Abstract Modeling the Climatic Niche of Wild

ABSTRACT

MODELING THE CLIMATIC NICHE OF WILD CARICA PAPAYA

by Hannah B. Scheppler

Crop wild relatives can be a source of beneficial traits to be used for crop improvement. Carica papaya is a staple crop throughout the globe, domesticated from wild relatives native to Central America. I used the program Maxent to model the bioclimatic aspects of wild papaya’s niche and compared these models to regions of known farmed areas and areas where papaya has invaded outside its native range. I developed two models after optimizing for resampling and bias-curbing schemes. A simpler model employing non- covariate predictors was used to identify the main aspects of wild papaya’s climatic niche while a more complex model, including all 19 bioclimatic predictors, was utilized to make predictions to new environments. Models were consistent with known farmed areas, supporting the utility of the model. Additionally, I compared predictions to known invasions, and results were consistent overall, minus one invasion in Texas. Global projections under climate change resulted in likely over-predictions, but the current climate model is likely to be accurate based on consistency with farmed and invaded areas. These results can be used to identify locations where farms may be successful, assess risk of invasions, and identify under-sampled areas for biological studies.

MODELING THE CLIMATIC NICHE OF WILD CARICA PAPAYA

Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Biology

Hannah B. Scheppler

Miami University

Oxford, Ohio

2019

Advisor: Dr. Richard C. Moore

Reader: Dr. Tereza Jezkova

Reader: Dr. David J. Berg

This thesis titled

MODELING THE CLIMATIC NICHE OF WILD CARICA PAPAYA

Hannah B. Scheppler

has been approved for publication by

The College of Arts and Sciences

and

Department of Biology

______Richard C. Moore

______Tereza Jezkova

______David J. Berg

Table of Contents

Introduction 1 Methods 3 Results 9 Discussion 10 References 16 Tables 25 Figures 26 Supplementary Tables 39 Supplementary Figures 40 Appendices 44

iii

List of Tables

Table 1. Model scores from those with best projections 25

List of Figures

Figure 1. Flow chart of methods 26

Figure 2. Map of wild papaya occurrences used in this study 27

Figure 3. Example of omission and predicted area graph from Maxent 28

Figure 4. Omission graphs from different combinations of resampling and bias- 29 curbing schemes

Figure 5. Study area projections for different combinations of resampling and bias- 30 curbing schemes

Figure 6. Response curves for individual variables from the best all-variable model 31

Figure 7. Model projections throughout the study area for the two best models in the 32 study

Figure 8. Mexico suitability map and papaya farm yield map from EarthStat 33

Figure 9. Brazil suitability map and papaya farm yield map from EarthStat 34

Figure 10. Suitability plotted by farm yield 35

Figure 11. Model projection compared to known invasions in Australia 36

Figure 12. Model projection compared to known invasion area in Florida 37

Figure 13. Model projection compared to known non-native populations in Texas 38

List of Supplementary Tables

Table S1. P-values between suitability values among papaya farm yield categories 39

List of Supplementary Figures

Figure S1. Study area model projections comparing use of threshold rules 40

Figure S2. Global projections comparing different climate scenarios and threshold rules 41

Figure S3. India suitability map and papaya farm yield map from EarthStat 42

Figure S4. West Africa suitability map and papaya farm yield map from EarthStat 43

vii

Appendices

Appendix A Table of GPS coordinates of individual papaya occurrences 44

Appendix B R script: converting bioclim rasters to ASCII format 56

Appendix C R script: cropping bioclim rasters 57

Appendix D R script: spatially thinning occurrence records 59

Appendix E R script: creating binary bias grids 60

Appendix F R script: creating pairwise correlation matrix of bioclim variable values 64 extracted from occurrence localities

Appendix G R script: calculating AICc 65

Appendix H R script: extracting suitability values at occurrence localities 69

Appendix I R script: combining yield and suitability values into one table 70

Appendix J R script: creating graphs from yield by suitability matrices 75

Appendix K R script: Welch’s and Student’s t-test 78

viii

Dedication

I dedicate this thesis to my brother, Noah Scheppler, and my nephew, Oliver Scheppler. This thesis is also dedicated to Ernie, Marmalade, and Ted Jones.

Acknowledgements

I am grateful to my thesis committee (Richard C. Moore, Tereza Jezkova, and David J. Berg) for providing counsel and direction for the project as well as professional guidance regarding future endeavors of mine. I would like to give special thanks to Justin Fain for his help in constructing R code for creation of bias grids. I acknowledge and thank Dr. Mariana Chávez-Pesqueira for sharing papaya occurrence records. Thank you to Robert A. Francis for encouraging me to test various settings in the models, helping me understand model outputs, and for introducing me to bias grids in Maxent. Additional thanks to Dr. Jessica McCarty and Jason Bracken for advice regarding the utility of Maxent and the selection of GIS data. I acknowledge and thank the staff (Dr. Andor Kiss and Ms. Xiaoyun Deng) of the Center for Bioinformatics and Functional Genomics (CBFG) at Miami University for computational support. Lastly, very special thanks to the Moore lab members at Miami University for their endless personal and professional support.

Introduction Crop wild relatives can have specific beneficial traits that are missing in cultivated strains and allow for crop improvement (Hajjar and Hodgkin, 2007). These traits include resistance to herbivores or plant pathogens, increased nutrient uptake, and overall resistance to abiotic stress, such as drought (Brumlop et al., 2013). Higher fitness can be achieved among cultivated strains by cross-breeding with wild relatives containing target traits (Warschefsky et al., 2014). This has been achieved for resistance to diseases in tomatoes, rice, and wheat (Hajjar and Hodgkin, 2007). Studies have been performed that identify higher genetic diversity among wild versus cultivated strains, which suggests further investigation into potential beneficial traits that may have been lost in agriculture due to genetic drift or bottlenecks (Sun et al., 2001; Tar'an et al., 2005). Methods to identify target traits include field and greenhouse experiments, QTL analyses, and environmental niche models (ENMs; Pelgrom et al., 2015; Johnson et al., 2000; Hajjar and Hodgkin, 2007; Kantar et al., 2015). Environmental niche models test combinations of environmental variables and model settings to reveal the best combination for predicting species occurrences. These models can be projected onto the globe to reflect an organism’s fundamental niche. The fundamental niche can be thought of as a region in multidimensional environmental space where a species could survive if individuals were able disperse to all potential environments (Hutchinson, 1957). In contrast, the realized niche is the region in environmental space where the species actually occurs, and it exists within the broader fundamental niche (Hutchinson, 1957). Environmental niche models aim to infer the fundamental niche, as predictor variables of realized niches are used to project potential niche space in environments where the species has not dispersed. However, the models utilize realized species occurrences. Therefore, ENMs can be considered to reflect niche space that is somewhere between the realized and fundamental niche; although, a greater fraction of the fundamental niche is revealed in ENMs when the species is sampled throughout a greater portion of its range (Phillips et al., 2006). Among other uses, ENMs can be used to detect potential areas where crops can be farmed. Model predictions can also be compared between crop wild relatives and cultivars to identify differences that may be informative for cross-breeding. Environmental niche models can identify areas where the wild relative is projected to grow, yet where it is not farmed, suggesting there may be beneficial traits left in the wild that give rise to a broader niche. In maize (Ureta et al., 2012), pigeonpea (Khoury et al., 2015), and sunflower (Kantar et al., 2015), projections and niche predictors were compared among wild and cultivated taxa. In all of these studies, some wild relatives exhibited higher resilience in harsher conditions (by having higher thresholds for growth) relative to crop strains. These wild relatives likely have beneficial traits that could be reintroduced into cultivars. Identifying the geographic area of a species’ niche is also helpful in understanding the potential for range expansion. This approach is common for studying invasive or non-native species (Peterson, 2003; Bradley et al., 2010), including a study of four invasive plant species in North America (Peterson et al., 2003), invasive bushmint in

