Conservation Potential of a Semi-Forested Agricultural Landscape: Diversity and Spatial
Distribution of Birds within a Large-Scale Ugandan Coffee Farm
A thesis presented to
the faculty of
the Voinovich School of Leadership and Public Affairs of Ohio University
In partial fulfillment
of the requirements for the degree
Master of Science
Michael F. McTernan
May 2019
© 2019 Michael F. McTernan. All Rights Reserved. 2
This thesis titled
Conservation Potential of a Semi-Forested Agricultural Landscape: Diversity and Spatial
Distribution of Birds within a Large-Scale Ugandan Coffee Farm
by
MICHAEL F. MCTERNAN
has been approved for
the Program of Environmental Studies
and the Voinovich School of Leadership and Public Affairs by
Nancy J. Stevens
Professor of Biomedical Sciences
Mark Weinberg
Dean, Voinovich School of Leadership and Public Affairs
3
ABSTRACT
MCTERNAN, MICHAEL F., M.S., May 2019, Environmental Studies
Conservation Potential of a Semi-Forested Agricultural Landscape: Diversity and Spatial
Distribution of Birds within a Large-Scale Ugandan Coffee Farm
Director of Thesis: Nancy J. Stevens
Protected area conservation is not enough to stem increasing biodiversity loss.
Therefore, it is important to consider private-owned lands when developing a long-term conservation strategy in a region. Agricultural lands, which cover more than 30 percent of the Earth’s terrestrial surface, are a good place to start. However, further research is needed to understand how species use the landscape, and what types of best practices should be encouraged to increase levels of biodiversity.
This study was conducted on a 2500 ha semi-forested Ugandan Robusta coffee farm. The system is a hybrid of shade and full sun coffee production, with intense cultivation on production land and corridors of reserved indigenous forest. Using point counts, I found that the forest and farm held substantial numbers of avian species, comparable to nearby protected areas. I also found that there were three distinct communities of birds, inhabiting the coffee, the edge, and the forest. The forest habitat seemed drive diversity, and held the highest effective number of species. This study showed that farmed land can act as a reserve, but that forest must be reserved in these landscapes.
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TABLE OF CONTENTS
Page
Abstract ...... 3 List of Tables...... 6 List of Figures ...... 7 Chapter 1: Introduction ...... 8 1.1 Current State of Human Impact on the Planet ...... 8 1.2 The Agricultural Footprint ...... 12 1.3 Conservation Potential of Agricultural Landscapes ...... 14 1.4 Land Sparing...... 16 1.5 Land Sharing: On-farm Reserves’ Effect on Biodiversity ...... 18 1.6 Coffee Agroecosystems...... 19 1.7 Biodiversity and People: Conservation, Economics and Political Ecology ...... 23 Chapter 2: Methods ...... 27 2.1 Hypotheses ...... 27 Avian Species Richness: ...... 28 Avian Community Composition: ...... 28 Avian Habitat Selection: ...... 29 2.2 Study Site ...... 30 2.3 Pilot Studies ...... 32 2.4 Bird Surveys ...... 33 2.5 Vegetation Data for Point Count Stations ...... 35 2.6 Functional Groups ...... 36 2.7 Data Analysis ...... 36 Estimating Species Diversity ...... 36 Comparisons of Community Similarity ...... 37 Chapter 3: Results...... 40 3.1 Overall Species Richness and Sample Coverage (2018 Point Count) ...... 40 3.2 Comparison of Species Richness and Composition Between Two Studies ...... 42 3.3 Avian Community Composition ...... 50 3.4 Avian Habitat Selection ...... 52 5
Chapter 4: Discussion ...... 56 4.1 Species Richness and Family Comparison of Two Studies ...... 56 4.2 Avian Community Composition Comparisons...... 59 4.3 Avian Habitat Selection ...... 62 4.4 Trends in Overall Species Richness and General Conservation Notes ...... 63 4.5 Future Studies ...... 63 4.6 Conservation at KWP ...... 64 References ...... 70 Appendix A: Opportunistic Observations ...... 96 Appendix B: Guilds ...... 97
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LIST OF TABLES
Page
Table 1. Number of Indivduals, Species, and Sample Coverage ...... 40 Table 2. Observed and Estimated Species Diversity for Hill Order 0, 1 ...... 41 Table 3. Species Observed in 2002 and 2018 ...... 43 Table 4. Family list of species found in KWP ...... 49 Table 5. Forest dependency with wetland species included ...... 51 Table 6. Forest dependency without wetland species included ...... 51 Table 7. Number of species and proportion of total of each foraging guild ...... 52 Table 8. Percent similarity between habitat types ...... 54 Table 9. Poisson results for species abundance/habitat type ...... 54 Table 10. Poisson results for species richness/habitat type ...... 54 Table 11. Poisson results for species abundance/canopy coverage...... 55 Table 12. Poisson results for species richness/canopy coverage ...... 55
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LIST OF FIGURES
Page
Figure 1. Number of years required for observed vertbrate species extinctions ...... 9 Figure 2. Human Influence on Planet Earth...... 10 Figure 3. Agricultural Impact on Planet Earth...... 12 Figure 4. Worldwide Coffee Production...... 20 Figure 5. Map of Kaweri Coffee Farm ...... 30 Figure 6. Rarefaction curves for Hill orders 0 and 1 ...... 41 Figure 7. NMDS plot with representative species labelled...... 53
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CHAPTER 1: INTRODUCTION
1.1 Current State of Human Impact on the Planet
The field of conservation is at a crossroads. The traditional method of securing land to create reserves that exclude or restrict human access appears insufficient to stem the vast losses of biodiversity around the globe (Laurance, 2012; Mora et al., 2011;
Heller, 2009). Even these sanctuaries are under siege from a variety of threats including population growth, resource extraction, and climate change, the effects of which could make many existing reserves obsolete (Hannah et al. 2002; Crist & Engelman., 2017).
Add to this the rising economic fortunes of many of the world’s people, and the increased consumption of meat and resources associated with the growing middle class, and it is easy to see conflict between conservation and developmenton the horizon (McDonald et al., 2008; McApline et al. 2009) As time progresses, it is realistic to expect only a small portion of the world’s biodiversity to remain truly protected within the reserve system
(Hannah et al., 2007). The time is now to think of other strategies to protect the world’s biodiversity from a threat that is not going away: humans.
Extinction of species is occurring, conservatively, at a rate 100 times the historical background rate and appears to be increasing (see figure 1; Cellabalos et al., 2015).
Translated into more simple terms: at “normal” background rates it would have taken somewhere between 800-10,000 years to lose the amount of vertebrate species lost in the past 100 years. Scientists estimate that only about 14 percent of the planet’s species have even been identified, making it likely that species are becoming extinct before they are even discovered and described (Mora et al., 2011). 9
Figure 1. Number of years required for observed vertebrate species extinctions in last 114 years to occur under background rate of 2 E/MSY (Ceballos et al., 2015)
This “sixth extinction” can clearly be laid at the feet of humankind (Barnosky et al., 2011; Kolbert, 2014; Brook et al., 2008). Humans dominate the planet; our ecological footprint is immense. Using surrogate data such as population density, the level of land transformation, ease of access, and type and amount of infrastructure, Sanderson et al.
(2002) estimated that over 80 percent of the terrestrial surface area of the planet is influenced by human activity and, incredibly, excluding land where it is not possible to grow the staple crops of wheat, maize or rice, that estimate jumps to 98 percent (see 10 figure 2). Considering that such crops grow almost anywhere with sun and thawed soil, clearly humans influence nearly every square inch of habitable earth in one way or another. And although these estimates point out only human influence and not environmental outcomes (e.g., there are places where human influence has a positive effect on the environment, though we hear mostly about the opposite) it is clear we must understand and take responsibility for ecological impacts if we hope to have a meaningful impact on reducing biodiversity loss (Brook et al., 2008).
Figure 2. Human Influence on Planet Earth (Sanderson et al., 2002)
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Human impact extends beyond the physical footprint of civilization to the very biogeochemical processes of our planet, disrupting the normal cycling of carbon, nitrogen, and phosphorous, as well as appropriating a vast share of the energy from the sun – the basis of all life on Earth (Bennett et al. 2001; Vitousek et al, 1997; Vitousek et al. 1997b). Net primary production (NPP) is a measurement of the amount of carbon produced by vegetation through photosynthesis (known as Gross Primary Production or
GPP), minus that used by vegetation for respiration (Haberl et al., 2007). It is more or less a measure of the annual accumulation of organic matter or, more simply put - the energy budget for life on Earth. The most recent estimates of human appropriation of net primary productivity (HANPP), according to data averaging the results from 1998-2002 puts human appropriation at a little over 23 percent, nearly a quarter of all the energy on
Earth (Haberl et al., 2007). This leaves precious little for the other 10 million or so species that are believed to exist.
Zooming in on the tropical regions, the situation is even more dire. In a band between the tropics of Cancer and Capricorn, and home to a large portion of the world’s biodiversity hotspots, HANPP is even higher, ranging from 30 percent to over 70 percent in regions like South-East Asia and India, Central America, and parts of Central Africa
(Myers et al., 2000; Haberl et al., 2007).
These data indicate we are stewards of the planet whether we want to be our not.
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1.2 The Agricultural Footprint
Not surprisingly, agriculture accounts for 50 percent of global HANPP, and if we add in pasturing, the number rises to 78 percent (Haberl et al., 2007). Considering as well the fact that 12 percent of the ice-free terrestrial surface is used for crops, and another 22 percent for pasturing animals (Ramankutty et al, 2008)– area converted from grassland, forest or savanna – the true extent of agriculture’s impact begins to reveal itself (see figure 3).
Figure 3. Agricultural Impact on Planet Earth (Ramankutty et al., 2008)
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Regardless of the intensity of farming practices - whether large-scale industrial monoculture, or smallholder polyculture systems - the impact of agriculture on the planet is immense (Donald, 2004). Clearing land for agricultural expansion is one of the leading drivers of deforestation around the globe, though the patterns and synergies underlying this trend have changed over the years (Giest et al., 2002). In the mid to late 20th century, the primary agents were small farmers, encouraged by government policies to colonize and settle frontier areas in the tropical regions (DeKonick and Dery, 1997, Rudel, 2007).
More recently, large scale agribusinesses driven by increased consumer demand have been the primary agents of change, clearing and leveling large sections of tropical forest in Southeast Asia and the Amazon (Rudel et al., 2009).
This shift is important when considering potential impacts and ways to minimize the negative effects of agriculture on biodiversity. Rather than small, irregularly shaped holdings interspersed with native forest (typical of smallholder fields), new holdings are larger and often cleared (leaving no remnant patches of forest) to accommodate the machines necessary for farming at this scale (Laurance et al., 2014).
