ASSESSING CONSERVATION PRIORITIES AND OPPORTUNITIES IN LOS SANTOS, PANAMA: A METHODOLOGY FOR SPATIALLY-EXPLICIT, SOCIOECOLOGICAL FOREST CONSERVATION PLANNING

By

MICHAEL L. BAUMAN

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2015

© 2015 Michael L. Bauman

To Larry A. Wilson, Anthony Martin, and Stephanie A. Bohlman

ACKNOWLEDGMENTS

Acknowledgements go to my graduate committee, Claudia, Stephen, and especially to Stephanie, for helping me get to the end. Acknowledgements also go to the Azuero Earth Project, the Farmer’s and Rancher’s association of Los Asientos.

Special thanks to the community of Los Asientos for sharing their time and knowledge and to Marie and Yeya for opening their homes. Similarly, thanks go to former Peace

Corps Volunteers Katie and Bracken Kilpatrick and Katie Van der Bilt, who introduced me to their communities. A special thanks to Nuevo Ocu, who helped me get a tow truck for my field vehicle. For funding and support of the project, acknowledgements also go to the Tropical Conservation and Development program, the School of Natural

Resources and Environment.

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TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF ABBREVIATIONS ...... 10

ABSTRACT ...... 11

CHAPTER

1 INTRODUCTION ...... 13

2 IDENTIFYING MULTIPLE FOREST TRANSITION PATHWAYS IN FOREST REGROWTH OF LOS SANTOS, PANAMA AND ITS IMPLICATIONS FOR REGIONAL CONSERVATION MANAGEMENT ...... 17

2.1 Background ...... 17 2.2 Study Area ...... 19 2.3 Methods ...... 20 2.3.1 Preliminary Assessment of Regrowth ...... 21 2.3.2 Spatial Unit for Analysis ...... 22 2.3.3 Regrowth Magnitude ...... 22 2.3.4 Preparation of Variables ...... 23 2.3.5 Methods of Analysis ...... 25 2.4 Results ...... 27 2.5 Discussion ...... 29 2.5.1 Correlations with Mean Slope ...... 29 2.5.2 Forest Transition Regions ...... 29 2.5.2.1 Forest transition in the High Slope region ...... 29 2.5.2.2 Forest transition in the Low Slope Southern region ...... 31 2.5.2.3 Forest transition in the Low Slope Northern region ...... 32 2.5.3 Data Limitations and Uncertainty ...... 34 2.5.3.1 Limitations and errors from census data ...... 34 2.5.3.2 Effects of sampling scale ...... 35 2.5.4 Applications into Conservation Planning ...... 35 2.6 Summary ...... 36

3 IDENTIFICATION OF CONSERVATION PRIORITY AREAS AND DEVELOPMENT OF CONSERVATION OPPORTUNITIES IN LOS SANTOS, PANAMA ...... 46

3.1 Background ...... 46

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3.2 Methods ...... 49 3.2.1 Overview of Modeling Conservation Area Networks ...... 49 3.2.2 Conservation Features ...... 51 3.2.2.1 Birds and amphibians ...... 51 3.2.2.2 Primates ...... 53 3.2.2.3 Forest habitats ...... 54 3.2.2.4 Ecoregions ...... 54 3.2.3 Model Cost Surfaces ...... 55 3.2.3.1 Measures of organizational support ...... 55 3.2.3.2 Joint area and organizational support cost surface ...... 58 3.2.4 Preparation of Model Inputs ...... 59 3.2.5 Model Assessment and Comparison...... 59 3.3 Results ...... 61 3.3.1 Model Comparisons ...... 61 3.3.2 Sensitivity to Individual Conservation Feature Groups ...... 62 3.3.3 Differences among Individual Conservation Feature Groups ...... 63 3.3.4 Assessing Effectiveness of Protected Areas and Organizational Support ...... 64 3.4 Discussion ...... 65 3.4.1 Analysis of Organizational Support and Conservation Features ...... 65 3.4.1.1 Organization support in systematic conservation planning ...... 65 3.4.1.2 Comparison of conservation features ...... 66 3.4.1.3 Comparison of organizational support and conservation features ...... 67 3.4.1.4 Assessment of methodology for including organizational support ...... 68 3.4.2 Caveats ...... 69 3.4.2.1 Evaluation of conservation features ...... 69 3.4.2.2 Evaluating planning unit size ...... 70 3.4.3 Implications for Conservation on the Azuero ...... 70 3.4.3.1 Areas with high conservation value and high organizational support ...... 70 3.4.3.2 Areas with high conservation value and low organizational support ...... 71 3.4.3.3 Areas with high organizational support and low conservation values ...... 72 3.5 Summary ...... 73

4 CONCLUSION ...... 84

APPENDIX

A LANDOWNER INTERVIEW FORM IN ENGLISH ...... 85

B LANDOWNER INTERVIEW FORM IN SPANISH ...... 89

C CONSERVATION FEATURE TARGETS ...... 93

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D ORGANIZATION SEMI-STRUCTURED INTERVIEWS IN ENGLISH ...... 99

E ORGANIZATION SEMI-STRUCTURED INTERVIEWS IN SPANISH ...... 102

F SUPPLEMENTAL MATERIAL ON THE CALCULATION OF JOINT COST (AREA AND ORGANIZATONAL SUPPORT) ...... 105

LIST OF REFERENCES ...... 108

BIOGRAPHICAL SKETCH ...... 119

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LIST OF TABLES

Table page

2-1 Non-government programs identified in 2012 that facilitate increased forest cover in the province...... 37

2-2 Classification of regrowth from Hansen et al. (2013) forest cover data...... 38

2-3 Biophysical and socioeconomic variables used to model regrowth...... 39

2-4 Conditional Aikaike Information Criterion (AICc) scores for single-variable geographically-weighted regression (GWR) models predicting regrowth magnitude...... 42

2-5 Correlation of GWR variables to mean slope...... 43

2-6 AICc scores of GWR models predicting regrowth magnitude...... 43

3-1 Summary of conservation feature targets...... 74

3-2 Organizations interviewed in the process of mapping areas of organizational support in the province of Los Santos...... 75

3-3 CAN Area, organization cost, and PA ratio comparisons for different models. ... 77

3-4 Similarities in model configuration between pairs of models with different cost scales (Model1 vs Model2) using Spearman rho (ρ) and Cohen’s kappa (К). .... 77

3-5 Similarities in model configuration between a model with all conservation features and models with selected sets of conservation features...... 77

C-1 Conservation feature targets and associated measurements...... 93

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LIST OF FIGURES

Figure page

2-1 Tree cover dynamics for Los Santos, Panama...... 44

2-2 Condition values for the two variable GWR model P_60 / X_Slope...... 45

2-3 Mean slope and mean slope coefficient values used to model regrowth magnitude...... 45

3-1 Steps used in Marxan for model configuration and calibration...... 78

3-2 Categorization of TNC Ecoregions across Los Santos province, Panama...... 79

3-3 Organizational support represented by project type...... 79

3-4 Frequency selection of planning units for models with different cost surfaces. .. 80

3-5 Frequency selection of planning units for the joint cost model with all conservation features...... 80

3-6 Frequency selection of planning units of models with different conservation features...... 81

3-7 Planning unit selection for the best CANs of joint cost models targeting different conservation feature groups...... 82

3-8 Comparison of areas with high selection frequencies (75% or greater) between the area only and organizational only models...... 83

F-1 Relationship between organization and area costs in models used to calibrate the joint cost model...... 107

F-2 Selected planning units for CANs used to calibrate the joint cost model...... 107

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LIST OF ABBREVIATIONS

AICc Conditional Aikakon Information Criterion

ANAM National Authority of Environmental Management (Autoridad Nacional de Ambiente)

ANATI Authority for the Administration of Lands (Autoridad Nacional de Administración de Tierras)

BLM Boundary length modifier

CAN Conservation area network

GWR Geographically-weighted regression

HS Region High Slope region

INEC National Institute of Statistics and Census (Instituto Nacional de Estadística y Censo)

IUCN International Union for Conservation of Nature

LSN Region Low Slope Northern region

LSS Region Low Slope Southern region

MIDA Ministry of Agricultural Development (Ministerio de Desarrollo Agropecuario)

OLS Ordinary least squares

PA ratio Perimeter:area ratio

SPF Species penalty factor

STRI Smithsonian Tropical Research Institute

TNC The Nature Conservancy

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

ASSESSING CONSERVATION PRIORITIES AND OPPORTUNITIES IN LOS SANTOS, PANAMA: A METHODOLOGY FOR SPATIALLY-EXPLICIT, SOCIOECOLOGICAL FOREST CONSERVATION PLANNING

By

Michael L. Bauman

August 2015

Chair: Stephanie A. Bohlman Major: Interdisciplinary Ecology

The criteria needed for systematic regional conservation planning have been expanded beyond ecological data, to include socioeconomic and institutional inputs, which provide information about the decision-making processes driving land use change and opportunities for conservation. This study constructs a methodology to provide guidance for regional conservation planning efforts in the Los Santos Province,

Panama, an agropastoral area once covered by tropical forest. The socioecological assessment incorporates ecological data and regional objectives and capabilities of local government and nongovernment organizations, and their interaction with possible forest transition drivers. Ecological data from published literature was summarized into a landscape index representing high, medium, and low areas of ecological value. Input from organizations, collected through semi-structured interviews and a focus group session was used to classify areas with relatively high potential for restoration, increased forest protection, and natural resource-based economic development. Forest transition drivers were identified through geographically weighted regression and detection analysis with socioeconomic and biophysical variables, derived from publicly

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available data and publications. The final analysis uses Marxan, a spatial optimization software, to highlight the sites with highest organizational support for conservation actions in areas with different ecological priorities. The assessment depicts many areas with high ecological value overlapping areas of organizational support. However, processes driving forest cover gain across this region indicate that maintaining and expanding these land use change patterns in a way that benefits the long-term conservation of the region’s faunal and floral biodiversity will require an increased level inter-institutional cooperation and coordination.

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CHAPTER 1 INTRODUCTION

A great challenge to tropical forest conservation is reestablishing forests in regions where widespread forest degradation and deforestation has occurred or is ongoing (Quesada et al., 2009). Much research has focused on defining ecological, social, economic, and political drivers of reforestation, focusing on areas of forest transition, where a net gain of tree cover has replaced a net loss (Kull, Ibrahim, &

Meredith, 2007; Mather & Needle, 1998; Rudel et al., 2005; Uriarte, Schneider, & Rudel,

2010). Understanding socioeconomic and biophysical drivers of forest transition may help guide stakeholders (i.e. landowners, governments, and non-government organizations) to plan and manage efforts to restore forest ecosystems (Rudel et al.,

2005).

Drivers of forest transition vary both spatially and temporally, and contain nonlinear feedbacks between different drivers at local, regional, national, and global scales (Lambin & Geist, 2003) that can be difficult to identify. Regression models represent one set of methods that have been used to identify forest transition drivers that can be incorporated into regional planning processes (Bolliger et al., 2011;

Clement, Orange, Williams, Mulley, & Epprecht, 2009; Kolb, Mas, & Galicia, 2013; Su,

Xiao, & Zhang, 2012). In regression modeling, patterns of forest cover change are modeled as linear or non-linear relationships with biophysical variables, such as such as slope and rainfall, and socioeconomic variables, such as employment and demographic statistics, that define the ecological, social, economic, and political characteristics of a given study area (Bolliger et al., 2011; Clement et al., 2009; Crk, Uriarte, Corsi, & Flynn,

2009; Du, Wang, & Guo, 2014).

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Designing and implementing effective conservation strategies requires the integration of three complex components: 1) species and ecosystems that are targeted for conservation (referred to as conservation features) 2) public and private landowners, whose land management decisions affect species and ecosystems, and 3) social, economic, and political organizations that influence the land use decisions made by landowners. Over the last thirty years, systematic conservation planning tools that simultaneously consider conservation features and their associated socioeconomic systems have been developed to help address complicated conservation planning problems (Linke, Watts, Stewart, & Possingham, 2011; Margules & Pressey, 2000;

Sarkar et al., 2006). In many systematic conservation planning tools, algorithms are used identify conservation area networks (CANs), spatial configurations of land parcels from a larger area that are identified for inclusion in user-defined conservation management goals, such as biodiversity and ecosystem service provisions (Ciarleglio,

Barnes, & Sarkar, 2008; Sarkar et al., 2006). Parcels used to identify CANs often include a mixture of those with no conservation management and those that are already being managed for conservation, such as protected areas and lands with conservation easements. Comparing the overlap of parcels selected in a CAN with those already being managed has been used to emphasize the value of their continued protection while the selected unprotected parcels are seen to represent important targets for actions that contribute to the specific conservation goals, such as increasing habitat availability of an endangered species or funding land management practices that can help restore ecosystem services (Izquierdo & Clark, 2012; Kremen et al., 2008;

Lagabrielle et al., 2011).

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Much of the effort in developing systematic conservation planning tools has focused on methodologies for defining conservation features (species and ecosystems) and conservation targets (how much of either species or ecosystems should optimally be protected in the CAN) (Knight & Cowling, 2007). Systematic conservation planning tools define the spatial distribution, characteristics, and conservation targets of conservation features using “objective” empirical observations, including published reports on the distribution and status of species and ecosystem (Cowling et al., 2003;

Margules & Pressey, 2000). Less effort has focused on how to include landowners and organizations that play a key role in defining opportunities for conservation and successful implementing conservation strategies (Knight, Cowling, Difford, & Campbell,

2010; Margules & Pressey, 2000). Attempts to include such inputs within conservation planning have encompassed measures of the willingness of landowners to participate in conservation initiatives within the systematic conservation planning tools and mapping organizational support for conservation actions using modelled outputs from such tools

(Ban, Picard, & Vincent, 2009; Bolliger et al., 2011; Lagabrielle et al., 2010; Morrison,

Loucks, Long, & Wikramanayake, 2009), but these have methods have formulate into a standardized methodologies in conservation planning. In particular, coordination among organizations has yet to be integrated into systematic conservation planning, despite its identification as a key obstacle in the implementation and effectiveness of regional conservation strategies on both public and private lands (Clement & Amezaga, 2009;

Knight, Cowling, Boshoff, Wilson, & Pierce, 2011; F. P. Smith, Gorddard, House,

McIntyre, & Prober, 2012). For supporters of systematic planning conservation processes, this means that regardless of how well a network of land units (CAN) is

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defined to optimally conserve species and ecosystems from objective data, if inputs from landowners and support and coordination of organizations are lacking, the implementation of the CAN is likely to fail.

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CHAPTER 2 IDENTIFYING MULTIPLE FOREST TRANSITION PATHWAYS IN FOREST REGROWTH OF LOS SANTOS, PANAMA AND ITS IMPLICATIONS FOR REGIONAL CONSERVATION MANAGEMENT

2.1 Background

While links between socioeconomic and biophysical variables is important, identifying how these variables interact with landowner motivation for reforestation is important for planning interventions. Some landowners may intentionally reforest their land because of perceived benefits, such as increased value of forest products, high cultural value on forests, or an increase in the economic potential of the services offered by forests, such as carbon sequestration (Kull et al., 2007; Lambin & Meyfroidt, 2010;

Rudel et al., 2005). For other landowners, reforestation may be an unintentional result of change in livelihoods, labor supply and other socioeconomic circumstances. Large- scale unplanned reforestation has often been tied to larger socioeconomic shifts that reflect changes in regional, national, or global economies. During these socioeconomic shifts, employment needs often shift from rural farms to urban centers, with landowners and farm laborers abandoning local, low wage agriculture and ranching jobs to seek higher wages in in urban areas. The resulting inattention to the land creates an opportunity for tree cover regrowth (Lambin & Meyfroidt, 2010; Rudel et al., 2005).In this study, we attempted to identify a suite of socioeconomic and biophysical variables that can be associated with intentional and unintentional reforestation.

The objective of this study was to apply a methodology that would identify the drivers of forest transition, determine whether tree cover regrowth from forest transition drivers has been planned or unplanned, and describe regional patterns of forest transitions drivers in the context of their significance to regional conservation planning

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efforts. This study specifically looked at the forest transition that has taken place in

Panama’s Los Santos province, where both planned and unplanned reforestation is occurring across the province (Garen et al., 2011; H. P. Griscom, Griscom, & Ashton,

2009). We hypothesized that older regrowth, which started prior to 2000, were largely an unplanned outcome that reflects a province-wide shift of populations from rural areas where household incomes were dependent on agriculture, beef, and dairy production to urban centers where incomes are predominately earned in non-agricultural economic sectors. Socioeconomic and biophysical variables were tested for the strength and significance of their association with regrowth and analyzed as to whether these relationships support our hypothesis of intentional and unintentional regrowth.

As a region with secure tenure, well-defined property boundaries and a large amount of government-collected socioeconomic data, Los Santos is well-suited for investigating drivers at multiple spatial scales. A better understanding of forest change dynamics in Los Santos can help address declines in ecosystem services, such as water purification, crop pollination, carbon sequestration, and habitat for endangered forest-dependent species like the Azuero spider monkey (Ateles geoffroyi azuerensis) and scarlet macaw (Ara macao), that have resulted from an estimated 96% of the forest having been removed or degraded (Garen et al., 2011). Furthermore, a province-wide understanding of forest transition drivers, and whether they are intentional or unintentional could identify early signs of forest regrowth and guide the design of programs to accelerate and sustain the regrowth with the cooperation of landowners

(Bolliger et al., 2011; Mena, 2008). Identifying forest transition drivers may provide

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insight into the possible fate of new tree cover and can help identify direct actions, if any, that have been successful in promoting tree cover regrowth.