India (Padalia et al., 2014), and over 70 non-native plant taxa in Australia (O'donnell et al., 2012). Carica papaya is cultivated world-wide as a tropical fruit crop, yet it can be found growing naturally in the Central American landscape, its region of origin. The species diverged from the genera Jarilla and Horovitzia ~27 Ma, coincident with the earliest stages of the formation of the Panamanian Isthmus (Antunes Carvalho and Renner, 2012). Wild papaya is found naturally growing in low-altitude, often disturbed, habitats from southern Mexico to Panama (Contreras, 2016). Wild papaya differ from most cultivars in that the former can be more tolerant to drought, produces many small fruits, and has much lower presence of hermaphrodites (Fuentes and Santamaría, 2014). Papaya domestication likely occurred near the Yucatan peninsula or southern Mexico, and cultivated papaya is now farmed throughout Central America and in locations where humans have dispersed the species, including Australia, Brazil, Cuba, Florida, Hawaii, India, Malaysia, South Africa, and Taiwan. The global distribution of papaya cultivation suggests papaya’s fundamental niche extends beyond its native range. This is typical, as species often have fundamental niches that are not fully exhibited due to dispersal constraints (Phillips et al., 2006). Modelling wild papaya’s niche would allow papaya growers and breeders to compare regions of high suitability with regions where cultivars are farmed by projecting habitat suitability onto the globe. Niche modeling can also help conservation biologists predict regions at risk of invasion by wild (or feral) papaya. Papaya is a fast-growing pioneer species with high seed output that thrives in disturbed habitats. It has invaded southern Florida (Kwit et al., 2000), Christmas Island and North Keeling Island (remote islands in the Indian Ocean between Singapore and Australia; Claussen, 2001), and two populations have been found in central Texas (Mink et al., 2017). Supporting the risk of non-native papaya establishment, Kwit et al., (2000) suggest that C. papaya has a broader abiotic regeneration niche (environmental requirements for seed germination) than native species throughout the Florida Keys, and it is able to establish more readily and at a broader temporal scale than native species. The species is considered to be naturalized throughout the Florida Keys and southern peninsular Florida (Wunderlin, 1998; Ward, 2011), likely due to dormant seed banks emerging after disturbance events (Kwit et al., 2000). Papaya has long been naturalized on Christmas Island (Lohr et al., 2016). The populations in Texas are comprised of few individuals with low risk of further invasion, likely due to unsuitable climate conditions (Mink et al., 2017). The species is known to thrive after hurricanes disturb the landscape (Horvitz et al., 1995; Horvitz et al., 1998; Franklin et al., 2004; Kwit et al., 2000), which is becoming more common due to global climate change (Knutson et al. 2010; Emanuel 2005). I used the program Maxent, a maximum entropy-based machine learning algorithm, to develop ENMs for wild papaya using present-day bioclimatic predictor variables. Maxent is convenient because it utilizes presence-only records, offers a variety of resampling options, provides ways to curb effects of sampling bias, and has settings that can be used to prevent

overfitting (Phillips et al., 2006). I performed a preliminary optimization of model settings including resampling and bias-curbing schemes. Final models used bootstrapping or subsampling as the resampling strategy and spatially thinned records to curb effects of sampling bias. Then, using the chosen settings, I optimized variable selection and regularization to produce the final models. I developed one model with non-correlating predictors to identify the main components determining papaya distribution, and I utilized a model with all variables to make projections to new environments. I also observed the effects of using threshold rules in Maxent to produce more conservative or liberal estimates under climate change. The all-variable model was compared to known papaya farming regions to test its utility in predicting farmed regions or potential areas papaya farms could be developed. The model was also compared to known areas of non-native papaya establishment to see if it is useful in predicting invasions. Overall, the model appears useful in predicting both farms and non-native establishments of papaya, and it may also be useful for researchers searching for under-sampled populations in the wild.

Methods Overview The methods consist of four main components. First, the GIS data was prepared for use in Maxent. Second, two primary Maxent settings were optimized: the resampling strategy and the bias-curbing scheme. Using optimal resampling and bias-curbing schemes, final models were then created by optimizing the variable selection and regularization multiplier in Maxent. Lastly, final models were systematically selected for analyses by various model scoring measures (Fig. 1). Data Preparation Occurrence Records. I used a total of 654 wild papaya occurrence records throughout its native range, including 593 collected in June 2008 and January 2014 from Nicaragua through Panama, 23 occurrences throughout Mexico, and 38 from herbarium specimens collected throughout Central America (Brown et al., 2012; Mardonovich et al., 2018; Chávez-Pesqueira et al., 2014; Chávez-Pesqueira and Núñez-Farfán 2016; Fig. 2). Individuals from the southern range were sampled from disturbed, natural habitats, mostly along roadsides (Brown et al., 2012). The Mexico samples were collected from four populations in continuous tropical rainforest, near the forest edge in a fragmented landscape in Los Tuxtlas region, southern Mexico (Chávez-Pesqueira et al., 2014; Chávez-Pesqueira and Núñez-Farfán 2016). Samples from the southern range include 418 samples throughout Costa Rica (248 collected in 2008 [Brown et al. 2012] and 170 in 2014), 93 throughout Nicaragua in 2014, and 82 throughout Panama in 2012 [Mardonovich et al. 2018]). Location data for 5 samples were acquired from Chávez-Pesqueira et al. (2014) in Veracruz, Mexico; 18 were acquired from Chávez-Pesqueira and Núñez-Farfán

(2016) throughout Mexico; and 38 sample coordinates were shared from Chávez-Pesqueira’s lab at Centro de Investigación Científica de Yucatán, Mexico by personal observations of Herbarium records (Appendix A). WorldClim Data. I utilized 30 arc-second bioclimatic raster data from WorldClim, which comprised 19 bioclim variables at approximately 0.0083 degree spatial resolution (Fick and Hijmans, 2017). I decided to use climatic variables in the models because occurrence records spanned a large area, occupying numerous landscapes where the climate is heterogeneous (allowing for discriminatory power in the model; Basso et al., 2001). The same variables were downloaded at a 2.5 arc-minute spatial resolution (approximately 0.0417 degrees) for years 2050 and 2070, using a Representative Concentration Pathway of 4.5 (a relatively conservative estimate of greenhouse gas concentration), and separate data from both the Community Earth System Model (CESM) and the Hadley Global Environment Model 2 - Earth System (hadGEM2-ES) were downloaded to account for differences among climate change models. The CESM is state-of-the-art and widely used in biodiversity studies (Kay et al., 2015; Dufresnes et al., 2016), and the hadGEM2-ES is an established climate model, also widely used in biodiversity studies (Pope et al., 2000; Peterson, 2004). The bioclim variables were converted to ASCII format for use in Maxent by using the raster package in R (Hijmans, 2019; Appendix B). The current-day ASCII bioclim files were then copied and cropped to the study area extent (1 degree around the widest occurrence points) in R for use in creating Maxent models, again using the raster package in R (Appendix C). The global extent rasters were reserved for current- climate global projections from finalized models. Preliminary Optimization of Model Settings Resampling Strategy. Resampling is employed to randomly divide the occurrence data into training and testing sets (Elith et al., 2006). There are three methods of resampling in Maxent: bootstrapping, cross-validation, and subsampling. Bootstrapping is a form of resampling with replacement, where test data may be used in more than one iteration. Bootstrapping partitions the training and test data randomly, therefore utilizing a “random background percentage” in Maxent (Elith et al, 2006). Cross-validation is resampling by partitioning the data into replicates, and for each iteration, one replicate is used as test data. By definition, cross-validation does not randomly select test data, as it is meant to methodically use one replicate at a time. Subsampling is resampling that uses random background points, like bootstrapping, but it resamples without replacement (Phillips, 2005). The receiver operating characteristic (ROC) plots the true positive rate by the false positive rate, and the area under the curve (AUC) reflects the model’s accuracy and discriminatory power. The AUC is commonly used in scoring Maxent models (Phillips and Dudík, 2008; Blach-Overgaard et al., 2010; Yang et al., 2013). It is important to optimize resampling strategy before employing final models because resampling can affect AUC scores depending on its effects on independence of training and test data (Veloz, 2009).

To optimize the resampling strategy, I ran a total of 18 models in Maxent (Phillips et al., 2016). In all models, the following settings were controlled: using all 19 bioclim variables; setting maximum number of background points to 10,000; maximum iterations to 5,000; 25% random background (standard for Maxent; Phillips, 2005; Beane et al., 2013); performing 10 replicates; and using Cloglog output format. Six models were performed for each resampling strategy, three of them using thinned records and three using all 654 records plus a 50-kilometer bias grid, using regularization multipliers (RMs) of 1, 7, and 15, respectively. Cao et al. (2013) tested RMs ranging from 1-40 and found that higher RMs resulted in over-predictions. Therefore, I started with a selection of RMs on the lower end, while also using a range of RMs to account for potential differences that they may cause in model scores. To spatially thin the records, I used the spThin package in R (Aiello-Lammens et al., 2019) and randomly thinned the 654 occurrences by 10 kilometers (Appendix D), resulting in 102 occurrences. I chose 10 kilometers because that was the minimum distance that provided an even visual representation of occurrences. Also, papaya populations can range from 2-3 kilometers apart to 15 kilometers or more, and a thinning parameter of 10 kilometers adequately represents population distribution. Additionally, several ENM studies used 10 kilometers as a thinning parameter (Kramer-Schadt et al., 2013; Radosavljevic and Anderson, 2014; Aiello- Lammens et al., 2015). Two different bias-curbing schemes (thinned records vs. bias grid) were used to account for differences caused by those settings. The best resampling strategy should maximize the independence of training and test data so that AUC can be properly used later to evaluate final models. Spatially auto-correlated training and test data typically result in higher AUCs (Veloz, 2009), which can be deceiving when model evaluation utilizes AUC scores. Maxent produces a graph that plots positive presences over different thresholds of omission; theoretically, there should be 1/10 total positive presences within 1/10 of a random sample, and a good model will have a slope of one (Phillips, 2005). Spatially auto-correlated training and test data cause the omission rate curve to be quite lower than the predicted omission rate (Phillips, 2005; Fig. 3). Therefore, models were also evaluated based on the proximity of omission rate curves to the predicted omission rate, with higher proximity being regarded as better. If there was not a discernible difference between qualities of omission graphs, I relied more on quality of predictions compared to known papaya occurrences. Bias-curbing scheme. One can curb the effects of bias by either spatially thinning the occurrence records or employing a bias grid in Maxent (also known as “biased prior” as in Merow et al., [2013] or “'FactorBiasOut'” described in Phillips et al., 2009). The bias grid is a raster that informs Maxent about sampling effort of the researcher; grid cells with a value of one indicate high effort, and values close to zero indicate low effort. Bias grids are usually reserved for models with few occurrence records (Kramer‐Schadt et al., 2013). Spatial thinning, on the other hand, is used when there are enough occurrence records to sacrifice without losing too much important information for the model (de Oliveira et al., 2014). It is important to optimize