In addition, the increased impact of large-scale farming challenges mental models that researchers and policy makers have traditionally assumed to be true, i.e. the idea that the rural poor, those who rely on the land for survival, are the primary drivers of deforestation and therefore are also driving extinctions related to habitat loss (e.g.,
Wright and Muller-Landau, 2006; for alternative view see Pfaff, 1999) These assumptions tend to shift the focus of conservation activities towards curtailing the 14 activities of small holder farmers at the expense of ignoring the growing impacts of agribusiness (Garcia-Barrios et al., 2009; De Schutter, 2011).
Despite the impact of agriculture, and the prevalence of agricultural landscapes around the globe, there are serious gaps in our understanding of how agriculture affects biodiversity over time, and whether or not agroecosystems can act as surrogate habitat for displaced species.
Researchers have long offered evidence of alternative “agroecological” models, pointing out the urgent need for a closer examination and development of methods to manage land for both food production and biodiveristy (Altieri, 1989; Gliessman, 1990
McNeely & Scheer, 2001). Yet, recognition of the value of these models is slow and further research is still needed.
Recently, Chazdon and colleagues (2009) posited an updated research agenda to aid in the targeted exploration of the biodiversity potential of tropical agroecosystems.
One of their primary concerns was the lack of baseline studies quantifying the current state of biodiversity in existing agroecosystems. Without such knowledge there is no way to understand short or long term population dynamics, types/numbers of species using agroecosystems for habitat, or whether certain land-use practices can foster preservation of particular species on agricultural lands.
1.3 Conservation Potential of Agricultural Landscapes
Worldwide, there are 100,000 protected areas covering about 12 percent of the planet’s terrestrial area (Chape et al., 2005), but data are lacking on how effective these
PAs are in meeting conservation goals. Area and shape, regulations, funding, and 15 enforcement are not uniform within PAs and this could limit their ability to help conserve biodiversity over the long term (Bruner et al., 2001).
Historically, protected areas were chosen on an ad hoc basis, placed on marginalized land with little commercial or economic value, and this has led to the establishment of PAs that do not meet the goals of protecting a representative biodiversity (Pressey, 1994). Add to this the current lack of funding for support or procuring large pieces of biologically productive and diverse land (Waldron et al., 2013), the lack of knowledge about where and what areas are most important (Scott et al., 2001), and a lack of budgeting clarity to best direct where these limited dollars can be spent
(Brooks et al., 2006), and the need for developing conservation strategies outside the
‘protected area paradigm’ becomes clear.
In the tropics approximately 90 percent of tropical forest lies outside PAs (WWF,
2002). Further, these PAs are embedded in a matrix of mixed types of land use such as agriculture, logging concerns, and human development placing pressure on species inhabiting parks through processes like edge effect, and lessened ability for wildlife to move through the landscape (Hanson and DeFries, 2007; Wilcove et al., 1986;
Woodroffe and Ginsberg, 1998;). Therefore, regardless of the extent of protected space, the fate of biodiversity within protected areas remains highly dependent on the makeup and land use policies of the surrounding landscape (Vandermeer and Perfecto, 2007).
After all, a park’s borders are not exclusionary, but instead act more like a filter, lessening the negative effects of land use practices outside the protected area but not eliminating them. Add to this the pressures put on a park’s borders by population growth 16 and the need for resources to support that growth (such as new land for agriculture or timber for housing and heat), and it is easy to see why effective conservation strategies must embrace a landscape level perspective (Tscharntke et al., 2005).
Two schools of thought currently dominate the discussion when it comes to reconciling conservation and agriculture. Land-sparing proponents suggest increasing intensification in order to increase production on existing farmland, which allows additional land to be spared from conversion - land which could theoretically be used for conservation purposes (Waggoner, 1996). Land sharing advocates, on the other hand, believe increasing on-farm heterogeneity in order to promote increased biodiversity within the agricultural landscape is more effective strategy when it comes to preserving biodiversity in the face of an ever-expanding agricultural footprint (Green et al., 2005;
Phalan et al., 2011). This school of thought believes that regardless of improvements in yields, agriculture will still expand into sensitive areas, and therefore it is best to develop a strategy that reconciles agriculture and conservation as best is possible. The jury is still out on which approach shows more promise, though, like most things, it appears context and specific implementation of each strategy has a lot to do with how successful either is at preserving biodiversity (Fischer et al., 2008; Ramnkutty and Rhemtulla, 2012)
1.4 Land Sparing
The crux of the land-sparing ethos is to maximize agricultural yields on any and all agricultural land (Waggoner 1996). The increase in yield is, in most cases, accomplished through adoption of an industrial model of agriculture: increased use of 17 inputs, efficiency through technology, and the favoring of large plots over smaller holdings (Tilman et al., 2011).
Land-sparing proponents argue that an agricultural ecosystem, regardless of how heterogeneous the landscape within, how large the patches of “natural” cover, or how benign the techniques used to grow and harvest food, will never sustain populations in the same way that an intact forest can. As such, they argue that our best route to preserving biodiversity is to avoid putting remaining natural land under production at all costs. The argument for this approach appears valid, but a deeper analysis raises questions about the approach’s long-term sustainability.
Waggoner (1996) estimated that double the amount of agricultural land will be needed to produce enough food, if current yields per area land are held constant, to feed the human population the projected for the year 2050. However, increased yields come at a cost, and this strategy is not always a viable option. In some areas yields may already be maximized (Ray et al., 2012). Another challenge is that institutional protections on land reserves, (the “spared” land which could provide a conservation benefit), may be inadequate to make a land-sparing approach truly effective (Dorrough et al. 2007; Persha et al. 2011).
Further complications with the land-sparing approach reveal themselves when you consider the fact that an agricultural landscape is not an island. Increased yields are often realized through agrochemical inputs, which do not always remain where they are sprayed or applied, compromising the health of ecosystems both near and distant 18
(Wauchope, 1978; Beman et al. 2005). These examples should make conservationists pause when considering land-sparing as the sole method of preserving biodiversity.
1.5 Land Sharing: On-farm Reserves’ Effect on Biodiversity
Land-sharing is tricky to directly define, as it involves a range of characteristics depending on context or researcher. It can encompass anything from the retention of
“keystone structures” – individual or small pockets of trees which encourage higher levels of biodiversity (Manning et al. 2006) – to embedding crops within a landscape of native vegetation, as is the case in traditionally grown shade coffee (Lopez-Gomez et al.,
2008). In some instances additional native or non-native trees are planted in effort to increase heterogeneity in a way that mimics that of natural forests found in the area or provides an economic benefit (i.e. fruit-producing trees [Tscharntke et al., 2011]).
Land-sharing tactics seem to have a positive effect on a range of species. In the agricultural landscapes of Central America, shade coffee is a commonly grown commodity. Within these agroecosystems researchers have found increases in tree cover are generally correlated with an increase of species richness in taxa including, birds, butterflies, beetles, epiphytes, bats, and ants (Perfecto et al. 2003, Harvey et al. 2006,
Moguel and Toledo 1999). But this trend is not always clear-cut. Perfecto et al. (2003) found that in one case, bird diversity increased with a decrease in tree cover (though nearby forest patches may contributed to this discrepancy), and Harvey et al. (2006) found that the strength of the correlation was taxon-specific and that different forest types were more or less effective depending on the species of interest. 19
Though the responses varied, all these studies point out the need to think about agricultural conservation on a landscape scale. Retaining a variety of tree cover nearby to agricultural sites, especially patches of native forest, can provide suitable habitat for species even in intensely farmed areas.
Given the current prevalence of agricultural landscapes, as well as the fact that food demand is expected to double by the year 2050 (Tilman et al., 2011), further expansion of the agricultural footprint is likely to occur. This expansion will likely be disproportionately heavy in the very tropical and subtropical areas where the biodiversity crisis is greatest (Phalan et al, 2013), and it is short-sighted to ignore the conservation potential of land sharing in this context (Tilman et al., 2002; Schaarlemann et al., 2004).
1.6 Coffee Agroecosystems
Coffee is a particular plant. It grows best in a small strip between the tropics of
Capricorn and Cancer, an area that also contains many of the world’s biodiversity hotspots (Myers et al., 2000). As such, coffee farms are in direct competition for land and resources with a multitude of threatened and endangered species. In 2014, 10.4 million ha of land, concentrated in 15 countries that contain global biodiversity hotspots, were used for coffee cultivation, (see Figure 4) (FAO 2015; Myers et al. 2000). The export value of the coffee grown on these farms tops 30 billion dollars
(http://www.worldstopexports.com/coffee-exports-country/).
Recently, there has been a decline in price for green coffee - demand has remained fairly steady, while production/ha, and therefore supply, has increased. This 20 drop in price is forcing farmers to increase intensification to boost yields over the short term or to transition their fields to other crops/pastureland (O’Brien and Kinnaird 2003).
Figure 4. Worldwide Coffee Production in Million Tonnes and Hectares (FAO, 2015)
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From a conservation standpoint these trends of intensification and crop transition are worrisome. Traditionally, coffee was typically “shade-grown”, meaning the coffee plants were interspersed beneath a canopy of native trees, providing shelter from direct sun and mitigating factors such as water loss and temperature fluctuations (Perfecto et al.,
1996). Although there is no standardized definition, nor labeling laws associated with the term “shade grown”, it is generally understood to mean the coffee is grown beneath a shaded canopy provided by native or planted trees, though the level of shade and the diversity of shade trees present are highly variable (see Mexican systems of Moguel and
Toledo, 1998).
Traditional coffee farms offer a strong model for how native biodiversity and commercial crop production can co-exist and even benefit from one another (e.g., pollination services [Gordon et al., 2007]; pest control [Perfecto et al., 2004]) . As such, early research into the conservation potential of agroecosystems lauded the conservation potential of coffee farm landscapes (Perfecto et al., 1996; Perfecto and Vandermeer,
1994; Wunderle, 1999; Wunderle and Latta, 2000). Such agroecosystems provide important data about how the retention of “natural” features within an agricultural landscape can offer resources to a range of species, either as permanent habitat or as a provisioning area for species such as migratory birds.
Indeed, research has demonstrated that many species are able to use the heterogeneous habitat of shade-grown coffee farms to their advantage, including ants
(Perfecto et al., 2002; Perfecto et al. 2003; Roberts et al. 2000), birds (Greenberg et al.
1997, Mas and Deitsch, 2004; Perfecto et al. 2003; Komar , 2006; Calvo and Blake 22
1998), amphibians (Perfecto et al., 2003; Murrieta-Galindo et al. 2013), mammals
(Gallina, 1996; Williams‐Guillén et al., 2006) and butterflies (Mas and Deitsch, 2004;
Perfecto et al., 2003), Most of these studies found the diversity to be lower than that in primary or secondary forests, yet significantly higher than in other human-modified landscapes such as pasture land or intensive cash-cropping systems, lending credence to the idea that shade coffee or agroforests can act as refuges or corridors for displaced species.
