hydrobiology and Fisheries 6, 51–53 (1995).

20. R. L. Welcomme, Notes on the present distribution and habits of the nonendemic species of tilapia which have been introduced to Lake Victoria, Rep. E. Afr. Freshwat. Fish. Res. Org. 1962/63, 36–39 (1964).

21. R. H. Lowe-McConnell, Observations on the biology of Tilapia nilotica Linne in East African waters. (pisces: Cichlidae), Rev. Zool. Bot. Afr. 57, 129–170 (1958).

249

22. R. D. Moralee, F. H. van der Bank, B. C. W. van der Waal, Biochemical genetic markers to identify hybrids between the endemic Oreochromis mossambicus and the alien species, O. niloticus (Pisces: Cichlidae), Water SA 26, 263–268 (2000).

23. P. B. Mather, A. H. Arthington, An Assessment of Genetic Differentiation among Feral Australian Tilapia Populations, J. Mar. Freshwater Res. 42, 721–728 (1991).

24. H. Y. Elder, D. J. Garrod, A natual hybrid of Tilapia nigra and Tilapia leucosticta from lake Naivasha, Kenya Colony, Nature 191, 722–724 (1961).

25. R. L. Welcomme, in Annual Report- East African Freshwater Fisheries Research Organization EAFRO, (EAFRO, 1965), pp. 18–24.

26. B. C. W. van der Waal, R. Bills, Oreochromis niloticus (Teleostei: Cichlidae) now in the Limpopo River system, South African Journal of Science 96, 47–48 (2000).

27. C. D. deSilva, Genetic variation in tilapia populations in man-made reservoirs in Sri Lanka, Aquaculture International 5, 339–349 (1997).

28. M. E. D’Amato, M. M. Esterhuyse, B. C. W. van der Waal, D. Brink, F. A. M. Volckaert, Hybridization and phylogeography of the Mozambique tilapia Oreochromis mossambicus in southern Africa evidenced by mitochondrial and microsatellite DNA genotyping, Conservation Genetics 8, 475–488 (2007).

29. H. Y. Elder, D. J. Garrod, P. J. P. Whitehead, Natural hybrids of the African cichlid fishes Tilapia spilurus nigra and T. leucosticta: a case of hybrid introgression, Biol. J. Linn. Soc. 3, 103–146 (1971).

30. C. D. De Silva, J. Ranasinghe, Biochemical evidence of hybrid gene introgression in some reservoir population of tilapia in southern Sri Lanka, Aquaculture and Fisheries Management 20, 269–277 (1989).

31. J.-C. Micha et al., in The third internation symposium on tilapia in aquaculture. ICLARM Conference Proceedings 41, P. R.S.V., J. Lazard, M. LEGENDRE, J. B. Amom Kothias, D. Pauly, Eds. (1996), pp. 354–360.

32. B. A. Costa-Pierce, Rapid evolution of an established feral tilapia (Oreochromis spp.): the need to incorporate invasion science into regulatory structures, Biological Invasions 5, 71–84 (2003).

33. R. E. Gregg, J. H. Howard, F. Shonhiwa, Introgressive Hybridization of tilapia in Zimbabwe, Journal of Fish Biology 52, 1–10 (1998). 250

34. J. N. Taylor, D. B. Snyder, J. Walter R. Courtenay, Hybridization between Two Introduced, Substrate-Spawning Tilapias (Pisces: Cichlidae) in Florida, Copeia 1986, 903–909 (1986).

35. E. Trewavas, G. Fryer, Species of Tilapia (Pisces, Cichlidae) in Lake Kitangiri, Tanzania, East Africa, Journal of Zoology 147, 108–118 (1965).

36. P. J. Whitehead, The relationship between Tilapia nigra (Gunther) and T. mossambica Peters in the eastern rivers of Kenya, Proc. Zool. Soc. Lond. 138, 605–637 (1962).

37. C. F. Hickling, The Malacca Tilapia Hybrids, Journal of Genetics 57, 1–10 (1960).

38. Y. Pruginin, in Proceedings of the FAO World Symposium on warm-water fish culture, T. V. R. Pillay, Ed. (Food and Agriculture Organization of the United Nations, Rome, 1968), vol. IV/E-12, pp. 223–229.

39. P. R.S.V., Tilapia Genetic Resources for Aquaculture: Proceedings of the Workshop on Tilapia Genetic Resources for Aquaculture 23-24 March 1987, Bangkok, Thailand (ICLARM International Center for Living Aquatic Resources Management, Manila, Philippines, 1988), p. 108.

40. L. Pinto, Hybridization Between Species of Tilapia, Transactions of the American Fisheries Society 111, 481–484 (1982).

41. U. Falter, M. Charlier, mate choice in pure-bred and hybrid females of oreochromis niloticus and O. mossambicus based upon visual stimulai (Pisces, Cichlidae), Biology of Behaviour 14, 265–276 (1989).

42. E. M. McKinna, S. Nandlal, P. B. Mather, D. a Hurwood, An investigation of the possible causes for the loss of productivity in genetically improved farmed tilapia strain in Fiji: inbreeding versus wild stock introgression, Aquaculture Research 41, e730–e742 (2010).

43. M. T. Ridha, Spawning performance and seed production from hybridization between Oreochromis spilurus and the GIFT strain of the Nile tilapia Oreochromis niloticus, Aquaculture Research 41, e723–e729 (2010).

44. J. Chernvinsky, Tilapia nilotica (Linne) from lake Rudolf, Kenya, and its hybrid resulting from a cross with T. aurea (Steindachner), Bamidgeh1 19, 81–96 (1967).

251

45. P. C. Goudswaard, F. Witte, E. F. B. Katunzi, The tilapiine fish stock of Lake Victoria before and after the Nile perch upsurge, Journal of Fish Biology 60, 838–856 (2002).

46. V. Espinosa-lemus, J. L. Arredondo-figueroa, I. de los A. Baringa-Sosa, Morphometric and genetic characterization of tilapia ( Cichlidae : Tilapiini ) stocks for effective fisheries management in two mexican reservoirs Caracterización morfométrica y genética de stocks de tilapias ( Cichlidae : Tilapiini ) para un efectivo man, Hidrobiologica 19, 95–107 (2009).

47. C. H. Fernando, H. H. A. Indrasena, The freshwater fisheries of Ceylon, Bull. Fish. Res. Stn., Ceylon 20, 101–134 (1960).

48. G. P. De Leon, L. Garcia-Prieto, V. Leon_Regagnon, A. Choudhury, Helminth communities of native and introduced fishes in Lake Patzcuaro, Michoacan, Mexico, Journal of Fish Biology 57, 303–325 (2000).

49. J. K. McCrary, B. R. Murphy, J. Jay R. Stauffer, S. S. Hendrix, Tilapia (Teleostei: Cichlidae) status in Nicaraguan natural waters, Environmental Biology of Fishes 78, 107–114 (2007).

50. E. van den Berghe, Biological Pollution: the tilapia problem, Nicaraguan Academic Journal 3 (2002).

51. M. I. Jimenez-Garcia, V. M. Vidal-Martinez, S. Lopez-Jimenez, Monogeneans in Introduced and Native Cichlids in Mexico: Evidence for Transfer, Journal of Parasitology 87, 907–909 (2001).

52. U. Strecker, The impact of invasive fish on an endemic Cyprinodon species flock (Teleostei) from Laguna Chichancanab, Yucatan, Mexico, Ecology of Freshwater Fish 15, 408–418 (2006).

53. D. C. Kritsky, V. M. Vidal, C. Rodrı´guez, “Neotropical Monogenoidea. 19. Dactylogryridae of Cichlids (Perciformes) from the Yucatan peninsula, with description of three new species of Sciadicleithrum Kritsky, Thatcher and Boeger, 1989.”, Journal of the Helminthological Society of Washington 61, 26–33 (1994).

54. E. Vareschi, The Ecology of Lake Nakuru (Kenya): II. Biomass and spatial distribution of fish (Tilapia grahami Boulenger = Sarotherodon alcalicum grahami Boulenger), Oecologia 37, 321–335 (1979).

252

55. P. L. Shafland, J. M. Pestrak, Suppression of largemouth Bass Production by Blue Tilapia in Ponds, Proceedings of the Annual Conference Southeastern Association of Fish and Wildlife Agencies 37, 441–446 (1983).

56. G. J. Piet, On the ecology of a tropical fish communityDepartment of Fish Culture and Fisheries , 189 (1996).

57. D. L. Morgan, H. S. Gill, M. G. Maddern, S. J. Beatty, Distribution and impacts of introduced freshwater fishes in Western Australia, New Zealand Journal of Marine and Freshwater Research 38, 511–523 (2004).

58. A. H. Arthington, D. R. Bludorn, M. Kennard, in The Third Asian Fisheries Forum, L. M. Chou et al., Eds. (Asian Fisheries Society, Manila, 1994), pp. 425–428.

59. D. R. Bluhdorn, A. H. Arthington, P. B. Mather, in Bureau of Rural Resource Proceedings, (Australian Govt. Pub. Service, Canberra, 1990), vol. 8, pp. 83–92.

60. A. H. Arthington, D. R. Bluhdorn, K. J. Delaney, in Proceedings of a workshop held at Queensland University of Technology on water weeds management in Queensland, Yezdani, Ed. (Center for Biological population management, Queensland University of Technology, Brisbane, 1992), pp. 142–162.

61. M. S. Peterson et al., Foraging in non-native environments: comparison of Nile Tilapia and three co-occurring native centrarchids in invaded coastal Mississippi watershed, Environ Biol Fish 76, 283–301 (2006).

62. S. Chellappa, N. T. Chellappa, E. A. Silva, F. A. Huntingford, M. C. M. Beveridge, The diet of hybrid red tilapia Oreochromis niloticus (L.) x Oreochromis mossambicus (Peters) reared in the freshwater ponds of north-eastern Brazil, Aquaculture Research 27, 945–952 (1996).

63. J. E. Randall, Introductions of marine fishes to the Hawaiian islands, Bulletin of Marine Science 41, 490–502 (1987).

64. J. Moreau, T. Petr, Ed. Inland fisheries in madagascar. Reports and papers presented at Indo-Pacific Fishery commission expert consultation on inland fisheries of the larger indo-pacific islands, Bangkok, thailand 4-9 august 1986. , FAO Fisheries Report 371, 72–103 (1986).

65. T. G. Pillai, Fish Farming Methods in the Philippines, Indonesia, and Hong Kong, FAO Fisheries Biology Technical Paper No. 18 FB/ (1962).

253

66. G. L. Vinyard et al., An Experimental-Study of the Plankton Community Impacts of 2 Omnivorous Filter-Feeding Cichlids, Tilapia-Galilaea and Tilapia-Aurea, Canadian Journal of Fisheries and Aquatic Sciences 45, 685–690 (1988).

67. L. Zambrano, E. Valiente, M. J. Vander Zanden, Stable isotope variation of a highly heterogeneous shallow freshwater system, Hydrobiologia 646, 327–336 (2010).

68. A. Barki, N. Gur, I. Karplus, Management of interspecific food competition in fish– crayfish communal culture: the effects of the spatial and temporal separation of feed, Aquaculture 201, 343–354 (2001).

69. L. Zambrano, E. Valiente, M. J. Vander Zanden, Food web overlap among native axolotl (Ambystoma mexicanum) and two exotic fishes: carp (Cyprinus carpio) and tilapia (Oreochromis niloticus) in Xochimilco, Mexico City, Biological Invasions 12, 3061–3069 (2010).

70. S. M. Starling, Effects of a reduction of Blue tilapia (Oreochromis aureus) on the ichthyofauna of a power-plant reservoirWildlife and Fisheries Science , 98 (1986).

71. D. Ippolito, thesis, The University of Texas Austin (1985).

72. A. Sreenivasan, F. Chandrasekaran, in Exotic Aquatic Speices in India. Proceedings of the workshop on exotic aquatic species in India, 25-26 Aprial, 1988. Special Publication 1, M. M. Joseph, Ed. (Asian Fisheries Society, Indian branch, Mangalore, India, 1989), vol. 1, pp. 67–74.

73. T. A. Zengeya, B. E. Marshall, Trophic interrelationships amongst cichlid fishes in a tropical African reservoir (Lake Chivero, Zimbabwe), Hydrobiologia 592, 175–182 (2007).

74. W. J. McConnell, Preliminary report on the Malacca tilapia hybrid as a sport fish in Arizona, The Progressive Fish Culturist 28, 40–46 (1966).

75. A. G. Jhingran, Performance of tilapia in indian waters and its possible impact on the native ichthyofauna, FAO Fisheries Report No. 458, 143–161 (1992).

76. M. F. Khan, P. Panikkar, Assessment of impacts of invasive fishes on the food web structure and ecosystem properties of a tropical reservoir in India, Ecological Modelling 220, 2281–2290 (2009).

77. M. G. Maddern, D. L. Morgan, H. S. Gill, Distribution, diet and potential ecological ipacts of the introduced Mozambique mouthbrooder Oreochromis mossambicus

254

Peters (Pisces: Cichlidae) in Western Australia, Journal of the Royal Society of Western Australia 90, 203–214 (2007).

78. J. Moreau, W. Ligtvoet, D. Palomares, in Trophic models of aquatic ecosystems, D. Christensen, V; Pauly, Ed. (ICLARM, Manila, ICLARM Conference Proceedings 26, Manila, 1993), pp. 144–152.

79. K. R. McKaye et al., African Tilapia in Lake Nicaragua, Bioscience 45, 406–411 (1995).

80. L. Fuselier, Impacts of Oreochromis mossambicus (Perciformes: Cichlidae) upon habitat segregation among cyprinodontids (Cyprinodontiformes) of a species flock in Mexico., Revista Biologia Tropical 49, 647–655 (2001).

81. R. L. Noble, R. D. Germany, C. R. Hall, interactions of blue tilapia and largemouth bass in a power plant cooling reservoir, Proceedings of the annual conference, Southeastern association of Game and Fish Commissioners 29, 247–251 (1976).

82. A. A. Schoenherr, A review of the life history and status of the desert pupfish, Cyprinodon macularius, Bulletin of the Southern California Academy of Science 87, 104–134 (1988).

83. J. Buntz, C. S. Manooch III, Tilapia aurea (Steindachner), a rapidly spreading exotic in south central florida, Proceedings of the annual conference, Southeastern association of Game and Fish Commissioners 22, 495–501 (1969).

84. W. R. Courtenay Jr., D. A. Hensley, Range expansion in Souther Florida of the introduced spotted tilapia, with comments on its environmental impress, Environmental Conservation 6, 149–151 (1979).

85. A. V Zale, R. W. Gregory, Food selection by Early Life stges of blue tilapia, Oreochromis aureus, in Lake George, Florida: Overlap with sympatric shad larvae, Florida Scientist 53, 123–129 (1990).

86. S. L. Traxler, B. Murphy, Experimental trophic ecology of juvenile largemouth bass, Micropterus salmides, and blue tilapia, Oreochromis aureus, Environmental Biology of Fishes 42, 201–211 (1995).

87. A. A. Schoenherr, in Fishes in North American Deserts, R. J. Naiman, D. L. Soltz, Eds. (Wiley-Interscience, Ney York, 1981), pp. 173–203.

88. J. P. Benstead, M. L. J. Stiassny, P. N. Loiselle, K. J. Riseng, N. Raminosoa, in Global Perspectives on River Conservation: Science, Policy, and Practice, P. J. Boon, B. R. Davies, G. E. Petts, Eds. (Wiley, Chichester, UK, 2000), pp. 205–231. 255

89. D. W. Fonticiella, L. Sonesten, in Tilapia Culture in the Americas Vol 2, B. A. Costa- Pierce, J. E. Rakocy, Eds. (The World Aquaculture Society, Baton Rouge, 2000), vol. 2, pp. 184–203.

90. A. Hardjamulia, S. E. Wardoyo, The role of tilapia in capture and culture-enhanced fisheries in indonesia, FAO Fisheries Report 458, 162–174 (1992).

91. M. V Natarajan, V. R. Menon, in Exotic aquatic species in India. Proceedings of the workshp on Exotic Aquatic Species in India, 25-26 Aprial, 1988. Special publication 1, M. M. Joseph, Ed. (Asian Fisheries Society, Indian branch, Mangalore, India, 1989), vol. 1, pp. 63–65.