2.2 Study Area

The area’s largest urban areas with most of the area’s 90,000 residents occur along the eastern edge are the province (H. P. Griscom et al., 2009). The highway connecting these urban centers extends around the southeastern tip of the peninsula and provides the main route to bring the area’s key exports of beef, milk, corn, and rice to predominately provincial and national markets (Garen et al., 2011). Although settlements in Los Santos began in the 1500s, the greatest population expansion in the area occurred starting in the 1940’s when Panama expanded its agricultural and ranching economy and supported colonization programs. By the 1960’s, intact forest in the area was already scarce. Falling beef prices in the 1970’s and 1980’s stimulated an emigration of landowners who left the province seeking economic opportunities in

Panama City from the Panama Canal (Heckadon Moreno, 2009). Remaining landowners secured private property rights and continued expanding the reach of their fields and pastures into the remaining forest cover. Today, most residents have secure tenure of their lands, having either inherited the property from family that was part of the colonization in the 1940s or having acquired them through direct purchase from those families emigrating from the province (Heckadon Moreno, 2006). Of the landowners that remain, many continue on as commercial or subsistence farmers. Fishing, both subsistence and commercial, is practiced along the coast. Out-migration from rural areas has continued as agricultural wages and profits remain low and better economic opportunities are sought elsewhere (Heckadon Moreno, 2009; Garen et al., 2011).

These opportunities stem from the economic growth of the Panama Canal Zone, from a

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new wave of colonization/agricultural expansion programs recruiting Panamanians to the Darien province, and from local construction jobs resulting from recent foreign interests in coastal real estate and tourism in Los Santos’s southern district of Pedasí

(Wright & Samaniego, 2008).

Among current landowners, there is interest in integrating forest restoration with their ongoing land uses, primarily agriculture and cattle ranching (Garen et al., 2009). In some areas of the province, technical and financial support is also available from government and non-government organizations (Table 2-1) to support tree planting and to facilitate natural regeneration. At the national level, forest restoration has been encouraged through tax incentives for landowners whose lands are delineated and registered with the National Authority for Environmental Management (Autoridad

Nacional del Ambiente – ANAM), though the impact of this program in Los Santos has not been studied. Additionally, “zero net-loss” forest policies, which offset deforestation from mining and highway expansion projects in the neighboring provinces of Herrera and Coclé with an equivalent amount of reforestation, have led to additional replanting of trees in Los Santos. With much of the limited public land already forested, reforestation to mitigate deforestation has occurred on private lands in cases where landowners volunteered a portion of their property for the projects.

2.3 Methods

For understanding forest transition drivers, a geographically-weighted regression

(GWR) was constructed by analyzing a large suite of biophysical and socioeconomic indicators. GWR was used because at the large spatial scale of the entire province, it was likely the patterns and drivers of reforestation were heterogeneous and varied local ecological, social, economic, and political processes (Clement et al., 2009; E. F. Lambin

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& Meyfroidt, 2010). A large suite of variables was chosen over a small number in part because spatial heterogeneity in drivers may lead some variables to be important in some areas versus others (Kolb et al., 2013). GWR specifically addresses the non- stationarity of independent variables by modeling geographically-weighted local coefficients of the variable at the scale of each spatial unit rather than applying the same weights for the entire study area. In this process, GWR is also able to capture patterns of variables at several spatial scales. Both the spatial extent of a variable’s influence and its relationship to regrowth, can be used to understand the types and spatial extents of forest transition drivers (Charlton & Fotheringham, 2009; O’Sullivan,

2003; Wheeler, 2008). The suite of drivers identified in the GWR analysis to test the hypothesis that older regrowth was unplanned, (Bonilla-Moheno, Aide, & Clark, 2011;

Nelson, 2001).

2.3.1 Preliminary Assessment of Regrowth

Temporal change in tree cover was calculated from the Hansen et al. (2013) global product of change in tree cover between 2000 and 2012 derived from Landsat imagery at a 30m x 30m resolution. The amount of older regrowth (defined as regrowth that was initiated prior to the year 2000) was derived from two products: forest cover in

2000 (measure as 1-100% in 1% increments) and the direction of change (forest gain, forest loss and no change) in forest cover between 2000 and 2012 (Table 2-2). The

Hansen et al. (2013) forest change product does not provide tree cover for 2012. In the change analysis, forest loss is defined as “a stand-replacement disturbance or the complete removal of tree canopy at the Landsat pixel scale” and forest gain as “the inverse of loss, or the establishment of tree canopy from a non-forest state” (Hansen et al., 2013).

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Areas of older forest regrowth were defined as which had between 26-75% forest cover in 2000 and either had no change or forest gain between 2000 and 2012. Areas with 76-100% forest cover in 2000 were considered relatively mature forest, and thus not considered regrowth. Areas with 0-25% forest in 2000 and forest gain between 2000 and 2012 were considered newer regrowth, but not included with this analysis. Areas with 0-25% forest in 2000 and no change between 2000 and 2012 were considered

“rastrojo”, which translates as “stubble”. The “rastrojo” category captures the practice of many cattle ranchers in Los Santos of letting fields fallow for 5-10 years before resuming agriculture. The dense, ever-green vegetation of the fallow becomes a feed source for cattle during the dry season, when most grasses have gone dormant. Once the vegetation has been grazed, what remains may be cleared for use as pasture or left to regrow as a source of feed for another season (Heckadon Moreno, 2006).

2.3.2 Spatial Unit for Analysis

Regrowth and the socioeconomic and biophysical variables were all scaled to 1 km2 grid-squares for analysis. Using data from the World Mangrove Atlas (Spalding et al., 1997), we removed grid cells that intersected with the presence of mangroves as the regrowth patterns of mangroves are distinct from other forest types because of the effects of tides and flooding and because mangroves are not generally converted to agriculture and pasture (Giri et al., 2011). Grid cells with less than 90% of their area within the province were also removed.

2.3.3 Regrowth Magnitude

In this study, a measure of relative regrowth was developed in relation to the amount of existing forest (and non-forest) cover in 2000 for each pixel. Absolute regrowth was not used because low rates of absolute regrowth might be due to lack of

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reforestation or because the pixel was already forested, thus leaving no space for regrowth. The amount of reforestation in relation to the amount of non-forest area could also not be constructed because there was not a land use classification analysis available (only forest and non-forest land cover types) that could indicate what percentage of non-forest could potentially be forest (i.e. pasture or agricultural land) and what percentage was not available for reforestation (i.e. paved areas and water).

The relative measure of regrowth, henceforth referred to as regrowth magnitude, was calculated as the amount of regrowth in relation to the amount of existing forest as follows. For each grid cell,

퐹 푦 = log 푟푒𝑔푟표푤푡ℎ (2-1) 퐹2000 where y represents the magnitude value for a given grid cell, Fregrowth represents the amount of regrowth from 2000 to 2012 and F2000 is forested area in 2000. The log transformation was added after initial testing of models showed that it improved overall model fits. Regrowth magnitude, regrowth, and 2000 forest cover are shown respectively in Figure 2-1, A-C.

2.3.4 Preparation of Variables

We derived a range of biophysical and socioeconomic variables to relate increased forest cover to specific forest transition pathways from three key sources: 1)

Smithsonian Tropical Research Institute’s (STRI) GIS Data Portal, 2) 2000 National

Authority for the Administration of Lands (ANATI - Autoridad Nacional de Administración de Tierras) Cadaster, and 3) 2000 and 2010 Panamanian National Census (Table 2-3).

It was assumed all the socioeconomic variables are potentially drivers of either planned or unplanned regrowth. For example, high levels of employment in the tourism sector

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may be associated with reforestation via planned reforestation, where higher tree cover provides aesthetic and wildlife benefits for tourism. However, high levels of employment in the tourism sector may be associated with reforestation via unplanned reforestation if higher-earning service jobs in tourism deplete agricultural labor to maintain pasture and fields. In addition, we paid special attention to colinearity between indicators, which may indicate that a suite of interrelated variables are affecting regrowth. Statistically, colinearity also necessitates that the correlated variables not be treated as independent.

Finally, our analysis took into account that there may be spatial variation in the relationship between biophysical and socioeconomic variables to a forest transition driver or set of drivers.

Biophysical indicators for older regrowth included slope, precipitation, and riparian zone (Crk et al., 2009; H. P. Griscom & Ashton, 2011; H. P. Griscom et al.,

2009; Helmer, Brandeis, Lugo, & Kennaway, 2008; Wright & Samaniego, 2008). A 30m x 30m resolution digital elevation model (LP DACC, 2014) was used to calculate mean slope (X_Slope). Precipitation at a 1km resolution (Hijmans, Cameron, Parra, Jones, &

Jarvis, 2005) was used to calculate mean annual precipitation. A ten meter stream buffer around all streams within the study area was derived from the STRI (2008) hydrology layer and used to indicate the percent of area which is legally protected as riparian zone for each watershed. Euclidian distance was used to calculate distance to the coast (D_Coast), distance to the national highway (D_Hwy), and distance to wealthy towns (D_Wealth), where the threshold for a “wealthy” was if a town’s average monthly household income was at or above the third quartile of income reported for all Los

Santos towns (< 390 USD/month) in the 2000 Panamanian national census.

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Town-level summaries for 17 variables from the 2000 census tables were spatially joined to a geodatabase of towns in Panama based on a nationally assigned identification number for each town (STRI, 2011). Only towns where more than 10 households participated in the 2000 census were included in the analysis. Using these towns, continuous surfaces of values for the 17 variables were created through krigging.

Krigged surfaces were optimized for each variable so that the resulting surface had the best combination of the following three measures: 1) a mean standard error closest to zero, 2) the smallest root-mean square error (root-mean-square standardized closest to one), and/or 3) the difference between the average standard error and the root-mean square closest to zero.

2.3.5 Methods of Analysis

GWR was used to model regrowth because initial data exploration indicated there were different relationships between drivers and regrowth in different areas of the province. GWR assumes that these relationships vary locally based on geographic location, described through a pair of coordinates (u,v) and expressed as:

푌(푢, 푣) = 푏0(푢, 푣) + 푏1(푢, 푣)푥1 + 푏2(푢, 푣)푥2 + ⋯ + 푏푛(푢, 푣)푥푛 + 휀(푢, 푣)

(2-2) where Y is the dependent variable, x1+x2+…xn are the selected predictor variables, b0 is the local intercept, b1+b2+…bn express both the effect and magnitude of effect, and e is the error term at a given point, (u,v) (Charlton & Fotheringham, 2009; Fotheringham &

Charlton, 1998). A bi-square kernel function with an adaptive bandwidth optimized to 80 neighbors was chosen so that of the nearest 80 neighboring points, those farther from the center point have less effect with increasing distance and when neighboring points are more dense (Fotheringham, Brundson, & Charlton, 2002; Pineda Jaimes, Bosque

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Sendra, Gómez Delgado, & Franco Plata, 2010; Gollini, 2015). The number of neighbors defined here reflects a value recommended for model robustness

(Fotheringham et al., 2002).

Variables for a given model were selected in a method adapted from a forward stepwise selection process used in OLS to create an iterative selection process that utilized conditional Akaike information criterion (AICc) and Spearman rho correlation matrix of all variables to determine variable inclusion (Du et al., 2014). First, single- variable GWR models were run for all variables and the variable from the best model was selected for inclusion in additional models. Then all remaining non-collinear variables were tested in a series of two variable models. This process was repeated to include additional variables until AICc values no longer decreased significantly (>3 delta

AICc). Variables with a Spearman’s rho value equal to or greater than 0.3 with variables already included in the model were not tested. Correlation matrices were calculated in

JMP Pro 9.0 (SAS Institute Inc., 2013) while AICc values of GWR models were calculated from GWmodel (Gollini et al., 2015) in R (R Development Core Team, 2010).

The local prediction reliability of the GWR was tested using Moran’s I (to test for spatial autocorrelation) and condition values (to test for local colinearity between model variables). Condition values are recommended to be below 30 for all spatial units and

Moran’s I with a p-value above 0.10 to indicate a reliable local model (ESRI, 2013;

Charlton& Fotheringham, 2009). Model reliability was also checked by comparing the average values from the GWR coefficient surface to the predicted relationship from

OLS, which is recommended this check to verify that coefficient patterns are driven by

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the relationship between predictor and response variables rather than generated as an artifact of estimated intercept values, which also vary locally (Wheeler 2008).

2.4 Results

Comparisons of single-variable GWR models predicting regrowth magnitude showed mean slope (X_Slope) to have the lowest AICc value (Table 2-4). As a result,

X_Slope was set as one of two variables in trials of two-variable models. Variables with medium to high correlation with mean slope were not considered for the two variable models (Table 2-5). Mean slope values from the regrowth analysis were highly or moderately correlated (Spearman’s |ρ| of >0.3) to 19 of the biophysical and socioeconomic variables tested (Table 2-5). Five additional variables that were not correlated with slope were tested in two-variable models: percent of residents over age

60 (P_60), percent of residents with community/municipal water (P_PublicH20), percent of residents employed in the private sector (P_Private), the amount of riparian area

(A_Riparian), and the percent of residents unemployed (P_Unemploy) (Table 2-6).

Addition of P_60 to the model with X_Slope lowered AICc the most. No three-variable had lower AICc values than the two-variable model.

Spatial clustering of residuals and model reliability, via the condition value, were tested for three of the models from Table 2-6: X_Slope, P_60 / X_Slope, and

P_PublicH20 / X_Slope. From these models, Moran’s I p-values were all greater than

0.10 indicating no significant spatial clustering of model residuals. However, in examining local colinearity of the models with the Arc GIS GWR tool, only X_Slope, not the two variables models, produced a model with acceptable condition values below the recommended threshold of 30. High condition values indicated that colinearity between the two variables in the two variable models was too high in one or more areas of the

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province to consider them as separate predictor variables of regrowth magnitude. As an example, areas shaded red in Figure 2-2 show spatial variation in the presence of multicollinearity, measured by condition values, from the P_60 / X_Slope model. The average coefficient value of X_slope in the single variable GWR model (mean = -0.0644

+/- 0.0017) was similar to the OLS global coefficient of X_slope (coefficient value = -

0.1082, r2 = .23, t-ratio -32, p<0.0001), which indicates the response variable (regrowth magnitude) was being modeled from values of the predictor variable (X_slope) rather than by changes to the GWR intercept value (Wheeler, 2008). Thus, mean slope

(X_slope) was determined to be the best overall predictor of regrowth magnitude for older regrowth.

Using the results of the GWR analysis, we defined three areas of the study site which had distinctly different responses between slope and regrowth magnitude in the

GWR analysis (Figure 2-3). High slope (HS) regions, found in the interior of the province, had slopes greater than 15 degrees, low regrowth magnitude, and a negative relationship between slope and regrowth magnitude (β = -0.40 and 0.00). The low slope southern (LSS) region, found along the eastern edge of the province and in isolated pockets within the interior province, had a mean slopes less than 15 degrees, high regrowth and a positive relationship between slope and regrowth magnitude (β > 0).

The low slope northern (LSN) region, a single area of notably low slope coefficient values (β < -0.30) in the north-northwestern part of the province, was similar to the LSS region in that it had low slopes and high regrowth magnitude, but unlike the LSS region, negative coefficient values were projected between slope and regrowth magnitude.

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2.5 Discussion

2.5.1 Correlations with Mean Slope

Mean slope was the most significant variable for modelling regrowth magnitude.

In testing for colinearity among variables, slope was found to have a strong correlation with several other biophysical and socioeconomic variables included within the analysis of regrowth magnitude. Slope has frequently been shown to have collinear relationships with variables that indicate ecological, social, economic, and political conditions (Crk et al., 2009; Du et al., 2014; Wear & Bolstad, 1998; Yackulic et al., 2011). Variables positively correlated to mean slope in this study included percentage of people working in agriculture, precipitation, distance to a major road, number of children in a household, percentage of people who had not completed elementary school, distance to the coast, number of children under 15 years old within a household, the likelihood of a male head of household, and the percentage of homes reporting no sources of income (Table 2-5).

Slope had negative relationship to the following variables: percentage of households with pension incomes, mean household income, the number of households in the area

(population), the percentage of household from ages 15 to 64, and the percentage of households where the head of household has fulltime employment (Table 2-5). These correlations may indicate a set of related variables that are related to regrowth as discussed below.

2.5.2 Forest Transition Regions

2.5.2.1 Forest transition in the High Slope region

In the High Slope (HS) region (light blue areas of Figure 2-3, B), regrowth magnitude values (which are relative to the amount of existing forest cover) in this region are generally low as a result of high levels of already-existing forest cover,

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accompanied by moderate to high absolute amounts of regrowth. Increasing slopes, which coincide with the higher elevations of the province, are accompanied by greater amounts of forest and reduce the available space for older regrowth to have taken place. These conditions in the HS region led to increases in mean slope being associated with areas with lower regrowth magnitude values.

The relationship that slope had to other socioeconomic variables meant that high mean slopes were associated with populations farther from roads where there were more children <15, lower levels of education, lower mean incomes and fewer sources for secondary incomes per household. These areas most likely have higher levels of employment in the agricultural sector but with a lower percentage of the population at prime working age (between 15 and 64). Both in literature specific to forest transition in

Panama and in forest transition theory more generally, low density, agriculturally- dependent populations have been associated with areas of unplanned forest cover increases (Hopkins, Gibbes, Inurreta Diaz, & Rojas, 2012; Lambin & Meyfroidt, 2010;

Thomlinson, Serrano, Lopez, Aide, & Zimmerman, 1996; Wright & Samaniego, 2008;

Yackulic et al., 2011). These studies list drivers of forest transitions as: 1) land abandonment from landowners emigrating due to higher wage jobs in urban centers, 2) lack of local labor for landowners to sufficiently keep pasture and crop land clear of secondary regrowth, and/or 3) a lack of capital for landowners to compete with wages from jobs outside of the agricultural sector and so secure local labor. From a series of

60 interviews conducted concurrently with this study (see questionnaires in Appendices

A and B), the second and third explanations seem the most probable drivers of forest transition in the HS region.