Much of the diversity present within a shade-grown coffee system seems to be linked to habitat heterogeneity and vegetative complexity, both offer a diversity of niches, allowing for complex biological interactions similar to those found in intact forest
(Perfecto et al. 2014). But species richness may mask other differences, such as the emergence of novel guild structures on coffee plantations.
For example, human-modified landscapes are more likely to support a higher number of omnivorous, frugivorous, or nectarivorous bird species, and less likely to support specialized or insectivorous bird species (Petit et al., 1999; Shahabuddin 1997;
Canaday, 1996; Greenberg et al. 1997). Edge species are also more likely to be found than deep forest specialists (Calvo and Blake, 1998). Although these results are not surprising, they highlight the importance of maximizing the amount of undisturbed habitat within a larger landscape to mitigate loss of rare or specialized species; as well as the need for studies that look beyond the purely quantitative aspects of diversity, such as species richness, and consider also qualitative elements of diversity including conservation concern and vulnerability to habitat loss or change (Petit et al., 1999). 23
Global intensification in the methods used to grow coffee has shifted the market away from traditional shade-grown settings. Accordingly, researchers have attempted to quantify decreases in biodiversity associated with transition from shade grown to full sun production methods. As early as 1994, researchers documented a decline in ant species richness as shade trees were removed from the system (Perfecto and Vandermeer, 1994).
A later review extended this premise for multiple taxa, including beetles, wasps, spiders, and birds (Perfecto et al. 1996). In Latin America (Dominican Republic and Guatemala), multiple studies have demonstrated a decrease in the number of bird species in the shift from shade to sun-grown coffee (Greenberg et al., 1997; Wunderle and Latta, 1996). A more recent review conducted by Philpott et al. examined results from 27 recent studies in Central and South America, finding similar decreases in bird richness as well as decreases in ant and tree species as intensification (i.e. – a decrease in shade trees) increased (Philpott et al., 2008).
These studies emphasize the need to gather and disseminate more research identifying landscape structures and farming methodologies that encourage biodiversity while maintaining profitability on agricultural landscapes, before a great deal of species are lost to rapid intensification practices.
1.7 Biodiversity and People: Conservation, Economics and Political Ecology
Though conservationists have in the past, and more recently in the development of a “war to save biodiversity”, framed efforts as a struggle against indigenous peoples and their use of land and resources, these strategies are ultimately counter-productive
(Peluso, 1993; Duffy, 2014). Research provides solid evidence that conservation areas 24 without community support are unsustainable over the long-term (Durban and Ralambo,
1994; Weladji and Tchamba, 2003; Wittemyer et al., 2008). Inclusion of local people is now commonplace in planning and management of conservation areas (PAs) and there is growing acknowledgment that shared governance is also needed in developing sustainable landscapes (Berkes, 2004; Pollnac et al. 2001; Porter-Bolland et al., 2012).
For conservation outside of protected areas, especially in agroecosystems, community buy-in is essential (Pretty and Smith, 2004; Pretty and Ward, 2001).
Establishing thoughtful relationships with local farmers and resource harvesters such as charcoal producers or those cutting timber is often first step in constructing a sustainable conservation plan (Pretty, 2003). Ensuring sustainable economic benefits for biodiversity and agroecosystems is a trending concern (McKenzie et al., 2013; McNeely, 1993;
Pascual and Perrings, 2007).
Recent studies have established a link between ecosystem function/ecosystem services and higher levels of biodiversity (Cardinale et al. 2012). A higher level of species diversity is associated with greater stability and resilience to environmental stochasticity (Allison, 2004; Elmqvist et al., 2003). This observation, known as the
“insurance hypothesis”, is built upon the idea of compensatory growth, a process whereby a species (or group of species) within a functional group increases productivity in response to loss of species within the same functional group (Fischer et al., 2006;
Walker et al., 1999).
It follows that higher levels of biodiversity should insulate an ecosystem from rapid declines in function, as well as allowing a community to rebuild itself over time 25
(Grime, 1998). One of the first examples of this phenomenon was demonstrated within microbial communities: an assortment of mini-communities were constructed with varying levels of species richness within functional groups (Naeem and Li, 1997), and communities with a higher number of species per functional group were found to be more predictable over time regardless of light or nutrient levels. Expanding upon this work,
Yachi and Loreau (1999) developed a stochastic dynamic model, basically a series of replicate ecosystems, each built upon a random sampling from a species pool. They then tested each ecosystem’s response to environmental fluctuations, using total productivity as a measurable proxy for ecosystem function (though they maintain the model holds for other processes as well). Those model ecosystems with more species in each functional group showed greater reliability over time in terms of biomass and density, and demonstrated what the researchers termed a “buffer” effect, a reduction in the temporal variance of productivity, as well as a “performance enhancing” effect, an increase in the temporal mean of productivity (Yachi and Loreau, 1999).
In agricultural areas, community composition and ecosystem function have ramifications in the face of climate change and sustainability across the globe.
Constructing a sustainable agriculture system which intentionally supports both planned
(crops) and associated biodiversity (remnant habitat patches, native trees and organisms) may allow farmers to better adapt to changing conditions, providing economic and food security over time (Thrupp, 2000). Quantifying levels of both biodiversity and agricultural yields in different agricultural production systems in a reliable and repeatable manner is essential to tracking changes over time to provide data on the most sustainable 26 practices (Gliessman, 1990; Tilman et al., 2002). Equally important is developing sound strategies of communicating this data to local farmers, helping to gain the buy-in needed to sustain positive biodiversity gains over the long-term (De Snoo et al., 2013; Issac et al., 2007; Siebert et al., 2006).
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CHAPTER 2: METHODS
My methods were based, to a degree, upon the 2002 study conducted by Makerere
University at KWP. Methodological detail in this document are expanded from survey techniques outlined in the original document, with an eye toward standardizing survey methodology for repeatability through time, and making contemporary comparisons with biodiversity surveys on other agroecosystems.
The study was conducted in in late January/ early February, 2018. I worked with two experienced local field assistants (Harriet Kemigisha and Rodger Arali) familiar with the local fauna (identified by my advisor, Nancy Stevens). I aimed to provide contemporary data on species richness and abundance at KWP and assemble a new baseline for measuring current levels of biodiversity at KWP, using quantitative and repeatable methodology for assessing species presence/absence and population densities.
These data can be used to inform future monitoring efforts and to track change over time.
2.1 Hypotheses
Kaweri coffee farm (KWP) has the potential to act as a biological refuge due to its structure and retention of natural landscape features in a region that has in recent decades undergone steady deforestation. It may be that species richness has decreased in conjunction with large-scale farming efforts at KWP, despite the retention of natural forest corridors and other natural landscape features. Alternatively, it may be that species richness has remained steady or even increased since the farm’s inception in 2002, should it serve as a refuge for taxa under increased pressure in surrounding areas. Quantifying 28 species richness at KWP is an important step in understanding the dynamics between human food production, human settlement, and biodiversity.
Avian Species Richness:
H0: Overall avian species richness at KWP remains relatively unchanged since
2002 (as identified by number of unique avian species recorded at KWP).
HA1: Overall avian species richness has increased since 2002.
HA2: Overall avian species richness has decreased since 2002.
It is also possible that avian species composition has changed in a mosaic pattern, with some species becoming locally extinct, and others migrating into the area. Recent methods have been established to delve more deeply into the structure of avian species composition, and those will be employed as a next step. Some level of stochastic changes might be expected over the 16 years of KWP’s existence, but to explore patterns that relate to ecosystem function in greater detail, I will further examine community composition.
Avian Community Composition:
H0: Avian community composition in terms of nesting requirements and feeding
guilds has remained the same since 2002.
HA1: Avian community composition has changed in KWP since 2002, with more
generalists than specialists remaining.
HA2: Avian community composition has changed in KWP since 2002, with more
specialists than generalists remaining. 29
HA3: Avian community composition has changed in KWP since 2002, with
specialists and generalists dominating different habitat sectors.
Zooming in a bit more, it will be necessary to examine what habitats at KWP are richest in species richness and ecological diversity.
Avian Habitat Selection:
H0: Species do not show preference for particular habitats within KWP.
Community composition and species richness are the same in coffee, forest,
savannah, and the same in each management section.
HA: Species show a preference for particular habitats within KWP. Community
composition and species richness differ in coffee, forest, savannah, or show
different patterns among management sections.
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2.2 Study Site
Figure 5. Kaweri Coffee Plantation
31
The Kaweri Coffee farm, run by the Neumann Kaffe Group based in Germany, is located in the Mubende district of Western Uganda, about 200 km west of Kampala, at an average elevation of 1,320 m (00’36’59.94 N, 31’28’29.46 E). The region is characterized by two wet seasons (March-May and October-November) and two dry seasons (December-February and June-August). Temperatures are steady throughout the year with an average of 21.5 Celsius, and an annual average high of 22.6 Celsius
(February) and an annual average low of 20.8 Celsius (June). Rainfall averages 1090 mm per year, with an annual low of 42mm in January and a high of 102 mm in October. This climate favors the Robusta variety, and indeed Uganda produces more Robusta coffee than any other African country (https://www.indexmundi.com/agriculture/?commodity
=green-coffee&graph=robusta-production) with green beans primarily grown for export rather than local consumption. The total area of Kaweri Coffee farm is 2512 hectares, making it the largest Robusta (Coffea canephora) coffee plantation in Uganda. In 2015, the planation expected to produce 2500 tons of coffee, from about 1.8 million plants on
1680 ha of planted land, an average of 1.5 tons/ha
(http://shillingscents.blogspot.com/2015/07/kaweri-coffee-plantation-ugandas-best.html).
Despite the fact that KWP is a large commercial farm, it has retained fairly large sections of intact forest, making it an attractive place to explore mixed-use landscapes for biodiversity conservation. Approximately 25 percent of the farm was originally left unplanted in 2002, and consists of indigenous highland forest and papyrus swamp. Forest and swamp lands are connected to one another to form a continuous corridor that organisms can use to move through the landscape without encountering planted land. The 32 farm is divided into five management sections, and each has a unique mixture of planted land and what I term “reserve” land, either forest or swamp. Although 25 percent of the land remains forested, the forest and the coffee are spatially divided with only the occasional non-coffee tree occurring on planted land. This offers an opportunity to look at a hybrid system; a sun-grown agricultural system that lacks the mosaic of trees that typically characterize traditional shade-grown coffee farms within the planted area, yet one that retains corridors of native habitat amongst the coffee fields. In this way, KWP exhibits characteristics of both land-sparing and land-sharing agricultural systems within a single interconnected landscape. Land-sparing proponents argue that interspersing trees within an agricultural matrix is ineffective due to the lack of significant intact habitat, essentially suggesting that breaking up a “native” habitat renders it useless to wildlife.