92. B. A. Martin, M. K. Saiki, Trophic Relationships of Small Nonnative Fishes in a Natural Creek and Several Agricultural Drains Flowing Into the Salton Sea, and Their Potential Effects on the Endangered Desert Pupfish, The Southwestern Naturalist 54, 156–165 (2009).

93. J. Alcocer, E. Escobar, A. Lugo, Water use (and abuse) and its effects on the crater- lakes of Valle de Santiago, Mexico, Lakes & Reservoirs: Research and Management 5, 145–149 (2000).

94. A. A. Schoenherr, Replacement of Cyprinodon macularis by Tilapia zilli in an irrigation drain near the Salton Sea., Proceedings Desert Fishes Council 13, 65–66 (1985).

95. N. Mercado-Silva, M. R. Helmus, M. J. Vander Zanden, The Effects of Impoundment and Non-Native Species an a River Food Web In Mexico’s Central Plateau, River Research And Applications 25, 1090–1109 (2009).

96. W. R. J. Courtenay, Exotic Fish: Environmental Roulette, Water Spectrum 12, 10–17 (1980).

97. C. W. Martin, M. M. Valentine, J. F. Valentine, Competitive interactions between invasive Nile tilapia and native fish: the potential for altered trophic exchange and modification of food webs., PloS one 5, e14395 (2010).

98. D. O. Oyugi, J. Cucherousset, J. Robert Britton, Temperature-dependent feeding interactions between two invasive fishes competing through interference and exploitation, Reviews in Fish Biology and Fisheries 22, 499–508 (2011).

99. A. M. Khan, Z. Ali, S. Y. Shelly, Z. Ahmad, M. R. Mirza, Aliens ; A Catastrophe For Native Fresh Water Fish Diversity In: The Journal of Animal and Plant Sciences 21, 435–440 (2011). 256

100. G. Ruiz-campos et al., Threatened fishes of the world : Fundulus lima Vaillant , 1894 ( Fundulidae ), Environmental Biology of fishes 1894, 20 (2000).

101. P. Steinhart, California’s Wild Heritage (California Department of Fish and game, 1990), p. 108.

102. A. Traore et al., Trophic structure and interactions in Lake Ayame (Cote d’Ivoire), Knowledge and management of aquatic ecosystems 288, 2 (2008).

103. G. Fryer, Observations on the biology of the cichlid fish Tilapia variabilis Boulenger in the northern waters of Lake Victoria (East Africa), Revue de zoologie et de botanique africaines 64, 1–33 (1961).

104. M. J. S. Wijeyaratne, H. H. Costa, Resource partioning of two commercially important cichlid species in three minor perennial reservoirs in the northwestern province of Sri Lanka, journal of aquaculture in the tropics 7, 131–140 (1992).

105. R. G. Doupé, J. Schaffer, M. J. Knott, D. W. Burrows, How might an exotic fish disrupt spawning success in a sympatric native species?, Marine and Freshwater Research 60, 379 (2009).

106. G. J. Piet, J. S. Pet, W. A. H. P. Guruge, J. Vijverberg, W. L. T. Van Densen, Resource partitioning along three niche dimensions in a size-structured tropical fish assemblage, Canadian Journal of Fisheries and Aquatic Science 56, 1241–1254 (1999).

107. M. K. Hendricks, R. L. Noble, Feeding interactions of three planktivorous fishes in Trinidad Lake, Texas, Proceedings of the Annual Conference Southeaster Association of Fish and Wildlife Agencies 33, 324–330 (1979).

108. R. O. Juliano, R. Guerrero. III, I. Ronquillo, in Workshop on Introduction of Exotic Aquatic Organisms in Asia, S. S. De Silva, Ed. (Asian Fisheries Society, Manila, Philippines, 1989), vol. Special Pu, pp. 83–90.

109. W. R. Brooks, R. C. Jordan, Enhanced interspecific territoriality and the invasion success of the spotted tilapia (Tilapia mariae) in south Florida, Biological Invasions 12, 865–874 (2010).

110. M. J. S. Wijeyaratne, W. M. D. S. K. Perera, Trophic interrelationships among exotic and indigenous fish co-occuring in some reservoirs in Sri Lanka, Asian Fisheries Science 14, 333–342 (2001).

257

111. F. H. C. Sanches et al., Aggressiveness overcomes body-size effects in fights staged between invasive and native fish species with overlapping niches., PloS one 7, e29746 (2012).

112. R. L. Noble, R. D. Germany, in Fish Culture in fisheries management, R. H. Stroud, Ed. (American Fisheries Society, Fish Culture Section, Bethesda, Maryland, 1986), pp. 455–461.

113. S. A. S. Ahmad, A. N. Bart, Y. Yi, J. E. Rokoey, J. S. Diana, The effect of the introduction of Nile tilapia (Oreochromis niloticus, L.) on small indigenous fish species (mola, Ambiypharyngodon mola, Hamilton; chela Chela cachius, Hamilton; punti, Puntius sophore, Hamilton), Aquaculture Research 41, 904–912 (2010).

114. M. Lindholm, D. O. Hessen, Competition and niche partitioning in a floodplain ecosystem: a cladoceran community squeezed between fish and invertebrate predation, African Zoology 42, 158–164 (2007).

115. F. Duponchelle, M. Legendre, Oreochromis Niloticus (Cichlidae) in Lake Ayame, Cote D’ivoire: Life History Traits of a Strongly Diminished Population, Cybium 24, 161–172 (2000).

116. A. V Zale, Applied aspects of the thermal biology, ecology, and life history of the Blue tilapia, Tilapia Aurea (Pisxes: Cichlidae), 238 (1984).

117. M. Gophen, R. W. Drenner, G. L. Vinyard, Cichlid stocking and decline of the galilee Sait Peter’s fish (Sarotherodon galilaeus) in Lake Kinneret, Israel, Canadian Journal of Fisheries and Aquatic Science 40, 983–986 (1983).

118. H. Ortega, H. Guerra, R. Ramirez, in Ecological and genetic Implications of Aquaculture Activities, T. M. Bert, Ed. (Springer, 2007), pp. 247–278.

119. G. Bernardi, G. Ruiz-Campos, F. Camarena-Rosales, Genetic isolation and evolutionary history of oases populations of the Baja California killifish, Fundulus lima, Conservation Genetics 8, 547–554 (2007).

120. G. Ruiz-Campos et al., An annotated distributional checklist of the freshwater fish from Baja California Sur, Mexico, Reviews in Fish Biology and Fisheries 12, 143– 155 (2002).

121. P. L. Shafland, Non-native fish introductions with special reference to Florida, Fisheries 4, 18–24 (1979).

258

122. H. Lopez-Fernandez, K. O. Winemiller, Status Of Dionda Diaboli and Report Of Established Populations Of Exotic Fish Species In Lower San Felipe Creek, Val Verde County, Texas, The Southwestern Naturalist 50, 246–251 (2005).

123. J. S. Pet, G. J. Piet, The consequences of habitat occupation and habitat overlap of the introduced tilapia Oreochromis mossambicus and indigenous fish species for fishery management in a Sri Lankan reservoir, Journal of Fish Biology 43, 193– 208 (1993).

124. R. L. Welcomme, Studies on the effects of abnormally high water levels on the ecology of fish in certain shallow regions of Lake Victoria, J. Zool. Lond 160, 405– 436 (1970).

125. M. Barton, C. Wilmhoff, Inland fishes of the Bahamas- ne distribution records for exotic and native species from New Providence Island, Bahamas Journal of Science 2, 7–11 (1996).

126. G. N. Bwanika, L. J. Chapman, Y. Kizito, J. Balirwa, Cascading effects of introduced Nile perch (Lates niloticus) on the foraging ecology of Nile tilapia (Oreochromis niloticus), Ecology of Freshwater Fish 15, 470–481 (2006).

127. G. J. Piet, J. S. Pet, W. A. H. P. Guruge, niche partitioning of indigenous riverine carps and the exotic lacustrine tilapia (Oreochromis mossambicus) in a reservoir in Sri Lanka, Verh. Internat. Verein. Limnol. 25, 2183–2187 (1994).

128. A. V Zale, Growth, survival, and foraging abilities of early life history stages of blue tilapia, Oreochromis aureus, and largemouth bass, Micropterus salmoides, Environmental Biology of Fishes 20, 113–128 (1987).

129. M. Dickman, H. Nanne, Impact of Tilapia Grazing on Plankton Composition in Artificial Ponds in Guanacaste Province, Costa Rica, Journal of Freshwater Ecology 4, 93–100 (1987).

130. M. Asaduzzaman et al., Effects of carbohydrate source for maintaining a high C:N ratio and fish driven re-suspension on pond ecology and production in periphyton-based freshwater prawn culture systems, Aquaculture 301, 37–46 (2010).

131. M. Asaduzzaman, M. A. Wahab, M. C. J. Verdegem, M. N. Mondal, M. E. Azim, Effects of stocking density of freshwater prawn Macrobrachium rosenbergii and addition of different levels of tilapia Oreochromis niloticus on production in C/N controlled periphyton based system, Aquaculture 286 (2009).

259

132. C. C. Figueredo, A. Giani, Ecological interactions between Nile tilapia (Oreochromis niloticus, L.) and the phytoplanktonic community of the Furnas Reservoir (Brazil), Freshwater Biology 50, 1391–1403 (2005).

133. M. Asaduzzaman et al., Effects of addition of tilapia Oreochromis niloticus and substrates for periphyton developments on pond ecology and production in C/N- controlled freshwater prawn Macrobrachium rosenbergii farming systems, Aquaculture 287, 371–380 (2009).

134. M. M. Rahman, MarcVerdegem, M. Abdul Wahab, Effects of tilapia (Oreochromis niloticus L.) stocking and artificial feeding on water quality and production in rohu–common carp bi-culture ponds, Aquaculture Research 39, 1579–1587 (2008).

135. M. R. Gonzalez, C. M. Hart, J. R. Verfaillie, S. H. Hurlbert, Salinity and fish effects on Salton Sea microecosystems: water chemistry and nutrient cycling, Hydrobiologia 381, 105–128 (1998).

136. F. Starling, X. Lazzaro, C. Cavalcanti, R. Moreira, Contribution of omnivorous tilapia to eutrophication of a shallow tropical reservoir: evidence from a fish kill, Freshwater Biology 47, 2443–2452 (2002).

137. P. B. Moyle, Inland Fishes of California (University of California Press, Berkeley, ed. 1, 1976), p. 405.

138. F. L. R. M. Starling, A. J. A. Rocha, Experimental study of the impacts of planktivorous fishes on plankton community and eutrophication of a tropical Brazilian reservoir, Hydrobiologia 200/201, 581–591 (1990).

139. J. U. J. Crutchfield, Establishment and Expansion of Redbelly Tilapia and Blue Tilapia in a Power Plant Cooling Reservoir, American Fisheries Society Symposium 15, 452–461 (1995).

140. S. S. De Silva, C. H. Fernando, Recent trends in the fishey of Parakrama Samudra, and ancient man-made lake in Sri Lanka, Tropical Ecology & Development, Proceedings of the Vth Internation Symposium of Tropical Ecolgy, 16-21 April 1979, Kuala Lampur Malaysia 2, 927–937 (1980).

141. F. Starling, X. Lazzaro, Experimental investigation of the feasibility of improving water quality by controlling exotic planktivore overpopulation in the eutrophic Paranoa Reservoir (Brasilia, Brazil)., Verhandlungen der Internationalen Vereinigung fu¨r Theoretische und Angewandte Limnologie 26, 789–794 (1997).

260

142. W. M. H. K. Wijenayake, U. A. D. Jayasinghe, U. S. Amarasinghe, Culture-based fisheries in non-perennial reservoirs in Sri Lanka: production and relative performance of stocked species, Fisheries Management and Ecology 12, 249–258 (2005).

143. J. W. jr. Avault, R. O. Smitherman, E. W. Shell, Evaluation of eight species of fish for aquatic weed control, FAO Fisheries Report 5, 109–122 (1968).

144. A. H. Hurlbert, T. W. Anderson, K. K. Sturm, S. H. Hurlbert, Fish and fish-eating birds at the Salton Sea: A century of boom and bust, Lakes and Reservoirs Management 23, 469–499 (2007).

145. A. F. V Howard, G. Zhou, F. X. Omlin, Malaria mosquito control using edible fish in western Kenya: preliminary findings of a controlled study, BMC Public Health 7, 199 (2007).

146. S. M. H. Huchette, M. C. M. Beveridge, D. J. Baird, M. Ireland, The impacts of grazing by tilapias (Oreochromis niloticus L.) on periphyton communities growing on artificial substrate in cages, Aquaculture 186, 45–60 (2000).

147. F. Elhigzi, S. Haider, P. Larsson, Interactions Between Nile Tilapia (Oreochromis- Niloticus) And Cladocerans In Ponds (Khartoum, Sudan), Hydrobiologia 307, 263– 272 (1995).

148. R. W. Drenner, S. B. Taylor, X. Lazzaro, D. Kettle, Particle-grazing and plankton community impact of an omnivorous Cichlid, Transactions of the American Fisheries Society 113, 397–402 (1984).

149. F. Schiemer, in Perspectives in Tropical Limnology, F. Schiemer, K. T. Boland, Eds. (SPB Academic Publishing, Amsterdam, 1996), pp. 65–76.

150. W. Sun et al., Effects of zooplankton refuge on the growth of tilapia (Oreochromis niloticus) and plankton dynamics in pond, Aquaculture International DOI 10.100 (2009).

151. R. W. Drenner, K. D. Hambright, G. L. Vinyard, M. Gophen, U. Pollingher, Experimental Study of Size-Selective Phytoplankton Grazing by a Filter-Feeding Cichlid and the Cichlid’s Effects on Plankton Community Structure, Limnology and Oceanography 32, 1138–1144 (1987).

152. C. M. Hart, M. R. Gonzalez, E. P. Simpson, S. H. Hurlbert, Salinity and fish effects on Salton Sea microecosystems: zooplankton and nekton, Hydrobiologia 381, 129– 152 (1998). 261

153. R. F. Menezes, J. L. Attayde, F. R. Vasconcelos, Effects of omnivorous filter-feeding fish and nutrient enrichment on the plankton community and water transparency of a tropical reservoir, Freshwater Biology 55, 767–779 (2010).

154. N. Okun, J. Brasil, J. L. Attayde, I. A. S. Costa, Omnivory does not prevent trophic cascades in pelagic food webs, Freshwater Biology 53, 129–138 (2008).

155. C. RONDEL et al., A cyanobacterial bloom prevents fish trophic cascades, Freshwater Biology 53, 637–651 (2008).

156. E. P. Simpson, M. R. Gonzalez, C. M. Hart, S. H. Hurlbert, Salinity and fish effects on Salton Sea microecosystems: benthos, Hydrobiologia 381, 153–177 (1998).

157. J. S. Diana, D. J. Dettweiler, C. K. Lin, Effect of Nile Tilapia (Oreochromis niloticus) on the Ecosystem of aquaculture ponds, and its significance to the trophic cascade hypothesis, Canadian Journal of Fisheries and Aquatic Science 48, 183– 190 (1991).

158. K. Lu, C. Jin, S. Dong, B. Gu, S. H. Bowen, Feeding and control of blue-green algal blooms by tilapia (Oreochromis niloticus), Hydrobiologia 568, 111–120 (2006).

159. S. Datta(Saha), B. B. Jana, Control of bloom in a tropical lake: grazing efficiency of some herbivorous fishes, Journal of Fish Biology 53, 12–24 (1998).

160. M. E. McDonald, Interactions between a phytoplankivorous fish, Oreochromis aureus, and two unialgal forage populations, Environmental Biology of Fishes 18, 229–234 (1987).

161. J. M. Diamond, The avifainas of Rennell and Bellona Islands, Nat Hist Rennen island british solomon islands (Zoology) 8, 127–168 (1984).

162. W. F. Childers, G. W. Bennett, Experimental vegetation control by largemouth bass- tilapia combinations, The Journal of Wildlife Management 31, 401–407 (1967).

163. E. F. Legner, C. A. Murray, Feeding rated and growth of the fish Tilapia Zillii (Cichlidae) on Hydrilla verticillata, Potamogeton Pectnatus and Myriophyllum spicatum var. exalbescens and interactions in irrigcation canals of southeastern California, Mosquito News 41, 241–250 (1981).

164. F. J. R. Junor, Tilapia melanopleura Dum in artificial lakes and dams in Rhodesia with special reference to its undesirable effects, Rhodesian Journal Agricultural Research 7, 61–69 (1969).