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Higher household dependency on agriculture observed in the census data suggests that landowners are not abandoning their land for other opportunities. On the other hand, lower numbers of adults from 15 to 64 suggests households may struggle to find the labor to manage the land for pasture or agriculture and prevent secondary regrowth. Similarly, lower incomes at the household level would create additional challenges in raising local wages to a rate that could compete with other employment sectors. In interviews, landowners stated their continued interest in ranching or agriculture but expressed concerns in finding labor or being able to compete with wages for employment in Panama City, Las Tablas, and Pedasí.

2.5.2.2 Forest transition in the Low Slope Southern region

In the Low Slope Southern (LSS) region (red and pink areas in Figure 2-3, B), amounts of existing forest cover in 2000 were low to zero so that even minimal amounts of regrowth produced a high regrowth magnitude value. In this region, as slopes increased, the regrowth magnitude also increased. In contrast to the HS region, mean slope in the LSS region correlated with a lower percentage of households dependent on income from the agricultural sector, a higher percentage of working age adults, and a higher percentage of adults in the households reporting employment from pensions, full- time jobs, and tourism. Compared to other parts of the province, these households tend to have better access to markets because: 1) they are closer to roads and larger towns and 2) the households are more likely to own a vehicle. Similarly, households have greater access to public resources, such as electricity, are likely to be smaller, and have higher completion rates of primary education. These trends would be expected given that these areas include the province’s largest, most dense urban centers (i.e. Los

Santos and its suburbs, Guararé, and Pedasí) and most productive agricultural lands.

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For the minimal amounts of regrowth detected in the LSS region, empirical and theoretical research on forest transition offer support of regrowth being either planned or unplanned under socioeconomic and biophysical conditions described in this region. As an unplanned outcome, agricultural intensification in low slope areas may lead to forest regeneration in marginal, lower producing lands being excluded from management practices (Griscom et al., 2009; Lambin & Meyfroidt, 2010). As a region of the province with higher wealth and greater access to public resources, regrowth may also represent a planned outcome with landowners planting trees and/or protecting regrowth for a variety of purposes including timber, construction, fruit production, live fence posts, supplemental forage to cattle, and attractiveness of native vegetation (Brooks, 2010;

Garen et al., 2011; Seabrook, McAlpine, & Fensham, 2008). Given the colinearity of socioeconomic variables, we cannot isolate any single forest transition pathway without more research targeting the land management decisions being made at the household level.

2.5.2.3 Forest transition in the Low Slope Northern region

In the Low Slope Northern (LSN) region (dark blue regions in Figure 2-3, B), as in the LSS region, existing forest cover in 2000 was low such that even small amounts of regrowth magnitude produced a high regrowth magnitude value (Figure 2-1). However, the LSN region had a similar, negative relationship between slope and regrowth magnitude as the HS region indicating increases in regrowth magnitude as slopes decreased.

The socioeconomic variables correlated with slope also had values similar to those observed across the LSS region. Like the LSS region, the LSN region had a lower percentage of households dependent on income from the agricultural sector and a

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higher number of households (i.e. higher population density). Similarly, there was a higher percentage of working age adults and a higher percentage of adults in the households reporting employment from pensions, full-time jobs, and tourism. Similar to the LSS households, these households may have better access to markets because: 1) they are closer to roads and larger towns and 2) the households are more likely to own a vehicle. Additionally, these households would likely have greater access to public resources, such as electricity, be smaller, and have higher completion rates of primary education. The most notable difference between the LSN region and the LSS region is that the LSN region is farther from the province’s largest towns.

As with the LSS region, empirical and theoretical research on forest transition offer support of regrowth being either planned or unplanned under socioeconomic and biophysical conditions described in this region. Since the relationship between mean slope and regrowth is similar to the HS region, drivers of forest transition may be similar to those suggested previously, mainly that landowners were unable to find sufficient labor to prevent secondary regrowth on their properties or that they lacked sufficient funds to pay wages at a level that could compete with other economic opportunities

(Mather & Needle, 1998; Rudel et al., 2005). Where these drivers are influencing land management decisions, regrowth would be an unplanned outcome.

However, the minimal amounts regrowth observed in the LSN region could also be the result of planned regrowth. With socioeconomic conditions allowing for a similar set of forest transition drivers described for the LSS region, increased wealth and access to public resources may help drive small-scale forestry projects across the LSN region (Rudel, Defries, Asner, & Laurance, 2009; Yackulic et al., 2011). As mentioned

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above, additional information at the household level would be needed to better understand the precise drivers of forest transition in this region.

2.5.3 Data Limitations and Uncertainty

2.5.3.1 Limitations and errors from census data

In estimating socioeconomic variables for the province, census tracts were used

Census tracts in Panama are not a contiguous set of polygons and so indirect methods, like kriging, were used to interpolate patterns between areas recorded in the census.

Though it was initially assumed that landowners were living near or on their properties, landowners frequently described owning multiple properties whose locations were likely to have been in more than one of the forest transition regions that we identified. The estimated values of socioeconomic variables likely provide a better description of residents’ households in a given area rather than the landowners’ households for properties in the area. This does not fundamentally change interpretation of forest transition drivers, but indicates that further investigation of the relationships between landowners and the availability of labor for the agricultural sector is warranted. In particular, a greater number of landowners in the HS region may be living in the LSS region of higher wealth and population density. As a result, lack of availability of labor, more so than capital, may be driving forest transition in the HS region.

To a lesser extent, inconsistencies in the data may have reduced some of the accuracy of the krigged surfaces used in analysis. In some cases, towns with available census and spatial data could not be used in calculating krigged surfaces because town codes changed and could not be tracked between the 2000 and 2010 several districts.

For example, there were additions of new corregimientos (counties) within the province that were not in the 2000 census. Additionally, typos and inconsistencies between town

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names and codes across datasets (i.e. STRI spatial dataset, 2000 census data, and

2012 census data) further reduced the number of points that could be used in kriging and potentially reduced the accuracy of krig surfaces.

2.5.3.2 Effects of sampling scale

Spatial scale is believed to have important effects on the results of GWR models

(Du et al., 2014; O’Sullivan, 2003; Su et al., 2012). Multi-scale GWR analyses have the ability to offer insights into the spatial trends at which processes occur. Yackulic et al.

(2011) showed that the strength of indicators of land use change, specifically forest transition, can vary depending on the spatial scale of the analyses. While a similar methodology would have been desirable for Los Santos, insufficient data from the cadaster and census tracts limited the ability to build a spatial model based on nested social institutions, such as neighborhoods or municipalities. Not having such a range of a priori spatial structures to apply for the province, our study applied a 1 km x1 km grid in an effort to extract information on drivers that occurred at smaller spatial scales. The grid was also advantageous in that it is easily replicable and was manageable for computational processing given the size of the study area.

2.5.4 Applications into Conservation Planning

Information on where regrowth has been planned and the variables that drive these land management decisions can provide important information to building forest conservation programs that appropriately target landowners and other natural resource stakeholders. From this study there was greater support that forest transition in the Los

Santos province has been an unplanned outcome of other actions rather than planned action. Though the results are not surprising, given the similarity in the ecological, social, economic, and political to other areas in Latin America, they suggest that the

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enthusiasm for planting and protecting a wide range of tree species noted in other regional studies and in interviews conducted alongside this study do not drive the province’s forest transition processes (Bray, 2010; Garen et al., 2011; H. P. Griscom &

Ashton, 2011; Wright & Samaniego, 2008). For conservation planners, unplanned regrowth can still indicate opportunities for forest conservation as regrowth patterns may be predicted using widely collected socioeconomic variables, such as those applied in this study, and regional plans can be adapted to incorporate the areas where regrowth is projected.

2.6 Summary

The main objectives of this study were to define drivers of forest transition taking place in Los Santos, Panama in order to inform regional plans for forest conservation.

GWR modelling was used to identify socioeconomic variables that indicated patterns of forest transition and define location and spatial scales at which different forest transition drivers may be occurring. Results from these analyses seem to indicate that much of the forest transition happening across the province has been the result of unplanned regrowth processes, such as labor shortages to manage lands with additional positive feedbacks to regrowth processes in rural areas because of the out-migration of residents to higher wage jobs in non-agricultural sectors and in urban centers. There was limited evidence for planned regrowth, the understanding of which would have been most useful to target forest restoration programs.

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Table 2-1. Non-government programs identified in 2012 that facilitate increased forest cover in the province. Organization Primary Role Funding Source United Nations - Global Supply funds and equipment for community World Bank Environment Facility (UN-GEF) projects that join improving cattle http://sgp.undp.org/ management with replanting native trees

Centro para la Investigacion en In coordination with UN-GEF, provide UN-GEF, Sistemas Sostenibles de technical assistance for community projects corporate and Producción Agropecuaria that join improving cattle management with private donations (CIPAV) replanting native trees http://www.cipav.org.co/

Mesoamerican Biological Corridor Provide funding and assistance to individuals World Bank of Panama II (CBMAPII) and community organizations in the promotion http://www-wds.worldbank.org* of wildlife conservation, organic-certified agriculture, improvement of forest resources, ecotourism, and production of non-timber forest products

Azuero Earth Project (AEP) Provide information and useful contacts to Corporate and http://azueroearthproject.org/ landholders interested protecting biodiversity private donations and sustainable development

Conservación, Naturaleza, y Vida Promote reserves and easements on private Corporate, (CONAVI) lands, protect sea turtles, and develop eco- government, and http://www.conavida.org/ tourism private donations

Environmental Leadership and Provide technical assistance in managing Yale University – Training Initiative (ELTI) native tree plantations to landholders who had School of http://environment.yale.edu/elti/ previously participated in the Native Species Forestry and Reforestation Project (PRORENA) Environment

*http://www- wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2006/01/23/000160016_2006012313 3712/Rendered/PDF/34757.pdf

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Table 2-2. Classification of regrowth from Hansen et al. (2013) forest cover data. 2000 Hansen 2000-2012 Hansen New Classification Forest Cover Forest Cover Change In this Analysis Classification Classifications 0-25% Non-forest Non-forest

26-50% Non-forest Rastrojo 51-75% Non-forest Older Regrowth

76-100% Non-forest Extant Forest

0-25% Forest gain Newer Regrowth

26-50% Remained forested Older Regrowth

51-75% Remained forested Older Regrowth

76-100% Remained forested Extant Forest

0-25% Deforestation Non-Forest

26-50% Deforestation Rastrojo 51-75% Deforestation Rastrojo 76-100% Deforestation Deforested

0-25% Both regrowth and Rastrojo deforestation

26-50% Both regrowth and Rastrojo deforestation

51-75% Both regrowth and Rastrojo deforestation

76-100% Both regrowth and Rastrojo deforestation

n/a Water Water

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Table 2-3. Biophysical and socioeconomic variables used to model regrowth. Variables have been given an abbreviation used in other tables. Empirical or theoretical justification explains each variable potential relationship to regrowth. Variable Abbreviation Relation Empirical or Theoretical Justification Biophysical characteristics Land abandonment and forest recovery tend to happen sooner at higher slopes Mean Slope X_Slope (+) where access is more difficult (Crk, Uriarte, Corsi, & Flynn, 2009) Growth is limited by water availability Mean Precipitation X_Precip (+) (Breugel et al., 2011) Riparian areas can offer a combination of higher slopes, water access, and remnant (+) forest cover, all of which complement forest recovery (Griscom, Griscom, & Ashton, Percent Riparian Area P_Riparian 2009) As a refuge for remnant forest cover, can act as a seed bank to newly abandoned

lands (Griscom et al., 2009) Population Size

Less populated areas are more likely to see (+) early land abandonment and forest recovery (Mather & Needle, 1998) Mean number of households X_House Areas near high populations may be prone (-) to urban expansion and deforestation (Thomlinson & Rivera, 2000) Age Structure

Households are likely to have more dependents and a greater need to maintain Percent below age 15 P_15 (-) fields and pasture for income (De Sherbinin et al., 2008) Middle-aged populations are more likely to Percent between ages of 15 have the labor available for maintaining or P_35 (-) -59 expanding fields and pastures (Lambin, Geist, & Lepers, 2003) Higher elderly populations may not be able to provide the labor necessary to maintain (+) fields and pastures (Seabrook, McAlpine, & Fensham, 2008) Elderly populations are more likely to Percent above age 59 P_60 support a land ethic that promotes agricultural/pastoral maintenance and (-) expansion and be resistant to adopting newer, pro-forest land management practices (Benjamin, Bouchard, & Domon, 2008)

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Table 2-3. Continued. Variable Abbreviation Relation Empirical or Theoretical Justification Gender

Agriculture and ranching are a traditional income source for households where the Percent of households with head is male and be contrary to land P_HeadM (-) a male head of household abandonment and forest recovery and indicative of agricultural expansion (Ahnström et al., 2008) Family Size

Families with fewer children do not need children to help with farm labor because Mean number of children X_Child (-) they are less dependent on agricultural income (De Sherbinin et al., 2008) Income

Rural, low-income areas are likely to (-) experience early land abandonment and Mean monthly household forest recovery (Mather & Needle, 1998) X_HouseInc income Higher income areas can indicate pressure (-) for urban expansion (Thomlinson & Rivera, 2000) Higher incomes can provide necessary capital to maintain economies of scale Distance to a wealthy town D_Wealth (+) where profits from agriculture and ranching are low (Benjamin et al., 2008) Education

People with higher education are more (-) likely to find employment outside of the Percent of people who have agricultural sector (Rudel et al., 2005) not completed primary P_NoSchool People with higher education have a better school understanding of the role of ecological and (-) conservation practices for fields and pastures (Raymond & Brown, 2011) Infrastructure

Areas with community-provided or municipal water supply receive greater Percent of people with focus of government assistance programs, P_PublicH20 (-) community/ municipal water which may extend to include agricultural subsidies that prevent land abandonment (Ribeiro Palacios et al., 2013) Areas without electricity are likely more Percent of people with isolated and will experience early land P_Electric (-) electricity abandonment and forest recovery (Bonilla- Moheno, Aide, & Clark, 2011) Areas farther from the highway are farther Distance to the national from product markets and more likely to be D_Hwy (+) highway abandoned and undergo forest recovery sooner (Crk et al., 2009) Employment

Higher unemployment may encourage Percent of people P_Unemployed (-) workers to seek income through illegal unemployed income sources, including illegal logging

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Table 2-3. Continued. Variable Abbreviation Relation Empirical or Theoretical Justification (Alemagi & Kozak, 2010; Bouriaud, 2005) People with no third-party income source Percent of people with no are more dependent maintaining or P_NoIncome (-) third-party income expanding fields and pasture for income (Raymond & Brown, 2011) A higher percentage of people working in Percent of people employed the agricultural sector can indicate a in the agricultural, fishing, or P_Agro (-) greater interest in maintaining or mining sectors expanding fields and pastures (Knight, Cowling, Difford, & Campbell, 2010) Potential income from eco-tourism Percent of people employed P_Tourism (+) encourages landowners to encourage in hospitality/tourism forest recovery (Lambin & Meyfroidt, 2010) Reduced reliance on agricultural sector for household income can encourage land (+) abandonment and forest recovery (Rudel et al., 2005) Additional income can provide necessary Percent of people employed capital to maintain low income-generating P_Private (-) in the private sector farm activities (i.e. crops and cattle; De Sherbinin et al., 2008) Areas with high employment outside of the agricultural sector are prone to a second (-) wave of deforestation from urban expansion (Thomlinson & Rivera, 2000) Elderly populations are more likely to support a land ethic that promotes agricultural/pastoral maintenance and (-) expansion and be resistant to adopting newer, pro-forest land management practices (Benjamin et al., 2008) Percent of people with Reduced reliance on agricultural sector for P_Pension pensions household income can encourage land (+) abandonment and forest recovery (Rudel et al., 2009). Additional income can provide necessary capital to maintain economies of scale (-) where profits from agriculture and ranching are low (De Sherbinin et al., 2008) Landowners encourage forest recovery (+) processes for potential income from eco- tourism (Lambin & Meyfroidt, 2010) Distance to the coast D_Coast Foreigners who have recently purchased (-) land have a pro-forest land use ethic (Lambin & Meyfroidt, 2010)

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Table 2-4. Conditional Aikaike Information Criterion (AICc) scores for single-variable geographically-weighted regression (GWR) models predicting regrowth magnitude. Lower scores represent variables that produced a better fit to regrowth magnitude. Variable AICc X_Slope 8950.179 D_Wealth 9048.397 D_Hwy 9084.109 X_Precip 9093.402 P_Electric 9093.948 D_Coast 9109.166 P_15 9110.095 P_PublicH20 9111.694 P_NoSchool 9119.343 X_HouseInc 9128.125 X_House 9130.225 P_60 9131.668 P_Private 9132.829 A_Riparian 9138.566 P_HeadM 9148.411 P_Agro 9150.547 P_FullTime 9151.269 X_Children 9151.677 P_Tourism 9154.293 P_NoIncome 9162.2 P_Pension 9165.314 P_Unemployed 9170.945 P_35 9189.073

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Table 2-5. Correlation of GWR variables to mean slope. Variables with positive relationships to mean slope (X_Slope) are listed in the left two columns, variables with a negative relationship to X_Slope are listed in the right two columns. Variable Spearman ρ Variable Spearman ρ P_Agro 0.5908 P_Tourism -0.5249 Precip 0.5895 P_Electric -0.4901 D_Hwy 0.5423 P_Pension -0.4868 X_Children 0.5210 X_HouseInc -0.4497 P_NoSchool 0.4843 Household -0.4226 D_Wealth 0.4819 P_35 -0.4144 D_Coast 0.4683 P_FullTime -0.3996 P_15 0.3865 P_60 -0.2062 P_HeadM 0.3848 P_PublicH20 -0.1587 P_NoIncome 0.3316 P_Private -0.1096

Table 2-6. AICc scores of GWR models predicting regrowth magnitude. Models were created through forward stepwise process, whereby the best single- variable model (X_slope) was carried into all two-variable models, and the best two-variable model (X_slope/P_60) was used as the base to all three variable models. V1 V2 V3 AICc X_Slope - 8950.179 X_Slope P_60 - 8937.079 X_Slope P_PublicH20 - 8941.661 X_Slope A_Riparian - 8954.959 X_Slope P_Private - 8960.536 X_Slope P_Unemployed - 8993.464 X_Slope P_60 P_PublicH20 8951.191 X_Slope P_60 A_Riparian 8943.878 X_Slope P_60 P_Unemployed 8986.217

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A

C Hectares B Hectares

Figure 2-1. Tree cover dynamics for Los Santos, Panama. Cells without data (not shown) were either mangroves or missing values for either the 2000 forest cover or 2000-2012 regrowth data layers. A) Regrowth magnitude with values shaded to reflect standard deviation from the mean. Areas shaded grey represent regrowth magnitude values within 0.5 standard deviations, areas shaded red represent regrowth magnitude values <0.5 standard deviations, and areas shaded blue represent regrowth magnitude values >0.5 standard deviations. B) Tree cover increase adapted from Hansen et al. (2013) 2000 and 2012 forest classification images. Darker shading reflects a greater area (hectares) of regrowth from 2000 to 2012. C) 2000 forest cover adapted from Hansen et al. (2013) forest classification. Darker shading indicate a greater area (hectares) of forest cover within the pixel in 2000.