the bias-curbing scheme before employing final models because high sampling bias affects model performance in several ways. Spatial sampling bias can cause overfitting to study area and high spatial autocorrelation between training and test data (Veloz, 2009). Three binary bias grids were created using the raster and spatstat packages in R (Hijmans, 2019; Baddeley et al., 2015; R Core Team, 2019; Appendix E). Each grid was made by creating a buffer around all occurrence records: one 30 kilometers, one 50 kilometers, and one 70 kilometers. The buffer areas were assigned values of one and non-buffer areas were assigned as zero. A total of 36 models were used to identify the best bias-curbing scheme. For each of the four schemes (thinned records and no bias grid, all records and 30 km bias grid, all records and 50 km bias grid, and all records and 70 km bias grid), I ran nine models including all combinations of resampling strategies and RMs of 1, 7, and fifteen. More RMs were tested after preliminary optimization of model settings. I determined the best bias-curbing scheme by observing the proximity of the omission rate curve to the predicted omission rate. Higher proximity was regarded as better because high omission curves suggest overfitting (Radosavljevic and Anderson, 2014) and low omission curves suggest spatial autocorrelation of training and test data (Phillips, 2005). I also utilized the visual quality of model projections compared to known occurrence hotspots because bias grids were associated with under-predictions throughout Mexico. Final Optimization of Models There are several approaches for performing variable selection for ENMs. One study suggested using complex models that include covariate predictors for projecting to new environments or climates (Braunisch et al., 2013), one study suggested intermediately complex models (Moreno-Amat et al., 2015), and other studies suggested removing covariates entirely (Rödder et al., 2009; Warren et al., 2014). I used two approaches, optimizing models using non- correlating predictors and using all nineteen. The simpler, non-covariate model was used to identify main niche predictors, following Rödder et al. (2009). The more complex model (using all 19 predictors) was used to project to new environments, following Braunisch et al. (2013). In Maxent, regularization allows the user to force the response curves of predictors to become smoothed, preventing overfitting of the model (Elith et al., 2011). The regularization multiplier (RM) is a coefficient that augments the regularization formula for each predictor (Phillips et al., 2006). The best RM for a model can be identified by testing a range of values, with the best option resulting in a balance of satisfactory model statistics and accurate predictions. Often researchers use an RM that results in the lowest AICc score (Akaike’s Information Criterion corrected for small sample size; Warren and Seifert, 2011). The AICc score represents the relative complexity of the model, with lower scores reflecting fewer model features. However, AICc-based evaluations alone can lead to over-predictions (Cao et al., 2013).

I simultaneously optimized regularization while performing variable selection. I tended to use higher RMs for models that used more predictors, as complex models caused restricted predictions. A correlation matrix of bioclim variables was generated in R using the variable measures at the occurrence locations. It was created using R packages dismo, raster, and rgdal (R Core Team, 2019; Hijmans et al., 2017; Hijmans, 2019; Bivand et al., 2019; Appendix F). Variables with over 70% or under -70% correlations were considered as covariates. A list of all possible combinations of non-correlating bioclim variables was systematically generated, totaling 440 combinations. Combinations of non-correlating predictors were eliminated from the list if they included any of the variables with zero permutation importance from a preliminary all-variable model, following Cao et al. (2013). Permutation importance represents the relative amount that AUC drops with absence of that particular variable (Phillips et al., 2006). In the end, there were 12 combinations of non-correlating variables to test, and one using all 19 variables. I ran models using all combinations of RMs ranging from 1-21 at increments of two, reserving the possibility to explore even-numbered RMs or those over 21 when necessary (i.e. when the model seemed to under- or over-predict). A range of RMs was used again here to account for potential differences it may cause in the model predictions. For several models, RMs of 7 and 9 produced under- and over-predictions, respectively, and therefore an RM of 8 was tested. Each model (the 12 combinations of non-correlating variables, and one with all 19) was run with bootstrapping and subsampling separately, as well as with spatially thinned records. Cloglog output was used so that the scale among projections was consistent and pictures could be adequately compared. Selecting Models Visual Accuracy of Predictions. For each variable selection strategy, I selected models for final evaluation based on visual quality of projections compared to known wild papaya habitat. This resulted in two models using all variables and seven using non-correlating variables that visually seemed accurate and overall undiscernible among each other. I re-ran those models in raw output format in order to calculate AICc in R using the ENMeval package (Muscarella et al., 2014; Appendix G), while confirming AICc using the program NicheA (Qiao et al. 2012; Qiao et al. 2016). AUC and AICc Scores. I further narrowed the candidate models by considering AUC and AICc scores. Models with AUC scores under 0.70 were eliminated from the list. The best model with non-correlates was identified by having relatively low AICc and projections consistent with known occurrences. Low AICc was used to choose that model because the purpose was to identify variables with high explanatory power in predicting papaya suitability. The best all-variable model was identified by having intermediate to high complexity (AICc) and accurate projections. I aimed for higher AICc for the all-variable model because the purpose of that model was to create predictions, and studies show that intermediate to complex models

produce more accurate predictions to new environments (Braunisch et al., 2013; Moreno-Amat et al., 2015). Higher RMs were used for the more complex models because lower RMs tended to cause high restrictions in habitat suitability. Suitability values from each model were extracted from occurrence points and averaged using the raster package in R to confirm the visual assessment of predictions (Hijmans, 2019; Appendix H). Projecting Papaya Suitability Globally The Maxent model selected for the following tasks was the best all-variable model. Maxent provides the option to apply a threshold rule to manipulate the amount of training data used, which can greatly affect model projections (Liu et al. 2005). The best all-variable model was re-run using a 10 percentile training presence threshold to represent a conservative estimate of suitability and minimum training presence threshold to represent a liberal estimate of suitability (Norris, 2014). Simultaneously the model was projected onto the globe. This was performed by using the “Projection layers” option in Maxent. The projections were performed for five separate global climate scenarios: year 2012 and years 2050 and 2070 from both CESM and hadGEM2-ES climate change models. The global projections using CESM and hadGEM2- ES were averaged in ArcGIS for 2050 and 2070, respectively, to account for differences between the climate change models. I compared current-day projections to known papaya farming regions. Geospatial data from EarthStat was downloaded for papaya farms (Monfreda et al., 2008). I utilized yield per hectare data from EarthStat and compared those maps to model projections. EarthStat warns that less popular crop data may be plotted throughout all farmed areas in a state or country, including areas where it may not be farmed. Therefore, using the data quality raster from EarthStat, I eliminated yield data that was likely to be projected to areas lacking papaya farms. I used ArcGIS Raster Calculator to assign grid cells a value of zero where data quality was relatively low: “Con(“Quality.tif”, “Yield.tif”, 0, “VALUE>0.5”)”. For some areas, papaya farm data was still consistent with country or state boundaries, suggesting it was still extrapolated to all farmed areas of the country or state, and I considered those areas to be low quality data. I visually compared farm yield data to habitat suitability from the best all-variable model by making maps in ArcGIS. A more quantitative comparison was also performed between habitat suitability from Maxent and papaya farm yield data. Using the raster package in R, the highest quality yield data was extracted (data quality = 1) (Hijmans, 2019, Appendix I). That resulted in yield data for only Brazil and Mexico. Yield data quality of 0.75 or higher was used to evaluate India. Suitability and the refined yield values were combined into tables for each country analyzed (Appendix I). Suitability and yield values were visualized in scatter plots and box-and-whisker plots showing low, medium, and high yield categories, which was performed in R (Appendix J). The correlation coefficients for the scatter-plots were calculated in Microsoft Excel. Means of suitability values within low, medium, and high yield categories were statistically compared

using both Welch’s Two Sample t-test and Student’s t-test in R (Appendix K). Both t-tests were used because the sample sizes of each category differed, potentially causing variances to differ enough to warrant a Welch’s t-test. The t-test results were confirmed in Microsoft Excel. To identify habitat suitability for wild papaya in areas where invasions are prone to occur, I used ArcGIS and visually compared global projections from the best all-variable model to areas where invasions have been documented (Environmental Systems Research Institute, 2019).