262

165. G. G. Scoppettone, J. A. Salgado, M. B. Nielsen, BLUE TILAPIA (OREOCHROMIS AUREUS) PREDATION ON FISHES IN THE MUDDY RIVER SYSTEM, CLARK COUNTY, NEVADA, Western North American Naturalist 65, 410–414 (2005).

166. D. S. Erdman, in Distribution, biology, and Management of Exotic Fishes, W. R. Courtenay Jr., J. R. Stauffer, Eds. (The Johns Hopkins University Press, Balitmore, 1984), pp. 162–176.

167. R. Victor, N. Al-Makki, escaped tilapia in an arabian pond: a biological assessment, International Association Of Theoretical And Applied Limnology 27, 762–767 (2000).

168. S. Uddin, M. Ekram-Ul-Azim, Abdul Wahab, M. C. J. Verdegem, The potential of mixed culture of genetically improved farmed tilapia (Oreochromis niloticus) and freshwater giant prawn (Macrobrachium rosenbergii) in periphyton-based systems, Aquaculture Research 37, 241–247 (2006).

169. E. F. Legner, R. A. Medved, F. Pelsue, Changes in chironomid breeding patterns in a paved river channel following adaptation of cichlids of the Tilapia mossambica- hornorum complex, Ann Ent. Soc. Am 73, 293–299 (1980).

170. W. J. Hauser, E. F. Legnar, F. Robinson, Biological control of aquatic weeds by fish in irrigation channels., Proc. water management for irrigation and drainage. ASCE/Reno, 139–145 (1977).

171. W. J. Hauser, E. F. Legner, R. A. Medved, S. Platt, Tilapia- A management tool, Fisheries 1, 24 (1976).

172. J. kear, G. Williams, Waterfowl at risk, Wildfowl 29, 5–21 (1978).

173. V. A. Wager, D. T. Rowe-Rowe, The effects of Tilapairendalli and T. mossambica on the aquatic macrophytes and fauna in five ponds, South African Journal of Science 68, 257–260 (1972).

174. D. W. Stinson, M. W. Ritter, J. D. Reichel, The Mariana Common Moorhen: Decline of an Island Endemic, The Condor 93, 38–43 (1991).

175. E. F. Legner, Efforts to control Hydrilla verticillate Royle with herbivorous Tilapia Zilli (Gervais) in imperial County irrigation canals, Proc. calif mosq and vector control assoc 46, 103–104 (1978).

263

176. R. E. Brummett, N. C. Alon, Polyculture of Nile tilapia (Oreochromis niloticus) and Australian red claw crayfish (Cherax quadricarinatus) in earthen ponds, Aquaculture 122, 47–54 (1994).

177. A. Sreenivasan, Tilapia mossambica- It’s ecology and status in Madras State, India, Madras Journal of Fisheries 3, 33–43 (1967).

178. D. G. Hughes, Tilapia: the biological solution, The Nicaraguan Academic Journal 3, 115–128 (2002).

179. E. R. A. de Zylva, The introduction of exotic fish in Sri Lanka with special reference to tilapias, Naga 22, 4–8 (1999).

180. D. M. Harper, K. M. Mavuti, S. M. Muchiri, Ecology and management of lake Naivasha, Kenya, in relation to climate change, alien species’ introduction, and agricultural development, Environmental Conservation 17, 328–336 (1990).

181. R. Riedel, L. M. Caskey, S. H. Hurlbert, Length-weight relations and growth rates of dominat fishes of the Salton Sea: implications for predation by fish-eating birds, Lake and Reservoir Management 23, 528–535 (2007).

182. H. Winkler, in Limnology of Parakrama Samudra - Sri Lanka, F. Schiemer, Ed. (Dr. W. Junk, The Hague, 1983), pp. 193–199.

183. P. S. Lobel, Invasion by the Mozambique tilapia (Sarotherodon mossambicus; Pisces; Cichlidae) of a pacific marine ecosystem, micronesia 16, 255–349 (1980).

184. R. G. Bailey, S. Churchfield, T. Petr, The ecology of the fishes in Nyumba ya mungu reservoir, Tanzania, Biological Journal of the Linnean Society 10, 109–137 (1978).

185. E. A. Baluyut, in Fish and Fisheries of Lakes and reservoirs in Southeast Asia and Africa, W. L. T. van Densen, M. J. Morris, Eds. (Westbury, 1999).

186. S. S. De Silva, R. P. Subasinghe, D. M. Bartley, A. Lowther, Tilapias as Alien Aquatics in Asia and the Pacific: A Review, 65 (2004).

187. R. I. Arthur, Adaptive learning and the management of small waterbody fisheries: a case study in Lao PDREnvironmental Science and Technology , 437 (2004).

188. J. Chandrasoma, in The First Asian Fisheries Forum., J. L. Maclean, L. B. Dizon, L. V Hosillos, Eds. (Asian Fisheries Society, Manila, Philippines, 1986), pp. 353–356.

264

189. H. P. C. Shetty, M. C. Nandeesha, A. G. Jhingran, in Exotic Aquatic organisms in Asia. Proceedings of the Workshop on Introduction of Exotic Aquatic Organisms in Asia. Asian Fisheries Society Special Publication 3., S. S. De Silva, Ed. (Asian Fisheries Society, Manila, Philippines, 1989), pp. 45–55.

190. R. G. Bailey, Changes in the fish and fisheries ecology of a large man-made lake in Tanzania, 1965-1994, Fisheries Management and Ecology 3, 251–260 (1996).

191. A. Sreenivasan, Fish production and fish population changes in some South Indian reservoirs, Indian Journal of Fisheries 23, 134–152 (1976).

192. A. Sreenivasan, in Indo-pacific Fisheries Council Proceedings 17th Session Colombo, Sri Lanka 27 October-5 November 1976, I.-P. F. C. IPFC, Ed. (FAO Regional Office for Asia and the Far east, Bangkok, 1977), pp. 292–302.

193. P. Hickley et al., The status and future of the Lake Naivasha fishery, Kenya, Hydrobiologia 488, 181–190 (2002).

194. L. L. Caskey, R. R. Riedel, B. Costa-Pierce, J. Butler, S. H. Hurlbrt, Population dynamics, distribution, and growth rate of tilapia (Oreochromis mossambicus) in the Salton Sea, California, with notes on bairdiella (Baidrdiella icistia) and orangemouth corvina (Cynoscion xanthulus), Hydrobiologia 576, 185–203 (2007).

195. T. Contreras-MacBeath, H. M. Mojica, R. C. Wilson, Negative impact on the aquatic ecosystems of the state of Morelos, Mexico from introduced aquarium and other commercial fish, Aquarium Sciences and Conservation 2, 67–78 (1998).

196. A. Sreenivasan, in Report Of The Second Session Of The Ipfc Working Party On Inland Fisheries, (1984), pp. 40–52.

197. Anon, Central inland fisheries research institute, Rep cent. Inl. Fish res. Stn. Barrackpore , 41 (1961).

198. I. B. Byrd, J. H. Crance, Fourteen Years of Management and Fishing Success in Alabama’s State-owned Public Fishing Lakes, Transactions of the American Fisheries Society 94, 129–134 (1965).

199. S. D. Henry, S. W. Barkley, R. L. Johnson, Exploitation of Nile Tilapia in a Closed- System Public Fishing Reservoir in Northern Arkansas, North American Journal of Fisheries Management 25, 853–860 (2005).

265

200. A. S. Sarnita, in Indo-pacific Fisheries Council Proceedings 17th Session Colombo, Sri Lanka 27 October-5 November 1976, I.-P. F. C. IPFC, Ed. (FAO Regional Office for Asia and the Far east, Bangkok, 1977), pp. 272–280.

201. U. S. Amarasinghe, in Reservoir Fishery management and development in Asia. Proceedings of a workshop held in Kathmandu, nepal, 23-28 November 1987, S. S. De Silva, Ed. (1988), pp. 105–112.

202. J. Pigg, The Tilapia Sarotherodon Aurea ( Steindachner ) In The North Canadian River In Central Oklahoma, Oklahoma Academy of Science Proceedings 112, 111– 112 (1978).

203. D. Pelrin, K. Carlander, Perlin and carlander 1971.pdf, Proc. Iowa Acad. Sci 78, 27– 29 (1971).

204. P. J. Schofield, W. T. Slack, M. S. Peterson, D. R. Gregoire, Assessment and control of an invasive aquaculture species: and update on Nile tilapia (Oreochromis niloticus) in Coastal mississippi after Hurricane Katrina, SFC Proceedings 49, 9–15 (2007).

205. H. N. Chandrasekharaiah, in Workshop on Exotic Aquatic Species in India, (Asian fisheries Society, Mangalore, India, 1988), vol. special pu, pp. 75–77.

206. P. L. Shafland, in The Florida game and Fish Commission’s Non-Native Fish Research Project (0284) Annual Report for 1978-179, (Florida Game and Fresh water Fish Commission, 1980), vol. 4(2), p. 31.

207. R. D. Germany, population dynamics of the blue tilapia and its effects on the fish populations of Trinidad Lake, TexasWildlife and Fisheries Science , 85 (1977).

208. W. Ligtvoet et al., in Fish stocks and fisheries of Lake Victoria, Witte, van Densen, Eds. (Samara Publishing Ltd, Cardigan, UK, 1995), pp. 11–53.

209. C. V MINTE-VERA, M. J. R. PETRERE, Artisanal fisheries in urban reservoirs: a case study from Brazil (Billings Reservoir, Sao Paulo Metropolitan Region), Fisheries Management and Ecology 7, 537–549 (2000).

210. G. C. Radonski, N. S. Prosser, R. G. Martin, R. H. Stroud, in Distribution, biology, and management of exotic fishes, (1984), pp. 313–321.

211. L. De Vos, J. Snoeks, D. T. van den Audenaerde, The effects of Tilapia introduction in Lake Luhondo, Rwanda, Environmental Biology of Fishes 27, 303–308 (1990).

266

212. P. N. Reinthal, M. L. J. Stiassny, The freshwater fishes of Madagascar: A study of the an endangered fauna with recommendations for a conservation strategy, Conservation Biology 5, 231–243 (1991).

213. E. H. Knaggs, Status of the genus Tilapia in California’s estuarine and marine waters, Cal-Neva Wildlife Transactions , 60–67 (1977).

214. J. P. Benstead et al., Conserving Madagascar’s Freshwater Biodiversity, Bioscience 53, 1101–1111 (2003).

215. R. Pethiyagoda, Threats to the indigenous freshwater fishes of Sri Lanka and remarks on their conservation, Hydrobiologia 285, 189–201 (1994).

216. A. Sreenivasan, in Ecology of Biological Invasion in the tropics, P. S. Ramakrishnan, Ed. (International Scientific publications , New Delhi, 1991), vol. Supplement, pp. 131–138.

217. P. L. Shafland, The continuting problem of non-native fishes in Florida, Fisheries 1, 25 (1976).

218. W. R. Courtenay Jr., C. R. Robins, R. M. Bailey, J. E. Deacon, Records of exotic fishes from Idaho and Wyoming, The Great Basin Naturalist 47, 523–526 (1987).

219. A. Chacon-Torres, C. Rosas-Monge, A restoration plan for pez blanco in Lake Patzcuaro, Mexico, American Fisheries Society Symposium 15, 122–126 (1995).

220. A. Maguilas, Tilapia crowding out other fishThe Manila Times , 14 (1999).

221. S. R. Banerji, M. S. Satish, in Workshop on exotic aquatic species in India, M. M. Joseph, Ed. (Asian Fisheries Society, Indian Branch, Mangalore, India, 1989), vol. Special Pu, pp. 97–99.

222. G. R. Batchelor, Aspects of the biology of Tilapia rendalli in the Doorndraai Dam Transvaal, J. Limnol. Soc. sth. Afr 4, 65–68 (1978).

223. K. F. Vaas, A. E. Hofstede, Studies on Tilapia Mossambica Peters (ikan mudjar) in Indonesia, Contributions of the Inland Fisheries Research Stations-DJakarta- Bogor Indonesia 1, 1–88 (1952).

224. B. Gindelberger, Why Sinarapan almost Disappered from Lake Buhi, Naga 4, 3–5 (1981).

267

225. S. Sarig, Culture of tilapia as a secondary fish in carp ponds, Bamidgeh 7, 41–45 (1955).

226. P. H. Skelton, South African Red Data Book - Fishes, 199 (1987).

227. R. J. Edwards, New additions and persistence of the introduced fishes of the upper San Antonio River, Bexar County, Texas, Texas Journal of Science 53, 3–12 (2001).

228. J. Leal-Florez, M. Rueda, M. Wolff, Role of the non-native fish Oreochromis niloticus in the long-term variation of abundance and species composistion of the native ichthyofauna in a caribbean estuary, Bulletin of Marine Science 82, 365–380 (2008).

229. J. A. Kushlan, Exotic Fishes Of The Everglades - A Reconsideration Of Proven Impact, Environmental Conservation 13, 67–69 (1986).

230. A. Sreenivasan, D. Sundararajan, A note on the tilapia fishery of an impoundment in Madras State, Science and Culture 33, 145–146 (1967).

231. G. G. Scoppettone, P. H. Rissler, M. B. Nielsen, J. E. Harvey, The Status of Moapa Coriacea and Gila Seminuda and Status Information on Other Fishes of the Muddy River, Clark County, Nevada, Fisheries 43, 115–122 (1998).

232. V. V Sugunan, Reservoir fisheries of India, FAO Fisheries Technical Paper no 345, 423p (1995).

233. M. Barton, Threatened fishes of the world: Cyprinodon laciniatus Hubbs & Miller, 1942 (Cyprinodontidae), Environmental Biology of Fishes 55, 422 (1999).

234. O. Dominguez-Dominguez, E. Martinez-Meyer, L. Zambrano, G. P.-P. D. E. L. E. On, Using Ecological-Niche Modeling as a Conservation Tool for Freshwater Species: Live-Bearing Fishes in Central Mexico, Conservation Biology 20, 1730–1739 (2006).

235. B. A. Martin, M. K. Saiki, Relation of desert pupfish abundance to selected environmental variables in natural and manmade habitats in the Salton Sea basin, Environmental Biology of Fishes 73, 97–107 (2005).

236. R. Raghavan, G. Prasad, P. H. Anvar-Ali, B. Pereira, Exotic fish species in a global biodiversity hotspot: observations from River Chalakudy, part of Western Ghats, Kerala, India, Biological Invasions 10, 37–40 (2008).

268

237. G. Ruiz-Campos Et Al., Distribution And Abundance Of The Endangered Killifish Fundulus Lima, And Its Interaction With Exotic Fishes In Oases Of Central Baja California, Mexico, The Southwestern Naturalist 51, 502–509 (2006).

238. A. O. Debrot, A review of the freshwater fishes of Curacao, with comments on those of Aruba and Bonaire, Caribbean Journal Of Science, 39, 100–108 (2003).

239. A. Varela-Romero, G. Ruiz-Campos, L. M. Yepiz-Velazquez, J. Alaniz-Garcia, Distribution, habitat and conservation status of desert pupfish (Cyprinodon macularius) in the Lower Colorado River Basin, Mexico, Reviews In Fish Biology And Fisheries, 12, 157–165 (2000).

240. L. Zambrano, C. Macias-Garcia, in Nonindigenous Freshwater Organisms: Vectors, Biology, and Impacts., R. Claudi, J. H. Leach, Eds. (CRC Press, Boca Raton, FL, 1999), pp. 113–123.

241. S. G. Nelson, L. G. Eldredge, Distribrution and status of introduced cichlid fishes of the genera Oreochromis and Tilapia in the islands of the South Pacific and Micronesia, Asian Fisheries Science 4, 11–22 (1991).

242. J. L. Attayde, J. Brasil, R. a. Menescal, Impacts of introducing Nile tilapia on the fisheries of a tropical reservoir in North-eastern Brazil, Fisheries Management and Ecology 18, 437–443 (2011).

243. A. P. Jenkins, S. D. Jupiter, I. Qauqau, J. Atherton, The importance of ecosystem- based management for conserving aquatic migratory pathways on tropical high islands : a case study from Fiji, Aquatic Conservation: Marine And Freshwater Ecosystems 238, 224–238 (2010).

244. S. Contreras, M. A. Escalante, in Distribution, Biology and management of Exotic Fishes., W. R. J. Courtenay, J. R. Stauffer, Eds. (1984), pp. 102–130.