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Condition Values

Figure 2-2. Condition values for the two variable GWR model P_60 / X_Slope. Condition values >3 (pink to red shaded areas) represent areas of high colinearity between the two predictor variables. High colinearity indicated poor model performance, since both variables were likely to reflect a single forest transition driver.

Mean Slope (degrees) β of Mean Slope

A B

Figure 2-3. Mean slope and mean slope coefficient values used to model regrowth magnitude. A) Mean slope values, in degrees, are shaded in increments of 3 degrees Darker blue shading represents flatter areas (<15 degrees) and darker red shading represents areas of steeper slopes (>15 degrees). B) Coefficient values of mean slope from the X_Slope GWR model.

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CHAPTER 3 IDENTIFICATION OF CONSERVATION PRIORITY AREAS AND DEVELOPMENT OF CONSERVATION OPPORTUNITIES IN LOS SANTOS, PANAMA

3.1 Background

In this study, we present a methodology for objectively quantifying organizational support, which is defined as technical or financial assistance from government and nongovernment organizations to support conservation actions, with a particular emphasis on coordination among organizations. Some conservation planning processes use an “expert-driven” approach, in which experts from organizations participate in helping define conservation priorities (Cowling & Pressey, 2003; Klein et al., 2008).

Because experts may have uneven knowledge of the region, biases from personal experience and/or agendas driven for the benefit of their organization (Cowling et al.,

2003; Smith, Muir, Walpole, Balmford, & Leader-Williams, 2003; Whitten, Holmes, &

MacKinnon, 2001), we wanted to step back from direct participation in organizational experts in defining conservation features and targets, and instead develop a methodology that transparently and objectively assesses current and planned support by organizations for conservation actions. By including organizational support in this way, we add actual “on-the-ground” organization’s actions to conservation planning, rather than influencing the planning process with individual experts’ opinions. If objectively assessed, organizational support for conservation is a critical piece to include in systematic conservation planning (Cowling et al., 2003; Moon et al., 2014).

Beyond the explicit objective to ensure the long-term persistence of species and ecosystems, organizations may promote conservation actions for reasons only tangentially related to conservation, such as promoting the economic and environmental resilience of vulnerable human populations (FAO, 2010; UN-REDD Programme, 2012).

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At time, there may be a geographical overlap between organizations explicitly setting conservation goals and organizations contributing indirectly to those same conservation goals. For example, one organization may be promoting the protection and planting of riparian corridors with landowners to conserve or extend the habitat availability of an endangered primate. However, a second organization may be working with landowners to protect and replant riparian corridors to reduce water contamination from sedimentation and agrochemical runoff. Recognizing where projects overlap provides a relative measure of identifying where synergies between organizations may exist and conservation and non-conservation objectives can become more effective. Using the example of protecting and replanting riparian corridors, each project alone offers opportunities for conservation, but examining both projects within a single spatial assessment may suggest where coordinating riparian protection and replanting efforts along a single river network could offer greater benefits for habitat increases and water quality than the resources of either individual project might allow. In this way, adding organizational support to systematic planning can provide a method to look at what types of conservation goals align or do not align with current organizational support.

The goal of this study was to develop methods for incorporating availability of organization resources for conservation into systematic conservation planning and to evaluate how a lack of organizational support can act as a constraint in conservation area network (CAN) design. One part of this evaluation analyzed the effectiveness of existing protected areas via their inclusion in selected CANs, similar to the work done by

Izquierdo & Clark (2012) and Kremen et al. (2008). A second part of the analysis sought to determine if different conservation targets, for example preserving primate habitat

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versus reforesting critical ecoregions, had different organizational support. To do so, we used a conservation planning software, Marxan (Ball & Possingham, 2000), applied to conservation of species and ecosystems (conservation features) in the Los Santos province on the Azuero Peninsula in Panama. Los Santos was chosen because the population has been undergoing a socioeconomic transition (H. P. Griscom et al., 2009) that may be accompanied by a change in attitudes about conservation. Also, as of

2012, there were at least nine separate government and non-government organizations assisting landowners in the province. Finally, there is a need for a systematic conservation plan for this area as we are aware of no unified regional plan to address declines in ecosystem services, such as water purification, crop pollination, and carbon sequestration, and significant reductions in local populations of endangered forest- dependent species like the Azuero spider monkey (Ateles geoffroyi azuerensis) and scarlet macaw (Ara macao) (Garen et al., 2011).

We hypothesized that incorporating organizational support would significantly alter CAN designs. Specifically, CANs that do not include organizational support in the planning algorithm will more often include protected areas, but not areas that offer opportunities to increase conservation value of the landscape via private landowners receiving organizational assistance. On the other hand, CANs that include organizational support in the planning algorithm will include areas outside of protected areas where the existing resources of established organizations that assist private landowners may increase species viability and ecosystem services. We discuss our results with respect to the location of existing protected areas in the province and land cover changes that have been occurring over the past 15 years.

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3.2 Methods

3.2.1 Overview of Modeling Conservation Area Networks

In many systematic conservation planning tools, such as the Marxan software used here, algorithms are used to evaluate spatial networks, known as conservation area networks (CANs), for their likelihood to achieve conservation goals defined by conservation features of interest and quantified by conservation targets (Moffett &

Sarkar, 2006). Conservation features can be represented as species, ecosystems, or ecosystem services and the corresponding targets needed to achieve conservation goals are defined as a proportion of the features distribution or occurrences (Ardron,

Klein, & Nicolson, 2008). The region of interest is subdivided into smaller planning units, which may be regular shapes, such as grid squares or hexagons, or irregular shapes, such watersheds or land parcels (Nhancale & Smith, 2011). In this study, the planning unit was defined by a 1 km x1 km “fishnet” square grid for the Los Santos province. The software algorithm uses machine learning to identify optimal CANs that include the conservation targets of all conservation features and minimize costs, which are representations of the factors that may limit implementation of conservation goals

(Ardron, Possingham, & Klein, 2010; Ball & Possingham, 2000; Watts et al., 2009).

Many planning tools also try to achieve spatially compact CANs. Compactness increases the connectivity among the planning units which is beneficial to species with large home ranges, promotes exchange and gene flow among a population, and focuses potentially costly conservation effort to a smaller area, among other benefits

(Klein et al., 2008; Margules & Pressey, 2000; Pulliam & Danielson, 1991).

Marxan reduces the large number of possible planning unit combinations to find feasible CAN solutions, which are defined as CANs that achieve all conservation

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targets. For each CAN, Marxan calculates a score based on costs, compactness and a penalty for missed conservation targets as shown below (Ball & Possingham, 2000):

푓(퐶퐴푁) = ∑푃푈푠 퐶표푠푡 + 퐵퐿푀 ∑푃푈푠 퐵표푢푛푑푎푟푦 + ∑퐶표푛푉푎푙푢푒 푆푃퐹푥푃푒푛푎푙푡푦 (3-1) where f(CAN) is the score for a given CAN, ∑푃푈푠 퐶표푠푡 is the total sum of costs across all planning units (PUs) in the CAN, ∑푃푈푠 퐵표푢푛푑푎푟푦 is perimeter of the CAN, BLM stands for boundary length modifier and is a coefficient that weights model compactness, and

∑퐶표푛푉푎푙푢푒 푆푃퐹푥푃푒푛푎푙푡푦 is the penalty for not adequately reaching targets for all conservation features. SPF stands for species penalty factor which is weight to adjust the importance meeting the target of the specific conservation feature (Ardron et al.,

2010) and is described below. The compaction term is based on the length (km) of shared boundary of all planning units selected in the CAN (the lowest compaction “cost” would be achieved with a CAN that is a single round patch).

In Marxan, feasible CANs are identified through simulated annealing, a process that iterates through different CAN configurations a large number of times (often > 1 million), calculates the objective function score for each iteration, and tests the score of the objective function against the previous iteration. The best CANS from all runs (100 runs per model in this study) are used to construct descriptive statistics, such as average CAN area and average costs, for model comparison and evaluation. As many similar feasible solutions exist for a given set of costs and conservation targets, it is often more useful to interpret these average feasible CAN statistics, rather than focus on the single “best” solution (Ardron et al., 2010; Game & Grantham, 2008).

Below we describe how the conservation targets, costs and compactness were input into the Marxan software to determine CANs for the Los Santos, Province. We

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focus in particular on how different costs, with and without organizational support, affect optimal CANs. Most procedures for choosing parameters followed standard Marxan protocols as described in Ardron, Possingham, & Klein, (2010), Ardron, Klein, &

Nicolson (2008), and Ball & Possingham (2000). Figure 3-1 presents an overview of the

Marxan process adapted from Ardron et al. (2010).

3.2.2 Conservation Features

Five conservation features were used in this study: birds, amphibians, primates, forest cover, and ecoregions (Table 3-1). The extent to which a conservation feature

(species or ecosystem) is prioritized in the selection of planning units in the CAN is determined by both its conservation target (indicated for each conservation feature below) and its species penalty factor (SPF), which is weight to adjust the importance meeting the target of the specific conservation feature (Ardron et al., 2010). The conservation target is the proportion or amount of the feature’s spatial distribution that is desired to be conserved, which is optimally provided by known habitat area requirements for the feature’s viability (Ardron et al., 2010; Game & Grantham, 2008). A full list of the targets used in this study has been placed in Appendix C. According to standard practice for Marxan (Ardron et al., 2010; Game & Grantham, 2008), the species penalty factor was kept constant across all features and calibrated with the methodology described by Game & Grantham (2008) until a sufficient percent of model runs (suggested as 70-90%) can produce feasible CANs.

3.2.2.1 Birds and amphibians

Bird and amphibian distribution data was provided by BirdsLife International

(2012) and IUCN Red List (2012), respectively. Distributions, created by the IUCN Red

List methodology under Criterion B (2013), are based on one or more of the following

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types of information: occurrence data, known occurrences, knowledge of habitat preferences (e.g. elevation, precipitation, and temperature), remaining suitable habitat, and others (IUCN, 2013). For this study, the extent to which these distributions represented occurrence data from within the province was not ascertained so it was assumed that a species may be present throughout all areas of its distribution. To be included in analysis, a species’ distribution had to be less than 90% of the province since species with 91 – 100% coverage would not have provided a basis of choosing one planning unit over another in the algorithm. Species with distributions of less than

10% coverage of the province were evaluated individually and removed from the model if the distribution within the province was limited to slivers along the edge of the province’s boundaries but occurred more widely in the neighboring province. These criteria led to a reduced list of 172 bird species and 14 amphibian species that were used in the analysis, none of which are listed as endangered, threatened, or vulnerable under the IUCN Red List (2012). The conservation target of a species within a taxonomic class (i.e. birds or amphibians) was set relative to the conservation target of the species with the smallest distribution. This gave greater conservation priority to less common species within the province; the species with the smallest distributions had the greatest conservation target (80%) and species with larger distributions in the province had lower conservation targets (as low as 8%) with the conservation targets calculated as follows:

0.5 (푥푝⁄푦푝) ≈ (푥푡⁄푦푡) (3-2)

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where y is the species with the smallest distribution; x is another species in the same taxonomic class; p proportion of the species total area to be targeted for conservation; t is the total area of the species (Ardron et al., 2010).

3.2.2.2 Primates

The study region has two endangered primate subspecies, a spider monkey subspecies Ateles geoffroyi azuerensis and a howler monkey subspecies Alouatta coibensis trabeata (Méndez-Carvajal et al., 2013). Both are listed as critically endangered on IUCN’s Red List with their populations under threat from habitat loss by deforestation from agricultural expansion and hunting pressure in remnant forests

(IUCN, 2012). We used the presence/absence data of Méndez-Carvajal (2011) to create a base map for each species, which was expanded based on home ranges

(Fedigan & Fedigan, 1988; Gavazzi, Cornick, Markowitz, Green, & Markowitz, 2008) and riparian corridors used by these species (Méndez-Carvajal, 2011). The base map was expanded as with buffers equal to the species home ranges’ around each occurrence, recorded as points or linear transects. The home ranges were 97.9 ha for

A.g. azuerensis (Fedigan & Fedigan, 1988) and 60 ha for A. c. trabeata (Gavazzi et al.,

2008). Additionally, if troops were located along a river, we expanded the base map to include the river and all its tributaries assuming all tributaries provided habitat for the species. Because buffers were not evaluated to exclude unsuitable habitat, it was assumed that there was a high level of uncertainty with the distributions and conservation targets of both primate species were set low (20% of their estimated distribution).

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3.2.2.3 Forest habitats

Three forest habitat categories were developed using the 30 m x 30 m global tree cover classification of Hansen et al. (2013). Pixels classified in 2000 as having 50-100% were considered forest. Categories of forest cover were then determined at the scale of

4 ha as follows 1) “non-forest” had 0-50% forested 30 x 30 m cells, 2) “low quality forest habitat” had 51-75% forested cells, and 4) “high quality forest habitat” had 76-100% forested cells. Since both low and high quality forest cover is rare across the study area

(neither covers more than 10% of the study area), their conservation targets were set relatively high, to 80%.

3.2.2.4 Ecoregions

The study area is comprised of four ecoregions: 1) Dry Tropical Panamanian

Forest, 2) Moist Tropical Panamanian Forest, 3) South American Mangroves, and 4)

Talamancan Montane Forests (Olson & Dinerstein, 2002) (Figure 3-2). Though much of the forest cover is gone from the dry and moist tropical forest ecoregions, the ecoregions themselves each cover a large portion of the province (Olson & Dinerstein,

2002). The conservation targets of dry and moist tropical forests were set to 20%, reflecting general estimates, such as the Aichi Biodiversity Targets (Secretariat of the

CBD, 2010), of what proportion may be needed of any terrestrial ecosystem to protect its biodiversity (Secretariat of the CBD, 2010; WCED, 1987). The mangrove and montane forest conservation targets were set to 80% because mangroves are recognized through legislation as priority areas for conservation (Isaza, 2002) and both ecoregions have limited distribution in province.

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3.2.3 Model Cost Surfaces

In addition to using conservation targets to select planning units, two types of costs were calculated for each planning unit: area and organizational support. Area, which is a common cost used in Marxan applications (Delavenne et al., 2011; Huber,

Greco, & Thorne, 2010; Rumsey et al., 2004), is simply area (in hectares) of the final

CAN with additional penalties applied for any unmet conservation targets. The rationale is that smaller CANs will be easier to achieve because they will take less resources, landowner cooperation, protected land purchases and organizational support (Adame,

Hermoso, Perhans, Lovelock, & Herrera-Silveira, 2014; Carwardine et al., 2007; Huber et al., 2010). For area, cost values were scaled to hectares so that interior cells all had a cost of 100 and planning units divide along the edge of the province were rounded to the nearest hectare.

3.2.3.1 Measures of organizational support

Data collection. In order to determine the level of organizational support available from organizations, we adapted the participatory methodology of Ban et al.,

(2009) to include four main steps: 1) semi-structured interviews with individual resource users 2) systematization of interview data 3) construction of a consensus map of areas perceived as important through a multi-stakeholder meeting, and 4) processing of data to the planning unit scale. Private semi-structured interviews were held with representatives from each organization that provides technical or financial assistance to support conservation actions, where representatives were shown a paper map of the

Los Santos province with district capitals and major roads (Appendices C and D). They were asked to identify the places in the province where their organization worked and describe the type of projects implemented or planned for that area. In addition, the

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representative was asked his/her organization’s relationship to forest conservation and overall environmental goals for the province. Typical follow-up questions to responses sought information on project participants’ overall demographics, challenges to project success, and concerns about the future, both for individual projects and regional environmental goals. A “snowball” sampling technique was used to determine when all landowner assistance organizations active within the study area had been identified and interviewed (Goodman, 1961).

Interviews were completed for nine organizations described in Table 3-2.