Results Optimal Model Settings When testing the three resampling strategies, cross-validation tended to cause high standard deviation of omission compared to bootstrapping and subsampling (Fig. 4). A range of RMs was utilized during this test, and the most accurate projections for bootstrapping and subsampling occurred with RMs where omission curves were best, and for cross-validation, when omission curves were worst. Among models that used cross-validation, habitat throughout the Yucatan peninsula would commonly be comparatively underrepresented (Fig. 5). Cross- validation was therefore eliminated as a candidate resampling method, and bootstrapping and subsampling were utilized in final models. The use of bias grids of 30 km, 50 km and 70 km buffers, with all 654 occurrences consistently caused omission rates to be below predicted omission, whereas use of thinned records and no bias grid caused omission rates to be much closer to the predicted omission (Fig. 4). Additionally, implementing a bias grid caused great under-fitting throughout Mexico (Fig. 5). Therefore, I utilized spatially thinned occurrence records and no bias grid in the final models. Variable Selection The best non-correlating variable model used subsampling and an RM of 8, with an AUC of 0.76 and relatively low AICc (Table 1). The variables in that model were bio06 (min. temperature of coldest month), bio10 (mean temperature of warmest quarter), bio15 (precipitation seasonality), and bio19 (precipitation of coldest quarter). Those variables are also within the highly permuting group of variables from the best all-variable model (Fig. 6). The best all-variable model used bootstrapping and an RM of 15, resulting in AUC of 0.74 and relatively high AICc (Table 1). The best all-variable model was used to reveal additional highly permuting variables that were missing from the best non-correlating variable model. Those variables included bio09 (mean temperature of driest quarter), bio01 (annual mean temperature), and bio17 (precipitation of driest quarter) (Fig. 6).

Projection Maps The use of a threshold rule versus no rule produced maps that globally appear similar, yet produced more visual under-representation in northern Mexico (Fig. S1). The best model using non-correlates produced projections that were slightly under-fit throughout Mexico compared to the best all-variable model (Fig. 7). Therefore, for discussing global projections, I utilized maps from the best all-variable model with no threshold rule. Climate change projections indicated vast increases in papaya suitability, with high suitability covering most of South America, Africa, and Australia, as well as southern United States (Fig. S2). For that reason, analysis of farmed and invaded areas utilized only the current climate predictions. Global suitability projections were visually consistent with EarthStat papaya yield data, minus a few areas where papaya is farmed yet has low suitability from the model. For example, maps of Mexico show that areas with high-yielding papaya farms are in locations where the best all-variable model projected medium to low suitability along the west coast (Fig. 8). That also happened in Southern Brazil (Fig. 9). Overall, other areas observed were visually consistent with the model (Figures S3-S4 for maps of India and West African regions, respectively). Quantitatively, scatter plots show a positive relationship between habitat suitability and papaya farm yields in India and Mexico, although the correlation coefficient was not significant (Fig. 10). Brazil had a negative relationship between habitat suitability and papaya farm yields (Fig. 10). Box and whisker plots showed that high yield corresponded to high suitability, and low and medium yield corresponded to medium suitability (Fig. 10). All yield categories had suitability values that were significantly different for Brazil, Mexico, and India, other than low vs. medium yields in Mexico (Table S1). The model showed high suitability where there are known invasions on Christmas Island and North Keeling Island. There was also high suitability throughout neighboring Indonesia and medium-to-high suitability on neighboring mainland Australia (Fig. 11). Where there were invasions in Florida, there was low-to-medium suitability on the mainland, and high suitability among the Florida Keys (Fig. 12). The region in Texas with known invasions showed medium to low suitability from the model (Fig. 13).

Discussion Model Optimization Optimizing settings and parameters in Maxent is essential for making accurate predictions and useful models out of biased or low-quality input data (Anderson and Gonzalez, 2011; Braunisch et al., 2013; Cao et al., 2013; Kramer‐Schadt et al., 2013). A multifaceted approach to evaluating Maxent models is preferable to relying on AICc or AUC alone, as AICc- based model selection tends to cause over-predictions, and AUC scores are sensitive to spatial auto-correlation of training and test data (Veloz, 2009; Cao et al., 2013; Escobar et al., 2018).

Here I attempted a multifaceted evaluation of Maxent settings and final models to develop an ENM of wild C. papaya, relying heavily on model projections compared to known species occurrences complemented by AUC and AICc metrics. I used climatic data to infer part of the abiotic component of wild papaya’s niche. Two levels of model optimization were performed: preliminary optimization, which focused on Maxent settings of resampling and bias-curbing schemes, and final optimization, which focused on variable selection and regularization multiplier (Fig. 1). A final, relatively complex model was developed to compare to known papaya farming hotspots as well as predict areas of farming suitability, to be utilized in predicting where papaya might invade, and to provide a resource to researchers who wish to sample in underrepresented areas. A simpler model with fewer, non-correlating predictors was used to identify main niche characteristics of wild papaya. Resampling schemes produced differences among omission graphs and model projections (Figures 4-5). Cross-validation was more commonly associated with higher standard deviation in omission (Fig. 4) and underrepresentation throughout Mexico (Fig. 5). Therefore, I used bootstrapping and subsampling in final models. Cross-validation uses all occurrence data for model validation, making it useful for small data sets (Phillips, 2005). The dataset was intermediate in size (see Proosdij et al., 2016 for small datasets), lessening the necessity to use cross-validation. The use of bias grids rather than spatial thinning led to omission rate curves that were much lower than predicted omission (Fig. 4), suggesting that bias grids increased the tendency for spatial autocorrelation of training and test data (Phillips 2005). Also, bias grids were associated with visual under-predictions throughout Mexico (Fig. 5). Kramer‐Schadt et al. (2013) found that spatial thinning resulted in more accurate predictions than using a bias grid, and bias grids led to higher omission and commission errors. They also recommended using a bias grid when spatial clustering of occurrences are biologically relevant. It is not likely that wild papaya is more clustered where the occurrence records are more clustered in the original dataset (as in Fig. 2). Therefore, I used spatially thinned records for final models. The scientific literature is unclear regarding the best variable selection techniques to use with Maxent. Braunisch et al. (2013) performed a thorough comparison that suggested eliminating correlating variables to increase model performance. However, they utilized a small portion of correlating bioclim variables to make models, which may be why each model showed different predictions under climate change. Warren et al. (2014) suggested avoiding covariates for climate change projections because they tended to cause stricter predictions. Moreno-Amat et al. (2015) performed an experiment that used all 19 bioclimatic variables, and found that including many covariates – along with regularization – produced the most visually accurate projections under climate changes. I employed two variable selection techniques using bioclimatic variables: modeling with non-correlates or all 19 predictors. I evaluated models based on visual quality of projections,

AUC, and AICc scores. However, AUC and AICc do not always correspond to accurate predictions (Cao et al., 2013; Escobar et al., 2018), and I was simultaneously tuning RM (which changes AICc). Therefore, I relied more heavily on comparing local projections to known occurrences, both visually and quantitatively (Table 1). The model with non-correlates was used to infer the most important climatic predictors of papaya distribution. The model with all bioclimatic variables was used for predictions to new environments and to identify highly permuting variables missing from the best non-correlate model. The non-correlating variable model resulted in slight under-fitting throughout northern Mexico compared to the all-variable model (Fig. 7), so I used the all-variable model for visual analyses. However, studies show that including covariates can cause stricter Maxent predictions (Braunisch et al., 2013; Warren et al., 2014). I attribute the relatively liberal predictions of the all-variable model to high regularization (Table 1). To provide visualizations of more conservative or liberal estimates of suitability under climate change, I re-ran the two final models using threshold rules of 10 percentile training presence and minimum training presence, respectively. Visually, the most conservative climate change ENM predictions were from models using a threshold rule versus no threshold rule, with no major visual differences between the two threshold rules (Fig. S1). However, no climate change ENM predictions were analyzed in this study because suitability seems to be vastly over- predicted for future climates, regardless of threshold rule (Fig. S2). The strong potential for over-predictions under climate change may be due to a high RM and Maxent’s lack of information regarding upper limits of response curves for variables, as many response curves were half-normal and left-skewed (Fig. 6). Climatic Niche of Wild Papaya Based on the variables used in the best non-covariate model, wild papaya has a strong tendency to exist in very high temperatures and precipitations (Fig. 6). Wild papaya exists under high mean temperature of warmest quarter, minimum temperature of coldest month, precipitation of coldest quarter, mean temperature of driest quarter, and annual mean temperature. Additional variables had non-zero permutation importance, yet they resulted in relatively horizontal response curves, suggesting they may not delimit the distribution of papaya. In fact, all permutating precipitation variables had horizontal response curves other than precipitation of coldest quarter. Precipitation of coldest quarter was also one of four predictors in the best non- covariate model, further supporting that it is an important predictor in wild papaya distribution. Overall, the two final models provided more information regarding temperature than precipitation, where papaya seems to prefer higher relative temperatures, low diurnal range in temperature, and high amounts of precipitation (Fig. 6). Temperature-related response curves from the model in this study are consistent with the idea that papaya can tolerate heat, however suitability is greatly reduced at lower temperatures (Fig. 6). That is consistent with common knowledge and the literature. Papaya is commonly known to be very sensitive to frost (Morton,