245. A. Jhingran, in Reservoir fisheries of Asia, S. S. De Silva, Ed. (1990), pp. 158–175.

246. M. L. Habel, Overwintering of the cichlid, Tilapia aurea, produced fourteen tons of harvestable size fish in a south Alabama bass-bluegill public fishing lake., The Progressive Fish Culturist 37, 31–32 (1975).

247. R. Guerrero. III, in Fish and fisheries of lakes and reservoirs in Southeast Asia and Africa, W. L. T. Van Densen, M. J. Morris, Eds. (Westbury Publishing, Otley, UK, 1999), pp. 151–157.

269

248. K. Lorenzen, C. J. Garaway, B. Chamsingh, T. J. Warren, Effects of access restriction and stocking on small water body fisheries in Loas , Journal of Fish Biology 53, 345–357 (1998).

249. R. I. Arthur et al., Assessing impacts of introduced aquaculture species on native fish communities: Nile tilapia and major carps in SE Asian freshwaters, Aquaculture 299, 81–88 (2010).

250. C. H. Fernando, in The scientific management of animal and plant communities for conservation, E. Duffey, A. S. Watt, Eds. (Blackwell Scientific Publications, London, 1971), pp. 295–310.

251. H. Hulsman, R. Vonk, M. Aliabadian, A. O. Debrot, V. Nijman, Effect of introduced species and habitat alteration on the occurrence and distribution of euryhaline fi shes in fresh- and brackish-water habitats on Aruba, Bonaire and Curaçao (South Caribbean), Contributions to Zoology 77, 45–52 (2008).

252. R. L. Welcomme, Chavalit Vidthayanon, The impacts of introductions and stocking of exotic species in the Mekong Basin and policies for their control. MRC Technical Paper No. 9, Mekong River Commission, Phnom Penh. (2003), p. 38.

253. C. H. Fernando, Impacts of Fish Introductions in Tropical Asia and America, Can. J. Fish. Aquat. Sci. 48, 24–32 (1991).

254. O. L. F. Weyl, Rapid invasion of a subtropical lake fishery in central Mozambique by Nile tilapia, Oreochromis niloticus (Pisces: cichlidae), Aquatic Conservation: Marine And Freshwater Ecosystems 18, 839–851 (2008).

255. G. C. Haldar, K. K. Ahmed, M. Alamgir, J. N. Akhter, M. K. Rahaman, in Management and ecology of lake and reservoir fisheries, I. G. Cowx, Ed. (Blackwell Publishing, Oxford, UK, 2002), pp. 145–148.

256. S. Akin, E. Buhan, K. O. Winemiller, H. Yilmaz, Fish assemblage structure of Koycegiz Lagoon-Estuary, Turkey: Spatial and temporal distribution patterns in relation to environmental variation, Estuarine, Coastal and Shelf Science 64, 671–684 (2005).

257. T. M. Berra, R. Moore, L. F. Reynolds, The freshwater fishes of the Laloki River system of New Guinea, Copeia 1975, 316–326 (1975).

258. R. L. Welcomme, The habitat and habitat preferences of the young of the Lake Victoria tilapia (Pisces Cichlidae), Revue de zoologie et de botanique africaines 70, 1–28 (1964). 270

259. P. D. Phan, S. S. De Silva, The fishery of the Ea Kao reservoir, southern Vietnam: A fishery based on a combination of stock and recapture, and self-recruting populations, Fisheries Management and Ecology 7, 251–264 (2000).

260. G. M. Bernacsek, large dam Fisheries of the Lower Mekong Countries: Review and AssessmentMKG/R 97023 1, 116 (1997).

261. D. Coates, Fish yeild estimates for the Sepik River, Papua New Guinea, a large floodplain system east of “Wallace”s Line’, Journal of Fish Biology 27, 431–443 (1985).

262. G. F. Black, Description of the Salton Sea Sport Fishery 1982-83 (1988).

263. D. Coates, Consideration of fish introduction into the Sepik River, Papua New Guinea, Aquaculture and Fisheries Management 18, 231–241 (1987).

264. K. De Silva, in Reservoir fisheries in Asia: Proceeding of the 2nd Asian reservoir fisheries workshop held in Hanqzhou, People’s Republic of China, 15-19 October 1990, S. S. De Silva, Ed. (IDRC, Ottawa, 1992), pp. 12–22.

265. S. S. De Silva, U. S. AMARASINGHE, C. Nissanka, W. Wijesooriya, M. J. J. Fernando, Use of geographical information systems as a tool for predicting fish yield in tropical reservoirs: case study on Sri Lankan reservoirs, Fisheries Management and Ecology 8, 47–60 (2001).

266. E. C. Mercene, M. P. Nasino, An assessment of the goby fishery in Laguna de Bay, Philippines, Asia Life Sciences 3, 25–34 (1994).

267. O. Pawaputanon, Current contribution of tilapia to capture fisheries in Thailand, FAO Fisheries Report 458, 238–243 (1992).

268. E. J. Hyslop, Longtitudinal variation in fish species composistion in he Angabanga river, Papua New Guinea with observastions on the trophic status of certain fish species, Ecology of Freshwater Fish 8, 102–107 (1998).

269. M. Njiru et al., Increasing dominance of Nile tilapia, Oreochromis niloticus (L) in Lake Victoria, Kenya: Consequences for the Nile perch Lates niloticus (L) fishery, Aquatic Ecosystem Health & Management 11, 42–49 (2008).

270. D. J. Russell, T. J. Ryan, A. J. McDougall, S. E. Kistle, G. Aland, Species diversity and spatial variation in fish assemblage structure of stream in connected tropical catchments in northern Australia with reference to the occurrence of

271

translocated and exotic species, Marine and Freshwater Research 54, 813–824 (2003).

271. R. Ogutu-Ohwayo, The Effects of Predation by Nile Perch, Lates niloticus L., on the Fish of Lake Nabugabo, with Suggestions for Conservation of Endangered Endemic Cichlids, Conservation Biology 7, 701–711 (1993).

272. U. S. AMARASINGHE, The Fishery and Population Dynamics of Oreochromis mossambicus and Oreochromis niloticus (Osteichthyes, Cichlidae) in a Shallow Irrigation Reservoir in Sri Lanka, Asian Fisheries Science 15, 7–20 (2002).

273. J. H. Chick, C. R. I. I. I. Ruetz, J. C. Trexler, Spatial Scale And Abundance Patterns Of Large Fish Communities In Freshwater Marshes Of The Florida Everglades, Wetlands 24, 652–664 (2004).

274. A. Sreenivasan, in Developments in Hydrobiology, J. Barica, L. R. Mur, Eds. (Dr. W. Junk Publishiers, The Hague, 1982), vol. 2, pp. 271–277.

275. C. H. Fernando, The fishery potential of man-made lakes in southeast asia and some stratigies for its optimization, BIOTROP Aniv. Publ. , 25–38 (1980).

276. C. H. Fernando, The development of Ceylon’s fisheries. XI. The role of inland waters in relations to the development of Ceylon's Fisheries, and a note on the pearl oyster fishery, Bull. Fish. Res. Stn. Ceylon 17, 291–297 (1965).

277. N. S. Mattson, V. Balavong, H. Nilsson, S. Phounsavath, W. D. Hartmann, in Reservoir and Culture Based Fisheries: Biology and Management, C. D. De Silva, Ed. (2001), pp. 48–55.

278. D. Coates, River fisheries and ways of enhancing them: a case study of the Sepik River, Papua new guinea and discussion of the approach being adapted, FAO Fisheries Report 317 (1987).

279. G. J. J. S. & F. C.H., Fisheries in semi-arid northeast Brazil with special reference to the role of tilapias., Internationale Revue der gesamten Hydrobiologie 79, 77–94 (1994).

280. F. G. Araujo, B. C. T. Pinto, T. P. Teixeira, Longitudinal patterns of fish assemblages in a large tropical river in southeastern Brazil: evaluating environmental influences and some concepts in river ecology, Hydrobiologia 618, 89–107 (2009).

272

281. P. G. Bianco, P. Turin, Record of two established populations of Nile tilapia, Oreochromis niloticus, in freshwaters of northern Italy, Journal of Applied Ichthyology 26, 140–142 (2010).

282. G. N. Bwanika, B. Makanga, Y. Kizito, L. J. Chapman, J. Balirwa, Observations on the biology of Nile tilapia, Oreochromis niloticus L., in two Ugandan crater lakes, African Journal of Ecology 42, 93–101 (2004).

283. J. BURGER et al., Dominance of Tilapia mossambica, an Introduced Fish Species, in Three Puerto Rican Estuaries, Estuaries 15, 239–245 (1992).

284. F. G. Araújo, L. N. Santos, Distribution of fish assemblages in Lajes reservoir, Rio de Janeiro, Brazil., Brazilian journal of biology = Revista brasleira de biologia 61, 563–76 (2001).

285. D. William B, Classification of texas Reservoirs in Relations to Limnology and fish community Associations, Transactions of the American Fisheries Society , 511– 520 (1990).

286. H. S. Swingle, Comparative evaluation of two tilapias as pond fishes in alabama, Transactions Of The American Fisheries Society 89, 142–148 (1960).

287. D. Baird, Aspects of the gorwth and reproduction of Labeo capensis in the Caledon River, Journal of the limnological Society of South Africa 2, 25–28 (1976).

288. W. R. Courtenay Jr., J. E. Deacon, Status of introduced fishes in certain spring systems in southern Nevada, Great Basin Naturalist 42, 266–361 (1982).

289. W. R. Courtenay Jr., H. F. Sahlman, W. W. Miley II, Exotic fishes in fresh and brackish waters of florida, Biological Conservation 6, 292–302 (1974).

290. R. Riedel, L. Caskey, B. A. Costa-Pierce, Fish biology and fisheries ecology of the Salton Sea, California, Hydrobiologia 473, 229–244 (2002).

291. C. H. Faunce, R. Paperno, Tilapia-dominated fish assemblages within an impounded mangrove Ecosystem in East-Central Florida, WETLANDS 19, 126–138 (1999).

292. J. J. Schmitter-Soto, C. I. Caro, Distribution of tilapia, Oreochromis mossambicus (Perciformes: Cichlidae), and water body characteristics in Quintana Roo, Mexico, Rev. Biol. Trop. 45, 1257–1261 (1997).

273

293. S. S. De Silva, Status of the introduced cichlid Sarotherodon mossambicus (Peters) in the reservoir fishery of Sri Lanka: a management strategy and ecological implications, Aquaculture Research 16, 91–102 (1985).

294. P. L. Cadwallader, G. N. Backhouse, R. Fallu, Occurrence of exotic tropical fish in the cooloing pondage of a power station in temperate South-eastern Australia, Aust. J. Mar. Freshw. Res. 31, 541–546 (1980).

295. W. R. Courtenay Jr., J. E. Deacon, Fish Introduction in the American Southwest: A case history of Rogers Spring, Nevada, The Southwestern Naturalist 28, 221–224 (1983).

296. T. Chukajorn, O. Pawapootanon, Annual catch statistics of freshwater fishes taken from seven reservoirs in northeastern Thailand, Proceedings of the Indo-Pacific Fisheries Council 17, 206–221 (1976).

297. R. G. Hogg, Established exotic cichlid fishes in Dade county florida, Florida Scientist 39, 97–103 (1976).

298. A. G. Jhingran, in Exotic Aquatic Species in India. Proceedings of the Workshop on Exotic Aquatic Species in India, 25-26 April, 1988. Special Publication 1, M. M. Joseph, Ed. (Assian Fisheries Society, Indian Branch, Mangalore, India, 1989), pp. 19–25.

299. C. Hubbs et al., Survival and abundance of introdcued fishes near San Antonio, Texas, The Texas Journal of Science 30, 369–376 (1978).

300. S. H. Bowen, Digestion and assimilation of periphytic detrital aggregate by Tilapia mossambica, Transactions of the American Fisheries Society 110, 239–245 (1981).

301. S. H. Bowen, Differential habitat utilization by sexes of Sarotherodon mossambicus in Lake valencia, Venezuela: significance for fitness, Journal of Fish Biology 24, 115–121 (1984).

302. R. D. Germany, R. L. Noble, Population Dynamics of Blue tilapia in Trinidad Lake, Texas , Proc. 31st Ann. Conf. S.E. assoc. fish and wildl. Agencies 31, 412–417 (1977).

303. L. Mhlanga, J. Day, M. Chimbari, N. Siziba, G. Cronberg, Observations on limnological conditions associated with a fish kill of Oreochromis niloticus in Lake Chivero following collapse of an algal bloom, African Journal of Ecology 44, 199– 208 (2006). 274

304. B. E. Marshall, in Restoration and management of tropical eutrophic lakes, Reddy, Ed. (Science Publishers, UK, 2005), pp. 165–185.

305. R. J. McKay, in Distribution, Biology, and Management of Exotic Fishes, Courtenay, Stauffer, Eds. (the Johns hopkins university press, Baltimore, 1984), pp. 177–199.

306. J. S. Balriwa, The evolution of the fishery of Oreochromis niloticus (Pisces: Cichlidae) in Lake Victoria, Hydrobiologia 232, 85–89 (1992).

307. R. g. hogg, Environmental hazards posed by exoticfish species newly established in Florida., Environmental Conservation 1, 176 (1974).

308. J. A. St. Amant, Addition of Tilapia mossambica Peters to the California fauna, Califnornia Fish and Game 52, 54–55 (1966).

309. J. R. Stauffer Jr., S. E. Boltz, J. M. Boltz, Cold shock susceptibility of blue tilapia from the Susquehanna River, Pennsylvania, North American Journal of Fisheries Management 8, 329–332 (1988).

310. A. H. Arthington, D. R. Bluhdorn, Distribution, genetics, ecology and status of the introduced cichlid, Oreochromis mossambicus, in Australia, Mitt. Internat. Verein. Limnol. 24, 53–62 (1994).

311. A. H. Arthington, D. A. Milton, Reproductive biology, growth and age composition of the introduced Oreochromis mossambicus (Cichlidae) in two reservoirs, Brisbane, Australia, Environmental Biology of Fishes 16, 257–266 (1986).

312. C. J. Kleynhans, A. Hoffman, First record of Oreochromis macrochir (Boulenger, 1912) (Pisces:Cichlidae) from the Limpopo River in southern Africa, Southern African Journal of Aquatic Sciences 18, 104–107 (1992).

313. M. S. Peterson, W. T. Slack, N. J. Brown-Peterson, J. L. Mcdonald, Reproduction in Nonnative Environments: Establishment of Nile Tilapia, Oreochromis niloticus, in Coastal Mississippi Watersheds, Copeia 2004, 842–849 (2004).

314. M. S. Peterson, W. T. Slack, C. M. Woodley, The Occurrence Of Non-Indigenous Nile Tilapia, Oreochromis Niloticus (Linnaeus) In Coastal Mississippi, Usa: Ties To Aquaculture And Thermal Effluent, Wetlands 25, 112–121 (2005).

315. J. S. Balriwa, Lake Vicotira wetlands and the ecology of the Nile tilapia, Oreochromis niloticus LinneInternational Insititute for Infrastructual, Hydraulic and Environmental Engineering , 247 (1998).

275

316. A. H. Arthington, R. J. McKay, D. J. Russell, D. A. Milton, Occurence of the Introduced cichlid Oreochromis mossambicus (peters) in Queensland, Aust. J. Mar. Freshw. Res. 35, 267–272 (1984).

317. W. Skinner, Oreochromis aureus(Steindacher: Cichlidae), an exotic fish species, accidentally introduced to the Lower Susquehanna River, Pennsylvania, proceedings of the Pennsylvania Academy of Science 58, 99–100 (1984).

318. H. J. Schrader, Invasive alien fishes in south west africa/Namibia, South African national Scientific Programme Report 199, 35–40 (1985).

319. J. Buntz, C. S. Manooch III, Fisherman utilization of Tilapia aurea (Steindachner) in Lake Parker, Lakeland, Florida, Proceedings of the annual conference, Southeastern association of Game and Fish Commissioners 23, 312–319 (1970).

320. D. Coates, Environmental management implications of aquatic species introductions: A case study of fish introductions into the Sepik-Ramu basin, Papua New Guinea, Asian Journal of Environmental Management 1, 39–49 (1993).

321. R. S. Dial, S. C. Wainright, New distributional records for non-native fishes in Flordia, Florida Scientist 46, 1–8 (1983).