Systematization of organizations’ project types and areas was done by grouping similar responses (i.e. repetition of phrases or key words) into three broad project categories: conservation, protection, and natural resource-based development as described below.

Conservation projects supported restoration or conservation of native forest ecosystems or species populations through incentives, training, or education. Protection project areas have, or are in need of, surveillance and enforcement against deforestation.

Natural resource-based development projects focus on the sustainable use of natural resources and economic development programs dependent upon natural resources.

Consensus mapping. Maps of project locations were scanned, geo-referenced, digitized and attributed with project category to create a database of project areas. From the database, we created spatial maps of areas where organizations work overlapped.

The maximum number of conservation and protection projects in one area was four, and maximum number of natural resource-based projects three. For each project category a series of maps was developed for the multi-stakeholder meeting to show the gradient from no organizational support, low organizational support (areas where a

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single organization worked), to high organizational support (areas where organizations’ projects overlapped).

At the multi-stakeholder meeting, participants from four of the nine organizations privately rated each map for how accurately the map covered each project category using a one to five Likert scale, with one meaning “strongly agrees” and five meaning

“strongly disagrees”. The map with the lowest score was chosen and a subsequent discussion was held with the stakeholders present on the map’s completeness as well as areas that should be added or removed. Maps were projected and edited with stakeholders in real-time to produce a finalized consensus map for each of the three project categories (Figure 3-3).

Organizational cost calculation. In order to create a single cost surface for use in Marxan’s objective function (Equation 3-1), the three consensus maps (one each for conservation, protection, and natural resources) were scaled and combined based on their proportional area in each planning unit. Project categories were ranked 1 to 3 with

3 having the greatest benefit and lowest cost; conservation projects received a 3, protection areas a 2, and natural resources-based development projects a 1. Maps were intersected spatially with the grid of planning units. Areas where projects overlapped were assigned the value of the highest ranking project. Areas without projects received a value of 0. This allowed us to calculate a score (0-100) based on the inverse weighted sum of all project types in a planning unit, which is represented in

Equation 3-3 as:

∑(푃 ∗1)+(푃 ∗2)+(푃 ∗3) 푦 = (1 − 푛 푝 푐 ) ∗ 100 (3-3) 3

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where y is the project score of a given planning unit, 푃푛 is the percent of the cell where the highest ranking project(s) was a natural resource-based development project, 푃푝 is the percent of the cell where the highest ranking project(s) was an area for protection, and 푃푐 is the percent of the cell where the highest ranking project(s) was a conservation project. The weighted sum was normalized to the highest possible score of any given cell (i.e., 3, a cell encompassed by a conservation project). Since the ultimate goal was to use this as a “cost” value, the inverse of the normalized weighted score was calculated so that planning units with a greater proportion of higher-ranked projects would have lower costs to incorporate into CANs.

3.2.3.2 Joint area and organizational support cost surface

In cases of more than one cost, Marxan’s objective function (Equation 3-1) requires that the costs be combined as a single cost surface in order to be included in the objective function. For this requirement, we created the joint cost model in which each cost (area and organizational support) had approximately equal weight based on visual inspection of model outputs. Joint costs were calculated using the equation:

퐶푗 = 훽푎퐶푎 + 훽푔퐶푔 (3-4) where 퐶푗 is the joint cost of a given planning unit, 퐶푎 is the area of the planning unit, 퐶푔 is the planning unit’s organizational cost, and 훽푎 and 훽푔 are the respective weights for area and organizational support costs. The approach used to calibrate the two costs in this study was based on optimization process described in Ardron, Klein, & Nicolson

(2008) and Ardron et al. (2010) and is described in Appendix F.

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3.2.4 Preparation of Model Inputs

To run a given model, four tables were constructed that summarized the relationships between planning units, costs, conservation features, and the compaction term for each planning unit. Because our main goal was to compare how organizational support affects conservation planning, we ran the model with three different cost surfaces: area only, organizational support only, and a combination of area and organizational support costs. The first table, with a version for each of the three cost type, listed all the spatial units of the analysis and their cost. The second table was the boundary length and described the lengths shared by a planning unit with its surrounding planning units. The third table was a list of all the conservation features and each feature’s conservation target (see Appendix C) and SPF value. The fourth table listed the area of a given conservation feature within each planning unit.

We followed the calibration steps shown in Figure 3-1 to ensure that parameters were able to identify feasible CANs. We began with the calibration of the SPF using the area only cost model and found an SPF of 40 was sufficient to identify a small number of feasible CANs from 100 runs. We adjusted the number of iterations per run to 1 million and found this to be the lowest number of iteration that could be used to produce feasible CANs for a recommended 70-90% of 100 runs (Ardron et al., 2010). These parameters were reevaluated with each alteration to cost or conservation feature, but did not require adjustment.

3.2.5 Model Assessment and Comparison

We compared the outcomes of the three models with different cost surfaces: area only, organizational support only, and a combination of area and organizational support costs. We also compared the model with individual conservation targets (birds,

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amphibians, primates, forest and ecoregions) rather than all conservation targets combined. All model configurations are given in Table 3-3.

For each model configuration, means and standard deviations were calculated across 100 runs for the following metrics: average area of the CAN, average perimeter- to-area (PA) ratio of the CAN, penalties accrued through missed conservation feature targets, and total costs (area and/or organization support, depending on the model).

The PA ratio, a common statistic to assess shape configuration, provided measure of compactness for model comparison (Cushman, Mcgarigal, & Neel, 2008; Li & Wu,

2004; Ribeiro & Lovett, 2009; Semwal et al., 2004). Higher PA ratio values indicates less compactness and greater fragmentation, while low PA ratio values indicated more compact solutions with fewer individual patches. Significant differences in the assessment metrics between pairs of models in these measures were tested using a pairwise Student’s t-test.

Spatial similarities between models (Table 3-3) could also be compared using the number of times (selection frequency: ranged between 0 and100) a planning unit was chosen in the final CAN of each of the 100 runs. We calculated the Spearman’s rho correlation (ρ) in selection frequency of all planning units between model pairs. High (ρ

>0.5) and moderate (ρ >0.3) values were interpreted as indicators of strong similarities in spatial configuration between models (Kendall, 1970). To compare models, we also calculated a measure of agreement between the best CANs of model pairs with

Cohen’s kappa (Agresti, 2002). Kappa scores between 0.3-0.5 indicate slight agreement, 0.5-0.7 moderate agreement, 0.7-0.8 strong agreement, and > 0.8 very strong agreement (Ardron et al., 2010; Game & Grantham, 2008).

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3.3 Results

3.3.1 Model Comparisons

Models with different cost surfaces (define briefly here) produced different conservation area networks (CAN). The model that included organizational support as the only cost surface had a much higher area cost and perimeter to area (PA) ratio than the model that included only area as a cost surface (Table 3-3). The organization-only model needed a large area to meet the conservation targets and was less compact than the area-only model. Conversely the area-only model had a higher organizational cost than the organization-only model (Table 3-3). The joint cost model was successful in reducing both area and organizational costs dramatically (Table 3-3). The joint cost model reduced the area costs more than it reduces the organizational costs by a small margin. Mean organizational support cost for the joint cost model (x̅ = 157,782 ± 65) was 18.4% higher the organization support cost for the organizational-only model (x̅ =

38,118 ±33). Mean CAN size from the joint cost model (x̅ = 157,782 ±65) was roughly equal (0.6% difference) to mean can area for the area only model (x̅ = 158,677 ±92;

Table 3-3). CAN configuration (specific planning units selected for the CANs) was the least similar between the area only and organization only models (weak agreement between model: ρ = 0.34, К= 0.22) and the most similar between the area only and joint cost model (moderate to strong agreement: ρ = 0.85, К= 0.55; Table 3-4). The organization only and joint cost model showed weak to moderate agreement among

CAN configurations: ρ = 0.64, К= 0.43.

Three key areas of the province were frequently selected for inclusion in the conservation area networks (CANs) of all three models (Figure 3-4). The first area encompassed the central, interior portion of the province and the area between the

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interior and western tip of the province along the Oria River basin. A second area included the southwestern portion of the province and overlapped with the location of four of the province’s protected areas: Cerro Hoya National Park, La Tronosa and

Tonosí Forest Reserves, and the Islas Cañas Wildlife Refuge (Figure 3-5). The third area was defined by a thin band running from the northernmost tip of the province, southwards along the coast and overlaps significant portions of protected mangrove forests. The main difference among these three models was that the organization-only model included more of the northern half of the province, which was selected with high to moderate frequency for the organization-only model, but not the other two models

(Figure 3-4).

3.3.2 Sensitivity to Individual Conservation Feature Groups

To better understand which conservation features had the greatest influence on the joint cost model, we ran a joint model with each of the five conservation features alone using the joint cost (area and organizational support) model and the compaction term. Visually the forest-only model most clearly resembles the model with all conservation features (Figure 3-6, A and F and 3-7, A and F) indicating that the forest cover had the greatest influence on the “all feature” model. Also, the forest-only model has the highest correlation coefficient and kappa coefficient with the model that included all conservation features (Table 3-5). The difference in area cost, organizational support cost, and PA ratio between the model with all conservation features and the model with a single feature was smallest for the forest-only model (Table 3-3). CANs were most sensitive to prioritizing forest habitat over any other conservation feature for two reasons: forest habitat covered a large area relative to other features and had a high proportion of its distribution included for its conservation target (80%).

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The forest-only model required the largest amount of area to meet its conservation target of any single conservation feature model, which can in the comparison of average CAN areas (i.e. area cost; Table 3-3). Models that included a single taxonomic group as in the conservation feature (amphibians only, birds only, and primates only; Figure 3-7, B-D) needed much smaller areas to create feasible CANs and thus had low area costs (Table 3-3). The ecoregions-only model had an intermediate amount of area required to construct a feasible CAN. A larger portion of the province in the northwest, which contains the dry tropical forest ecoregion, was selected for CANs of the ecoregion-only model (Figure 3-7, E).

3.3.3 Differences among Individual Conservation Feature Groups

The models of single conservation feature groups (Figure 3-6 and 3-7) allowed us to compare where conservation features overlapped with each other, with protected areas and with specific areas of organizational support. The planning units with high selection frequencies in the forest-only model (red areas in Figure 3-6, F) encompassed most of the high selection areas of the other conservation features (Figure 3-6, B-D), except the ecoregion-only model (Figure 3-6, E) that included a greater proportion of the mangroves along the northeastern coast. All single conservation feature models included La Tronosa Forest Reserve and Cerro Hoya National Park, located in the southwest section of the province (Figure 3-6). Mangrove areas, many of which fall within protected areas, had high selection frequency for the forest-only, ecoregion-only, and bird-only models. The primate-only and ecoregion-only models had high selection frequency in the southern interior areas of the province with high organizational support

(Figure 3-6, D and E). The ecoregion-only, amphibian-only, and bird-only models

(Figure 3-6, B, C, and E) also had moderately high selection frequency in the north-

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northwestern area of the province, which were also selected in organization-only models (Figure 3-4, C) and are areas of high levels of organization activity.

3.3.4 Assessing Effectiveness of Protected Areas and Organizational Support

We assessed the effectiveness of protected areas in relation to the final CAN network (joint cost model with all conservation features included) using two criteria: 1) the average selection frequency value of CAN planning units in protected areas, and 2) the percent of planning units in the CAN with a selection frequency of 75% or higher that remained outside of the protected area system (Figure 3-5). In Los Santos, where protected areas have been established around large intact forested areas, sea turtle nesting beaches, and mangrove forest, mean selection frequencies for planning units occurring on protected lands were very high (x̅ = 91.82, n=323). However, protected areas only covered 20.2% (n=1,335) of the highly selected planning units, which were defined as planning units with a selection frequency equal to or above 75%.

Areas of the province that had planning units with high selection frequencies in both the area-only and organizational-only cost models (Figure 3-8, purple areas) were located in the province’s protected areas, mangroves, and throughout the Oria River basin. Areas of the province receiving high organizational support but selected infrequently in the area-only cost CANs (Figure 3-8, blue areas) included a large part of the northern part of the province, the southern coast, and the eastern area of the province, near Pedasí. Areas of high conservation value in the province not receiving organizational support (Figure 3-8, red areas) had a patchy distribution across the interior of the province, but more concentrated areas in the southwest.

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3.4 Discussion

3.4.1 Analysis of Organizational Support and Conservation Features

3.4.1.1 Organization support in systematic conservation planning

Including organizational support in systematic planning altered the outcome of the planned conservation network compared to a model that focused just on the landscape features to be conserved. It illuminated potential mismatches between organizational support and conservation priority areas. Although the model that included only organization support in determining costs had a larger amount of area in the final

CAN than the model that used only area as a cost, the organization-only model did not cover all priority areas of the conservation features used in this study (birds, amphibians, reptiles, ecoregions, and forest cover). These results indicated that organizations’ projects do not fully target conservation priority areas as defined in this study. As a result, CANs based on organizational support alone must include more area to achieve all conservation targets for all conservation features.

It was possible to develop CAN’s that struck a balance between prioritizing the inclusion of organizational support and the protection of conservation features within a minimal and compact CAN, as demonstrated with a joint cost model that included total area and organizational support as costs. Combining area and organizational support costs (i.e. the joint cost model) provided CANs able to meet conservation targets of all conservation features with only a slight increase to area in comparison to the area-only cost model, with only modest increase (18.4%) in the organizational costs. This indicates that some adjustment in where organizational support is provided to new areas (i.e. planning units) within the province can more effectively cover conservation priority areas and minimize amount of area with efforts at conservation.

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3.4.1.2 Comparison of conservation features

Of the five conservation features included in the full model, forest cover had the strongest effect in defining the configuration of CANs. The planning units with high selection frequency for the forest cover only model was inclusive of most of the areas with high selection frequency for the other four features (birds, amphibian, primates and ecoregions). In future iterations of the model, the importance of the forest cover feature could be decreased by lowering its conservation target value, which was set high.

However, the conservation target for forest was set high because of the limited amount of forest cover in the province and the strong association between forest and ecosystem services, including animal habitat, carbon sequestration and soil stabilization (Aerts &

Honnay, 2011; Griscom, 2004). For example, Griscom (2004) found that greater forest cover was associated with higher animal abundance and diversity in the southern

Azuero peninsula.

The model that only used ecoregions as a conservation feature showed a greater selection of planning units within the dry tropical forest ecoregion than the model that used only forest cover. This was a result of the relatively high amount of organizational activity, but the relatively small amount of forest cover remaining in the dry tropical forest ecoregion. The differences between the two models indicate areas where restoration for the critically endangered dry tropical forest ecoregion could potentially be aided by existing organizational support.

Though it was assumed that forest cover would be associated with higher animal abundance, and an associated overlap between planning units with high selection frequencies, there was a noted the lack of congruence between the frequently selected planning units of species (birds, amphibians and primates). This, in part, may be due to

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how these species distributions were modelled and targets estimated. Though distributions from IUCN are based on several forms of data that vary in their level of precision, most distributions in the study area appeared based on general knowledge of species habitat preferences. In particular, it was noted that divisions in the distribution data provided by IUCN Red List (2012) and BirdsLife International (2012) closely reflected gradients in environmental conditions (e.g. elevation, precipitation, temperature). Ideally, animal distributions would be based on observational data and modeling specific to Los Santos, which does not currently exist. Additionally, the conservation targets would ideally be based on published viability assessments of regional populations and ecosystems, rather than targets formed from the legal protection and general conservation status of different conservation features, as well as statistical data normalization techniques (Ardron et al., 2010). However, the Marxan framework allows conservation features to be updated then analysis re-run, providing a flexible, updatable framework for conservation planning as the quality of information improves and conservation priorities change.

3.4.1.3 Comparison of organizational support and conservation features

By including organizational support in systematic conservation planning methodology and running sensitivity analysis on individual conservation features, we were able to analyze how constraints that minimize area and maximize organizational support interacted with priority areas of single conservation features. The model that included only forest cover had high organizational costs (Table 3-3), indicating that many areas with the greatest current forest cover did not have organizational support

(Figure 3-8). If forest cover is considered of high conservation priority, as assumed in

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this analysis, and maintaining existing forest cover is a priority, then organizational support should be adjusted to include more areas with current forest cover.

The conservation priorities of animal species and ecoregions aligned better with organizational support, having much lower organizational costs (Table 3-3). This is in part due to the much lower spatial coverage of these conservation features, especially for animal distributions. It may also reflect poor modeling of animal distributions, which do not take into account current forest cover, which possibly enhances animal populations (Griscom, 2004).

The high organizational support in the priority areas for ecoregions and animal species was generally not due to the organization’s explicit goals to conserve these features. Particularly in the northern area of Los Santos, interviews revealed that organizational objectives are primarily focused on water conservation to meet the needs of growing urban populations (Las Tablas and Guararé in Los Santos province and

Chitré in the Herrara province), rather than restoration of native forest cover of this ecoregion or protecting animal species. This is an example of organizational support existing in areas of high conservation priority as a by-product of goals other than those used in the Marxan analysis. It is possible that water resources, scarcity and/or quality could be added explicitly as a conservation feature, which would reflect the organization’s goals (Izquierdo & Clark, 2012).

3.4.1.4 Assessment of methodology for including organizational support

The methodology used in this study to assess organizational support is a thorough and systematic approach that can be applied to other planning scenarios. It incorporates regional knowledge on the needs and status of conservation efforts into an organizational support cost, rather than separating expert knowledge as its own

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methodology used to define conservation priorities. As a result, the method in this study avoids biases in allowing the organizations to define conservation priorities, yet provides accountable and transparent techniques for existing and planned support of conservation to influence the identification of important areas for conservation and futures steps in forming conservation strategies.