Land-sparers would argue that intensifying production in all areas of a given farm is preferable to mixed-use scenarios and that nearby forests should be kept out of the agro- ecosystem. Kaweri practices intense farming within cultivated areas of the plantation, yet retains substantial pockets of intact habitat across the larger farming landscape. I explored this model as a potential solution that bridges the gap between the land sharing and land sparing strategies.
2.3 Pilot Studies
Before coffee was planted in 2002, the owners of KWP initiated a survey of the property to catalogue the biodiversity present in January of that year (Wathyso et al., unpublished). A team from Makerere University surveyed birds, butterflies, vegetation, 33 and small mammals (avian survey results (130 species observed) detailed in Table 1 in the Results section).
In 2014, Dr. Nancy Stevens and Ugandan ornithologist Harriet Kemigisha conducted a 3-day survey of bird, mammal and amphibian species across habitats at
KWP to assess the habitat and develop logistics for longer-term work. The number of species (e.g., 112 bird species) recorded was similar to the baseline recorded in the 2002 study (Stevens, unpublished). In 2016, I conducted a 3-day pilot survey with a small team, developing experience in bird surveys, identifying target habitats in the study area, and recording 110 avian species based on visual and call data.
Both of these recent informal studies suggested that KWP had maintained significant avian species richness since the 2002 baseline study. This study gathered additional data required to explore species abundance and habitat use. My aim was to augment species richness data with measures of abundance and community structure, offering insight into whether KWP offers habitat stability and developing a methodology for long-term monitoring efforts. My data offers insights into what species can persist within an agricultural landscape and what aspects of the landscape maximize biological diversity and richness.
2.4 Bird Surveys
Avian species richness and community structure at KWP were assessed following a modified version of the BBIRD field protocol developed by the University of Montana, an established point count and data recording strategy (Martin et al., 1997). Time and radius of plots were based on a thorough review of coffee farm avian diversity studies, 34 using count durations of ten minutes and a survey radius of 50m to enable effective detection and surveying of species across a patchy landscape (Calvo and Blake, 1998;
Perfecto et al. 2003; Bael et al. 2013; Greenberg et al., 1996; Petit et al., 1999; Petit et al.
1995)
I divided the plantation into a series of 50m circular plots using ArcMap. I used a random number generator to choose 4 plots of each habitat type, in each management section, for a total of 20 plots per habitat type and 60 plots overall. On site, stakes were placed in the center of each plot prior to the surveying effort. I walked the two field assistants through the sites on the day prior to their surveys to ensure that the correct sites were surveyed.
To cut down on travel time between points, each surveyor worked within one management section each morning and surveyed 6 points between the hours of 7-11am (2 coffee, 2 edge, and 2 forest). These same six plots were then surveyed twice more on subsequent days, resulting in three temporal replications for each of the 60 points over a period of approximately two weeks. The order of points surveyed was changed for each surveyor to try and minimize the effects of time of day on counts. No two plots were within 200m of each other on a given day to cut down on autocorrelation, or double counting.
Surveyors arrived at each plot and waited five minutes before beginning data collection in order to allow for birds to acclimate to their presence. Counts took place for ten minutes per plot. A fifty meter fixed radius was used as a cut-off point for counts to ensure that counts were representative of the vegetation present and to allow for more 35 accurate comparisons among plots, though birds outside of fifty meters were also recorded to help determine detection probabilities (species recorded outside this range were given a special notation and are included in Appendix A). Species and number of individuals were recorded, as well as the sex and age of each individual when possible to determine. Song and call observations will bewere recorded in addition to visual observations, and designated as such. Fly-overs and fly-throughs were not counted but were recorded and included in Appendix data.
Point count surveys took place between 7am-11 am. To supplement standardized surveys, we also walked the next day’s points each afternoon, recording any species we saw. We also recorded any species of note seen during vegetation surveys or free time.
These observations were not used to inform species richness or abundance data, but are included as opportunistic observations in Appendix A.
2.5 Vegetation Data for Point Count Stations
To provide context on habitat type at each point count station, vegetation was sampled using a modification of the techniques laid out by the BBird protocol and the
Point-Centered Quarter Method (PCQM) (Cottam and Curtis, 1956; Martin et al., 1997).
I used a densiometer to record the canopy coverage at the center of each point. I also measured the canopy coverage of the two nearest trees in each of four quadrants demarcated by the cardinal directions. I averaged the eight tree measurements to estimate average canopy coverage for each point. 36
I also measured the height (m), using a clinometer, and circumference at breast height (cm) of each of the two nearest trees, in each of the four quadrants. Circumference measurements were converted in excel to diameter at breast height (DBH).
2.6 Functional Groups
Often when land use shifts from one function to another, generalists are able to persist or migrate into the space (Petit and Petit, 2003; Gray et al. 2007). This usually comes at the expense of specialists that require a narrower range of conditions for survival.
Bird species recorded during point counts were sorted into functional groups to assess community structure at different sites. I categorized the species into groups based on feeding habits and forest dependency (see Appendix B). This provided an indication of how avian species utilized the different habitats and whether particular areas of the plantation supported habitat or feeding specialists, offering insights into KWP conservation potential for such species.
2.7 Data Analysis
Estimating Species Diversity
Raw species counts are useful, but often do not give a complete picture of the diversity present at a given site. Imperfection detection is a given (Preston, 1979), and the actual diversity of a site is likely higher than the number of species observed. I used rarefaction to estimate the total species richness present at KWP. Rarefaction draws on the sampled population to create a curve of the number of species as a function of the number of individuals sampled. By extrapolating the curve past the study sample size, 37 until it begins to level off, you can estimate the species richness of a site. For my study I estimated the total species richness for KWP, as well as for each habitat type sampled, using the iNEXT package in R (Chao et al., 2014).
iNext also allows for the calculation of three orders of Hill Numbers – q=0
(species richness), q=1 (exponential of Shannon-Wiener diversity index), and q=2 (the reciprocal of Simpson’s γ). Hill numbers 1 and 2 provide insight into the relationship between richness and evenness within a site. Translating the Shannon-Weiner and
Simpson’s diversity indices using hill numbers returns a calculation of an “effective number of species”, which is more easily understood and comparable across studies (Jost,
2006). Rather than the non-linear values returned by the indices, which can cause confusion during comparison (a community with Shannon-Weiner of 4.5 is not half as diverse as one of 9), the hill number calculations always provide a value measured in units of species. This enables comparison regardless of the index used.
Comparisons of Community Similarity
Species for both the 2002 study and the 2018 study were categorized into foraging and forest dependency guilds (based on Bennun et al., 1996). The results were arranged in a contingency table with counts/guild as rows and the study year as columns.
Differences in the proportions were tested for significance using Fisher’s exact test.
Fisher’s exact test is a non-parametric test that determines whether the relative proportions of one variable are independent of the second variable – the null hypothesis being they are not independent. It tends to be more accurate than the often used chi- square test when sample sizes are small, as was the case in this study. 38
Comparisons of Species Habitat Preferences (PermAnova and Non-Metric Multi-
Dimensional Scaling):
Each plot was one of three habitat types: coffee, edge or forest. Species type and abundance were recorded for each plot. In addition species were categorized into foraging and forest dependency guilds. I tested for evidence of differences in the type and number of species found in each habitat type, as well as differences between guild use of habitat using a Permutational Multivariate Analysis of Variance (PERMANOVA). The null hypothesis was no difference between species richness and abundance between habitat types, and no difference in use of habitat type amongst the foraging and forest dependency guilds. The PERMANOVA test is similar to the ANOVA test in that it is used to examine variation between groups, however the PERMANOVA is better suited to deal with multiple variables and uses permutations to avoid potential biases. As a semi- parametric test it also is able to examine data that does not conform to the assumptions of multi-variate normality (Anderson, 2014). The PERMANOVA test is therefore uniquely suited to complex natural systems which may not have normal distributions.
Results of a PERMANOVA test can be visualized using a variety of ordination techniques. In this study, Non-Metric Multi Dimensional Scaling (NMDS) was used.
Multi-Dimensional Scaling (MDS) uses algorithms to place any number of objects in an
N-Dimensional space while preserving the distance between these objects. NMDS is a more flexible version of MDS in that it uses rank order rather than linear correlation to determine the distance between points, making it more suitable (similar to the 39
PERMANOVA) for the analysis of complex systems which may not follow normal distributions.
A Poisson regression was used to test for correlations between species abundance and species richness in the three habitat types and the five management sections. The outcome variables were richness and abundance and the predictor values were habitat type and management section.
40
CHAPTER 3: RESULTS
3.1 Overall Species Richness and Sample Coverage (2018 Point Count)
In total I recorded 3301 individuals across all three habitats (Table 1). The highest abundance (1228) was observed in the coffee habitat. Identical species richness was found in the coffee and edge habitats (113), with 100 species observed in the forest habitat.
I was able to achieve estimated total sample coverage of .993. Individual habitat sample coverages ranged from .976 (edge) to .984 (coffee) (Table 1).
Table 1. Number of Individuals, Species, and Sample Coverage
Site N Number of Species
Coffee 1228 113 .984
Edge 1133 113 .976
Forest 940 100 .977
Total 3301 159 .993
Estimated species richness (Hill Order =0) for all habitats surveyed was 172.71 ±
7.348 species. To estimate the “effective number of species” I chose to calculate the Hill
Order of 1 (Shannon Diversity) which was 59.17 ± 1.601 (Figure 6). Estimated diversity for Hill’s orders 0 and 1 were also calculated for each of the 3 habitat types (Figure 6;
Table 2).
41
Figure 6. Rarefaction curves for Hill orders 0 and 1
Table 2. Observed and Estimated Species Diversity for Hill Order 0, 1
Site Diversity Observed Estimated S.E. LCL UCL Coffee Species richness (0) 113 122.516 5.651 116.24 140.946 Coffee Shannon diversity (1) 37.925 40.029 1.62 37.925 43.204 Edge Species richness (0) 113 146.107 17.179 125.714 199.211 Edge Shannon diversity (1) 46.828 50.161 2.188 46.828 54.45 Forest Species richness (0) 100 118.355 10.517 106.46 152.155 Forest Shannon diversity (1) 55.111 58.981 1.965 55.13 62.832 Total Species richness (0) 159 172.71 7.348 164.121 195.708 Total Shannon diversity (1) 57.54 59.172 1.433 57.54 61.98
42
It should be noted that although the forest habitat had the lowest estimated species richness, the forest had a higher level of diversity under Hill Order 1, which takes into account evenness. Calculations of evenness based on the Shannon-Weaver Index confirmed that the forest species abundances were the most evenly distributed (forest
=.87, edge = .81, coffee = .77).