322. A. S. Sarnita, in Fish and fisheries of lakes and reservoirs in Southeast Asia and Africa, W. L. T. van Densen, M. J. Morris, Eds. (Westbury Publishing, Otley, UK, 1999), pp. 143–150.

323. S. Y. Lin, Fish culture projects in haiti and the Dominican Republic, FAO Fisheries Bulletin 6, 230–234 (1953).

324. W. R. Courtenay, in Proceedings, 1986 conference science national parks, VOL 5 Management of exotic species in natural communities, L. K. J. Thomas, Ed. (US Park Service and George Wright Society, Washington DC, 1986), pp. 3–10.

325. A. S. Mendis, in Indo-pacific Fisheries Council Proceedings 17th Session Colombo, Sri Lanka 27 October-5 November 1976, I.-P. F. C. IPFC, Ed. (FAO Regional Office for Asia and the Far east, Bangkok, 1977), pp. 247–257.

326. K. B. C. Pushpalatha, J. Chandrasoma, Culture based fisheries in minor perennial reservoirs in Sri Lanka: Varibility in production, stocked species and yeild implications, Journal of Applied Ichthyology 26, 99–104 (2010).

276

327. W. S. Devick, in New directions in research, management and conservation of Hawaiian freshwater stream ecosystems. Proceedings of the 1990 Symposium on Freshwater Stream Biology and Fisheries Management, (Division of Aquatic Resources, Hawaii Department of Land and Natural Resources., 1992), pp. 189– 213.

328. J. S. Pet, J. W. M. Wijsman, P. J. Mous, M. A. M. Machiels, Characteristics Of A Sri- Lankan Reservoir Fishery And Consequences For The Estimation Of Annual Yield, Fisheries Research 24, 9–33 (1995).

329. D. R. Bluhdorn, A. H. Arthington, Somatic characteristics of an Australian population of Oreochromis mossambicus (Pisces: Cichlidae), Environmental Biology of Fishes 29, 277–291 (1990).

330. A. Getabu, Growth parameters and total mortality in Oreochromis niloticus (Linnaeus) from Nyanza Gulf, Lake Victoria, Hydrobiologia 232, 91–97 (1992).

331. L. Bunkley-Williams et al., The South American Sailfin Armored Catfish , Liposarcus multiradiatus ( Hancock ), a New Exotic Established in Puerto Rican Fresh Waters, CARIBBEAN JOURNAL OF SCIENCE 30, 90–94 (1994).

332. S. De Silva, J. Chandrasoma, Reproductive-Biology Of Sarotherodon-Mossambicus, An Introduced Species, In An Ancient Man-Made Lake In Sri-Lanka, Environmental Biology Of Fishes 5, 253–259 (1980).

333. G. B. Concepcion, S. G. Nelson, Effects of a Dam and Reservoir on the distributions and densities of macrofaunca in tropcial streams and Guam (Mariana Islands0, Journal of Freshwater Ecology 14, 447–454 (1999).

334. S. Piazzini et al., A tropical fish community in thermal waters of southern Tuscany, Biological Invasions 12, 2959–2965 (2010).

335. G. Grammer, W. Slack, M. Peterson, M. Dugo, Nile tilapia Oreochromis niloticus (Linnaeus, 1758) establishment in temperate Mississippi, USA: multi-year survival confirmed by otolith ages, Aquatic Invasions 7, 367–376 (2012).

336. R. Mert, E. Cicek, Range expansion of introduced Tilapia species (Oreochromis niloticus, L. 1758, Cichlidae) in Turkey, Journal of Animal and veterinary Advances2 9, 1753–1756 (2010).

337. D. J. Russell, P. a Thuesen, F. E. Thomson, Reproductive strategies of two invasive tilapia species Oreochromis mossambicus and Tilapia mariae in northern Australia., Journal of fish biology 80, 2176–97 (2012). 277

338. W. Courtenay, D. Hensley, J. Taylor, J. McCann, in Zoogeography of North American freshwater fishes, C. H. Hocutt, E. O. Wiley, Eds. (John Wiley & Sons, 1986), pp. 675–698.

339. P. L. Shafland, Exotic Fishes of Florida - 1994, Reviews in fisheries science1 4, 101– 122 (1996).

340. W. Brown, First record of of the african mouthbreeder Tilapia mossambica Peters in Texas., Texas Journal of Science 13, 352–354 (1961).

341. J. H. Finucane, G.R.Rinckey, A study of the African Cichlid Tilapia Heudeloti Dumeril, in Tampa Bay , Florida, Southeastern Association of Game and Fish Commissioners 18, 259–269 (1964).

342. F. G. Hoover, J. A. St. Amant, Establishment of Tilapia mossambica Peters in Bard Valley, Imperial County, California, California Fish Game 56, 70 (1970).

343. V. G. Springer, J. H. Finucane, The African cichlid, Tilapia heudeloti Dumeril, in the commericial fish catch of Florida., Transactions of the American Fisheries Society 92, 317–318.

344. A. S. Mendis, The role of man-made lakes in the development of freshwater fisheries in Sri Lanka, Proceedings of the indo-pacific fisheries council 17, 247– 254 (1964).

345. M. J. Penrith, Otkikoto lake, South west africa annual , 138–139 (1978).

346. A. Yashouv, On the possibility of mixed cultivation of various tilapia with carp, Bamidgeh 10, 21–29 (1958).

347. I. J. de Moor, M. N. Bruton, Atlas of alien and translocated indigenous aquatic animals in southern Africa (Foundation for Research Development Council for scientific and industrial research, Pretoria, 1988), p. 310.

348. R. G. Doupe, M. J. Knott, J. Schaffer, D. W. Burrows, Investigational piscivory of some juvenile Australian freshwater fishes by the introduced Mozambique tilapia Oreochromis mossambicus, Journal of Fish Biology 74, 2386–2400 (2009).

349. A. Yashouv, A. Halevy, Studies on growth and productiveity of Tilapia aurea and its hybrid “gan shmuel” in experimental ponds at Dor, Bamidgeh 19, 16–22 (1967).

350. I. J. De Moor, Case studies of the invasion by four alien fish species (Cyprinus carpio, Micropterus salmoides, Oreochromis macrochir and O-mossambicus) of 278

freshwater ecosystems in southern Africa, Transactions of the Royal Society of South Africa 51, 233–255 (1996).

279

TABLE A. 2.

THE LOGISTIC REGRESSION MODELS OF ECOLOGICAL EFFECTS OF TILAPIA

INTRODUCTION FOR ALL ECOLOGICAL EFFECT CATEGORIES COMBINED

MODEL SELECTION, CONTROL AND YEAR PARAMETERS

Contro ID Model AIC dAIC l Year L1 143.10 0.00 NA Effects~1 L2 144.85 1.76 -0.01 Effects~1+ year L3 144.98 1.88 -0.05 -0.01 Effects~1+ (1|Species) L4 145.04 1.94 -0.12 NA Effects~1+ Q.C L5 145.10 2.00 -0.05 -0.01 Effects~1+ (1|Region) L6 146.84 3.74 -0.06 -0.01 Effects~1+ Q.C+year L7 146.85 3.75 -0.05 -0.01 Effects~1+ year+(1|Species) L8 146.85 3.76 -0.05 -0.01 Effects~1+ year+(1|Region) L9 146.97 3.87 -0.05 -0.01 Effects~1+ Q.C+(1|Species) L10 146.98 3.88 -0.05 -0.01 Effects~1+ (1|Species)+(1|Region) L11 147.04 3.94 -0.05 -0.01 Effects~1+ Q.C+(1|Region) L12 148.84 5.74 -0.05 -0.01 Effects~1+ Q.C+year+(1|Species) L13 148.84 5.74 -0.05 -0.01 Effects~1+ Q.C+year+(1|Region) L14 148.85 5.75 -0.05 -0.01 Effects~1+ year+(1|Species)+(1|Region) L15 148.97 5.87 -0.05 -0.01 Effects~1+ Q.C+(1|Species)+(1|Region) L16 150.84 7.74 -0.05 -0.01 Effects~1+ Q.C+year+(1|Species)+(1|Region)

280

TABLE A. 3.

THE LOGISTIC REGRESSION MODELS RESULT OF ECOLOGICAL EFFECTS OF TILAPIA

INTRODUCTION FOR ALL ECOLOGICAL EFFECT CATEGORIES COMBINED

SPECIES RESULTS

ID O.aur O.moss O.nil Oreo.nei T.mar T.S.nei T.zill L1 L2 L3 0.06 -0.02 -0.24 0.09 0.02 -0.02 0.09 L4 L5 L6 L7 0.02 0.00 -0.07 0.03 0.01 -0.01 0.02 L8 L9 0.05 -0.02 -0.21 0.08 0.02 -0.02 0.08 L10 0.06 -0.02 -0.24 0.09 0.02 -0.02 0.09 L11 L12 0.00 0.00 -0.02 0.01 0.00 0.00 0.01 L13 L14 0.02 0.00 -0.07 0.03 0.01 -0.01 0.02 L15 0.05 -0.02 -0.21 0.08 0.02 -0.02 0.08 L16 0.00 0.00 -0.02 0.01 0.00 0.00 0.01

281

TABLE A. 4

THE LOGISTIC REGRESSION MODELS RESULT OF ECOLOGICAL EFFECTS OF TILAPIA

INTRODUCTION FOR ALL ECOLOGICAL EFFECT CATEGORIES COMBINED

REGION RESULTS

ID Africa Asia Europe NeoTropics Oceania Sri.Lanka USA 1 2 3 4 5 0 0 0 0 0 0 6 7 8 0 0 0 0 0 0 9 10 0 0 0 0 0 0 11 0 0 0 0 0 0 12 13 0 0 0 0 0 0 14 2E-12 -1E-12 7E-13 1E-12 -2E-12 -3E-13 15 1E-13 -6E-14 3E-14 6E-14 -2E-13 -2E-14 16 2E-12 -1E-12 7E-13 9E-13 -2E-12 -2E-13

282

APPENDIX B:

SUPPLEMENT TO CHAPTER 4:

THE POTENTIAL TRADE-OFF BETWEEN ARTISANAL FISHERIES PRODUCTION AND

HYDROELECTRICITY GENERATION ON THE KAFUE RIVER, ZAMBIA

B.1 Modeling the impact of flood regime on fishery production

Gillnet catch per unit effort (CPUE) was compiled from the literature and

Zambian department of Fisheries (DoF) records for the years 1954-2010. We assumed that the net and mesh sizes for the years before 1980 which were not reported by

Williams (1960), CSO (1970, 1978, 1984) and Kapetsky (1974) were the same as those reported in Everett (1974) for the year 1970. CPUE for 1976 (Dudley & Scully, 1980) was converted to kilograms by multiplying fish number by the average weight of each fish species in 1985 (Mung’omba, 1992). For the 1980 to 2006 post-dam era, the DoF recorded experimental gillnet surveys on the Kafue (Nyimbili, 2006). In a standard DoF gillnet fleet, they used top-set 90m2 (stretched) multifilament gillnets ranging from 25 to 140 mm stretched mesh in 13 mm increments and hung to 50%, for a hanging length of 45 m each. We supplemented these data with collections in 2008 and 2010, following the standard DoF protocol.

283

We used a multivariate auto-regressive state-space (MARSS) model to fit time series of experimental CPUE, fisheries effort and water regime data to population growth models using maximum likelihood estimation. This state-space approach allowed the simultaneous estimation of the unobserved state process of fish abundance

(CPUE) and fisheries effort (meters of gillnet) with observation error and including the effect of water level as a covariate to the CPUE process.

The univariate auto-regressive (1) growth model for a population time series with effort, E, and water, W, takes the form

N  N *exp u  b N  b E  c W   t t1  1 t1 2 t1 1 t1 t  (S1)

-1 -1 Where Nt is the experimental gillnet catch per unit effort (CPUE kg*m *night ) in year t, and b and c are parameters to be estimated, with u the intrinsic population growth rate, and σt is random, independent, identically normally distributed observation error.

Taking the natural log of Nt such that Xt = ln(Nt) yields a linear equation which assumes

Gompertz growth,

X  u  (b 1)X b E  cW  t 1 t1 2 t1 t1 t (S2)

In multivariate state-space, the state and observation processes are arranged into a system of equations in matrix form including covariates (Holmes & Ward, 2011).

284

 (v)   (v)  (v)   (v)    (v)  x B C x u  Q 0            wt ,wt ~ MVN 0,   (S3a) x(cv)   0 B(cv) x(cv)  u(cv)    0 Q(cv)   t   t1       (v)   (v)  (v)   (v)    (v)  y Z 0 x a  R 0            vt , vt ~ MVN 0,   (S3b) y(cv)   0 Z(cv) x(cv)  a(cv)    0 R(cv)   t   t     

Where xi are state vectors at time t defined by a state process equation

(Equation S3a) with i superscripts representing estimated variates (v) CPUE and effort, or the covariates (cv; in this case the CCA score representing water regime). Bi are matrices which correspond to parameters b in the univariate case, while C corresponds

i to covariate parameters c and u are growth rates. Process error, wt, is modeled as a multivariate normal distribution with variance-covariance matrix of process

(environmental) stochasticity Q. Vectors of observed data yi are related to the process states through the observation process equation (Equation S3b). Z are identity matrices which associate one or more observations to unobserved state processes, with a parameters which scale multiple observations of the same state and multivariate normal observation error vt with R variance-covariance matrixes.

In total we specified 3 state processes for CPUE, effort, and water (Equation S4).

We demeaned and standardized all data and used the resulting z-scores for estimation.

 xcpue  b1 b3 b5 ucpue   q1 0 0  effort    effort   x   b2 b4 0 Xt1  u   MVN0, 0 q2 0 (S4a)  water     x  0 0 1  0  0 0 1  t   t

285

 cpue  1 0 0  0   cpue  y  r 0 0 0   gill      gill  y  0 1 0  0   0 r 0 0  (S4b)  Xt  boats  MVN0,   yboats 0 1 0 a  0 0 rboats 0         water 0 0 1 0   y       0 0 0 1e -5t

b1=CPUE density dependence, b2= effect of CPUE on fishing effort, b3= effect of harvest on CPUE, b4= density dependence of effort, b5= effect of water regime on CPUE, and dashes represent

The process errors, wt were modeled as a multivariate normal distribution with mean 0 and variance-covariance matrix Q. We assumed that processes do not co-vary and fixed

CCA score variance at unity to give the process model the flexibility to exactly equal the true covariate values; thus the covariates processes are not modeled but exactly specified (Holmes & Ward, 2011). Initial results indicated estimated process errors less than 1e-15 in all cases, leading to instability in the estimation algorithm. We therefore fixed process error for CPUE and effort at a trivially small value, 1e-5. Observation processes, y, consist of the observed data with measurement error (Equations S3b and

S4b). We specified two observation vectors for the effort process, gillnet meters and boat counts, where boats counts are linearly scaled to gillnets by estimating the number of gillnets meters per boat, aboats in Equation S4b.

B.2 Estimating the Impact of Flood Regime on Hydroelectric Generating Capacity

We measure the impact of the Itezhi-tezhi Dam on hydroelectric generating capacity at the Kafue Gorge generating station. In order to measure the impact of the

Itezhi-tezhi Dam on hydroelectric generating capacity, we apply the widely used

286

Muskingum method of flood routing, or predicting downstream flows based on known upstream flows (McCarthy, 1938) (Flood routing is also sometimes known as channel routing). Other studies have extensively modeled the hydrological system of the Kafue

Flats and developed sophisticated systems for informing dam operation in real-time response to contemporaneous rainfall information (Fromelt, 2009; Meier, 2010a b).

This simpler method has the advantages of flexibility and transparency, at the cost of some statistical efficiency. We also calibrate our model using longer time series of daily discharge measurements than has previous work.

To determine the depth and velocity of the flood wave at any point in time, we use two ordinary differential equations. The first equation is

dV(t)/dt = I(t) – O(t) (S5) where V(t) is the volume of water stored in the given reach (river segment) at time t, I(t) is the flow into the reach, and O(t) is the flow out of the reach. This equation governs conservation of mass. For our modeling purposes, we ignore losses to evaporation and seepage to groundwater, although some authors suggest that these losses can be substantial (Anon, 2007). We also ignore contributions from runoff between the two dams, since such runoff is marginal in comparison to the Kafue‘s discharge and in any case unlikely to be substantially affected by the river‘s flood regime. We use daily discharge data at the Itezhi-tezhi Dam (Error! Reference source not found.) to represent

287

inflows into the reach, and discharge at Kafue Gorge Dam represents outflows (Figure

S1). The latter measure is influenced by flow through the turbines, but for much of the year water must be spilled at the Kafue Gorge Dam due to excess supply, such that during these spillage events, discharge through Kafue Gorge closely approximates the counterfactual, natural flow.