3.4.2 Caveats

3.4.2.1 Evaluation of conservation features

Each of the conservation feature data sets used in this model had limitations that affected the development of CANs and the resulting recommendations for conservation actions. The bird and amphibian data were modelled based on regional occurrence data that modelled on a range of general environmental conditions, such as climate and topography, but did not include critical information on the state of the current habitat, such as current forest cover, nor directed observational studies of species occurrences in the study area (IUCN, 2013). The primate data was based on local observations

(Méndez-Carvajal, 2011) then tied to local landscape features, like riparian forest, known to be used be primates. However, with incomplete knowledge of habitats used by these endangered subspecies, we most likely did not include many landscape elements that were important for maintaining these species. The ecoregion data was based on historic natural vegetation cover, not actual current land cover. Finally, the forest cover data was based on the Landsat-derived forest cover change product of

(Hansen et al., 2013), which has been shown to have poor performance in some localized area (Tropek et al., 2014). In the case of the Azuero, our experience in the area indicates the amount of reforestation may be too low and the amount of deforestation too high. However, as the quality of information improves Marxan

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analyses are able to be rerun with finer scale data so that conservation goals can be re- evaluated and updated.

3.4.2.2 Evaluating planning unit size

Model outputs can be also sensitive to planning unit size. Smaller planning units tend to produce more efficient (i.e. lower cost) results (Huber et al., 2010; Nhancale &

Smith, 2011), but require higher resolution data for conservation features, which we did not have for the all features but forest cover. The 1km x 1km planning unit likely introduced potential errors in the analysis since species from a coarser resolution grid may not be present in the whole grid cell, just some fraction of it (Guisan & Thuiller,

2005). As finer-scale, region-specific data becomes available it can be incorporated into systematic conservation analysis in tools like Marxan to provide for evolving, rather than static, conservation planning (Ball & Possingham, 2000).

3.4.3 Implications for Conservation on the Azuero

3.4.3.1 Areas with high conservation value and high organizational support

Areas with high conservation value and high organizational support, as defined by the Marxan analysis, included both protected (the southwestern corner of the province, coastal mangroves along the eastern edge of the province and Cañas Island) and unprotected areas (the more highly forested areas of the Oria River basin) (Figure

3-8, purple areas). The existing protected areas network alone did not include all areas of high conservation value determined by this model. One strategy to extend protection for remaining high conservation areas would be to expand the protected areas system into the Oria River basin. However, landowners in this area still rely on income from agricultural activities so forest protection would have to be balanced with maintenance of local livelihoods (INEC, 2010). Landowners in Los Santos traditionally allow cattle to

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graze in forested areas during the dry season, when pasture grass is dormant and the green understory and fallen seeds of the forest offer a continued supply of forage

(Heckadon Moreno, 2004; pers. obs.). Establishing off-limits protected areas would limit landowners’ livelihoods. On the other hand, because forested area provides benefits for ranching, there may be opportunities to institute more limited protection and incentives for forest protection that benefit both conservation and landowners (Dagang & Nair,

2003; Garen et al., 2009; Murgueitio, Calle, Uribe, Calle, & Solorio, 2011). Within this context, strategies would need to reduce the grazing pressure and prevent further degradation of forested areas in high conservation value areas and potentially encourage some limited natural regeneration. Potential mechanisms to increase forest protection include payment for environmental services , investment into developing an eco-tourism sector and land acquisition into a private land trust (Calvo-Alvarado &

Sanchez-Azofeifa, 2013; de Koning et al., 2011; Ferraro, Hanauer, & Sims, 2011; Holl &

Aide, 2011; Kull et al., 2007).

3.4.3.2 Areas with high conservation value and low organizational support

Areas with high conservation value and low organizational support consisted of mangroves along the eastern edge of the province and interior forests outside of protected areas both south of the Oria River basin and in higher elevations adjacent to protected areas. These areas may represent opportunities for organizations looking to expand conservation actions or initiate organizational support. The lack of existing organizational support in these areas creates some barriers, such as lack of a previously established network of stakeholders with an awareness of conservation issues (Clement & Amezaga, 2009; Macura et al., 2011; Pannell et al., 2006). These barriers might lead to a more extensive set of actions to implement new programs than

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had efforts been placed towards expanding conservation strategies into areas where conservation programs are already present.

3.4.3.3 Areas with high organizational support and low conservation values

Areas with high organizational support and low conservation values were most abundant in the northern third of the province, but also occurred along the southern edge of province and in the district of Pedasí. These areas had low amounts of forest cover and regeneration (Hansen et al., 2013) and high agricultural production. Conflict between forest-based conservation and production would potentially be the greatest in these areas (F. P. Smith et al., 2012). Conservation strategies would likely have greater success of implementation when centered on the restoration of forest and species, rather than the conservation of existing forest and species, as in other areas of the province where there is higher forest cover (Holl & Aide, 2011). The high levels of organizational support suggest that resources are already directed towards these efforts, but the high agricultural production and low forest cover suggest costs for conservation in this area may be higher compared to other areas of the province.

Restoration techniques, including natural regeneration, planting trees in riparian areas, or agroforestry, may require higher levels of financial assistance to offset the economic losses from agricultural production or include high costs for providing technical assistance, if the techniques required to implement projects on properties are not familiar to land managers (Harvey et al., 2008; Le, Smith, Herbohn, & Harrison, 2012; F.

P. Smith et al., 2012). As a result, organizations may expend more effort with less impact to increasing province-wide conservational goals in these areas, than equivalent effort in extending organization support to areas with high conservation values but low organizational support.

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3.5 Summary

To identify areas of high conservation value and expand the need to incorporate socioeconomic and institutional costs into systematic conservation planning methodologies, we tested the design of CANs using increased area and lack of organizational support presence as costs that could impact final CAN design. We found that designs with just area or with area and organizational support costs combined could find feasible CAN solutions using approximately the same area. In models with only organizational costs, however, we found that area costs were increased when organizational and area costs were combined. In general, we found that configuration of

CANs demonstrated low variation in planning unit selection, regardless of cost scale, as a result of high conservation targets from conservation features. Between different conservation features groups, we found that CANs were most sensitive to the locations of dense forest cover. As with other studies, we were able to assess the general effectiveness of protected areas in targeting areas of high conservation value and found that where protected areas occurred there was high overlap with the presence of priority conservation areas. Still, the majority of high conservation value areas remained outside of the protected area network.

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Table 3-1. Summary of conservation feature targets. Conservation Feature Feature's target Feature's Feature's target (% of distribution) distribution (% of province) (% of province) Amphibians 8%-80% 1%-96% 1%-10% Birds 8%-80% 1%-98% 1%-10% Primates Alouatta coibensis trabeata 20% 43% 9% Ateles geoffroyi azuerensis 20% 43% 9% Ecoregions Isthmian pacific moist forests 20% 54% 11% Panamanian dry forests 20% 37% 7% South American Pacific 80% 7% 6% mangroves Talamancan montane forests 80% 1% 1% Forest Cover High quality forest habitat 80% 13% 10% Low quality forest habitat 80% 9% 7%

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Table 3-2. Organizations interviewed in the process of mapping areas of organizational support in the province of Los Santos. The “Primary role” describes the primary roles that organization has in relation to impacting landowner forest conservation practices. An asterisk (*) indicates those organizations also present for helping in consensus mapping. Organization Title Primary Role Funding Source National government * National Authority for the Environment Manages tax incentives for landowners maintaining a set Central government (Autoridad Nacional del Ambiente – ANAM) level of forest (including plantation) on their property.

Maintains an inventory of privately owned forests (including plantations) that have been voluntarily registered with agency.

Provides technical assistance to landowners for timber and restoration tree-planting activities.

Performs outreach about forest and wildlife conservation to primary schools.

Manages conservation and wildlife restoration projects initially begun by the Mesoamerican Biological Corridor of Panama II (CBMAPII).

Manages permits for logging.

Manages national parks and reserves.

Responsible for monitoring and preventing illegal logging from public and private lands.

* Ministry for Agricultural Development Provides technical assistance to landowners for agriculture Central government (Ministerio de Desarrollo Agropecuario – MIDA) (including fruit crops) and ranching (including beef and dairy)

Offers farmers Guacimo saplings alongside of improved pasture species for improving silvopastoral practices

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Table 3-2. Continued. Organization Title Primary Role Funding Source National government (continued) Institution for Agricultural Research in Panama Coordinate and conduct research on agricultural and Central government (Institucion de Investigacion Argpecuaria Panama – ranching practices and their impacts. IDIAP)

National Agricultural Development Bank Controls the distribution of loans and loans’ interest rates on Central government (Banco de Desarollo Agropecuario - BDA) land management projects. and interest collected from loans

Municipal governments * Guarrare Creation and maintenance of municipal parks. Taxes collected from within the * Las Tablas Ability to levy taxes on the sale of goods, (including timber) municipality and and land use permitting. central government Pocri funds

Pedasi

Nonprofit Organizations * Azuero Earth Project (AEP) Provide information and useful contacts to landholders Corporate and private http://azueroearthproject.org/ interested protecting biodiversity and sustainable donations development

Environmental Leadership and Training Initiative Provide technical assistance in managing native tree Yale University – (ELTI) plantations to landholders who had previously participated in School of Forestry http://environment.yale.edu/elti/ the Native Species Reforestation Project (PRORENA) and Environment

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Table 3-3. CAN Area, organization cost, and PA ratio comparisons for different models. Conservation CAN Area Organization PA Model Cost surface feature (ha) cost Ratio Cost surface models Area cost only area only all features 160,234 62,205 1.12 Organization cost only organization only all features 240,995 40,149 2.61 Joint cost area/organization all features 160,629 43,725 1.00

Conservation feature models Bird only area/organization birds 48,021 3,360 0.64 Amphibian only area/organization amphibians 41,083 875 0.67 Primate only area/organization primates 35,343 1,219 0.84 Ecoregion only area/organization ecoregions 93,834 4,447 0.85 Forest only area/organization forests 141,271 44,876 0.96

Table 3-4. Similarities in model configuration between pairs of models with different cost scales (Model1 vs Model2) using Spearman rho (ρ) and Cohen’s kappa (К). All measures of similarity were significant with a p-value < 0.001. All conservation features were used in these models. Model1 Model2 ρ К Area cost only Organization cost only 0.34 0.22 Area cost only Joint cost 0.85 0.55 Organization cost only Joint cost 0.64 0.43

Table 3-5. Similarities in model configuration between a model with all conservation features and models with selected sets of conservation features. Spearman rho (ρ) and Cohen’s kappa (К) are calculated between the model with all conservation features and the comparison model. All measures of similarity were significant with a p-value < 0.001. All models used the joint cost surface (area and organizational support). Comparison Organization ρ К Area cost PA Ratio Model cost Bird only 0.20 0.04 -119,546 -42,850 -0.33 Amphibian only 0.32 0.14 -112,607 -40,366 -0.35 Primate only 0.23 0.10 -125,285 -42,507 -0.16 Ecoregion only 0.41 0.19 -66,794 -39,278 -0.15 Forest only 0.87 0.70 -19,358 1,151 -0.03

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Assemble all data

1 For each planning unit , For each conservation 2 3 calculate costs and feature , set 3 4 Alter costs or conservation feature conservation targets features abundance

Set model parameters

5 7 Set BLM to produce Set SPF values to Set no. of iterations to appropriate degree of produce sufficient produce sufficient 6 8 compactness feasible solutions near-optimal solutions

Revise parameters Produce and evaluate sets 9 of CANs

Figure 3-1. Steps used in Marxan for model configuration and calibration. The figure is adapted from Game & Grantham (2008). Key terms are numbered throughout the figured and are defined underneath the chart.

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Figure 3-2. Categorization of TNC Ecoregions across Los Santos province, Panama. Talamancan montane forest is shown in blue, Isthmian-Pacific moist forest in green, Panamanian dry forest in yellow, and South American Pacific mangroves in purple.

A B C

Figure 3-3. Organizational support represented by project type. A) Coverage of conservation projects coverage (blue), B) Coverage of protection projects (red), and C) Coverage of natural resource based projects (yellow).

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A B C

Figure 3-4. Frequency selection of planning units for models with different cost surfaces. From 100 runs for each model, blue areas show planning units never selected and red areas show planning units selected in all runs. All conservation features were used in the models. Cost surface for each model are: A) area only B) organization only, and C) joint (area and organization).

Figure 3-5. Frequency selection of planning units for the joint cost model with all conservation features. Protected areas are shown in black hatch and labelled. From 100 runs for each model, blue areas show planning units never selected and red areas show planning units selected in all runs.

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A B C

D E F

Figure 3-6. Frequency selection of planning units of models with different conservation features. Protected areas are shown with black hatches. All models include the joint cost surface (area and organizational support) and a compaction term. Conservation features in the models are: A) all conservation feature groups, B) amphibians only, C) birds only, D) primates only, E) ecoregions only, and F) forests only.

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A B C

D E F

Figure 3-7. Planning unit selection for the best CANs of joint cost models targeting different conservation feature groups. Based on the 100 runs of each model, blue areas show planning units included in the best CAN (lowest cost) and grey areas show areas not selected. Models include A) all conservation feature groups, B) amphibians only, C) birds only, D) primates only, E) ecoregions only, and F) forests only.

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Figure 3-8. Comparison of areas with high selection frequencies (75% or greater) between the area only and organizational only models. All conservation features were used in the models. Areas with high selection frequency in both the area only and organization only models are shaded purple. Areas with high selection frequency in the area only model, but not the organization only model are shaded red. Areas with high selection frequency in the organization only model, but not the area only model are shaded blue. Areas with low selection frequencies in both models are left grey. Protected areas are shown with black hatch and labeled.

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CHAPTER 4 CONCLUSION

As an approach to systemically incorporate the objectives of regional organizations into conservation planning, the analysis presented here provided insight as to where areas of high conservation value are being met with organizational support, where high conservation value areas are unsupported, and where opportunities may be present to coordinate between organizations for restoration of areas with low conservation values. Identification of these areas would hopefully aid in the implementation of conservation strategies, though careful consideration should be given to the quality and accuracy of the conservation feature data supporting a CAN will be essential. Given these limitations, the next step towards implementation of conservation actions would be to present CANs under the different cost scenarios with stakeholders and reevaluate how organization costs were modeled and select areas where stakeholders feel confident that conservation initiatives are ready to be implemented.

Though ensuring that conservation efforts are able to secure the needs of conservation features remains tenable, we point again to the lack of land management programs in the area, and in particular forest conservation programs, as opportunity for the region. We suggested that the first step towards implementation of regional CAN that includes partnerships between government reserves and private landowners will have to begin by raising landowners’ awareness of how to conservation values have a place among more traditional land uses such as ranching and agriculture. Finally, we recognized that given limited funding and staffing within land management organizations operating in the province, collaboration among institutional stakeholders will be essential for achieving regional goals.

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APPENDIX A LANDOWNER INTERVIEW FORM IN ENGLISH

In the summer of 2012, 60 landowners were interviewed to provide a more in depth understanding of how decisions at the household level might play a role in the forest transition drivers observed across the province. Surveys included questions about household demographics, socioeconomic status, environmental attitudes, land use history, and land use management ongoing practices and interests. The interviews represented a small, incomplete coverage of households in the province so results from the interviews are only presented anecdotally in the text. The tables below represent the survey forms used during interviews in English. Interviews were conducted in Spanish.

After receiving consent from participants, questions were asked to participants with responses recorded by the author.