1987; Gross, 2003). One study on the “Maradol” cultivar suggests that papaya utilizes crop- common transcription factors to tolerate extreme cold and heat (Figueroa-Yañez et al., 2016). Papaya’s apparent preference for high precipitation is consistent with greenhouse experiments on various papaya cultivars. Drought stress induces flower abscission, reduced number of leaves, and overall small papaya plants (Aiyelaagbe et al., 1986; Masri et al., 1990). From this study, non-horizontal precipitation-related response curves include precipitations of wettest times of year, and response curves for precipitations during dry season are horizontal (Fig. 6). That may reflect a dependence on wet season to regenerate after the dry season. This is interesting because the papaya cultivar “Baixinho de Santa Amalia’’ displayed physiological mechanisms to resist drought, such as an ability to increase ion content, potentially related to osmotic adjustment. That cultivar also displayed quick regeneration after rehydration (Mahouachi et al., 2006). Comparing Model to Known Farm Hotspots Projections were also visualized in regions where papaya is commonly farmed. Overall, the two data sets are visually consistent (Figures 8-9, S3-S4). However, at the tip of Baja California Peninsula (in Baja California Sur) and throughout southern Brazil, there are farms yet medium to low suitability predicted by the model (Figures 8-9). The model may under-predict suitable habitat due to sampling bias. The result could also be due to low frequency (and low sampling) of individuals adapted to the environment at the distribution edge. Some researchers suggest that edges of species distributions may house individuals with adaptations to abiotic stress, as the climate is likely to be relatively extreme there compared to the center of the species range (Williams et al., 2009; Khoury et al., 2015). On the other hand, this result may be due to increased efforts in maintaining papaya farms. Papaya farms typically require large scale human intervention for crop survival (such as irrigation; Almeida et al., 2003). Therefore, a niche model might under-predict where farms are able to occur due to human aid in an otherwise unsuitable environment. An agricultural economics study suggested that non-poor farms in Mexico are typically in areas where irrigation is common, and Baja California Sur was found to be one of the least poor areas studied (Bellon et al., 2005). The quantitative comparison of papaya suitability versus farm yield showed that high- yielding farms are present in regions of statistically higher suitability than low- or medium- yielding farms (Fig. 10, Table S1). However, in Brazil, there is an overall slight negative relationship between suitability and papaya farm yield. This result may be due to the lower suitability among medium-yielding farms compared to low-yielding farms in Brazil. Another study found a stronger relationship between observed yields and model predictions for farmed mussels in Italy (Vincenzi et al., 2007). Additionally, there is no significant difference between suitability among low- and medium-yielding papaya farms in Mexico (Fig. 10). Those two results may be due to the fact that papaya farms, like any farms, are likely preferentially constructed on relatively suitable habitat. The relatively low yields among some farms might not

13 be due to low suitability, as the mean suitability among all farms exceeds ~0.4 (Fig. 10). Instead, lower yields may be partly due to other factors, such as economic stress or lack of human intervention on farms. Invasion Risks Papaya has been known to thrive and establish populations after disturbance events (Kwit et al., 2000; Australian Government Department of Health, 2003). Model projections from the best all-variable model were compared to known non-native papaya populations. For the Christmas and North Keeling Islands populations in the Indian Ocean, the model shows high suitability, which is consistent with the established papaya populations in those areas. Suitability is also high on surrounding Indonesia islands and the Australia mainland, suggesting a risk for establishment considering papaya is cultivated in those areas (Australian Government Department of Health, 2003; Fig. 11). A survey by Kwit et al. (2000) found that papaya was more common in Dade County, Florida (on the peninsula) than in some areas observed throughout the Florida Keys; authors attributed that to lower tree density and higher light conditions in Dade County. Papaya is indeed considered naturalized in some locations on the mainland (Wunderlin 1998; Ward 2011). However, the model projection from this study shows high suitability among the Florida Keys and intermediate suitability on the southern mainland (Fig. 12). This may be a flaw in the model or real and due to other factors, such as differential competition scenarios or human management. Also, papaya seed pools may have increased throughout the Florida Keys since the Kwit et al. (2000) study, as two major hurricanes (Hurricanes Wilma and Irma) occurred since then, likely thinning the landscape and promoting papaya germination and potentially reproduction. For the Texas population, the model does not suggest papaya is likely to survive there (Fig. 13). Indeed, there were only two mature individuals recorded among the populations in Texas, while the rest were seedlings (Mink et al., 2017). Although the climate change projections may show over-predictions, they suggest that suitability will increase throughout Texas in the future, placing the area at risk under climate change (Fig. S2). Papaya is likely a strong competitor (Kwit et al., 2011), which may promote range expansion as endangered species decline due to climate change. Future Directions One can add predictors to the model that reflect disturbance, which has been performed in several studies to predict species distributions under a changing climate (Mod et al., 2016). Papaya is sensitive to habitat fragmentation (Chávez-Pesqueira et al., 2014), and distance or relationship to habitat edges may be used as a predictor. Papaya is also symbiotic with certain mycorrhizal fungi (Cruz et al., 2000; Rodríguez-Romero et al., 2011), and ENMs can be performed and compared among the symbionts to predict range shifts. There are also known interactions between papaya, pollinators, and co-flowering species, making pollinators or co- flowering species data useful in predicting papaya distribution (Badillo‐Montaño et al., 2019). A previous study developed separate ENMs for orchard species and their pollinators, and examined

14 their range shifts under climate change to infer potential pollinator deficits (Polce et al., 2014). It would be interesting to examine potential effects that future pollinator distributions may have on papaya ranges throughout climate change. Additionally, photoperiod affects growth of papaya, and photoperiod can be tested as a predictor variable (Lange, 1961). A previous study used photoperiod as a predictor variable for an ENM of a dragonfly species (Söndgerath et al., 2012). Further investigation can be performed to identify any differences in niche breadth between wild and cultivated strains. One can accomplish this by performing separate ENMs for wild and cultivated feral papaya, and then comparing predictions and response curves between the two models. Similar studies have been performed with maize (Ureta et al., 2012), pigeonpea (Khoury et al., 2015), and sunflower (Kantar et al., 2015). This may inform breeders about proper regimes for future farms, or provide geneticists with information regarding stress tolerances to identify target genes or gene families. Additionally, more statistics can be utilized to evaluate models, such as the true skill statistic or threshold comparisons (Allouche et al., 2006; Jiménez-Valverde, 2014). The fundamental niche may be difficult to elucidate in ENMs (Phillips et al., 2006), but with spatially even sampling efforts, variety in predictor variables, and additional quantitative evaluations, the model projections will extend further beyond the realized niche and closer to the fundamental niche.

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Tables

Table 1. Model scores from those with best projections. Highlighted rows indicate the two final models used in this study. Using AICc, a simpler model using non-covariate predictors was chosen to identify the main niche characteristics of wild papaya, and a more complex model was used to make predictions about range expansion.

Average Suitability at Occurrence Locations Variables Resampling RM AUC AICc Thinned Records All Records all bootstrap 15 0.74 ± 0.05 2756.3 ± 4.37 0.67 ± 0.22 0.81 ± 0.13 all subsample 7 0.72 ± 0.04 2748.2 ± 4.27 0.67 ± 0.24 0.83 ± 0.15 6,10,14,19 bootstrap 8 0.74 ± 0.04 2749.2 ± 4.28 0.66 ± 0.22 0.81 ± 0.14 6,10,15,19 bootstrap 8 0.76 ± 0.03 2749.9 ± 7.28 0.66 ± 0.24 0.83 ± 0.15 2,10,11,14,19 subsample 5 0.74 ± 0.04 2745.8 ± 2.76 0.65 ± 0.26 0.82 ± 0.17 2,10,11,15,19 subsample 5 0.74 ± 0.04 2750.0 ± 3.55 0.66 ± 0.26 0.84 ± 0.16 6,10,14,19 subsample 8 0.75 ± 0.04 2746.4 ± 2.95 0.66 ± 0.24 0.83 ± 0.15 6,10,15,19 subsample 8 0.76 ± 0.03 2747.8 ± 3.58 0.66 ± 0.24 0.83 ± 0.15 6,10,17,19 subsample 8 0.74 ± 0.05 2745.8 ± 4.57 0.66 ± 0.25 0.83 ± 0.16

Figures

Figure 1. Flow chart of methods.