The second differential equation governs conservation of momentum. This equation necessarily varies from stream to stream, depending on the shape of the terrain. In order to close the equation, it must define V. In the simple case of straight riverbed with polygonal, parallel cross-sections and linear edges, the riverbed can be modeled as a frusta, with the following equation for volume:

 A A  A A  (S6) V  h 1 2 1 2     3  where h is the height of the frusta and A1, A2 are the areas of the cross-sections at either end. As A1 and A2 are to remain unchanged parameters, the volume can also be modeled as equivalent to a prism whose base is weighted average of the cross-sections:

V = h [X A1 + (1 – X) A2], (S7)

288

where 0 < X < 1. Since A2 is typically greater than A1, X must be less than one-half. If we assume that the flow is proportional to the area of its cross section, then Equation (S7) becomes

V = K [X I + (1 – X) O], (S8) where K is a constant. Given these assumptions, K can be interpreted as the time it takes for a wave to travel from one end of the reach to the other, and reflects the rate of decrease of the height of a wave as it travels through the reach. During a flood, the volume stored in the reach necessarily increases. The Muskingum method assumes that this volume is a weighted linear function of both the inflow rate and the outflow rate. X varies their respective weights, and K scales them to the volume of the reach.

Substituting (equation S8) into (equation S5) yields the differential equation

dO dI (S9) O  K1 X   I  KX dt dt

This continuous equation is then discretized so that it can be estimated empirically, with small time step Δt ≡ t2 - t1:

O  O  O  O  I  I  I  I  (S10) 1 2  K(1 X ) 2 1   1 2  KX  2 1  2  t  2  t 

289

Given an initial value of outflow O1, we would like to be able to predict subsequent outflows using only new inflow measurements. As such, we solve for O2 in terms of O1, I1, and I2:

1  K(1 X )O2  1  K(1 X )O1  I1  I2  I2  I1  (S11) O2      O1      KX   2  t  2  t  2  t 

1  1 K(1 X )   1 KX   1 KX   (S12) O2    O1    I2    I1 1  K(1 X )   2 t   2 t   2 t       2  t 

We then calibrate the model. K, the travel time of the flood wave, is calculated to be 54 days, the lag which yields the maximum correlation between the discharges at Itezhi- tezhi and Kafue Gorge. X is taken to be 0.2, a typical value for most rivers (Hornberger,

1998). This calibration yields predictions that fit the wet seasons well, but which do not over fit the turbine flows. Figure S2 shows the flows simulated using the estimated values of K and X. We take these predictions to be estimates of the discharge entering the Kafue Gorge reservoir.

We then similarly estimate counterfactual discharge for Itezhi-tezhi, using discharge at the Hook Bridge monitoring station (Figure S2) to estimate the inflows into the reservoir at Itezhi-tezhi. Since Hook Bridge is just upstream from the Itezhi-tezhi

Reservoir, the pre-dam time series of the daily discharges at Itezhi-tezhi and Hook

Bridge are highly correlated, especially during the wet season. We put this

290

counterfactual Itezhi-tezhi discharge through the simulation described above, constructing a counter factual “no-dam” scenario of what the Kafue Gorge reservoir inflows would be if the Itezhi tezhi did not exist. We take the annual average for each simulated Kafue Gorge hydrograph as an expected difference in flow attributed to the

Itezhi-tezhi Dam‘s influence on the Kafue River‘s flood regime (Figure 4.5).

The KG generating station can operate until the reservoir is emptied, and even then it can use the reduced inflow directly, thus, reduced discharges into the reservoir at KG do not necessarily imply reduced generating capacity. We compute the minimum reduction of power output as that which uses the full capacity of the reservoir, solving the following constrained optimization problem:

(S13) ∫ ( )

where G is the outflow through the turbines (m3d-1), I(t) is discharge entering the

3 -1 KG reservoir (m d ), and time t is measured in days. The value ta represents the date when the reservoir begins to drain, and tb is the date at which the reservoir is completely empty and begins to refill.

B.3 References

Anon (2007) Strategy for Flood Management for Kafue River Basin, Zambia. Associated Programme Flood Management. Lusaka, Zambia.

291

CSO (1970) Fisheries Statistics (Natural waters) 1969. Central Statistical Office, Zambia

CSO (1978) Fisheries Statistics 1972 (Natural Waters): Volume I. Central Statistical Office, Zambia

CSO (1984) Fisheries Statistics 1973 (Natural Waters): Volume 1. Central Statistical Office, Zambia

Dudley R.G. & Scully R.J. (1980) Changes in experimental gillnet catches from the Kafue Floodplain, Zambia, since construction of the Kafue Gorge Dam. Journal of Fish Biology, 16, 521-537.

Everett G.V. (1974) An Analysis of the 1970 commercial fish catch in three areas of the Kafue Flodplain. The African Journal of Tropical Hydrobiology and Fisheries 3, 147-159.

Fromelt A. (2009) Hydrological modeling of the Kafue watershed using remote sensing data. Thesis, ETH University, Zurich

Holmes E.E. & Ward E.J. (2011) Analysis of multivariate timeseries using the MARSS package, version 2.8. Mathematical Biology Program Northwest Fisheries Science Center, Seattle, WA.

Hornberger G.M. (1998) Elements of Physical Hydrology. The Johns Hopkins University Press, Balitmore.

Kapetsky J.M. (1974) Growth, Mortality, and Production of Five Fish Species of the Kafue River Floodplain, Zambia. PhD Thesis, The University of Michigan.

McCarthy G.T. (1938) The unit hydrograph and flood routing. In: Conference of the North Atlantic Division, US Corps of Engineers. US Corps of Engineers, New London, CT.

Meier P. (2010a) Remote Sensing Applications in the Kafue River Basin, Zambia. Working Paper.

Meier P. (2010b) Remote Sensing for Water Resources Management.

Mung’omba J. (1992) Kafue Flood Plain Gillnet Survey Data and Summary Tables & Figures. Zambian Department of Fisheries, Chilanga, Zambia.

Nyimbili B. (2006) An evaluation of fish population changes in the Kafue Flats floodplain fishery of Zambia from 1980 to 2005. Ms Thesis, University of Bergen, Norway.

292

Williams N.V. (1960) A Review of the Kafue River Fishery. Rhodesia Agricultural Journa,l 57, 86-92.

293

TABLE B. 1

MESH SIZES AND SAMPLING LOCATIONS ON THE KAFUE RIVER, ZAMBIA

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 67,

78, Kapetsky, 1954 89, Unspecified (1,2) 1974 294 100, 110 67, 78, Kapetsky, 1955 89, Unspecified (1,2) 1974 100, 110 67, 78, Kapetsky, 1956 89, Unspecified (1,2) 1974 100, 110

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 1957 67, 78, Kapetsky, 89, Unspecified (1,2) 1974 100,

110 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, Williams 1958 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, Unspecified (2) 1960 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 295 110 110 110 110 110 110 110 110 110 110 110 110 67, 78, Kapetsky, 1959 89, Unspecified (1,2) 1974 100, 110 67, 78, Kapetsky, 1961 89, Unspecified (1,2) 1974 100, 110

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 67, 78, Kapetsky, 1962 89, Unspecified (1,2) 1974 100, 110 67, 1963 78, Kapetsky, 89, Unspecified (1,2) 1974

296 100, 110 67, 78, Kapetsky, 1964 89, Unspecified (1,2) 1974 100, 110 67, 78, Kapetsky, 1965 89, Unspecified (1,2) 1974 100, 110

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 1966 67, 78, Kapetsky, 89, Unspecified (1,2) 1974 100,

110

67, 78, Kapetsky, 1967 89, Unspecified (1,2) 1974 297 100, 110 Lusaka District, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, Mazabuka 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, district, 1968 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, CSO, 1970 (2) Mumbwa 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, district, 110 110 110 110 110 110 110 110 110 110 110 110 Namwala district

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note Lusaka 1969 District, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, Mazabuka 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, district, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, CSO, 1970 (2) Mumbwa 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

district, 110 110 110 110 110 110 110 110 110 110 110 110 Namwala district 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 298 Maala, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, Chunga Everett, 1970 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, Lagoon, 1974 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, Chinyanya 110 110 110 110 110 110 110 110 110 110 110 110 67, 67, 67, 67, Maala, 78, 78, 78, 78, Chunga Everett, 1971 89, 89, 89, 89, Lagoon, 1974 100, 100, 100, 100, Chinyanya 110 110 110 110

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 1972 Lusaka District, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, Mazabuka 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, district, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, CSO, 1978 (2) Mumbwa 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, district, 110 110 110 110 110 110 110 110 110 110 110 110 Namwala district

Lusaka 299 District, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, Mazabuka 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, district, 1973 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, CSO, 1984 (2) Mumbwa 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, district, 110 110 110 110 110 110 110 110 110 110 Namwala district 1974

1975

59, Chunga Dudley & 1976 74, Lagoon, Scully, 1980 90, Nyimba

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 106, 121

1977

1978

1979

63, 63,

1980 76, 76, Chunga DoF 89, 89, Lagoon, database, 300 102, 102, Namwala unpublished 114 114 63, 76, Chunga DoF 1981 89, Lagoon, database,

102, Nyimba unpublished 114 63, 63, 63, 76, 76, 76, Chunga DoF 1982 89, 89, 89, Lagoon, database,

102, 102, 102, Nyimba unpublished 114 114 114

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 63, 63, 63, 63, 63, Chunga 76, 76, 76, 76, 76, DoF Lagoon, 1983 89, 89, 89, 89, 89, database, Namwala, 102, 102, 102, 102, 102, unpublished Nyimba 114 114 114 114 114 1984

63, 63, 63, 63,

76, 76, 76, 76, Chunga DoF

1985 89, 89, 89, 89, Lagoon, database, 301 102, 102, 102, 102, Namwala unpublished 114 114 114 114

63, 63, 63,

76, 76, 76, Chunga DoF

1986 89, 89, 89, Lagoon, database,

102, 102, 102, Nyimba unpublished

114 114 114

1987

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 1988 63, 63, 63, 63, 63, 76, 76, 76, 76, 76, Chunga DoF

89, 89, 89, 89, 89, Lagoon, database,

102, 102, 102, 102, 102, Nyimba unpublished 114 114 114 114 114

63, 63, 63, 63, Chunga 76, 76, 76, 76, DoF 102, Lagoon, 1989 89, 89, 89, 89, database, 114 Namwala, 102, 102, 102, 102, unpublished 302 Nyimba 114 114 114 114

1990

63, 63, 63, 76, 76, 76, Chunga DoF 1991 89, 89, 89, Lagoon, database,

102, 102, 102, Nyimba unpublished 114 114 114

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 1992 63, 63, 63, 76, 76, 76, Chunga DoF 89, 89, 89, Lagoon, database,

102, 102, 102, Nyimba unpublished 114 114 114 63, 63, 63, 63, 63, 63, Chunga 76, 76, 76, 76, 76, 76, DoF Lagoon, 1993 89, 89, 89, 89, 89, 89, database, Namwala,

303 102, 102, 102, 102, 102, 102, unpublished Nyimba 114 114 114 114 114 114 63, 63, 63, 63, 63, 63, 63, Chunga 76, 76, 76, 76, DoF 89, 89, 89, Lagoon, 1994 89, 89, 89, 89, database, 102, 102, 102, Namwala, 102, 102, 102, 102, unpublished 114 114 114 Nyimba 114 114 114 114 63, 63, Chunga DoF 89, 1995 76, Lagoon, database, 102, 89 Namwala unpublished 114

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 1996 63, 63, 63, 63, 76, 76, 76, 76, Chunga DoF 89, 89, 89, 89, Lagoon, database,

102, 102, 102, 102, Nyimba unpublished 114 114 114 114 63, 63, 63,

76, 76, 76, Chunga DoF 1997 89, 89, 89, Lagoon, database,

304 102, 102, 102, Nyimba unpublished 114 114 114 63, 63, 63, 63, 76, 76, 76, 76, Chunga DoF 1998 89, 89, 89, 89, Lagoon, database,

102, 102, 102, 102, Nyimba unpublished 114 114 114 114 63, 63, 76, 76, Chunga DoF 1999 89, 89, Lagoon, database,

102, 102, Nyimba unpublished 114 114 2000

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 2001

2002 63, 76, Chunga DoF 89, Lagoon, database,

102, Nyimba unpublished 114 63, 63, 63, 63, 63, 63, 76, 76, 76, Chunga DoF 76, 76, 76,

305 2003 89, 89, 89, Lagoon, database, 89, 89, 89, 102, 102, 102, Nyimba unpublished 114 114 114 114 114 114 2004

63, 63, 63, 63, Chunga 76, 76, 76, 76, DoF Lagoon, 2005 89, 89, 89, 89, database, Namwala, 102, 102, 102, 102, unpublished Nyimba 114 114 114 114 2006

2007

TABLE B. 1. CON’T

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Locations Source Note 2008 Chinyanya, 63, 63, Chunga 76, 76, Lagoon, DoF 89, 89, Kasaka, database,

102, 102, Mazabuka, unpublished 114 114 Mutukuzhi,

Namwala 2009

306 63, 63, Chinyanya, 76, 76, Chunga DoF 2010 89, 89, Lagoon, database, 102, 102, Mazabuka, unpublished 114 114 Nyimba NOTE: (1) Only a total for the year is given, placed in Jan for convience. (2) Mesh sizes used assumed same as those reported in Everett 1974

TABLE B. 2.

FISH SPECIES REPORTED IN EACH DATASET COMPILED

Years Species Family Source Note 1954-7 & 1959-67 Unspecified total Kapetsky 1974 1958 Oreochromis andersonii Cichlidae Williams 1960 1958 Oreochromis macrochir Cichlidae Williams 1960 Sargochromis 1958 codringtonii Cichlidae Williams 1960 Serranochromis 1958 angustceps Cichlidae Williams 1960 Serranochromis 1958 macrocephalus Cichlidae Williams 1960 Serranochromis 1958 robustus Cichlidae Williams 1960 Serranochromis 1958 thumbergi Cichlidae Williams 1960 1958 Tilapia rendalli Cichlidae Williams 1960 1958 Tilapia sparrmanii Cichlidae Williams 1960 1958 Clarias gariepinus Clariidae Williams 1960 1958 Clarias ngamensis Clariidae Williams 1960 1958 Labeo cylindricus Cyprinidae Williams 1960 Marcusenius 1958 macrolepidotus Mormyridae Williams 1960 1958 Mormyrus spp. Mormyridae Williams 1960 1958 Schilbe intermedius Schilbeidae Williams 1960 1968- Oreochromis andersonii DoF 1969 Fisheries 1969 Cichlidae Statistics 1968- Oreochromis macrochir DoF 1969 Fisheries 1969 Cichlidae Statistics 1968- DoF 1969 Fisheries 1969 Sargochromis carlottae Cichlidae Statistics

307

TABLE B. 2. CON’T

Years Species Family Source Note 1968- Sargochromis DoF 1969 Fisheries 1969 codringtonii Cichlidae Statistics 1968- Serranochromis DoF 1969 Fisheries 1969 angustceps Cichlidae Statistics 1968- Serranochromis DoF 1969 Fisheries 1969 macrocephala Cichlidae Statistics 1968- Serranochromis DoF 1969 Fisheries 1969 robustus Cichlidae Statistics 1968- Serranochromis DoF 1969 Fisheries 1969 thumbergi Cichlidae Statistics 1968- Tilapia rendalli DoF 1969 Fisheries 1969 Cichlidae Statistics 1968- Tilapia sparrmanii DoF 1969 Fisheries 1969 Cichlidae Statistics 1968- DoF 1969 Fisheries 1969 Clarias gariepinus Clariidae Statistics 1968- DoF 1969 Fisheries 1969 Clarias ngamensis Clariidae Statistics 1968- DoF 1969 Fisheries 1969 Labeo cylindricus Cyprinidae Statistics 1968- DoF 1969 Fisheries 1969 Hepsetus odoe Hepsetidae Statistics 1968- DoF 1969 Fisheries 1969 Synodontus spps Mochokidae Statistics 1968- Marcusenius DoF 1969 Fisheries 1969 macrolepidotus Mormyridae Statistics 1968- DoF 1969 Fisheries 1969 Mormyrus lacerda Mormyridae Statistics 1968- DoF 1969 Fisheries 1969 Schilbe intermedius Schilbeidae Statistics 1970- 1971 Haplochromis spps Cichlidae Everett 1974 1970- Oreochromis andersonii 1971 Cichlidae Everett 1974 1970- Oreochromis macrochir Cichlidae Everett 1974 1971