Control Information Task Date(s) Name Healthy? If no, provide comments Interview Revision of the interview Coding the interview Entering the data Editing of the data

I. Identification 1. Identification and location of the home 1. House ID number 2. Community *(name) (census community##) 3. Distrito 4. Name and ID number of personal identification number (PIN; see II *(name) (PID) below) of the primary interviewee. 5. Name and ID number of personal identification number (PIN; see II *(name) (PID) below) of the secondary interviewee. 6. GPS location of the home (UTM) 7. Distance from the home and the 1. 2. center of the community (by walking or in km) min km

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II. Composition of the home 1. Who are the members of the home? 1. Personal Name of the member of the home 2. Relation to 3. Year of 4. Sex 5. ID Number the head of the birth/age (0=man Educaci (PIN) home1 (aaaa/bb) 1=woman ón2 (level ) of educatio n) 1 2 3 4 5 6 7 8 9 10 1) Codes: 0=head of home; 1=spouse; 2 child; 3=child in-law; 4=grandchild; 5=parent; 6=parent in-law; 7=sibling; 8=sibling in-law; 9=uncle/aunt; 10=niece/nephew; 11=other family; 12=not family; 2) Codes: 0=none; 1=pre-school; 2=elementary school, incomplete; 2=elementary school, complete; 3=elementary school, not stated; 4=high school, incomplete; 5=high school, complete;6=high school, not stated; 7=university, up to 3 years; 8=university,4+ years; 9=university, not stated; 10=post-graduate; 11=Masters; 23=Doctorate; 13=High education, non-university; 14=Vocational; 15=Special education; 16=Not stated

2. We would like to ask two additional questions about family relationships: 1. What is the marital state of the head of home? Codes:: 1=married/living together; 2= separated from spouse; 3=separated from a union; 4=divorced; 5=widowed; 9=single; 10= >15 yr old: 2. How many children do you have living? 3. Do you have family living in another country? (0/1) III. Home, Property, Work 1. Please indicate the type of home you have: 1. Do you own your home? Codes: 0=mortgaged; 1=rented; 2=own; 3=borrowed; 4=condemned; 9=other, specify: 2. What type material are the (majority of the) walls? Codes: 1=cement block; 2=wood; 3=thatch/adobe; 4=metal; 5=cane/grass/palm 6= no walls 9= other, specify: 3. What type material are the (majority of the) roof? Codes: 1=concrete; 2=clay tile; 3= other tile type; 4=metal; 5=Wood; 6=grass/palm; 9=other, specify: 4. What type material are the (majority of the) floors? Codes: 1=paved; 2=wood; 3=earthen; 9=other, specify:

2. Please indicate the tools and objects that you have in the house: Tools/Objects Tools/Objects 1. Television 2. Electric fan 3. Radio 4. Air conditioner 5. Cell phone 6. Sewing machine 7. House phone 8. Computer 9. Refrigerator 10. Vehicle 11. Washing machine

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3. Please indicate the type of service you have 1. What is your fuel source for cooking? Codes: 1=gas; 2=firewood; 3=kerosene; 4=charcoal; 5=electricity; 9= no kitchen: 2. What is your water source? Codes: 1=publicly piped, IDAAN; 2=publicly piped, community; 3=personal piping; 4=artisanal well; 5=unprotected well; 6=surface well; 7=river/stream; 8= other, specify: 3. What is your type of restroom? Codes: 1=hole or latrine; 2=piped plumbing; 3= septic tank; 4=none 4. What is your method of garbage disposal? Codes: 1=public garbage truck; 2= private garbage truck; 3=bare earth; 4=river/stream/ocean; 5=burn; 6= bury; 9= other, specify: 4. Please indicate the area(s) in which you work, circle the area in which you spend the majority of your time, and put a star next to the area from which you derive the majority of your income: 1. Agriculture 10. Finance 2. Fishing 11. Real estate 3. Mining 12. Public Administration 4. Manufacturing 13. Teaching 5. Electrician 14. Social services 6. Construction 15. Community activities 7. Business 16. Private home 8. Hospitality 17. International organization 9. Transportation 18. Non-specified activities 5. Please indicate the type(s) of work you have: Types of Work Types of Work 1. Seasonal government employee 9. Permanent employee of a cooperative 2. Seasonal employee of a non-profit organization 10. Permanent employee in the private sector 3. Seasonal employee of a cooperative 11. Permanently employed in domestic services 4. Seasonal employee in the private sector 12. Independently employed/self-employed 5. Seasonally employed in domestic services 13. Owner 6. Seasonal employee of the Canal Commission or defense sites 14. Member of a production cooperative 7. Permanent government employee 15. Family worker 8. Permanent employee of a non-profit organization 16. Other type of work: IV. Environmental Attitudes 1. Please indicate the extent to which you agree/disagree with the below statements1: 1. We are approaching the limit of the number of people the earth can support. 2. Humans have the right to modify the natural environment to suit their needs. 3. When humans interfere with nature it often produces disastrous consequences. 4. Human ingenuity will insure that we do NOT make the earth unlivable. 5. Humans are severely abusing the environment. 6. The earth has plenty of natural resources if we just learn how to develop them. 7. Plants and animals have as much right as humans to exist. 8. The balance of nature is strong enough to cope with the impacts of modern industrial nations. 9. Despite our special abilities humans are still subject to the laws of nature. 10. The so–called ‘‘ecological crisis’’ facing humankind has been greatly exaggerated. 11. The earth is like a spaceship with very limited room and resources. 12. Humans were meant to rule over the rest of nature. 13. The balance of nature is very delicate and easily upset. 14. Humans will eventually learn enough about how nature works to be able to control it. 15. If things continue on their present course, we will soon experience a major ecological catastrophe. 1) Codes: 0=no response; 1=strongly agree; 2=agree; 3=neither agree/disagree; 4=disagree; 5=strongly disagree

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V. Land use 1. Please indicate and rank the three most common land uses on your land(s) during the given time period1: Rank 1. Present day? 2. 10 years ago? 3. 10 years from now? 1. 2. 3. 1) Codes: 1=cattle, beef, young cows only; 2=cattle, beef, all ages; 3=cattle, dairy; 4=agriculture, corn; 5=agriculture, legumes; 6=agriculture, rice; 7=agriculture, other, specify:; 8=no management, 0-5yrs; 9=no management, 5-10yrs; 10=secondary/degraded forest; 11=mature forest; 12=plantation, teak; 13=plantation native; 14=plantation, mixed; 15=other, specify:

2. If your rankings changed between 10 years ago and present day, why do you think they changed? ______3. If your rankings changed from present day to 10 years from now, why do you think they might change? ______VI. Land Extension Programs 1. We would like to ask you about the type of extension programs you use or are interested in using: 1. Type of activity 2.Current 3. Interest 4. Limits to use of this in participating activity on expanding/s in said your land? tarting the activity2? activity1? *If answer to 3 was not 0 Rank 1. 2. 3. 1. Plant improved pasture species *(0/1) *(0-5) 2. Irrigate agricultural lands 3. Reforest areas near water and water sources 4. Plant cover crops to recuperate soil fertility 5. Increase cattle rotation 6. Incorporate (more) trees into pasture (non-fence) 7. Incorporate (more) trees in agricultural lands 8. Install cattle drinking pond(s) 9. Reforest with exotic timber species (e.g. Teak) 10. Develop an ecotourism business 11. Incorporate certified-organic practices into crop management 12. Incorporate certified-organic beef practices into livestock management 13. Incorporate certified-organic dairy practices into livestock management 14. Plant and maintain timber for FSC (Forestry Stewardship Council) certification 15. Reforest with plantations of fruit crops 16. Diversify pasture with non-grass cattle forages 17. Use live fences to separate fields 18. Develop a tree nursery business 19. Develop a bush meat business 20. Reforest with native timber species 21. Reforest areas to attract wildlife 1) Codes: 0=no response; 1=strongly agree; 2=agree; 3=neither agree/disagree; 4=disagree; 5=strongly disagree 2) Codes: 1=Financial assistance, cash; 2=financial assistance, in-kind; 3=Credit assistance, cash; 4=Credit assistance, in-kind; 5=Credit assistance, loan interest rates; 6=Land, available space; 7=Land, fertility limitation; 8=Contrary to other uses; 9=Personal benefit; 10=available extension; 11=participation of other community/family members; 13=other, specify;

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APPENDIX B LANDOWNER INTERVIEW FORM IN SPANISH

The tables below represent the survey forms used during interviews. Interviews were conducted in Spanish. After receiving consent from participants, questions were asked to participants with responses recorded by the author. Additional information can be found in Appendix A.

Información de Control Tarea Fecha(s) ¿Por quién? ¿Buen estado? Si no, proporcionar comentarios Entrevista Revisión del cuestionario Codificación Ingreso de la información Revisión y aprobación del ingreso de la información I. Identificación 1. Identificación y localización del hogar Número de identificación del hogar Comunidad *(nombre) (comunidad##) Distrito Nombre y número de identificación personal NIP (Ver II abajo) del *(nombre) (PID) entrevistado primario Nombre y número de identificación personal NIP del entrevistado *(nombre) (PID) secundario. (Ver II abajo). Referencia de la geoposición del hogar (formato UTM) Distancia entre el hogar y el centro de la 1. 2. comunidad (en minutos a pie y en km) min km II. Composición del hogar 1. ¿Quiénes son los miembros del hogar? 1. Número de Nombre del miembro del hogar 2. Relación con el 3. Ano de 4. Sexo 5. identificación jefe hogar nacimiento (0=hombre, Educación2 personal (PIN) (aaaa/bb) 1=mujer) (nivel de educación) 1 2 3 4 5 6 7 8 9

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1) Códigos: 0=jefe del hogar; 1=esposo/a; 2=niño/a; 3=yerno/nuera; 4= (bis) nieto/a; 5=padre/madre; 6=suegro/a; 7=hermano/a; 8=cunado/a; 9=tío/a; 10=sobrino/a; 11=otros; 12=no pariente 2) Códigos: 0=Ningún grado, 1=Pre-escolar, 2=Primaria incompleta, 3=Primaria completa, 4=Primaria no declarado, 4=Secundaria incompleta, 5=Secundaria completa, 6=Secundaria no declarado, 7=Universitaria hasta 3, 8=Universitaria 4 y más, 9=Universitaria no declarado, 10=Especialidad (post-grado), 11=Maestría, 12=Doctorado, 13=Superior no universitaria, 14=Vocacional, 15=Enseñanza especial, 16=No declarado

2. Nos gustaría hacer algunas preguntas con relación de las relaciones familiares: 4. ¿Cuál es el estado civil del jefe de hogar? Códigos: 1=unido(a); 2= casado(a); 3=separado(a) de matrimonio; 4= separado(a) de unión; 5=viudo(a); 9=soltero(a); 10= menor de 15 años: 5. ¿Cuántos hijos vivos tiene usted? 6. ¿Usted tiene familia afuera del país? (0/1) III. Hogar, propiedad, trabajo 1. Nos gustaría hacer algunas preguntas con relación del hogar: 1. ¿Tiene casa propia? Código: 0=hipotecada; 1=alquilada; 2=propia; 3=cedida; 4=condenada; 9= Otros materiales 2. ¿De qué tipo de material están hechas (la mayoría de) las paredes? Código: 1=Bloque, ladrillo, piedra, concreto; 2=Madera (tablas, troza); 3=Quincha, adobe; 4=metal; 5=Palma, paja, penca, caña, palos; 6= sin paredes 9= Otros materiales: 3. ¿De qué tipo de material está hecho (la mayor parte de) el techo? Código: 1=Losa de concreto; 2=teja; 3= Otro tipo de tejas (tejalit, panalit, techolit); 4=metal; 5=Madera; 6=Palma, paja o penca; 9=otro materiales: 4. ¿De qué tipo de material está hecho (la mayor parte de) el piso? Código: 1=Mosaico, baldosa, mármol, parquet; 2=Pavimento; 3=Ladrillo; 4=madera 5=Tierra, 6=otros materiales

2. Por favor indicar los implementos y objetos que posee el hogar: Herramientas/Objetos Herramientas/Objetos 12. Televisor 13. Abanico eléctrico 14. Radio 15. Aire acondicionador 16. Teléfono celular 17. Machina para coser 18. Teléfono residencial 19. Computadora 20. Refrigeradora 21. Automóvil 22. lavadora

3. Por favor indicar el tipo de servicio que tienes: 1. ¿Cuál es su combustible para cocinar?

Código: 1=gas; 2=leña; 3=querosín; 4=carbón; 5=electricidad; 9= no cocina: 2. ¿Cuál es su abastecimiento del agua? Código: 1=Acueducto público del IDAAN; 2=Acueducto público de la comunidad; 3=Acueducto particular; 4=Pozo sanitario;

5=Pozo brocal no protegido; 6=Pozo superficial; 7=Río, quebrada o lago; 8= otro: 3. ¿Cuál es su tipo de servicio sanitario? Código: 1=hueco o latina; 2=conectado a alcantarillado; 3= tanque séptico; 4=no tiene 4. ¿Cuál es su manera para la recolección de basura? Código: 1=carro recolector público; 2= carro recolector privado; 3=terreno baldío; 4=río, quebrada, lago o mar; 5=incineración o quema; 6= Entierro; 9= otra forma:

4. Por favor indicar las áreas en que trabaja, marcar con círculo lo que representa la mayoridad de su tiempo y con estrella lo que representa la mayoridad de sus ingresos: 1. Agropecuaria 10. Actividades financieras 2. Pesca 11. Actividades inmobiliarias 3. Explotación de minas y canteras 12. Administración pública y defensa 4. Industrias Manufactureras 13. Enseñanza 5. Suministro de electricidad 14. Servicios sociales y relacionados con la Salud humana

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6. Construcción 15. Actividades administrativas y servicios de apoyo 7. Comercio al por mayor y al por menor 16. Actividades de los hogares 8. Hoteles y Restaurantes 17. Actividades de organizaciones y órganos extraterritoriales 9. Transportación 18. Actividad no especificada

5. Por favor indicar el tipo de trabajo que tienes: 1. Eventual Gobierno 9. Permanente Cooperativa 2. Eventual Organización sin fines de lucro 10. Permanente Empresa privada 3. Eventual Cooperativa 11. Permanente Servicio doméstico 4. Eventual Empresa privada 12. Independiente o cuenta propia 5. Eventual Servicio doméstico 13. Patrono(a) o dueño(a) 6. Eventual Comisión del canal 14. Miembro de una cooperativa de producción 7. Permanente Gobierno 15. Trabajador familiar 8. Permanente Organización sin fines de lucro 16. Otro tipo de trabajo: IV. Actitudes ambientales 1. Por favor, indique hasta qué punto usted está de acuerdo / desacuerdo con las siguientes afirmaciones 1: 1. Nos estamos acercando al límite de la cantidad de personas que puede sostener la Tierra. 2. Los seres humanos tienen el derecho de modificar la naturaleza para satisfacer sus necesidades. 3. Cuando los humanos interfieren con la naturaleza que a menudo producen consecuencias desastrosas 4. El ingenio humano se asegurará de que NO hagamos la tierra inhabitable. 5. Los seres humanos están abusando gravemente el medio ambiente. 6. La tierra tiene un montón de recursos naturales, solo que deberíamos de aprender a desarrollar. 7. Las plantas y los animales tienen el mismo derecho a existir como los seres humanos. 8. El equilibrio de la naturaleza es lo suficientemente fuerte como para enfrentar los impactos de las modernas naciones industrializadas. 9. A pesar de nuestras habilidades especiales los seres humanos todavía están sujetos a las leyes de la naturaleza. 10. La llamada “crisis ecológica” que enfrenta la humanidad se ha exagerado mucho. 11. La tierra es como una nave espacial con espacio y recursos muy limitados. 12. Los seres humanos estaban destinados a gobernar sobre el resto de la naturaleza 13. El equilibrio de la naturaleza es muy delicado y se irritan fácilmente. 14. Los seres humanos eventualmente aprenderán lo suficiente sobre cómo funciona la naturaleza al ser capaz de controlarla. 15. Si las cosas siguen su curso actual, pronto experimentaremos una gran catástrofe ecológica. 1) Códigos: 0=sin respuesta; 1=muy de acuerdo; 2=de acuerdo; 3=ni de acuerdo no desacuerdo; 4=desacuerdo; 5=muy desacuerdo V. Uso del terreno 1. Por favor, indicar y clasificar a los tres usos de la tierra más comunes en su propia durante el período de tiempo determinado1: Posición 1. ¿Día de hoy? 2. ¿Qué hace 10 años? 3. ¿10 años a partir de ahora? 4. 5. 6. 1) Códigos: 1=ganado, para ceba (engordar); 2=ganado, adultos para carne; 3=ganados, leche; 4=agricultura, maíz; 5=agricultura, leguminosos; 6=agricultura, arroz; 7=agricultura, otro; 8=sin manejo, 0-5 años; 9= sin manejo, 5-10 años; 10=bosque secundario/intervenido; 11=bosque maduro; 12=plantación, teca; 13=plantación, nativos; 14=plantación, mesclada; 15=otro:

2. Si sus clasificaciones han cambiado entre 10 años y hoy en día, ¿por qué crees que ha cambiado? ______

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3. Si sus clasificaciones han cambiado de día de hoy a 10 años a partir de ahora, ¿por qué crees que podrían cambiar? ______VI. Programas de extensión de tierras 1. Nos gustaría preguntarle sobre el tipo de programas de extensión que usa o en lo que está interesado: 1. Tipo de actividad 2. ¿Uso 3. ¿Interés 4. ¿Limites a actual de en ampliar participar2? esta / iniciar el actividad actividad1 en su ? Posición tierra? 1. 2. 3. 1. Sembrar pastos mejorados *(0/1) *(0-5) 2. Implementar un sistema de riego para cultivas 3. Reforestar cerca de agua y ojos de agua 4. Sembrar abonos verdes para recuperar la fertilidad del suelo 5. Aumentar la rotación de ganado de los potreros 6. Incorporar (más) árboles en potreros (no cerca vivas) 7. Incorporar (más) árboles con cultivas 8. Instalar bebederos para ganado 9. Reforestar con especies exóticas (como teca) 10. Desarrollar una ingresa de eco-turismo 11. Incorporar practicas orgánicas certificadas en el manejo de cultivos 12. Incorporar practicas orgánicas certificadas en el manejo de ganado 13. Incorporar practicas orgánicas certificadas en el manejo de lecheras 14. Sembrar y mantener árboles para la venta de madera certificada de FSC (Forestry Stewardship Council) 15. Reforestar con árboles frutales 16. Sembrar otro tipos de forraje que el pasto 17. Sembrar cercas vivas para la separación de fincas y potreros 18. Desarrollar un vivero para la venta de arboles 19. Desarrollar una criadero zoológico para la venta de carne 20. Reforestar con especies nativas maderables 21. Reforestar para atraer a la fauna silvestre

1) Códigos: 0=sin respuesta; 1=muy de acuerdo; 2=de acuerdo; 3=ni de acuerdo no desacuerdo; 4=desacuerdo; 5=muy desacuerdo 2) Códigos: 1= asistencia financiera, en efectivo; 2= asistencia financiera, en especie; 3=asistencia con créditos, en efectivo; 4= asistencia con créditos, en especie; 5= asistencia con créditos, en efectivo, tasas de interés; 6=Terreno, espacio disponible; 7=Terreno, fertilidad limitada; 8=conflicto con otro usos; 9=beneficio personal; 10=asistencia técnica; 11=participación de otro miembros de la comunidad/familia; 13=otro;

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APPENDIX C CONSERVATION FEATURE TARGETS