Figure 2. Map of wild papaya occurrences used in this study. Each point represents one individual.

Figure 3. Example of omission and predicted area graph from Maxent. In a good model, the mean omission +/- one standard deviation (yellow curve) will be close to predicted omission (black line). Predicted omission is a straight line with a slope of 1 due to the definition of cumulative threshold. The fraction of background predicted as positive (“mean area +/- one stddev”, blue curve) is also shown as a function of cumulative threshold (Phillips, 2005; Phillips, 2017). A yellow curve far below predicted omission suggests spatial autocorrelation of training and test data, which affects AUC (Phillips, 2005; Veloz, 2009).

Figure 4. Omission graphs from different combinations of resampling and bias-curbing schemes. The training AUC minus the test AUC value is shown in bold red text within the graphs. Higher values indicate overfitting of model to training data, and values close to zero indicate no over- or under-fitting. These models used all variables and an RM of seven. X-axis: cumulative threshold. Y-axis: fractional value.

Figure 5. Study area projections for different combinations of resampling and bias-curbing schemes. These models used all variables and an RM of 7. Bias grids led to strong visual under-fitting throughout Mexico, increasing in intensity with increase in bias grid buffer distance. Cross-validation resampling strategy led to mild under-fitting throughout Mexico, particularly visible in the northernmost region.

Figure 6. Response curves for individual variables from the best all-variable model. Bold text indicates non-zero permutating variables. Within graphs, text on top (red) is sample average, and text on bottom (black) is permutation importance. X-axis: range of values of variable through study area. Y-axis: probability of presence. Horizontal response curves indicate low influence on predicting distribution of the species.

Figure 7. Model projections throughout study area for the two best models in the study. The best all-variable and non-correlating variable models are shown. Papaya occurrences are shown with white bulls-eye points. The non-correlating variable model (right) resulted in more under-fitting throughout the Yucatan region of Mexico.

Figure 8. Mexico suitability map and papaya farm yield map from EarthStat. Farm map shows yield in tons/hectare (Monfreda et al., 2008). The model is mostly consistent with known farmed areas, minus a few areas where there are farms and low suitability (such as on Baja California Peninsula).

Figure 9. Brazil suitability map and papaya farm yield map from EarthStat. Farm map shows yield in tons/hectare (Monfreda et al., 2008). The model is mostly consistent with known farmed areas, minus a few areas where there are farms and low suitability throughout southern Brazil.

Figure 10. Suitability plotted by farm yield. Suitability values are from best all-variable model, and yield values are from EarthStat (Monfreda et al., 2008). Stars indicate means that are significantly different than all other groups, as calculated with Student’s and Welch’s t-tests (Table S1). Overall the highest-yielding papaya farms are in regions of highest suitability, and low- and medium- yielding farms are in regions of medium suitability.

Figure 11. Model projection compared to known invasions in Australia. Known invasions occur on North Keeling Island (among Cocos Islands) and Christmas Island, indicated by black stars with zoomed model projections linked to them. Invasions documented by Claussen (2001). The model is consistent with those invasions as it predicts high suitability on those islands.

Figure 12. Model projection compared to known invasion area in Florida. Invasions documented by Kwit et al. (2000). The model is consistent with known invasions in Florida.

Figure 13. Model projection compared to known non-native populations in Texas. Populations documented by Mink et al. (2017). The model shows low suitability at that location.

Supplementary Tables

Table S1. P-values between suitability values among papaya farm yield categories. All comparisons show significantly different habitat suitability other than low vs. medium yield in Mexico.

Welch's Two Sample t-test Low vs. Medium Medium vs. High Low vs. High Brazil < 2.20E-16 < 2.20E-16 < 2.20E-16 India < 2.20E-16 < 2.20E-16 < 2.20E-16 Mexico 0.4578 < 2.20E-16 < 2.20E-16

Two Sample t-test Low vs. Medium Medium vs. High Low vs. High Brazil < 2.20E-16 < 2.20E-16 5.70E-16 India < 2.20E-16 < 2.20E-16 < 2.20E-16 Mexico 0.4642 < 2.20E-16 < 2.20E-16

Supplementary Figures

Figure S1. Study area model projections comparing use of threshold rules. Minimum training presence rule (“minTP”) and ten percentile training presence rule (“10TP”). Overall, threshold rules produced slightly stricter predictions.

Figure S2. Global projections comparing different climate scenarios and threshold rules. Threshold rules were used to generate a conservative (10 percentile training presence; “T10”) and liberal (minimum training presence; “minTP”) projection. However, there were no major visual differences among rules on a global scale.

Figure S3. India suitability map and papaya farm yield map from EarthStat. Farm map shows yield in tons/hectare (Monfreda et al., 2008). Here, papaya farms are mapped among all farmed areas in each state, and geospatial farming trends cannot be inferred within states. Overall, the model shows high suitability throughout the country, which is consistent with farm data from EarthStat.

Figure S4. West Africa suitability map and papaya farm yield map from EarthStat. Farm map shows yield in tons/hectare (Monfreda et al., 2008). Here, papaya farms are mapped among all farmed areas in each country with available data, and geospatial farming trends cannot be inferred within countries. Overall, the model shows high suitability throughout Guinea-Bissau and Nigeria, which is consistent with farm data from EarthStat.

Appendix A Table of GPS coordinates of individual papaya occurrences.

Longitude Latitude Location Citation/Herbarium -82.87268333 9.750416667 Caribbean, Costa Rica Brown et al., 2012 -82.8706 9.749183333 Caribbean, Costa Rica Brown et al., 2012 -82.90911667 9.775083333 Caribbean, Costa Rica Brown et al., 2012 -82.90896667 9.77535 Caribbean, Costa Rica Brown et al., 2012 -82.90895 9.7754 Caribbean, Costa Rica Brown et al., 2012 -82.9091 9.775666667 Caribbean, Costa Rica Brown et al., 2012 -82.90918333 9.775666667 Caribbean, Costa Rica Brown et al., 2012 -82.9091 9.775633333 Caribbean, Costa Rica Brown et al., 2012 -82.90913333 9.775466667 Caribbean, Costa Rica Brown et al., 2012 -82.91588333 9.79285 Caribbean, Costa Rica Brown et al., 2012 -82.91051667 9.803783333 Caribbean, Costa Rica Brown et al., 2012 -82.90848333 9.811916667 Caribbean, Costa Rica Brown et al., 2012 -82.9077 9.8105 Caribbean, Costa Rica Brown et al., 2012 -82.90776667 9.810566667 Caribbean, Costa Rica Brown et al., 2012 -82.90768333 9.810283333 Caribbean, Costa Rica Brown et al., 2012 -82.90871667 9.812216667 Caribbean, Costa Rica Brown et al., 2012 -82.90946667 9.812916667 Caribbean, Costa Rica Brown et al., 2012 -82.90946667 9.812916667 Caribbean, Costa Rica Brown et al., 2012 -82.90953333 9.813 Caribbean, Costa Rica Brown et al., 2012 -82.90938333 9.812883333 Caribbean, Costa Rica Brown et al., 2012 -82.90938333 9.812883333 Caribbean, Costa Rica Brown et al., 2012 -82.92191667 9.828116667 Caribbean, Costa Rica Brown et al., 2012 -82.92243333 9.828766667 Caribbean, Costa Rica Brown et al., 2012 -82.92253333 9.828883333 Caribbean, Costa Rica Brown et al., 2012 -82.94516667 9.857483333 Caribbean, Costa Rica Brown et al., 2012 -82.95091667 9.864583333 Caribbean, Costa Rica Brown et al., 2012 -82.95091667 9.864583333 Caribbean, Costa Rica Brown et al., 2012 -82.9523 9.86655 Caribbean, Costa Rica Brown et al., 2012 -82.95585 9.871066667 Caribbean, Costa Rica Brown et al., 2012 -82.95815 9.873683333 Caribbean, Costa Rica Brown et al., 2012 -82.9641 9.8799 Caribbean, Costa Rica Brown et al., 2012 -82.9641 9.8799 Caribbean, Costa Rica Brown et al., 2012 -82.9641 9.8799 Caribbean, Costa Rica Brown et al., 2012 -82.97136667 9.891416667 Caribbean, Costa Rica Brown et al., 2012 -82.96741667 9.886216667 Caribbean, Costa Rica Brown et al., 2012 -82.976 9.897433333 Caribbean, Costa Rica Brown et al., 2012 -82.97583333 9.897283333 Caribbean, Costa Rica Brown et al., 2012 -82.97583333 9.897283333 Caribbean, Costa Rica Brown et al., 2012 -82.98355 9.90715 Caribbean, Costa Rica Brown et al., 2012 -82.96876667 9.88765 Caribbean, Costa Rica Brown et al., 2012 -82.92231667 9.82855 Caribbean, Costa Rica Brown et al., 2012 -82.92231667 9.82855 Caribbean, Costa Rica Brown et al., 2012 -82.9171 9.82215 Caribbean, Costa Rica Brown et al., 2012 -82.84275 9.739433333 Caribbean, Costa Rica Brown et al., 2012 -82.87178333 9.7495 Caribbean, Costa Rica Brown et al., 2012 -83.25225 10.0273 Caribbean, Costa Rica Brown et al., 2012 -83.25196667 10.02735 Caribbean, Costa Rica Brown et al., 2012 -83.27855 10.04401667 Caribbean, Costa Rica Brown et al., 2012 -83.27855 10.04401667 Caribbean, Costa Rica Brown et al., 2012 -83.27895 10.04403333 Caribbean, Costa Rica Brown et al., 2012 -83.27946667 10.04425 Caribbean, Costa Rica Brown et al., 2012 44