308

TABLE B. 2. CON’T

Years Species Family Source Note 1970- Serranochromis 1971 angustceps Cichlidae Everett 1974 1970- Serranochromis 1971 macrocephala Cichlidae Everett 1974 1970- Serranochromis 1971 robustus Cichlidae Everett 1974 1970- Serranochromis 1971 thumbergi Cichlidae Everett 1974 1970- Tilapia rendalli 1971 Cichlidae Everett 1974 1970- Tilapia sparrmanii 1971 Cichlidae Everett 1974 1970- 1971 Clarias gariepinus Clariidae Everett 1974 1970- 1971 Clarias ngamensis Clariidae Everett 1974 1970- 1971 Barbus spps. Cyprinidae Everett 1974 1970- 1971 Labeo molybdinus Cyprinidae Everett 1974 1970- 1971 Hepsetus odoe Hepsetidae Everett 1974 1970- 1971 Synodontus spps Mochokidae Everett 1974 1970- Marcusenius 1971 macrolepidotus Mormyridae Everett 1974 1970- 1971 Mormyrus lacerda Mormyridae Everett 1974 1970- 1971 Schilbe intermedius Schilbeidae Everett 1974 1972 Brycinus grandisquamis Alestidae CSO 1978 (1) 1972 Brycinus lateralis Alestidae CSO 1978 (1) 1972 Brycinus peringueyi Alestidae CSO 1978 (1) 1972 Micralestes acutidens Alestidae CSO 1978 (1) Rhabdalestes 1972 rhodesiensis Alestidae CSO 1978 (1) 1972 Ctenopoma multispine Anabantidae CSO 1978 (1) 309

TABLE B. 2. CON’T

Years Species Family Source Note Haplochromis 1972 adolphifrederici Cichlidae CSO 1978 (1) 1972 Oreochromis andersonii Cichlidae CSO 1978 (1) 1972 Oreochromis macrochir Cichlidae CSO 1978 (1) Pseudocrenilabrus 1972 philander Cichlidae CSO 1978 (1) 1972 Sargochromis carlottae Cichlidae CSO 1978 (1) Sargochromis 1972 codringtonii Cichlidae CSO 1978 (1) 1972 Sargochromis giardi Cichlidae CSO 1978 (1) Serranochromis 1972 macrocephala Cichlidae CSO 1978 (1) Serranochromis 1972 robustus Cichlidae CSO 1978 (1) Serranochromis 1972 thumbergi Cichlidae CSO 1978 (1) 1972 Tilapia rendalli Cichlidae CSO 1978 (1) 1972 Tilapia sparrmanii Cichlidae CSO 1978 (1) 1972 Citharines Citharinidae CSO 1978 (1) 1972 Clarias gariepinus Clariidae CSO 1978 (1) 1972 Clarias ngamensis Clariidae CSO 1978 (1) 1972 Clarias stappersii Clariidae CSO 1978 (1) 1972 Clarias theodorae Clariidae CSO 1978 (1) 1972 Barbus spps. Cyprinidae CSO 1978 (1) 1972 Barbus spps. Cyprinidae CSO 1978 (1) 1972 Labeo annectens Cyprinidae CSO 1978 (1) 1972 Labeo cylindricus Cyprinidae CSO 1978 (1) 1972 Hepsetus odoe Hepsetidae CSO 1978 (1) Mastacembelus Mastacembelida 1972 frenatus e CSO 1978 (1) 1972 Synodontus spps Mochokidae CSO 1978 (1) Marcusenius 1972 macrolepidotus Mormyridae CSO 1978 (1) 1972 Mormyrus lacerda Mormyridae CSO 1978 (1) 1972 Aplocheilichthys spps Poeciliidae CSO 1978 (1) 1972 Schilbe intermedius Schilbeidae CSO 1978 (1) 1973 Oreochromis andersonii Cichlidae CSO 1984 (2) 310

TABLE B. 2. CON’T

Years Species Family Source Note 1973 Oreochromis macrochir Cichlidae CSO 1984 (2) 1973 Sargochromis carlottae Cichlidae CSO 1984 (2) Sargochromis 1973 codringtonii Cichlidae CSO 1984 (2) Serranochromis 1973 angustceps Cichlidae CSO 1984 (2) Serranochromis 1973 macrocephalus Cichlidae CSO 1984 (2) Serranochromis 1973 robustus Cichlidae CSO 1984 (2) Serranochromis 1973 thumbergi Cichlidae CSO 1984 (2) 1973 Tilapia rendalli Cichlidae CSO 1984 (2) 1973 Tilapia sparrmanii Cichlidae CSO 1984 (2) 1973 Clarias gariepinus Clariidae CSO 1984 (2) 1973 Clarias ngamensis Clariidae CSO 1984 (2) 1973 Labeo spps Cyprinidae CSO 1984 (2) 1973 Hepsetus odoe Hepsetidae CSO 1984 (2) 1973 Synodontus spps Mochokidae CSO 1984 (2) Marcusenius 1973 macrolepidotus Mormyridae CSO 1984 (2) 1973 Schilbe intermedius Schilbeidae CSO 1984 (2) 1973 other CSO 1984 (2) 1976 Brycinus lateralis Alestidae Dudley & Scully 1980 1976 Oreochromis andersonii Cichlidae Dudley & Scully 1980 1976 Oreochromis macrochir Cichlidae Dudley & Scully 1980 1976 Sargochromis carlottae Cichlidae Dudley & Scully 1980 1976 Sargochromis giardi Cichlidae Dudley & Scully 1980 Serranochromis 1976 angustceps Cichlidae Dudley & Scully 1980 Serranochromis 1976 macrocephalus Cichlidae Dudley & Scully 1980 Serranochromis 1976 robustus Cichlidae Dudley & Scully 1980 Serranochromis 1976 thumbergi Cichlidae Dudley & Scully 1980 1976 Tilapia rendalli Cichlidae Dudley & Scully 1980 311

TABLE B. 2. CON’T

Years Species Family Source Note 1976 Tilapia sparrmanii Cichlidae Dudley & Scully 1980 1976 Clarias gariepinus Clariidae Dudley & Scully 1980 1976 Clarias ngamensis Clariidae Dudley & Scully 1980 1976 Labeo molybdinus Cyprinidae Dudley & Scully 1980 1976 Hepsetus odoe Hepsetidae Dudley & Scully 1980 1976 Synodontis spps Mochokidae Dudley & Scully 1980 Marcusenius 1976 macrolepidotus Mormyridae Dudley & Scully 1980 1976 Mormyrus lacerda Mormyridae Dudley & Scully 1980 1976 Schilbe intermedius Schilbeidae Dudley & Scully 1980 1980- Brycinus imberi DoF database, 2010 Alestidae unpublished 1980- Brycinus lateralis DoF database, 2010 Alestidae unpublished 1980- Ctenopoma spp DoF database, 2010 Anabantidae unpublished 1980- Oreochromis andersonii Cichlidae DoF database, 2010 unpublished 1980- Oreochromis macrochir DoF database, 2010 Cichlidae unpublished 1980- Oreochromis niloticus DoF database, 2010 Cichlidae unpublished 1980- Pseudocrenilabrus DoF database, 2010 philander Cichlidae unpublished 1980- Sargochromis carlottae DoF database, 2010 Cichlidae unpublished 1980- Sargochromis DoF database, 2010 codringtonii Cichlidae unpublished 1980- Sargochromis giardi DoF database, 2010 Cichlidae unpublished 1980- Serranochromis DoF database, 2010 angusticeps Cichlidae unpublished 1980- Serranochromis DoF database, 2010 macrocephalus Cichlidae unpublished 1980- Serranochromis Cichlidae DoF database, 2010 robustus unpublished

312

TABLE B. 2. CON’T

Years Species Family Source Note 1980- Serranochromis DoF database, 2010 thumbergi Cichlidae unpublished 1980- Tilapia rendalli DoF database, 2010 Cichlidae unpublished 1980- Tilapia sparrmanii Cichlidae DoF database, 2010 unpublished 1980- Distichodus schenga DoF database, 2010 Citharinidae unpublished 1980- DoF database, 2010 Clarias gariepinus Clariidae unpublished 1980- DoF database, 2010 Clarias ngamensis Clariidae unpublished 1980- DoF database, 2010 Clarias theodorae Clariidae unpublished 1980- DoF database, 2010 Barbus spps. Cyprinidae unpublished 1980- Labeo cylindricus DoF database, 2010 Cyprinidae unpublished 1980- Labeo molybdinus DoF database, 2010 Cyprinidae unpublished 1980- Hepsetus odoe DoF database, 2010 Hepsetidae unpublished 1980- Mastacembelus frenatu Mastacembelida DoF database, 2010 s e unpublished 1980- Synodontus spps DoF database, 2010 Mochokidae unpublished 1980- Marcusenius DoF database, 2010 macrolepidotus Mormyridae unpublished 1980- Mormyrus larceda DoF database, 2010 Mormyridae unpublished 1980- DoF database, 2010 Mormyrus longirostris Mormyridae unpublished 1980- Mormyrus macrodon DoF database, 2010 Mormyridae unpublished 1980- Petrocephalus Mormyridae DoF database, 2010 catastoma unpublished

313

TABLE B. 2. CON’T

Years Species Family Source Note 1980- Pollimyrus castelnaui DoF database, 2010 Mormyridae unpublished 1980- Schilbe intermedius DoF database, 2010 Schilbeidae unpublished NOTES: (1) Reference does not clarify if listed species are those included in the catch, or a total species list for the Kafue River. (2) Species reported only using local names. Translated using Mortimer 1965. Species synonyms were reconciled using Skelton (2001) and Fishbase.org (Froese and Pauly 2012).

314

TABLE B. 3.

FULL MODELING RESULTS. REFER TO TEXT AND SUPPLEMENTARY INFORMATION TEXT FOR DESCRIPTION

CPUE Effort Water

CPUE on on Effort Regime

315 DD effort CPUE DD on CPUE

Rank -LL AICc dAICc AICc.wgt Ucpue Ueffort (b1-1) (b2) (b3) (b4-1) (b5) Rcpue Rgillnets Rboats Notes 1 -93.2 210.7 0.00 0.23 0.07 0.08 -0.22 0.01 -0.14 0.00 0.00 0.14 0.22 0.24 2 -93.2 210.7 0.06 0.23 0.06 0.08 -0.23 0.00 -0.13 0.00 -0.02 0.14 0.22 0.24

3 -93.2 210.7 0.06 0.23 0.07 0.08 -0.22 0.00 -0.14 0.00 0.00 0.14 0.22 0.24 na -95.8 211.0 0.38 0.19 -0.07 0.08 0.00 0.00 0.01 0.00 0.00 0.16 0.22 0.24 (1) na -95.0 211.9 1.19 0.13 -0.03 0.08 0.00 0.00 -0.03 0.00 0.06 0.16 0.22 0.24 (1) na -95.3 212.5 1.86 0.09 -0.08 0.09 0.00 0.00 0.01 -0.01 0.00 0.16 0.22 0.24 (1) na -95.3 212.5 1.88 0.09 -0.08 0.08 0.00 0.01 0.01 0.00 0.00 0.16 0.22 0.24 (2) 4 -93.1 212.9 2.25 0.08 0.08 0.08 -0.26 0.01 -0.14 0.00 -0.03 0.14 0.22 0.24 na -91.9 212.9 2.27 0.07 0.47 1.36 -0.74 -1.64 -0.56 -1.36 -0.03 0.15 0.22 0.19 (2) 5 -93.2 213.0 2.35 0.07 0.08 0.08 -0.25 0.00 -0.14 0.00 -0.02 0.14 0.22 0.24

TABLE B. 3 CON’T

CPUE Effort Water

CPUE on on Effort Regime DD effort CPUE DD on CPUE

Rank -LL AICc dAICc AICc.wgt Ucpue Ueffort (b1-1) (b2) (b3) (b4-1) (b5) Rcpue Rgillnets Rboats Notes 6 -99.1 213.2 2.53 0.07 0.06 0.08 -0.21 0.00 -0.13 0.00 0.00 0.19 0.32 0.86 na -95.0 214.2 3.51 0.04 -0.03 0.08 0.00 0.00 -0.03 0.00 0.05 0.16 0.22 0.24 (2) na -95.0 214.2 3.53 0.04 -0.03 0.08 0.00 0.00 -0.03 0.00 0.05 0.16 0.22 0.24 (2) na -101.1 214.9 4.20 0.03 -0.07 0.08 0.00 0.00 0.01 0.00 0.00 0.19 0.32 0.86 (1) 7 -99.1 215.4 4.73 0.02 0.06 0.08 -0.23 0.00 -0.13 0.00 -0.02 0.19 0.32 0.86 8 -99.1 215.4 4.77 0.02 0.07 0.08 -0.22 0.00 -0.14 0.00 0.00 0.19 0.32 0.86 9 -99.1 215.5 4.80 0.02 0.07 0.08 -0.21 0.00 -0.13 0.00 0.00 0.19 0.32 0.86 na -94.6 215.8 5.11 0.02 -0.03 1.05 0.00 -1.18 -0.03 -0.90 0.04 0.16 0.22 0.22 (2)

316 10 -97.0 215.8 5.13 0.02 1.38 0.08 -1.89 0.00 -1.42 0.00 0.00 0.17 0.22 0.24 na -100.5 215.8 5.17 0.02 -0.03 0.08 0.00 0.00 -0.03 0.00 0.06 0.19 0.32 0.86 (1) na -94.9 216.4 5.73 0.01 -0.62 1.75 0.68 -2.04 0.54 -1.59 0.00 0.16 0.21 0.24 (1) na -100.8 216.6 5.89 0.01 -0.08 0.08 0.00 0.01 0.01 0.00 0.00 0.19 0.32 0.86 (2) na -100.8 216.6 5.90 0.01 -0.08 0.09 0.00 0.00 0.01 -0.01 0.00 0.19 0.32 0.86 (1) 11 -99.1 217.6 6.97 0.01 0.07 0.08 -0.25 0.01 -0.14 0.00 -0.02 0.19 0.32 0.86 12 -99.1 217.7 7.02 0.01 0.07 0.08 -0.24 0.00 -0.14 0.00 -0.02 0.19 0.32 0.86 na -100.5 218.1 7.44 0.01 -0.03 0.08 0.00 0.00 -0.03 0.00 0.06 0.19 0.32 0.86 (2) na -100.5 218.1 7.44 0.01 -0.03 0.08 0.00 0.00 -0.03 0.00 0.06 0.19 0.32 0.86 (2) na -100.6 218.5 7.79 0.00 -0.07 1.31 0.00 -1.50 0.01 -1.17 0.00 0.19 0.32 0.86 (1) na -98.5 218.8 8.15 0.00 0.48 -0.39 -0.75 0.61 -0.55 0.47 -0.03 0.19 0.32 0.86 (2) na -100.5 220.6 9.89 0.00 -0.05 1.16 0.00 -1.31 -0.01 -1.03 0.02 0.19 0.32 0.86 (2) 13 -100.8 221.0 10.35 0.00 0.22 1.13 -0.37 -1.28 -0.27 -1.00 0.00 0.19 0.32 0.86 na -120.8 265.8 55.14 0.00 -0.07 -0.01 0.00 0.01 -3.70 -1.84 0.00 0.16 0.95 0.94 (1)