3.2 Comparison of Species Richness and Composition Between Two Studies
Overall species richness has increased since 2002, in terms of raw numbers.
Researchers in 2002 used mist nets and physical observation and recorded a total of 130 unique species (Table 3). My 2018 study, conducted during the same season, using point counts exclusively, identified 159 unique species (Table 3). This number excludes fly- overs and identifications made outside of 50m - inclusion of these observations resulted in an addition of 9 species observed during point counts (see Appendix A).
A total of 76 species were observed in both 2002 and 2018, indicating the persistence of at least 58% of the species found in 2002 (Table 3). If you exclude birds primarily associated with wetland habitats, which were not specifically surveyed in 2018, the number rises to 71.5% indicating a possible turnover of 28.5% of species.
43
Table 3. Species Observed in 2002 and 2018
Species 2002 2018 Afep Pigeon x x African Black Headed Oriole x x
African Blue Flycatcher x
African Broadbill x
African Citril x
African Crowned Eagle x
African Cuckoo x
African Dusky Flycatcher x African Emerald Cuckoo x x
African Goshawk x African Green Pigeon x x
African Harrier Hawk x
African Hobby x
African Open Billed Stork x African Paradise Flycatcher x x
African Pied Wagtail x
African Pygmy Kingfisher x
African Shrike Flycatcher x African Thrush x x Angola Swallow x x
Ashy Flycatcher x Baglafecht Weaver x x
Barn Swallow x
Bateleur x Black and White Casqued Hornbill x x Black and White Mannikin x x
Black Billed Turaco x
Black Billed Weaver x Black Crowned Waxbill x x
Black Cuckoo x
Black Headed Heron x Black Headed Weaver x x
Black Kite x
Black Necked Weaver x
Black Shouldered Kite x Black Throated Apalis x x Blue Breasted Kingfisher x x
Blue Flycatcher x 44
Table 3 continued Blue Headed Coucal x
Blue Naped Mousebird x Blue Shouldered Robin Chat x x Blue Spotted Wood Dove x x
Blue Throated Brown Sunbird x
Bocage's Bushshrike x
Brimstone Canary x Broad Billed Roller x x Bronze Mannikin x x
Bronze Sunbird x Brown Backed Scrub Robin x x
Brown Chested Alethe x
Brown Crowned Tchhagra x
Brown Eared Woodpecker x Brown Illadopsis x x
Brown Parrot x
Brown Snake Eagle x Brown Throated Wattle Eye x x
Buff Spotted Woodpecker x
Buff Throated Apalis x Cardinal Woodpecker x x
Chestnut Wattle Eye x
Chin Spot Batis x Collared Sunbird x x Common Bulbul x x
Common Fiscal x
Common Waxbill x
Copper Sunbird x Crowned Hornbill x x Diederic Cuckoo x x Double Toothed Barbet x x
Dusky Blue Flycatcher x Dusky Long Tailed Cuckoo x x
Dusky Tit x Eastern Gray Plantain Eater x x
European Beeeater x
Fan Tailed Widowbird x
Fire Crested Alethe x
Giant Eagle Owl x
Golden Breasted Bunting x 45
Table 3 continued Gray Apalis x Gray Backed Cameroptera x x Gray Capped Warbler x x Gray Headed Negrofinch x x Gray Headed Sparrow x x
Gray Throated Barbet x
Gray Throated Tit Flycatcher x
Gray Winged Robin Chat x Great Blue Turaco x x
Great Reed Warbler x
Great Sparrow Hawk x
Green Backed Twinspot x Green Crombec x x Green Headed Sunbird x x Green Hylia x x
Green Sandpiper x
Green Sunbird x
Green Throated Sunbird x
Green Woodhoopoe x
Grey Backed Fiscal x
Grey Parrot x
Grey Wagtail x
Grey Woodpecker x
Grosbeak Weaver x Hadada Ibis x x
Hairy Breasted Barbet x
Hamerkop x
Helmeted Guineafowl x Holub's Golden Weaver x x
Honeyguide Greenbul x
Klass Cuckoo x
Laughing Dove x
Lead Colored Flycatcher x
Lemon Dove x
Lesser Honeyguide x
Lesser Swamp Warbler x
Little Beeeater x Little Gray Greenbul x x
Little Green Sunbird x Little Greenbul x x 46
Table 3 continued Little Sparrowhawk x
Little Swift x
Lizard Buzzard x
Long-Crested Eagle x
Mackinnon's Shrike x
Marico Sunbird x
Marsh Tchagra x Narina Trogon x x
Narrow Tailed Starling x Northern Black Flycatcher x x
Northern Puffback x
Olive Bellied Sunbird x
Olive Long Tailed Cuckoo x
Olive Pigeon x Olive Sunbird x x Papyrus Gonolek x x
Papyrus Yellow Warbler x Plain Greenbul x x
Purple Headed Starling x
Purple Heron x
Red Bellied Paradise Flycatcher x
Red Capped Robin Chat x Red Chested Cuckoo x x Red Eyed Dove x x
Red faced Cisticola x Red Headed Bluebill x x
Red Headed Malimbe x
Red Headed Weaver x
Red Napped Widowbird x
Red Shouldered Cuckoo Shrike x Red Tailed Antthrush x x Red Tailed Bristlebill x x
Red Throated Pipit x Ring Necked Dove x x Ross Turaco x x Rufous Flycatcher Thrush x x
Ruppels Long Tailed Glossy Starling x
Sand Martin x
Scaly Breasted Illadopsis x Scaly Francolin x x 47
Table 3 continued Scarlet Chested Sunbird x x
Senegal Plover x
Sengal Coucal x
Shikra x Slender Billed Greenbul x x Snowy Crowned Robin Chat x x
Sooty Chat x Speckled Mousebird x x Speckled Tinkerbird x x
Spectacled Weaver x
Splendid Starling x
Spotted Greenbul x
Squacco Heron x Striped Kingfisher x x
Striped Swallow x
Swamp Flycatcher x Tambourine Dove x x
Tawny Flanked Prinia x
Toro Olive Greenbul x
Tree Pipit x Tropical Boubou x x
Vanga Flycatcher x
Village Weaver x Violet Back Starling x x Viollets Black Weaver x x
Wattled Lapwing x Western Black Headed Oriole x x Western Nicator x x
Whinchat x
White Browed Scrub Robin x White Chinned Prinia x x
White Headed Sawing x
White Rumped Swift x White Spotted Flufftail x x White Tailed Ant Thrush x x
White Thighed Hornbill x White Throated Beeeater x x
Willow Warbler x
Winding Cisticola x
Wood Warbler x 48
Table 3 continued Woodland Kingfisher x x
Wooly-Necked Stork x
Yellow Bellied Hyliota x Yellow Billed Barbet x x
Yellow Breasted Apalis x
Yellow Crested Woodpecker x
Yellow Longbill x Yellow Rumped Tinkerbird x x
Yellow Spotted Barbet x
Yellow Throated Leaflove x
Yellow Throated Longclaw x
Yellow Wagtail x Yellow Whiskered Greenbul x x
Yellow White Eye x Yellowbill x x
Birds observed in 2018 belonged to 43 different families, a decrease of 1 when compared to the 44 families found in 2002. Species from 33 families were observed during both studies (Table 4).
49
Table 4. Family list of species found in KWP
Family 2002 2018 Accipitridae x x Acrocephalidae x Alcedinidae x x Apodidae x Ardeidae x Bucerotidae x x Calyptomenidae x Campephagidae x Charadriidae x x Ciconiidae x x Cisticolidae x x Coliidae x x Columbidae x x Coraciidae x x Cuculidae x x Emberizidae x Estrildidae x x Falconidae x Fringillidae x Hirundinidae x x Hyliotidae x Indicatoridae x Laniidae x x Macrosphenidae x x Malaconotidae x x Meropidae x x Monarchidae x x Motacillidae x x Muscicapidae x x Musophagidae x x Nectariniidae x x Nicatoridae x x Numididae x Oriolidae x x Paridae x Passeridae x x Pellorneidae x Pellorneidae x 50
Table 4 continued Phasianidae x x Phoeniculidae x Phylloscopidae x Picidae x x Platysteiridae x x Ploceidae x x Psittacidae x x Pycnonotidae x x Rallidae x x Ramphastidae x x Scolopacidae x Scopidae x Scotocercidae x x Stenostiridae x x Strigidae x Sturnidae x x Threskiornithidae x x Trogonidae x x Turdidae x x Zosteropidae x
3.3 Avian Community Composition
I found a significant difference in the proportions of species displaying varying levels of forest dependency between the 2002 and 2018 studies (p = <0.01; Table 5). The percentage of forest dependent (FF) and forest generalist (F) species increased by 14 percent (40% versus 54%; Table 5). This is primarily explained by the differences in wetland species observed (12 percent drop from 2002 to 2018). Indeed, if you exclude the wetland species from the analyses there is no significant difference in the community composition (p = .46; Table 6)
51
Table 5. Forest dependency with wetland species included (number of species and proportion of total)
Dependency 2002 2018 2002 2018
FF 19 34 0.15 0.22
F 32 51 0.25 0.32 f 46 55 0.35 0.35 wl 18 4 0.14 0.03 g 5 7 0.04 0.04 op 10 7 0.08 0.04
FF = Forest Specialist; F = Forest Generalist; f = Forest visitor; wl = wetland; g = generalist; op=open country
Table 6. Forest dependency without wetland species (number of species and proportions of total)
Dependency 2002 2018 2002 2018
FF 19 34 0.17 0.22
F 32 51 0.29 0.32 f 46 55 0.41 0.35 g 5 7 0.04 0.04 op 10 7 0.09 0.04
FF = Forest Specialist; F = Forest Generalist; f = Forest visitor; g = generalist; op=open country
I removed wetland associated species (to try and reduce habitat sampling effects) and found no significant difference in the proportions of species found in each foraging guild between the two studies (p = 0.72) (Table 7).