-83.34218333 10.04711667 Caribbean, Costa Rica Brown et al., 2012 -83.36795 10.061 Caribbean, Costa Rica Brown et al., 2012 -83.36743333 10.06476667 Caribbean, Costa Rica Brown et al., 2012 -83.40053333 10.07736667 Caribbean, Costa Rica Brown et al., 2012 -83.40053333 10.07736667 Caribbean, Costa Rica Brown et al., 2012 -83.40053333 10.07736667 Caribbean, Costa Rica Brown et al., 2012 -83.40053333 10.07736667 Caribbean, Costa Rica Brown et al., 2012 -83.40671667 10.07773333 Caribbean, Costa Rica Brown et al., 2012 -83.43731667 10.09961667 Caribbean, Costa Rica Brown et al., 2012 -83.43475 10.09958333 Caribbean, Costa Rica Brown et al., 2012 -84.76076667 10.06341667 N Pacific, Costa Rica Brown et al., 2012 -84.76978333 10.06928333 N Pacific, Costa Rica Brown et al., 2012 -84.77201667 10.07063333 N Pacific, Costa Rica Brown et al., 2012 -84.7968 10.0917 N Pacific, Costa Rica Brown et al., 2012 -84.92963333 10.1766 N Pacific, Costa Rica Brown et al., 2012 -85.00446667 10.24611667 N Pacific, Costa Rica Brown et al., 2012 -85.0052 10.24621667 N Pacific, Costa Rica Brown et al., 2012 -85.0052 10.24621667 N Pacific, Costa Rica Brown et al., 2012 -85.00601667 10.24606667 N Pacific, Costa Rica Brown et al., 2012 -85.00601667 10.24606667 N Pacific, Costa Rica Brown et al., 2012 -85.00601667 10.24606667 N Pacific, Costa Rica Brown et al., 2012 -85.00601667 10.24635 N Pacific, Costa Rica Brown et al., 2012 -85.00601667 10.24635 N Pacific, Costa Rica Brown et al., 2012 -85.04223333 10.3116 N Pacific, Costa Rica Brown et al., 2012 -85.04223333 10.3116 N Pacific, Costa Rica Brown et al., 2012 -85.13961667 10.46 N Pacific, Costa Rica Brown et al., 2012 -85.14763333 10.46258333 N Pacific, Costa Rica Brown et al., 2012 -85.14783333 10.46255 N Pacific, Costa Rica Brown et al., 2012 -85.14783333 10.46255 N Pacific, Costa Rica Brown et al., 2012 -85.14788333 10.4626 N Pacific, Costa Rica Brown et al., 2012 -85.14798333 10.4626 N Pacific, Costa Rica Brown et al., 2012 -85.14925 10.46298333 N Pacific, Costa Rica Brown et al., 2012 -85.15381667 10.46433333 N Pacific, Costa Rica Brown et al., 2012 -85.15381667 10.46433333 N Pacific, Costa Rica Brown et al., 2012 -85.17586667 10.47116667 N Pacific, Costa Rica Brown et al., 2012 -85.20776667 10.4791 N Pacific, Costa Rica Brown et al., 2012 -85.20776667 10.4791 N Pacific, Costa Rica Brown et al., 2012 -85.19326667 10.4768 N Pacific, Costa Rica Brown et al., 2012 -85.14485 10.4615 N Pacific, Costa Rica Brown et al., 2012 -85.14536667 10.46163333 N Pacific, Costa Rica Brown et al., 2012 -85.14536667 10.46163333 N Pacific, Costa Rica Brown et al., 2012 -85.1384 10.4635 N Pacific, Costa Rica Brown et al., 2012 -85.13741667 10.46933333 N Pacific, Costa Rica Brown et al., 2012 -85.13696667 10.47098333 N Pacific, Costa Rica Brown et al., 2012 -85.1286 10.22615 N Pacific, Costa Rica Brown et al., 2012 -85.12848333 10.22611667 N Pacific, Costa Rica Brown et al., 2012 -85.12851667 10.22593333 N Pacific, Costa Rica Brown et al., 2012 -85.12831667 10.226 N Pacific, Costa Rica Brown et al., 2012 -85.12811667 10.22611667 N Pacific, Costa Rica Brown et al., 2012 -85.1281 10.22621667 N Pacific, Costa Rica Brown et al., 2012 -85.1281 10.22621667 N Pacific, Costa Rica Brown et al., 2012 -85.1282 10.22613333 N Pacific, Costa Rica Brown et al., 2012 -85.12818333 10.22613333 N Pacific, Costa Rica Brown et al., 2012 -85.12818333 10.22613333 N Pacific, Costa Rica Brown et al., 2012 -85.12771667 10.22603333 N Pacific, Costa Rica Brown et al., 2012 -85.12765 10.22605 N Pacific, Costa Rica Brown et al., 2012

-85.12775 10.22608333 N Pacific, Costa Rica Brown et al., 2012 -85.26148333 10.23463333 N Pacific, Costa Rica Brown et al., 2012 -85.26053333 10.2348 N Pacific, Costa Rica Brown et al., 2012 -85.26053333 10.2348 N Pacific, Costa Rica Brown et al., 2012 -85.25836667 10.23543333 N Pacific, Costa Rica Brown et al., 2012 -85.2586 10.23528333 N Pacific, Costa Rica Brown et al., 2012 -85.2588 10.23535 N Pacific, Costa Rica Brown et al., 2012 -85.25705 10.23626667 N Pacific, Costa Rica Brown et al., 2012 -85.25705 10.23626667 N Pacific, Costa Rica Brown et al., 2012 -85.25703333 10.236 N Pacific, Costa Rica Brown et al., 2012 -85.25713333 10.23615 N Pacific, Costa Rica Brown et al., 2012 -85.25755 10.2358 N Pacific, Costa Rica Brown et al., 2012 -85.25755 10.23595 N Pacific, Costa Rica Brown et al., 2012 -84.9629 9.931916667 Nicoya, Costa Rica Brown et al., 2012 -84.96221667 9.931833333 Nicoya, Costa Rica Brown et al., 2012 -84.9578 9.923116667 Nicoya, Costa Rica Brown et al., 2012 -84.9593 9.9219 Nicoya, Costa Rica Brown et al., 2012 -84.95945 9.921933333 Nicoya, Costa Rica Brown et al., 2012 -84.95936667 9.921883333 Nicoya, Costa Rica Brown et al., 2012 -84.95936667 9.922 Nicoya, Costa Rica Brown et al., 2012 -84.9593 9.922033333 Nicoya, Costa Rica Brown et al., 2012 -84.95926667 9.922016667 Nicoya, Costa Rica Brown et al., 2012 -84.95925 9.921983333 Nicoya, Costa Rica Brown et al., 2012 -84.95928333 9.922016667 Nicoya, Costa Rica Brown et al., 2012 -84.95926667 9.922066667 Nicoya, Costa Rica Brown et al., 2012 -84.95925 9.92205 Nicoya, Costa Rica Brown et al., 2012 -84.9593 9.922033333 Nicoya, Costa Rica Brown et al., 2012 -84.95921667 9.922016667 Nicoya, Costa Rica Brown et al., 2012 -84.95906667 9.92225 Nicoya, Costa Rica Brown et al., 2012 -84.94028333 9.840333333 Nicoya, Costa Rica Brown et al., 2012 -84.93105 9.851716667 Nicoya, Costa Rica Brown et al., 2012 -84.93583333 9.893783333 Nicoya, Costa Rica Brown et al., 2012 -84.9593 9.920566667 Nicoya, Costa Rica Brown et al., 2012 -84.95935 9.920616667 Nicoya, Costa Rica Brown et al., 2012 -84.95936667 9.9207 Nicoya, Costa Rica Brown et al., 2012 -84.95941667 9.920683333 Nicoya, Costa Rica Brown et al., 2012 -84.95941667 9.920633333 Nicoya, Costa Rica Brown et al., 2012 -84.96053333 9.916416667 Nicoya, Costa Rica Brown et al., 2012 -84.9605 9.916316667 Nicoya, Costa Rica Brown et al., 2012 -84.96038333 9.91365 Nicoya, Costa Rica Brown et al., 2012 -84.95908333 9.912666667 Nicoya, Costa Rica Brown et al., 2012 -84.96005 9.90965 Nicoya, Costa Rica Brown et al., 2012 -84.95995 9.909566667 Nicoya, Costa Rica Brown et al., 2012 -84.95868333 9.90855 Nicoya, Costa Rica Brown et al., 2012 -84.95088333 9.910116667 Nicoya, Costa Rica Brown et al., 2012 -84.95071667 9.910133333 Nicoya, Costa Rica Brown et al., 2012 -84.94831667 9.910966667 Nicoya, Costa Rica Brown et al., 2012 -84.94835 9.9109 Nicoya, Costa Rica Brown et al., 2012 -84.9445 9.909733333 Nicoya, Costa Rica Brown et al., 2012 -84.94188333 9.907266667 Nicoya, Costa Rica Brown et al., 2012 -84.94021667 9.904766667 Nicoya, Costa Rica Brown et al., 2012 -84.93785 9.9036 Nicoya, Costa Rica Brown et al., 2012 -84.93645 9.902283333 Nicoya, Costa Rica Brown et al., 2012 -84.9366 9.9023 Nicoya, Costa Rica Brown et al., 2012 -84.93655 9.902266667 Nicoya, Costa Rica Brown et al., 2012 -84.93598333 9.902233333 Nicoya, Costa Rica Brown et al., 2012