TABLE B. 3 CON’T

CPUE Effort Water

CPUE on on Effort Regime DD effort CPUE DD on CPUE

Rank -LL AICc dAICc AICc.wgt Ucpue Ueffort (b1-1) (b2) (b3) (b4-1) (b5) Rcpue Rgillnets Rboats Notes na -128.5 269.8 59.11 0.00 -0.07 0.08 0.00 0.00 0.01 0.00 0.00 1.00 1.00 1.00 (1) 14 -128.2 271.3 60.59 0.00 0.06 0.08 -0.21 0.00 -0.13 0.00 0.00 1.00 1.00 1.00 na -128.4 271.8 61.10 0.00 -0.03 0.08 0.00 0.00 -0.03 0.00 0.06 1.00 1.00 1.00 (1) na -128.5 271.8 61.17 0.00 -0.08 0.09 0.00 0.00 0.01 -0.01 0.00 1.00 1.00 1.00 (1) na -128.5 271.8 61.17 0.00 -0.08 0.08 0.00 0.01 0.01 0.00 0.00 1.00 1.00 1.00 (2) 15 -128.1 273.5 62.80 0.00 0.08 0.08 -0.23 0.01 -0.14 0.00 0.00 1.00 1.00 1.00 16 -128.1 273.5 62.82 0.00 0.08 0.09 -0.22 0.00 -0.14 0.00 0.00 1.00 1.00 1.00 17 -128.2 273.5 62.85 0.00 0.06 0.08 -0.23 0.00 -0.13 0.00 -0.02 1.00 1.00 1.00 317 na -128.4 274.0 63.32 0.00 -0.07 1.32 0.00 -1.53 0.01 -1.16 0.00 1.00 1.00 1.00 (1) na -128.4 274.0 63.33 0.00 -0.04 0.09 0.00 0.00 -0.02 -0.01 0.04 1.00 1.00 1.00 (2) na -128.4 274.0 63.34 0.00 -0.04 0.08 0.00 0.01 -0.02 0.00 0.04 1.00 1.00 1.00 (2) 18 -128.1 275.7 65.08 0.00 0.08 0.08 -0.27 0.01 -0.15 0.00 -0.03 1.00 1.00 1.00 19 -128.1 275.8 65.10 0.00 0.08 0.09 -0.26 0.00 -0.14 -0.01 -0.03 1.00 1.00 1.00 na -128.3 276.1 65.39 0.00 -0.03 1.03 0.00 -1.15 -0.03 -0.88 0.04 1.00 1.00 1.00 (2) na -128.4 276.2 65.58 0.00 -0.58 1.68 0.63 -1.96 0.49 -1.50 0.00 1.00 1.00 1.00 (1) na -127.8 277.5 66.88 0.00 0.48 1.36 -0.75 -1.63 -0.57 -1.34 -0.02 1.00 1.00 1.00 (2) NOTE: (1)Excluded from analysis because the intrinsic population growth rate (Ucpue) was estimated as negative. (2) xcluded from analysis because the B matrix is non-stationary

APPENDIX C:

SUPPLEMENT TO CHAPTER 5:

CAN MARKET-DRIVEN HARVEST OF INVASIVE SPECIES CONTRIBUTE TO CONSERVATION

GOALS? MODEL DESCRIPTION

C.1 Open access bioeconomic model

We assume Gordon’s (1954) open access bioeconomic equilibrium for fishery harvests where the total cost of harvest is equal to the total revenue received from the harvest. Entry into the fishery occurs until the economic rents (i.e. “profits” in the vernacular) that can be earned by harvesting are equal to other opportunities in the economy. This point is where total costs=total revenue, i.e. economic rents are zero.

We assume fixed marginal costs (MC) of effort, that is, one unit of effort costs the same no matter how much effort is expended, and thus the average cost (AC) is equal to MC.

Under open access, we do not equate the MC with the Marginal Revenue (MR) from production as is typical in economics, but rather the Average Revenue (AR).

Following (Clark 2010), we define the revenue of harvesters to be a function of the price received for harvests as determined by a demand curve, and the costs of

318

harvesting as a function of the size of the population which is being harvested. We make P the price which is a function of Xh the biomass of the harvest

X Eq. 10 P(X )  a(1 h ) h b where a is the maximum willingness to pay for a unit of invasive species catch (at a low catch) and b is the maximum quantity of harvest that could be sold at any price.

The cost of harvesting a given biomass increases as the population biomass declines, because it takes more effort to harvest from a small population. If we assume that the cost of a unit of effort is constant, c, and that a unit of effort removes a constant proportion of the population biomass, q (the catchibility), we can define an equation for cost of one unit of harvest, the marginal cost Cmar, as a function of population biomass, Xb and the total cost, Ctot.

c Eq. 11a Cmar(Xb )  qxb

c Eq. 11b Ctot  xh qxb

To parameterize these equations we draw from experience with crayfish harvesting for Rusty Crayfish (Orconectes rusticus) invasion in the upper mid-west U.S.

(Peters 2010), and from the Signal Crayfish (Pacifastacus leniusculus) invasion in Lake

Tahoe as examples (Table C 1). From Tahoe Lobster Co, Inc in Nevada crayfish are selling for $3.85-$4.75/lb (Pers. Comm. 1-31-13, 1-775-358-0304), and they are

319

expecting about 100lbs for the next harvest. Because this small of harvest is probably less than what the market would bear, and because it leads to an uninteresting example, assume for a baseline that they could sell 300x as much, if they could harvest it. This 300% increase was chosen based solely on convenience for creating demonstrative figures, and should be taken as an evaluation of this crayfish market.

For the costs of harvest estimates, c, and other biological parameters I draw from (Peters 2010) for Rusty Crayfish in Lake Ottawa, assuming the most efficient of their techniques tested, the double bucket trap with protected bait. We also assume a mass of 9.7g/crayfish, which is roughly a crayfish 30mm carapace length (Lorman 1980).

For a unit of effort, we assume as in (Peters 2010) 16 man-hours to pull 150traps at $20 per hour. To estimate catchibility, q, we assume the average trap catch from one double bucket trap (Peters 2010) catch of females = 1650 crayfish/person hour=

176crayfish/trap or 1.7Kg/trap= 2.93e-5 of the carrying capacity. We assume catchibility is constant. For illustrative purposes we increase q by 10x.

To find the equilibrium harvest, we describe the relationship between the demand and the supply. First, we take the total net revenue from harvest, Rtot,

R  (P  C)X tot h Eq. 12

We have assumed open-access, such that harvesters will enter the market until

Rtot is zero. We can therefore find the bio-economic equilibrium harvest level from this equation by substituting Eq. 10 and Eq. 11a into Eq. 12 and solving for Xh. The substitution yields a quadratic, however, one of the solutions is uninterestingly Xh=0. 320

The equilibrium harvest is therefore found by equating P=C and solving for Xh. The “*” denotes equilibrium.

TABLE C 1.

PARAMETERS, THEIR EXAMPLE VALUES DRAWN FROM CRAYFISH, AND THE SOURCE OF

THOSE VALUES

Parameter definition Value in source a Maximum willingness to pay $10.55/kg Eq.1 Tahoe Lobster Co (pers. comm) b maximum amount (Kg) that 45kg * 300 Eq.1 Tahoe Lobster Co could be sold at any price =13500 (pers. comm) c cost of a unit of effort (# of $320/150 Eq.2 (Peters 2010) traps) traps =$2.1 q Catchibility, percent 2.93e-4 Eq.2 (Peters 2010) caught/trap K Carrying capacity 6,000,000CF = Eq.5 (Peters 2010) 58200KG r Growth rate 1.2 Eq.5 (Peters 2010)

Eq. 13 *  c  X h  b1   aqXb 

321

Next, we model the biological population growth of invasive species being harvested. We use a logistic population growth where Xb is the population biomass, r is the intrinsic population growth rate and K is the carrying capacity of the ecosystem.

dxb  xb  Eq. 14  rxb 1  dt  K 

The intersection of the growth equation (Eq. 14) and the harvest equation (Eq.

13) is a visual representation of the equilibrium population biomass (Error! Reference source not found.). To analytically determine this bio-economic equilibrium population biomass, we assume that at equilibrium the removal of biomass by harvesting exactly equals the addition of biomass via natural growth processes (hence the intersection).

To find this intersection substitute assume dXb/dt=0 and equate Eq. 13 with Eq. 14 and solve for Xb.

r bc Eq. 15 x3  rx2  bx   0 K b b b aq

The solution for the equilibrium biomass is the above cubic (Eq. 15), which indicates that there are potentially three bio-economic equilibriums, though not all equilibriums may be sensible. It should be noted that not all demand curves and growth functions will yield a cubic. For example, the traditional price- only demand curve such as used in (Gordon 1954) yields a quadratic. Solutions to the cubic polynomial were found in R (R Core Team 2012) using the polyroot function.

322

The harvest, Xh at equilibrium can be found by entering the real (i.e., not imaginary) solutions of Eq. 15 into the harvest function in Eq. 13. The equilibrium price,

P* can then be found by entering the equilibrium harvest into the demand function in

Eq. 10.

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C.2 Density-Impact model

The next step in the bioeconomic framework is to estimate by how much impacts to ecosystem services are reduced by market driven reductions in population size. We now describe density-impact curves, which relate impacts on ecosystem services to the density of the invasive species populations. We employ a set of phenomenological density-impact curves (Yokomizo et al. 2009) to describe the monetary value of ecosystem services, I, as function of population biomass

I(X b )  v  vD1/1 exp xb / K  u/  Z Eq. 16a

D  1 exp 1 u/ /1 Z1 exp 1 u/   Eq. 5b

Z  1/1 expu /  

where μ,β are parameters which determine the shape of the density-impact curves, v is the maximum value of ecosystem services, and D and G are accessory functions. The complexity of these equations allows a wide variety of density-impact curves (Figure C. 2) which are constrained by the carrying capacity of invasive species population as well as a maximum value, v.

To estimate the value of recovered ecosystem services, solve Eq. 5 in terms of the equilibrium population biomass, for example from the solutions to Eq. 15 for the open access scenario.

C.3 Alternative market scenarios

In this section we extend the open-access scenario developed above to consider alternative market scenarios: harvester monopoly, pure competition, and social

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welfare/amenity values including ecosystem services. In these scenarios, we are concerned with optimizing a flow of goods and maximizing their value over the long term.

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C.3.1 Monopoly

C.3.1.1 Static monopoly

In the monopoly case static bioeconomic equilibriums are defined where the marginal cost of effort = marginal revenue received. The marginal revenue can be found by taking the derivative of the price curve (Eq. 10)

X Eq. 17 R  P * X  a(1 h )* X total h b h

dR 2a Eq. 18 R  total  a  X marg d b h Xh

And the marginal cost is constant over dXh (Eq. 11), thus we can find the equilibrium harvest, Xh*, by equating marginal cost to marginal revenue and solving for

Xh.

c 2a * Eq. 19  a  X h qXb b

* 1  bc  X h,mono  b   2  aqXb 

With constant marginal cost and linear demand the static monopoly harvest is always half of the competitive open-access equilibrium.

The monopoly equilibrium biomass Xb can be found by substituting Eq. 19 into the growth function (Eq. 14) and solving for equilibrium where dXb/dt=0. 326

Eq. 20  Xb  1  bc  rXb1   b    K  2  aqXb 

 r b bc X 3  rX 2  X   0 K b b 2 b 2aq

Which yields a polynomial with three possible equilibrium solutions. As before

(Eq. 15) we solve with the polyroot function in R.

C.3.1.2 Long-term monopoly

This above solution is only for a static snapshot in time, however a monopolist would consider long-term profits, which are dependent on discount rate, δ, (i.e. the perceived future value of value today). This long-term maximization problem is given by

(Clark 2010).

 Eq. 21 maximizex (t) et P X  C X dt Monopolistic h   Xh  h Xb  h  0

At equilibrium, Xh is found from Eq. 14, P(Xh) is the price function in Eq. 10, and

C(Xb) is the cost function in Eq. 11a. We numerically find the population biomass Xb which maximizes Eq. 21 using the integrate and optimize functions in R. It should be noted however that (Clark 2010) provides an analytical solution to this and the other maximization problems discussed below.

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C.3.2 Pure Competition

The maximization problem for pure competition gives the traditional Maximum

Economic Yield (MEY). And we solve this as previously were p is a fixed price.

 Eq. 22 maximizex (t) et p  C X dt Pure Competition h   Xb   h  0

C.3.3 Amenity values

This scenario imagines finding a harvest and population biomass that maximizes welfare or utility of both the market for invasive species and the recovered value of ecosystem services. First, we describe static welfare maximization, finding a harvest and population that maximizes the sum of consumer and producer surplus. That is, the area under the curve of the demand function and above the harvest function up to the equilibrium harvest. Then we add the ecosystem service values and maximize over the long-term.

C.3.3.1 Static social welfare maximization

The producer surplus, Sp is the difference in total revenue (Eq. 17) and total cost

(Eq. 11b) for a given harvest:

 a  cX * Eq. 23 S  R C  a  X * X *  h p tot tot  h  h *  b  qXb

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Where population growth function (Eq. 14) can be substituted for Xh* at equilibrium. The consumer surplus is defined as the area under the demand curve (Eq.

10) and above the equilibrium price

* X h Eq. 24 S  P  P* dX cons   X h  h 0

X * h  aX   aX *  S  a  h   a  h dX cons   b  b  h 0    

X * h  a  S   X *  X dX cons  b h h h 0  

In this particular case the demand function is linear and we can solve using the equation for a triangle:

1 Eq. 25 Area  *base*height 2 1 S  * X * *a  P*  cons 2 h a 2 S  X *  cons 2b h

Where again, the population growth function (Eq. 14) can be substituted for Xh* at equilibrium.

The task then is to find the harvest level which maximizes welfare. We find this maximum using the optimize function in R.

* * Eq. 26 Welfare  max Xh Scons  S prod,where Xh  GrXb 

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C.3.3.2 Amenity values with ecosystem services recovery

Now we consider the Amenity value harvest if we internalize the value of ecosystem service returns into the welfare equation Eq. 26 and include the long-term maximization. That is, we are finding an equilibrium harvest rate and population size that maximizes the sum of consumer and producer surplus and the value of the recovered ecosystem service due to population reduction. The long-term analytical solution is given by (Clark 2010), but we again solve numerically in R. Again, we assume pure competition, that is, a fixed price, p.

 Eq. 27 maximizex (t) et p C X  I dt Amenity value h   Xb  h Xb  0

Where again Xh* is from Eq. 14, and I(Xb) is the density-impact function

C.3.3.3 Amenity subsidy

Given a level of harvest/population size that maximizes welfare from Eq. 27, we find the level of subsidy to pay harvesters to achieve it. This subsidy is envisioned simply as the difference in the value of revenue received for the equilibrium harvests under the amenity scenario, and the costs of harvesting that equilibrium level (if the difference is negative).

For a given equilibrium harvest size from Eq. 27, we can calculate the equilibrium population from Eq. 14. This harvest and population sizes can then be substituted into

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the total cost function (Eq. 11b), yielding the cost of harvests at this level. The total revenue received from the social optimum with impact reduction harvest can then be found via Eq. 17. The net revenue of total revenue minus total costs, if negative, reveals the amount of the subsidy required.

C.4 Demand simulations and figures

The goal of the remainder of this supplement is to explore increases in demand, both in terms of maximum price willing to pay (parameter a) and maximum amount that can be cleared (parameter b). Included below are only the Long-term results. In the next three sections, we graphically demonstrate by simulation that the maximum price consumers are willing to pay for invasive species products, a, does not substantially affect the long-term population equilibriums for open-access, monopoly or pure competition scenarios.

C.4.1 Open-access equilibrium populations

There is little effect of increasing a (Figure C. 3) and open-access population equilibrium (Eq. 15).

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C.4.2 Monopoly equilibrium populations

In the monopoly scenario, the a parameter for maximum willing to pay has little or no effect on the final population biomass (Figure C. 4).

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Figure C. 4. Long-term Monopoly population biomass equilibriums over varying levels of demand by “a”-( over the panels) and “b”, the x-axis.

C.4.3 Pure Competition equilibrium populations.

In the Pure Competition scenario, the a parameter for maximum willing to pay has little or no effect on the final population biomass (Figure C. 5).

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Figure C. 5. The equilibrium population biomass of pure competition (price-taker) harvest over a range of fixed prices determined by sifting the demand curve by a (by panels) and b (x- axis). The equilibrium population size is the MEY.

C.5 References

Clark, C.W. (2010) Mathematical Bioeconomics: The Mathematics of Conservation, Third Edit. John Wiley & Sons, New York.

Gordon, H.S. (1954) The Economic Theory of a Common-Property Resource : The Fishery. Journal of Political Economy 62, 124–142.

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Lorman, J.G. (1980) Ecology of the crayfish Orconectes rusticus in northern Wisconsin.

Peters, B.W. (2010) Evaluating Strategies For Controlling Invasive Crayfish Using Human And Fish Predation. Biological Science, 80.

R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Yokomizo, H., Possingham, H., Thomas, M.B. and Buckley, Y.M. (2009) Managing the impact of invasive species: the value of knowing the density – impact curve. Ecological Applications 19, 376–386.

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