52
Table7. Number of species and proportion of total of each foraging guild
2002 2018 2002 2018 AfIN 5 5 0.04 0.03 BgIN 2 5 0.02 0.03 CAR 14 15 0.13 0.10 FcIN 11 20 0.10 0.13 FgIN 8 11 0.07 0.07 FRUG 8 12 0.07 0.08 GRAN 7 4 0.06 0.03 NEC 5 11 0.04 0.07 OMN 33 38 0.29 0.24 TrIN 12 17 0.11 0.11 UsIN 7 18 0.06 0.12
AfIN: Aerial-Foraging Insectivores; BgIN: Bark-Gleaning Insectiovres; CAR: Carnivores; FcIN: Fly-
Catching Insectivores; FgIN: Foliage-Gleaning Insectivores; FRUG: Frugivores; GRAN: Granivores; NEC:
Nectivores; OMN: Omnivores; TrIN: Terrestrial Insectivores; UsIN: Understory Insectivores
3.4 Avian Habitat Selection
PERMANOVA results reveal evidence of species’ preference for particular habitats (coffee, edge, forest) within the Kaweri Coffee Plantation [F(2,57)=5.49, p
<0.001]. Groups of species also showed a preference for varying levels of average canopy coverage across all habitats [F(2,57)=6.86, p <0.001].
PERMANOVA results did not reveal a significant preference based on management section or any other measured habitat features.
Non-Metric Multi-Dimensional Scaling plots depicts species grouped according to the habitat where they were most often found, a shorter distance indicates a stronger preference for that habitat (figure 7). Most of the difference is found along the x-axis which corresponds to habitat type (and also closely to average canopy coverage). 53
Figure 7. NMDS plot with representative species labeled (gray = coffee, red = edge, green = forest, blue line = percent canopy coverage)
The forest and edge habitats exhibited higher overlap between species, with some separation from the coffee habitat. Percent similarity calculations showed that species richness and abundance were also more similar between the edge and forest habitats, and that the forest and coffee communities were least similar (Table 8).
54
Table 8. Percent similarity between habitat types
% habitats
63 forest to edge
56 edge to coffee
43 coffee to forest
Poisson regression results show exhibit a significant difference in species abundance and richness between the coffee and forest habitats, though not between either habitat and the edge (Tables 9,10). Species richness increased from coffee to edge to forest, whereas abundance generally decreased.
Table 9. Poisson results for species abundance/habitat type
Estimate Robust SE Pr(>|z|) LL
UL
(Intercept) 4.12390336 0.0698572 0.000000000 3.9869833 4.26082348 habitatedge -0.09409732 0.1118307 0.400108946 -0.3132855 0.12509082 habitatforest -0.27269250 0.1007836 0.006815577 -0.4702283 -0.07515668
Table 10. Poisson results for species richness/habitat type
Estimate Robust SE Pr(>|z|) LL UL
(Intercept) 3.3393220 0.03881319 0.000000000 3.26324813 3.4153958 habitatedge 0.1010961 0.06038458 0.094090474 -0.01725766 0.2194499 habitatforest 0.1811388 0.05534240 0.001063911 0.07266772 0.2896099 55
Overall, there was a significant correlation between canopy coverage and both richness and abundance. Abundance decreased with an increase in canopy, while richness increased (Table 11, 12).
Table 11. Poisson results for species abundance/canopy coverage
Estimate Robust SE Pr(>|z|) LL UL
(Intercept) 4.343645175 0.172263050 2.729883e-140 4.00600960 4.6812807540
CanopyCover -0.005221975 0.002490959 .03604 -0.01010425 -0.0003396956
Table 12. Poisson results for species richness/canopy coverage
Estimate Robust SE Pr(>|z|) LL UL
(Intercept) 3.168544572 0.111910074 2.369934e-176 2.9492008259 3.387888317
CanopyCover 0.004073776 0.001588162 .01031497 0.0009609777 0.007186573
56
CHAPTER 4: DISCUSSION
4.1 Species Richness and Family Comparison of Two Studies
I observed 29 more species in 2018 than the researchers in 2002 (38 including fly- overs and birds identified at a distance greater than 50m). After I removed wetland associated species from the counts the turnover of species was 28.5%, evidence that a majority of species are persisting on the land despite active cultivation. However, we caution against direct comparison between the studies.
All the information on final bird counts and methods used in the 2002 study, conducted by researchers from Makere University, was gleaned from a report created for the management at KWP (Wathyso et al., unpublished). This report was primarily an inventory of species present at KWP before coffee was planted. In addition to bird species the report also included inventories on plants, small mammals and butterflies.
And although this report included avian species lists, and some notes on forest dependency, it was not a final peer-reviewed document Limited documentation is available on the survey methods or statistical tests utilized, sampling effort in terms of person-hours, and/or habitats where bird species were recorded. Abundance data are also not available from the study. Efforts to contact the researchers to gain clarification on these points were unsuccessful.
In the absence of precise methods utilized, a standardized methodology was selected based on repeatability, simplicity of measures, and minimizing equipment or funding needed to continue monitoring. Mist netting, although mentioned in the original study, was omitted from this protocol as it was unclear from the report exactly to what 57 degree, and at how many sites the researchers employed mist nets. This study relied exclusively on point counts to estimate species diversity within KWP.
For comparative purposes, the elimination of mist netting is significant. Mist netting targets different species than point counts, tending to miss larger birds and capturing more small or rare species and recording less uncommon species (Whitman et al., 1997, Wang & Finch, 2002; Derlindati & Caziani, 2005).
Studies have also found a significant interaction between data collection methods and habitat type in terms of the number of species recorded. Two studies, one in the
Ozarks and one in Costa Rica, found that point counts tend to detect more canopy and sub-canopy species in mature forest than does mist-netting, yet fewer of these species in younger forests; another study in the Chaco forest of Argentina found the opposite to be true (Blake and Loiselle, 2001; Pagen et al., 2002; Derlindati & Caziani, 2005). In terms of species richness and abundance, point counts tend to observe a higher number of individuals and record more families overall than do mist nets (Blake & Loiselle, 2001;
Derlindati & Caziani, 2005; Zakaria & Rajpar, 2010). This evidence points towards a significant effect of survey methodology on the kinds and numbers of species a researcher may find. The lack of detection in 2018 of small passerine species observed in
2002, such as the Brown Chested Alethe, the Little Bee-Eater, and the Whinchat, may be a result of this survey-type effect.
Another factor confounding direct comparison lies in differences between the habitats surveyed in 2002 and those surveyed in 2018. My study focused primarily on three habitats – coffee, edge, and forest. These three habitats account for a majority of the 58 land within KWP’s boundaries, but other types of habitat can be found including sections of papyrus swamp, and areas with more open savanna-like conditions. It is likely the
2002 study sampled these other habitats in addition to the forest and farmland present at the time. This showed in forest dependency results in which a large proportion (15 percent) of species observed in 2002 were associated primarily with wetland habitat
(Table 4, above). The proportions of generalists and open country species was similar between two studies, which may be expected due to the large areas of open space with the cultivated land (Table 4, above).
Of the families found in 2002, but not 2018, 4 of the 12 are considered families of
“water birds”: Acrocephalidae (Reed Warblers), Ardeidae (Herons), Scolopacidae
(Sandpipers), and Scopidae (Hamerkops). Given these species’ habitat preferences, it would be unlikely for my point count study to detect their presence in 2018. However, some of these species were observed opportunistically in 2018 or during the pilot studies in 2014 and 2016, evidence they may still be residing within the KWP borders. Similar to the 2002 study, the pilot studies were designed to survey a wider range of habitats and actively search for rare and cryptic species, species a point count might miss. A full list of my opportunistic observations in 2018, as well as and species observed during the pilot studies, are included in the appendices A and B
Given the differences in methodology and types of habitat surveyed it is not possible to definitively determine how species richness has changed since 2002. The fact that this study documented more species than did researchers in 2002 is promising, 59 however, and may indicate that species are seeking refuge within KWP’s borders as deforestation continues in the surrounding area.
4.2 Avian Community Composition Comparisons
Analysis of forest dependency showed that any significant difference between studies can most likely be attributed to the paucity of wetland habitat surveys in 2018.
Despite this, further exploration of species records can help to develop an understanding of the conservation potential of KWP.
Of 19 forest specialists observed in 2002, only 5 were not found during the 2018 point counts. These species were the African Broadbill, the Brown Chested Alethe, the
Fire Crested Alethe, the Grey Parrot, and the Yellow Longbill. The Yellow Longbill was identified during the pilot study in 2016, and it is possible that the two species of Alethe and the Broadbill are present but were undetected as they are small, cryptic species (these were observed in 2002, not caught in mist nets). The species of most concern in this group is the Grey Parrot, listed by the IUCN as endangered. A major cause for the decline is harvesting by humans, though habitat loss is also implicated (IUCN, 2019). The reason this species is no longer found within KWP is unclear. Gray parrots exploit a variety of tree species seasonally, and it could be that during the clearing of land for coffee planting, fruit trees the species relied on were removed (Tamungang and Ajayi, 2003).
This theory is buttressed by a study conducted in 2015, which observed higher abundances of Grey Parrots in secondary forests and agricultural land rather than parkland, presumably due to the higher diversity of tree species in the former
(Tamungang et al., 2016). Additional vegetation surveys within KWP and a focused 60 search for this species may help clarify the reason for non-detection and provide useful information for future conservation efforts. Since KWP tends to keep some shade trees in the coffee fields, a better understanding of how individual trees may act as keystone structures could help to better inform clearing efforts and allow for an easy compromise between conservation and production (Tews et al., 2014).
There was also a slight increase in forest specialists/generalists in 2018 indicating a possible immigration of forest birds into KWP as deforestation has increased on its borders. Excluding wetland species from the analysis had no impact upon this increase.
In the time since the 2002 survey, large patches of forest have disappeared from the surrounding area. Though I was unable to find surveys of the area previous to the deforestation it can be assumed that populations of birds living in patches outside KWP were forced to move due to deforestation. Griffith (2000) found evidence of birds taking refuge in agroforestry areas in Guatemala after devastating fires destroyed large sections of primary forest, and Turner and Corlett (1996) have also demonstrated the conservation value of small forest patches in otherwise deforested areas. Given these findings, it is likely that on a landscape of rapid habitat alteration forest patches and corridors in KWP provide a refuge for displaced interior forest species.
I found no significant difference in the numbers of birds in each foraging guild between the two studies. Most likely this reflects that slight changes in KWP since 2002 have not been drastic enough to impact guild structure (as would perhaps a shift from park or reserve land to clear-cut agriculture). 61
According to a timeline published by NKG, the land was home to a military base in the 1990’s that housed around 2500 people; the northern portion of the land was heavily farmed to provide food – primarily cultivated with maize and tapioca. In total around 200 families, relocated after the purchase of the land by NKG, lived on the property in small clay huts spread throughout the area, and many kept small gardens. This suggests that the land was not primary forest when the survey was conducted in 2002, and it can be assumed that many of the same landscape characteristics presently found – high amounts of edge, open farmland, and small patches of interior forest – existed then as well.