Table C-1. Conservation feature targets and associated measurements. Feature's Feature's Feature's Target distribution target (% Scientific name/Description target (% of area (ha) (% of of distribution) province) province) Amphibians Bolitoglossa lignicolor 2,774 80% 1% 1% Craugastor azueroensis 4,436 52% 2% 1% Craugastor crassidigitus 8,016 26% 8% 2% Elachistocleis ovalis 33,891 8% 89% 9% Hypsiboas crepitans 32,191 8% 84% 8% Hypsiboas pugnax 6,729 34% 5% 2% Hypsiboas rosenbergi 12,596 16% 22% 3% Leptodactylus bolivianus 23,326 10% 61% 6% Leptodactylus fuscus 12,464 16% 22% 3% Leptodactylus poecilochilus 14,001 18% 18% 4% Leptodactylus savagei 36,553 8% 96% 10% Pleurodema brachyops 18,194 11% 48% 5% Rhinella granulosa 19,837 13% 35% 5% Scinax altae 32,039 8% 84% 8% Birds Accipiter bicolor 21,649 11% 57% 6% Accipiter superciliosus 31,517 9% 83% 8% Amaurolimnas concolor 23,290 10% 61% 6% Amazona autumnalis 18,847 11% 49% 5% Amazona ochrocephala 30,663 9% 80% 8% Anas acuta 14,056 16% 25% 4% Anas clypeata 33,073 9% 87% 9% Anhinga anhinga 30,016 9% 79% 8% Aphriza virgata 9,551 25% 10% 3% Ara macao 15,150 18% 20% 4% Aramides axillaris 13,708 19% 18% 4% Aratinga pertinax 35,764 8% 94% 9% Ardea herodias 32,889 9% 86% 9% Arenaria interpres 9,551 25% 10% 3% Arremon aurantiirostris 17,291 12% 45% 5% Atlapetes albinucha 11,517 20% 15% 3% Attila spadiceus 17,895 12% 47% 5% Aythya affinis 35,625 8% 94% 9% Aythya collaris 14,277 18% 19% 4%

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Table C-1. Continued. Feature's Feature's Target Feature's distribution target (% Scientific name/Description area target (% of (% of of (ha) distribution) province) province) Birds (continued) Basileuterus culicivorus 13,644 19% 18% 4% Busarellus nigricollis 30,631 9% 80% 8% Cacicus cela 37,082 8% 97% 10% Calidris alba 9,551 25% 10% 3% Calidris canutus 9,551 25% 10% 3% Calidris fuscicollis 33,596 8% 88% 9% Calidris himantopus 31,610 9% 83% 8% Calidris minutilla 32,879 9% 86% 9% Camptostoma obsoletum 37,041 8% 97% 10% Campylopterus hemileucurus 14,976 18% 20% 4% Cathartes burrovianus 18,484 14% 32% 5% Catharus aurantiirostris 5,762 41% 4% 2% Catoptrophorus semipalmatus 9,551 25% 10% 3% Cercomacra tyrannina 20,720 11% 54% 5% Chaetura spinicaudus 18,800 11% 49% 5% Charadrius collaris 21,880 10% 57% 6% Charadrius semipalmatus 31,666 9% 83% 8% Charadrius wilsonia 9,551 25% 10% 3% Chlidonias niger 10,514 24% 11% 3% Chloroceryle aenea 36,274 8% 95% 10% Chloroceryle amazona 37,124 8% 97% 10% Chlorophanes spiza 23,512 10% 62% 6% Chondrohierax uncinatus 22,636 10% 59% 6% Circus cyaneus 30,919 9% 81% 8% Claravis pretiosa 35,257 8% 93% 9% Cochlearius cochlearius 15,581 12% 41% 4% Columbina talpacoti 35,257 8% 93% 9% Contopus cinereus 20,274 11% 53% 5% Corapipo altera 22,800 10% 60% 6% Cyanerpes lucidus 9,862 22% 13% 3% Cyanocompsa cyanoides 19,089 14% 33% 5% Cyclarhis gujanensis 29,485 9% 77% 8% Dacnis cayana 18,245 11% 48% 5% Dacnis venusta 15,231 15% 27% 4% Dendroica virens 17,055 15% 30% 4% Dendroplex picus 13,217 16% 23% 3% Elaenia frantzii 3,670 65% 1% 1%

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Table C-1. Continued. Feature's Feature's Target Feature's distribution target (% Scientific name/Description area target (% of (% of of (ha) distribution) province) province) Birds (continued) Empidonax flaviventris 14,260 16% 25% 4% Eucometis penicillata 15,470 12% 41% 4% Eudocimus albus 17,579 12% 46% 5% Eurypyga helias 25,679 10% 67% 7% Falco femoralis 22,802 13% 40% 6% Falco rufigularis 36,286 8% 95% 10% Florisuga mellivora 22,514 10% 59% 6% Fulica americana 36,903 8% 97% 10% Gallinula chloropus 36,508 8% 96% 10% Gampsonyx swainsonii 34,680 8% 91% 9% Geotrygon montana 19,596 11% 51% 5% Geranospiza caerulescens 36,641 8% 96% 10% Haematopus palliatus 9,551 25% 10% 3% Harpagus bidentatus 30,204 9% 79% 8% Heliothryx barroti 23,237 10% 61% 6% Henicorhina leucophrys 4,362 55% 2% 1% Henicorhina leucosticta 14,395 18% 19% 4% Herpetotheres cachinnans 26,006 10% 68% 7% Heteroscelus incanus 9,551 25% 10% 3% Hylocharis eliciae 19,674 11% 52% 5% Hylophilus aurantiifrons 2,989 80% 1% 1% Hylophilus decurtatus 22,204 10% 58% 6% Hylophilus ochraceiceps 9,847 25% 10% 3% Ibycter americanus 7,979 27% 8% 2% Jacana jacana 30,222 9% 79% 8% Larus argentatus 9,551 25% 10% 3% Larus atricilla 15,935 12% 42% 4% Larus pipixcan 10,656 24% 11% 3% Laterallus albigularis 6,951 32% 6% 2% Laterallus exilis 4,574 51% 2% 1% Leptodon cayanensis 22,299 10% 59% 6% Leptotila rufaxilla 7,905 33% 6% 2% Leucopternis albicollis 24,622 10% 65% 6% Leucopternis princeps 11,853 20% 16% 3% Limnodromus griseus 31,655 9% 83% 8% Lurocalis semitorquatus 20,009 11% 53% 5% Malacoptila panamensis 19,079 11% 50% 5%

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Table C-1. Continued. Feature's Feature's Target Feature's distribution target (% Scientific name/Description area target (% of (% of of (ha) distribution) province) province) Birds (continued) Megascops guatemalae 22,932 10% 60% 6% Melanerpes pucherani 4,097 57% 2% 1% Micrastur ruficollis 14,971 15% 26% 4% Micrastur semitorquatus 24,760 10% 65% 6% Microcerculus marginatus 9,698 22% 13% 3% Mionectes oleagineus 37,029 8% 97% 10% Molothrus oryzivorus 16,651 15% 29% 4% Mycteria americana 26,834 9% 70% 7% Myiarchus tuberculifer 17,866 12% 47% 5% Myiobius atricaudus 19,091 11% 50% 5% Myrmeciza exsul 13,670 16% 24% 4% Myrmotherula schisticolor 15,043 18% 20% 4% Notharchus hyperrhynchus 37,026 8% 97% 10% Numenius phaeopus 9,551 25% 10% 3% Nyctibius griseus 23,679 10% 62% 6% Onychorhynchus coronatus 19,091 11% 50% 5% Ortalis cinereiceps 24,009 10% 63% 6% Oryzoborus funereus 36,553 8% 96% 10% Pachyramphus cinnamomeus 12,279 20% 16% 3% Panyptila cayennensis 21,372 11% 56% 6% Parula pitiayumi 17,618 12% 46% 5% Patagioenas cayennensis 34,374 8% 90% 9% Penelope purpurascens 10,362 21% 14% 3% Phaeochroa cuvierii 22,248 10% 58% 6% Phaeomyias murina 35,758 8% 94% 9% Phaethornis longirostris 27,299 9% 72% 7% Phaethornis striigularis 19,150 11% 50% 5% Phalacrocorax brasilianus 36,553 8% 96% 10% Picumnus olivaceus 26,411 10% 69% 7% Pionus menstruus 22,026 10% 58% 6% Pipra mentalis 12,478 20% 16% 3% Piranga olivacea 34,157 8% 90% 9% Pitangus sulphuratus 5,892 40% 4% 2% Platalea ajaja 22,472 10% 59% 6% Pluvialis dominica 33,501 8% 88% 9% Pluvialis squatarola 17,880 14% 31% 5% Porphyrio martinicus 28,074 9% 74% 7%

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Table C-1. Continued. Feature's Feature's Target Feature's distribution target (% Scientific name/Description area target (% of (% of of (ha) distribution) province) province) Birds (continued) Porzana flaviventer 6,699 35% 5% 2% Progne subis 32,488 9% 85% 9% Psarocolius decumanus 22,932 13% 40% 6% Psarocolius wagleri 15,752 12% 41% 4% Pyrilia haematotis 17,675 14% 31% 5% Pyrrhura picta 12,076 20% 16% 3% Ramphastos sulfuratus 25,991 10% 68% 7% Ramphocaenus melanurus 21,566 11% 57% 6% Rhynchocyclus brevirostris 11,017 21% 14% 3% Rynchops niger 9,551 25% 10% 3% Schiffornis turdina 16,790 15% 29% 4% Sclerurus guatemalensis 10,733 24% 11% 3% Sporophila schistacea 19,546 14% 34% 5% Stelgidopteryx serripennis 36,093 8% 95% 9% Sterna antillarum 9,551 25% 10% 3% Sterna hirundo 9,551 25% 10% 3% Sterna maxima 9,551 25% 10% 3% Sterna nilotica 9,551 25% 10% 3% Sterna sandvicensis 9,551 25% 10% 3% Sublegatus arenarum 24,421 10% 64% 6% Tachycineta albilinea 34,907 8% 92% 9% Tangara gyrola 17,184 12% 45% 5% Tangara icterocephala 4,612 51% 2% 1% Terenotriccus erythrurus 19,091 11% 50% 5% Thalurania colombica 18,017 14% 32% 5% Thamnophilus bridgesi 26,908 9% 71% 7% Thamnophilus doliatus 36,103 8% 95% 9% Thraupis palmarum 21,036 13% 37% 6% Thryothorus rutilus 16,319 12% 43% 4% Tigrisoma mexicanum 28,343 9% 74% 7% Tityra inquisitor 37,202 8% 98% 10% Tringa flavipes 33,559 8% 88% 9% Trogon collaris 22,264 13% 39% 6% Trogon massena 19,079 11% 50% 5% Trogon rufus 19,920 11% 52% 5% Turdus assimilis 21,792 13% 38% 6% Tyrannulus elatus 36,963 8% 97% 10%

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Table C-1. Continued. Feature's Feature's Target Feature's distribution target (% Scientific name/Description area target (% of (% of of (ha) distribution) province) province) Birds (continued) Tyrannus dominicensis 18,762 14% 33% 5% Vanellus chilensis 7,892 33% 6% 2% Xenops minutus 20,453 11% 54% 5% Xiphorhynchus erythropygius 9,193 26% 10% 2% Zimmerius vilissimus 20,327 11% 53% 5% Primates Alouatta coibensis trabeata 32,427 20% 43% 9% Ateles geoffroyi azuerensis 32,991 20% 43% 9% Ecoregions Isthmian pacific moist forests 41,324 20% 54% 11% Panamanian dry forests 28,540 20% 37% 7% South American Pacific mangroves 21,883 80% 7% 6% Talamancan montane forests 1,983 80% 1% 1% Forest Cover High quality forest habitat 39,006 80% 13% 10% Low quality forest habitat 26,960 80% 9% 7%

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APPENDIX D ORGANIZATION SEMI-STRUCTURED INTERVIEWS IN ENGLISH

Below is the English version of semi-structured interview forms used in one-on- one interviews with organizations:

Control Information Task Date(s) Name Healthy? If no, provide comments Interview Revision of the interview Coding the interview Entering the data Editing of the data I. Identification 1. Identification and location of the organization 8. Organization ID number 9. Community *(name) (census community##) 10. District 11. Generic title of the primary interviewee. *(nombre) (PID) 12. Generic title of the secondary interviewee *(nombre) (PID) 13. GPS location of the office (UTM) 14. Date established in Los Santos 15. Principle activities of the organization a. b. c. 9. Principle sources of funding

II. Concerns for conservation 1. Please describe your organization’s relationship to forest conservation in Los Santos:

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III. Goals for conservation 2. Please describe your organization’s environmental conservation goals in Los Santos: (Types of programs, geographic areas of interest, general demographic of clientele, etc.)

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2. Please use the map of Los Santos to illustrate your organization’s area of influence and the types of projects taking place in different areas:

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APPENDIX E ORGANIZATION SEMI-STRUCTURED INTERVIEWS IN SPANISH

Below is the Spanish version of semi-structured interview forms used in one-on- one interviews with organizations:

Información de Control Tarea Fecha(s) ¿Por quién? ¿Buen estado? Si no, proporcionar comentarios Entrevista Revisión del cuestionario Codificación Ingreso de la información Revisión y aprobación del ingreso de la información I. Identificación 1. Identificación y localización de la organización 16. Número de identificación de la organización 17. Comunidad *(nombre) (censos ##) 18. Distrito 19. Titulo general del entrevistado primerio *(titulo) (OID) 20. Titulo general del entrevistado secundario *(titulo) (OID) 21. Referencia de la geoposición de la organización (formato UTM) 22. Fecha de establecimiento en Los Santos 23. Actividades principales de la a. organización b. c. 9. Recursos principales de financiamiento

II. Preocupaciones de conservación 1. Por favor, describa las preocupaciones de su organización, los conflictos percibidos, y sugerencias de manejo para la restauración y conservación de bosques en Los Santos:

III. Objetivos para la conservación

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2. Por favor, describa la participación de su organización en la conservación ambiental de Los Santos: (Los tipos de programas, áreas geográficas de interés, demográficas generales de la clientela, etc.)

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2. Por favor, use el mapa de Los Santos para ilustrar el espacio de influencia de su organización y donde están los proyectos diferentes:

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APPENDIX F SUPPLEMENTAL MATERIAL ON THE CALCULATION OF JOINT COST (AREA AND ORGANIZATONAL SUPPORT)

Marxan’s objective function (Equation 3-1) requires that the costs be combined as a single cost surface (Ardron et al., 2010). For this requirement, we created the joint cost model in which each cost (area and organizational support) had approximately equal weight based on visual inspection of model outputs. Joint costs were calculated using the equation:

퐶푗 = 훽푎퐶푎 + 훽푔퐶푔 (F-1)

where 퐶푗 is the joint cost of a given planning unit, 퐶푎 is the area of the planning unit, 퐶푔 is the planning unit’s organizational cost, and 훽푎 and 훽푔 are the respective weights for area and organizational support costs.

The approach used to calibrate the two costs in this study was based on optimization process described in Ardron, Klein, & Nicolson (2008) and Ardron et al.

(2010). In this method, one cost is assigned a fixed coefficient of 1, which in our study was assigned to the area cost coefficient (훽푎). The coefficient of the second cost is determined by calculating the equilibrium point between the lowest scoring area-only cost model and the lowest scoring organization-only cost model. The costs are plotted for each model as (퐺푎, 퐴푎) and (퐺푔, 퐴푔), where 퐺푎 represents the organizational cost from the area-only model, 퐴푎 represents the area cost from the area-only model, 퐺푔 represents the organizational cost from the organization-only model, and 퐴푔 represents the area cost from the organization-only model (Figure F-1). The absolute value of the slope that connects the two points becomes 훽푔, calculated in Equation F-2 as:

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퐴푎−퐴𝑔 훽푔 = | | (F-2) 퐺푎−퐺𝑔

The equilibrium point between the area costs and organization costs of the area- only and organization-only models could be plotted with the area cost and organization cost of the lowest cost joint cost model (Figure F-1, C). In visually comparing the area- only, organization-only, and the first joint cost calibration output, we discerned little to no impact on the joint cost model from organizational costs (Figure F-2). We increased the coefficient value by calculating the slope between first joint cost model and the organization only cost model, similar to Equation F-2. With 훽푔 adjusted the new value

(훽푔 = 7.1), outputs from this second iteration were judged to sufficiently balance the prioritization of organizational support and area (Figure F-2).

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220,000 210,000 200,000

190,000 180,000

Area(ha) 170,000 160,000 150,000 140,000 35,000 40,000 45,000 50,000 55,000 60,000 65,000 Organization Cost

Figure F-1. Relationship between organization and area costs in models used to calibrate the joint cost model. Models represented by each point include: A) the area only cost model B) the organization only cost model C) the first iteration of the joint cost model, and D) the joint cost model.

A B

C D

Figure F-2. Selected planning units for CANs used to calibrate the joint cost model. Based on the 100 runs of each model, blue areas show planning units included in the best CAN (lowest cost) and grey areas show areas not selected. Models represented include: A) the area only cost model B) the organization only cost model C) the first iteration of the joint cost model, and D) the joint cost model.

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BIOGRAPHICAL SKETCH

Michael L. Bauman was born in Atlanta, Georgia. He graduated from Emory

University in 2005 Summa Cum Laude with a Bachelor of Science in environmental studies and a minor in anthropology. In the course of his degree, he spent one year abroad studying at James Cook University in Queensland, Australia, an experience that instilled the importance of participatory conservation planning. From December 2007 to

January 2011, he served three years with the United States Peace Corps as an agroforestry and rural community development extensionist, first in Paraguay and later in Costa Rica. His service provided him on-the-ground experience in understanding the challenges facing conservation and development goals in Latin America. He advanced his technical skill set for addressing these challenges through his master’s degree from the School of Natural Resources and Environment at the University of Florida, which he completed in the summer of 2015.

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