If true, then the resources available to bird species in 2002 and 2018 was likely similar as well. Both studies observed high numbers insectivores (40 and 49%; see table
4). This makes sense considering the high amount of edge habitat, which tends to favor insectivores over frugivores (Watson et al., 2004). The relative proportions of the foraging guilds observed in 2018, especially the increase in understory foraging insectivores (+ 6 percent (highest increase)), may indicate a positive shift in community structure. Recent studies in Africa demonstrated a strong negative impact upon understory insectivores with the loss of primary forest area (Serkerciolglu, 2012;
Buechley et al., 2015; Cordeiro et al., 2015). A higher number of understory foraging insectivores, coupled with a decrease in the number of granivores observed (-3 percent), could indicate a shift of more specialized species into KWP. However, the differences may simply be attributable, at least in part, to methodological differences between the 62
2002 and 2018 studies, Future, more targeted studies should be developed to examine the shift in greater detail.
4.3 Avian Habitat Selection
Species recorded in this study exhibited spatial distribution patterns corresponding to habitat type. The most significant differences were found between species occupying the coffee and forest habitats. The edge community was most similar in composition to that of the forest habitat, though not in a statistically significant way.
This is not surprising given the fact that the edge habitats acts as an ecotone between the forest and coffee habitats, and likely reflect zones of species overlap with both. Though not statistically significant, the fact that species composition in edge habitats is more similar to that observed in the forest habitat rather than the coffee habitat has important implications.
Although the significance was variable, both species richness and abundance appeared to decrease in response to the average amount of canopy coverage within all three habitats. This was especially clear in the edge habitat where both species richness and abundance showed a significant negative correlation with an increase in average canopy coverage. It is possible these findings are the result of detectability differences between the coffee habitat and the forest, as some studies have found a positive correlation between shade cover (increased canopy coverage) in coffee plantations and species richness and abundance (Greenberg et al., 1997; Philpott and Biecher, 2012).
Other studies have found the opposite to be true (Sekercioglu, 2002). 63
4.4 Trends in Overall Species Richness and General Conservation Notes
The total number of species observed within the borders of KWP (159) compares favorably with other studies of avian diversity conducted in nearby Ugandan protected areas (Owiunji & Plumptre, 1998; Sekercioglu, 2002). My study also found a higher number of species than a similar presence/absence study examining the effects of smallholder agriculture on species richness near Mabira reserve in eastern Uganda
(Naidoo, 2004), and is nearly double the highest richness found in a timed species count
(TSC) study of avian species richness amongst 12 small and large agricultural sites in central and western Uganda (Bolwig et al., 2006). Though the differences in methodology makes direct comparison anecdotal, this may indicate that KWP is a promising locale to enhance biodiversity conservation.
Rarefaction estimates put total diversity in the range of 164-195 species; a feasible number given that addition of casual observations raises the observed species richness to 172. Further analysis of the abundance data also indicate that species in the forest were most evenly distributed, and therefore the forest habitat holds the most
“effective number of species” (table 2). The significance of these observations for biodiversity conservation are clear: forest patches within KWP are an integral driver of overall species diversity and should be protected to preserve the integrity of species richness and abundance.
4.5 Future Studies
Future studies could look more closely at the habitat features associated with varying levels of species abundance and richness at KWP. This data could be particularly 64 important to KWP management when considering the types and number of trees to keep on cultivated land. While on site, all three observers noticed that birds tended to flock on particular fruit trees (most often figs) within the coffee matrix. Determining whether these trees may be acting as keystone structures could increase the value of the land in terms of conservation potential. Going further, studies should look at how richness and diversity vary within the other habitats in KWP, in particular the papyrus swamps not surveyed during this study.
Beyond more detailed analysis of the landscape, it is important to develop a structured monitoring plan for assessing species richness and local abundance over time.
The 2002 study offers a tantalizing glimpse into baseline conditions in KWP, yet it lacks precise documentation of species abundance and location records. This study offers a cost-effective, simple methodology that can be conducted by a small team at regular intervals to improve understanding of trends in biodiversity richness across seasons, years, and sections of the farm. Documenting changes over time will be important to avian biodiversity on agricultural land in the largest robusta coffee farm in Uganda.
4.6 Conservation at KWP
Determining how best to support high levels of species diversity on agricultural lands requires us to think on a landscape scale (Tscharntke et al., 2005). It has been shown that proximity to forest is important both in terms of species diversity (Perfecto and Vandermeer, 2002; Anand, et al., 2010), as well as to the provision of ecosystem services (Ricketts et al.; Karp et al. 2013). This study found that the forest held a higher
“effective number of species” (Table 4), the number of equally abundant species required 65 to produce a particular level of diversity (Jost, 2006), than either of the other two habitats.
Translating the observed diversity in this way reduces the weight of species observed only once or twice and gives more weight to repeated observations (and therefore evenness). It also allows for a more intuitive comparison between habitats because the scale is linear, unlike that used in the popular diversity indices. Thus, using this definition, the forest’s diversity is 1.45 times that of the coffee, and 1.17 times that of the edge.
Digging deeper reveals that in coffee-planted areas, 41 of the 113 bird species
(36%) were observed only once or twice. Within edge habitats, 38 of 113 bird species
(34%) were observed just once or twice. In the forest habitats, 33 of 100 species (33%) were observed only once or twice. Expanding this to three observations, reveals that species such as the red-tailed bristlebill, the tambourine dove, and the green hylia - observed once (or twice in the case of the green hylia) in the coffee were observed over
20 times in the forest. And whereas the opposite is true in a few cases – single or double observations in the forest found more often in the coffee - the shift is not as drastic and, in some cases, it appears these species are utilizing the forest edge more than the coffee.
This is suggestive that avian species richness observed in the coffee-planted habitats is likely due to species moving through the cultivated land to reach adjacent forest corridors.
Where does this leave Kaweri? Given that forested habitat accounts for only 25% of the land area in KWP, but supports similar (if not higher) levels of diversity than the cultivated land, it is clear the forest holds an outsized importance in terms of 66 conservation. The above evidence makes a strong case for maintaining forest coverage at current levels if biodiversity conservation is the primary goal. The fact there is no difference in the richness and abundance across management sections, and that there is little deviation between the same across the 20 forest points sampled, suggests that much of the approximately 600 ha of forest are contributing to the land’s conservation value equally. Therefore, any reduction in forest could be expected to have a negative effect.
There are altruistic reasons for KWP to forego any yield gains that could be realized by transitioning forest to cultivated land. Though protected areas and laws are a vital piece of the puzzle when confronting biodiversity loss (Hoffman et al., 2010), there is a growing consensus that current conservation efforts will not prevent biodiversity loss over the long term (Newmark, 1996; Woodruffe & Ginsberg, 1998; Carroll et al., 2004;
Pimm et al., 2014). These researchers point out the need for integration between conservation and human development, recognizing that beyond local scale effects, many of the factors effecting persistence relate to large-scale spatial and temporal processes.
Across the tropics, and specifically in Africa, protected areas are increasingly isolated from each other (Newmark, 2008). Often, the isolation is caused by a transition of forest to agricultural land - a trend that many predict will only increase over the next few decades, most drastically in sub-Saharan Africa (Tilman, 2001; FAO, 2011). By retaining forest cover at current levels, KWP could make a significant contribution to maintaining connectivity on a landscape facing increased deforestation, thereby acting as a refuge for the area’s displaced species. 67
It has been argued that small-scale farming is one way to increase both food security and biodiversity (Tscharntke, 2012; Whitman et al., 2017), and it is easy to see the value of forest patches on agro ecological landscapes. However, I argue that single- mindedly pursuing a strictly small-holder strategy does biodiversity conservation a disservice by not engaging with large land owners whose practices may be most at odds with biodiversity conservation, but who nevertheless have control over large chunks of private land and thus the greatest potential shift in land use practices. As the world moves towards a more instituting a more equitable food system, we cannot ignore the reality of the present moment. Developing and presenting profitable alternatives to the current model of monoculture, heavy input systems seen on large-scale farms should be a priority moving forward.
However noble the cause of biodiversity conservation for its own sake, (a cause management at KWP has expressed an interest in supporting), KWP, like any farm, is a commercial enterprise and must consider the bottom line. Fortunately, new research has shown that steps to maintain local levels of biodiversity, such as the conservation of nearby forest corridors and patches have a positive effect on the ecosystem services reaped by agricultural lands (Ricketts et al, 2004; Priess et al., 2007; Classen et al., 2014;
Milligan et al., 2016).
Ricketts et al. (2004) demonstrate that pollination services provided by forest fragments within 1km increased coffee yields by 20 percent, and reduced the frequency of small, misshapen berries (commanding a lower price) by almost one-third. Along this vein, Classen et al. (2014) were able to demonstrate that exclusion of vertebrate predators 68 reduced coffee set by approximately 9 percent, and that the presence of pollinators increased coffee weight by an average of 7.4 percent across a range of coffee management systems near Mount Kilimanjaro. Many other studies are discovering similar connections between increased levels of biodiversity and the economic benefits provided through ecosystem services. Milligan et al. (2016) observed decreases in pest control provided by ants and birds as distance to forest increased. And Priess et al. (2007) found that retention of forest patches within an agricultural matrix may offset loss of pollination services in areas experiencing heavy deforestation, as well as valuing pollination services at 46/ ha presently on coffee farms in Indonesia.
Considering the amount of cultivated land (~1700 ha) and the tonnage (~2500 tons) of coffee produced, the monetary benefit of the ecosystem services provided by
KWP’s forest patches is likely substantial and helps to offset any unrealized gains from transitioning forest to production land.
Similarly, certification standards can also contribute to offsetting the profit lost by unrecognized yields due to forest retention. A recent study by Mitiku and Maertens
(2017) found that certification can increase the economic viability of land-sharing on small coffee farms by providing a premium on the coffee produced. Astuti et al. observed similar effects, but on a smaller scale. However, the overall impacts of certification, particularly on the economic front are still uncertain. The main issue with certification schemes is that they are run by a variety of organizations. Biodiversity and economic benefits, as well as enforcement of standards, across schemes are not uniform resulting in mixed effects for species and farmers (Philpott et al., 2007; Perfecto et al., 2005). Further 69 research is needed to explore how size and location of farms affect the benefits of certification, as well as how to best develop standards that promote conservation while providing the proper economic incentives.
KWP is a hybrid-system, where plots of high intensity sun-grown coffee are intermixed with both narrow forest corridors and substantial forest patches. There is growing evidence that this type of agricultural matrix approach may be effective in maintaining biodiveristy of birds and other species in the region (Blanco and Waltert,
2013; Rodrigues et al., 2018). This study provides further evidence that this system could be a useful model for agricultural conservation, bridging the gap between land-sparing and land-sharing techniques, offering a way to reconcile conservation and economic goals.
70
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APPENDIX A: OPPORTUNISTIC OBSERVATIONS
Species > 50m
Blue Headed Coucal Centropus monachus
Common Scimitarbill Rhinopomastus cyanomelas
Crested Barbet Trachyphonus vaillantii
Helmeted Guineafowl Numida meleagris
White Browed Coucal Centropus superciliosus
White Winged Warbler Xenoligea montana
Fly-Overs
White-Headed Vulture Trigonoceps occipitalis
Little Swift Apus affinis
Opportunstic Observations
Black Winged Cuckoo Shrike Coracina melaschistos
Black Headed Heron Ardea melanocephala
Yellow-Rumped Seedeater Serinus xanthopygius
African Firefinch Lagonosticta rubricata
97
APPENDIX B: GUILDS
2002 Species
Common Name Dependency Foraging African Emerald Cuckoo F FgIN Gray Headed Negrofinch F FgIN Green Crombec F FgIN Lesser Honeyguide f FgIN Red Chested Cuckoo F FgIN Yellowbill F FcIN Cardinal Woodpecker f BgIN Grey Woodpecker f BgIN African Green Pigeon F OMN Blue Spotted Wood Dove f OMN Red Eyed Dove f OMN Tambourine Dove F OMN African Black Headed Oriole f OMN Common Bulbul f OMN Double Toothed Barbet f OMN Little Greenbul F OMN Speckled Tinkerbird F OMN Violet Back Starling f OMN Western Black Headed Oriole F OMN Yellow Billed Barbet F OMN Yellow Rumped Tinkerbird F OMN Yellow Whiskered Greenbul F OMN Black and White Casqued Hornbill F FRUG Crowned Hornbill f FRUG Eastern Gray Plantain Eater f FRUG Great Blue Turaco F FRUG Purple Headed Starling F FRUG Ross Turaco F OMN Sooty Chat f OMN African Citril f GRAN Black and White Mannikin f GRAN Brimstone Canary f GRAN Red Headed Bluebill F GRAN Red Napped Widowbird f GRAN Baglafecht Weaver f OMN Black Crowned Waxbill f OMN 98
Gray Headed Sparrow f OMN Laughing Dove f OMN Ring Necked Dove f OMN Scaly Francolin F OMN Spectacled Weaver f OMN Viollets Black Weaver f OMN Bronze Sunbird f NEC Collared Sunbird F NEC Green Headed Sunbird F NEC Scarlet Chested Sunbird f NEC Holub's Golden Weaver f OMN Ruppels Long Tailed Glossy Starling f OMN Blue Headed Coucal f CAR African Hobby F CAR Bateleur f CAR Long-Crested Eagle f CAR Shikra f CAR African Paradise Flycatcher f FcIN Blue Breasted Kingfisher F CAR Blue Flycatcher F FcIN Broad Billed Roller f AfIN Brown Throated Wattle Eye f FcIN Little Beeeater f FcIN Mackinnon's Shrike f CAR Narina Trogon F FcIN Northern Black Flycatcher f FcIN Whinchat f FcIN White Throated Beeeater f FcIN Woodland Kingfisher f CAR African Thrush f TrIN Grey Wagtail F TrIN Hadada Ibis f CAR White Spotted Flufftail F TrIN Blue Shouldered Robin Chat F TrIN Brown Backed Scrub Robin f TrIN Snowy Crowned Robin Chat F TrIN Gray Backed Cameroptera f UsIN Gray Capped Warbler f UsIN Green Hylia F UsIN Tropical Boubou f CAR Western Nicator F UsIN 99
White Chinned Prinia F UsIN Dusky Long Tailed Cuckoo FF FgIN Yellow Longbill FF FgIN Afep Pigeon FF OMN Little Gray Greenbul FF OMN Plain Greenbul FF OMN Slender Billed Greenbul FF OMN Spotted Greenbul FF OMN Grey Parrot FF FRUG White Thighed Hornbill FF FRUG Olive Sunbird FF NEC Brown Chested Alethe FF FcIN Fire Crested Alethe FF FcIN African Broadbill FF UsIN Brown Illadopsis FF TrIN Red Tailed Antthrush FF TrIN Red Tailed Bristlebill FF TrIN Rufous Flycatcher Thrush FF TrIN White Tailed Ant Thrush FF TrIN Black Throated Apalis FF UsIN Diederic Cuckoo g FgIN Bronze Mannikin g GRAN Helmeted Guineafowl g OMN Black Headed Weaver g OMN Striped Kingfisher g CAR Little Swift op AfIN White Rumped Swift op AfIN Speckled Mousebird op FRUG Common Waxbill op GRAN Black Kite op CAR Black Shouldered Kite op CAR Angola Swallow op AfIN Grey Backed Fiscal op CAR Striped Swallow op AfIN Yellow Throated Longclaw op TrIN
2018 Species
Common Name Forest Dependency Foraging Guild Afep Pigeon FF OMN 100
African Black Headed Oriole F OMN African Blue Flycatcher g FcIN African Crowned Eagle FF CAR African Cuckoo FF UsIN African Dusky Flycatcher F FcIN African Emerald Cuckoo F FgIN African Goshawk f CAR African Green Pigeon FF OMN African Harrier Hawk F CAR African Open Billed Stork f CAR African Paradise Flycatcher F FcIN African Pygmy Kingfisher F FcIN African Shrike Flycatcher F FcIN African Thrush f TrIN Angola Swallow FF AfIN Ashy Flycatcher F FcIN Baglafecht Weaver wl OMN Barn Swallow F AfIN Black and White Casqued Hornbill f FRUG Black and White Mannikin FF GRAN Black Billed Turaco FF FRUG Black Billed Weaver f OMN Black Crowned Waxbill F OMN Black Cuckoo f UsIN Black Headed Weaver f OMN Black Necked Weaver f OMN Black Throated Apalis FF UsIN Blue Breasted Kingfisher FF CAR Blue Naped Mousebird F FRUG Blue Shouldered Robin Chat FF TrIN Blue Spotted Wood Dove F OMN Blue Throated Brown Sunbird FF NEC Bocage's Bushshrike F UsIN Broad Billed Roller FF AfIN Bronze Mannikin f GRAN Brown Backed Scrub Robin F TrIN Brown Crowned Tchhagra F UsIN Brown Eared Woodpecker F BgIN Brown Illadopsis F TrIN Brown Parrot f FRUG Brown Snake Eagle F CAR 101
Brown Throated Wattle Eye F FcIN Buff Spotted Woodpecker FF BgIN Buff Throated Apalis g UsIN Cardinal Woodpecker F BgIN Chestnut Wattle Eye f FcIN Chin Spot Batis f FcIN Collared Sunbird F NEC Common Bulbul F OMN Common Fiscal F CAR Copper Sunbird FF NEC Crowned Hornbill op FRUG Diederic Cuckoo FF FgIN Double Toothed Barbet f OMN Dusky Blue Flycatcher g FcIN Dusky Long Tailed Cuckoo FF FgIN Dusky Tit F FgIN Eastern Gray Plantain Eater f FRUG European Beeeater FF FcIN Giant Eagle Owl f CAR Golden Breasted Bunting f OMN Gray Apalis f UsIN Gray Backed Cameroptera f UsIN Gray Capped Warbler f UsIN Gray Headed Negrofinch f FgIN Gray Headed Sparrow f OMN Gray Throated Barbet F FRUG Gray Throated Tit Flycatcher g FgIN Gray Winged Robin Chat f TrIN Great Blue Turaco wl FRUG Great Sparrow Hawk FF CAR Green Backed Twinspot F GRAN Green Crombec f FgIN Green Headed Sunbird F NEC Green Hylia FF UsIN Green Sunbird F NEC Green Throated Sunbird F NEC Green Woodhoopoe op TrIN Grosbeak Weaver F OMN Hadada Ibis FF CAR Hairy Breasted Barbet f FRUG Holub's Golden Weaver g OMN 102
Honeyguide Greenbul f OMN Klass Cuckoo F FgIN Lead Colored Flycatcher f FcIN Lemon Dove FF OMN Little Gray Greenbul F OMN Little Green Sunbird F NEC Little Greenbul g OMN Little Sparrowhawk F CAR Lizard Buzzard F CAR Marico Sunbird f NEC Narina Trogon f FcIN Narrow Tailed Starling f FRUG Northern Black Flycatcher F FcIN Northern Puffback f UsIN Olive Bellied Sunbird f NEC Olive Long Tailed Cuckoo FF FgIN Olive Pigeon op OMN Olive Sunbird F NEC Papyrus Gonolek op FcIN Plain Greenbul F OMN Red Bellied Paradise Flycatcher f FcIN Red Capped Robin Chat f TrIN Red Chested Cuckoo FF FgIN Red Eyed Dove f OMN Red Headed Bluebill op GRAN Red Headed Malimbe F BgIN Red Headed Weaver f OMN Red Shouldered Cuckoo Shrike FF UsIN Red Tailed Antthrush F TrIN Red Tailed Bristlebill F TrIN Red Throated Pipit f TrIN Ring Necked Dove wl OMN Ross Turaco FF OMN Rufous Flycatcher Thrush op TrIN Sand Martin g AfIN Scaly Breasted Illadopsis f TrIN Scaly Francolin f OMN Scarlet Chested Sunbird f NEC Sengal Coucal f CAR Slender Billed Greenbul f OMN Snowy Crowned Robin Chat F TrIN 103
Speckled Mousebird f FRUG Speckled Tinkerbird wl OMN Splendid Starling f OMN Striped Kingfisher F FcIN Tambourine Dove F OMN Tawny Flanked Prinia op UsIN Toro Olive Greenbul FF OMN Tree Pipit FF TrIN Tropical Boubou F CAR Vanga Flycatcher FF FcIN Village Weaver FF OMN Violet Back Starling FF OMN Viollets Black Weaver FF OMN Wattled Lapwing F OMN Western Black Headed Oriole f OMN Western Nicator FF UsIN White Browed Scrub Robin f TrIN White Chinned Prinia f UsIN White Headed Sawing FF AfIN White Spotted Flufftail F TrIN White Tailed Ant Thrush f TrIN White Throated Beeeater FF FcIN Willow Warbler FF UsIN Wood Warbler f OMN Woodland Kingfisher f CAR Yellow Bellied Hyliota F UsIN Yellow Billed Barbet F OMN Yellow Breasted Apalis f UsIN Yellow Crested Woodpecker f BgIN Yellow Rumped Tinkerbird f OMN Yellow Spotted Barbet F FRUG Yellow Throated Leaflove F OMN Yellow Whiskered Greenbul f OMN Yellow White Eye f FgIN Yellowbill f FcIN
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Thesis and Dissertation Services ! !