-84.9353 9.902633333 Nicoya, Costa Rica Brown et al., 2012 -84.93553333 9.898333333 Nicoya, Costa Rica Brown et al., 2012 -84.93616667 9.897533333 Nicoya, Costa Rica Brown et al., 2012 -84.93621667 9.897583333 Nicoya, Costa Rica Brown et al., 2012 -84.93625 9.897783333 Nicoya, Costa Rica Brown et al., 2012 -84.93633333 9.897833333 Nicoya, Costa Rica Brown et al., 2012 -84.9363 9.897816667 Nicoya, Costa Rica Brown et al., 2012 -84.93623333 9.897883333 Nicoya, Costa Rica Brown et al., 2012 -84.13951667 9.452583333 Central Pacific, Costa Rica Brown et al., 2012 -84.12606667 9.433716667 Central Pacific, Costa Rica Brown et al., 2012 -84.12603333 9.43375 Central Pacific, Costa Rica Brown et al., 2012 -84.09581667 9.415666667 Central Pacific, Costa Rica Brown et al., 2012 -84.08811667 9.409633333 Central Pacific, Costa Rica Brown et al., 2012 -84.0806 9.4041 Central Pacific, Costa Rica Brown et al., 2012 -84.07906667 9.402933333 Central Pacific, Costa Rica Brown et al., 2012 -84.07885 9.402316667 Central Pacific, Costa Rica Brown et al., 2012 -84.07898333 9.402316667 Central Pacific, Costa Rica Brown et al., 2012 -84.07891667 9.4025 Central Pacific, Costa Rica Brown et al., 2012 -84.07871667 9.40205 Central Pacific, Costa Rica Brown et al., 2012 -84.07883333 9.402183333 Central Pacific, Costa Rica Brown et al., 2012 -84.0616 9.389733333 Central Pacific, Costa Rica Brown et al., 2012 -84.06151667 9.389733333 Central Pacific, Costa Rica Brown et al., 2012 -84.06155 9.38975 Central Pacific, Costa Rica Brown et al., 2012 -84.05578333 9.3859 Central Pacific, Costa Rica Brown et al., 2012 -84.05576667 9.385916667 Central Pacific, Costa Rica Brown et al., 2012 -84.05576667 9.385933333 Central Pacific, Costa Rica Brown et al., 2012 -84.0404 9.373983333 Central Pacific, Costa Rica Brown et al., 2012 -84.04048333 9.374033333 Central Pacific, Costa Rica Brown et al., 2012 -84.04016667 9.373833333 Central Pacific, Costa Rica Brown et al., 2012 -84.03973333 9.373616667 Central Pacific, Costa Rica Brown et al., 2012 -84.03301667 9.369983333 Central Pacific, Costa Rica Brown et al., 2012 -84.03263333 9.370033333 Central Pacific, Costa Rica Brown et al., 2012 -84.03106667 9.370433333 Central Pacific, Costa Rica Brown et al., 2012 -83.98423333 9.352183333 Central Pacific, Costa Rica Brown et al., 2012 -83.81998333 9.213916667 S Pacific, Costa Rica Brown et al., 2012 -83.82 9.214033333 S Pacific, Costa Rica Brown et al., 2012 -83.82001667 9.213966667 S Pacific, Costa Rica Brown et al., 2012 -83.82001667 9.21395 S Pacific, Costa Rica Brown et al., 2012 -83.81868333 9.213583333 S Pacific, Costa Rica Brown et al., 2012 -83.81708333 9.21385 S Pacific, Costa Rica Brown et al., 2012 -83.80391667 9.208816667 S Pacific, Costa Rica Brown et al., 2012 -83.77378333 9.190716667 S Pacific, Costa Rica Brown et al., 2012 -83.77381667 9.19065 S Pacific, Costa Rica Brown et al., 2012 -83.77396667 9.190766667 S Pacific, Costa Rica Brown et al., 2012 -83.77386667 9.190716667 S Pacific, Costa Rica Brown et al., 2012 -83.77566667 9.1908 S Pacific, Costa Rica Brown et al., 2012 -83.76585 9.1857 S Pacific, Costa Rica Brown et al., 2012 -83.66451667 9.082 S Pacific, Costa Rica Brown et al., 2012 -83.64435 9.060833333 S Pacific, Costa Rica Brown et al., 2012 -83.64433333 9.06075 S Pacific, Costa Rica Brown et al., 2012 -83.64433333 9.060683333 S Pacific, Costa Rica Brown et al., 2012 -83.62638333 9.051316667 S Pacific, Costa Rica Brown et al., 2012 -83.61983333 9.05175 S Pacific, Costa Rica Brown et al., 2012 -83.57015 9.003683333 S Pacific, Costa Rica Brown et al., 2012 -83.57013333 9.003683333 S Pacific, Costa Rica Brown et al., 2012 -83.57008333 9.003666667 S Pacific, Costa Rica Brown et al., 2012

-83.56171667 8.998033333 S Pacific, Costa Rica Brown et al., 2012 -83.56348333 8.999383333 S Pacific, Costa Rica Brown et al., 2012 -83.56343333 8.9993 S Pacific, Costa Rica Brown et al., 2012 -83.56303333 8.999116667 S Pacific, Costa Rica Brown et al., 2012 -84.42583333 9.523033333 Central Pacific, Costa Rica Brown et al., 2012 -84.42613333 9.52315 Central Pacific, Costa Rica Brown et al., 2012 -84.4261 9.523183333 Central Pacific, Costa Rica Brown et al., 2012 -84.4277 9.523583333 Central Pacific, Costa Rica Brown et al., 2012 -84.42753333 9.523516667 Central Pacific, Costa Rica Brown et al., 2012 -84.42681667 9.523366667 Central Pacific, Costa Rica Brown et al., 2012 -84.42696667 9.523466667 Central Pacific, Costa Rica Brown et al., 2012 -84.42693333 9.523483333 Central Pacific, Costa Rica Brown et al., 2012 -84.50493333 9.536166667 Central Pacific, Costa Rica Brown et al., 2012 -84.50488333 9.536183333 Central Pacific, Costa Rica Brown et al., 2012 -84.50488333 9.536216667 Central Pacific, Costa Rica Brown et al., 2012 -84.5049 9.536216667 Central Pacific, Costa Rica Brown et al., 2012 -84.5049 9.536216667 Central Pacific, Costa Rica Brown et al., 2012 -84.62158333 9.587816667 Central Pacific, Costa Rica Brown et al., 2012 -84.62156667 9.587783333 Central Pacific, Costa Rica Brown et al., 2012 -84.6436 9.676466667 Central Pacific, Costa Rica Brown et al., 2012 -84.64343333 9.67585 Central Pacific, Costa Rica Brown et al., 2012 -84.65106667 9.685416667 Central Pacific, Costa Rica Brown et al., 2012 -84.64991667 9.688183333 Central Pacific, Costa Rica Brown et al., 2012 -84.64641667 9.688033333 Central Pacific, Costa Rica Brown et al., 2012 -84.64636667 9.6881 Central Pacific, Costa Rica Brown et al., 2012 -84.64588333 9.6884 Central Pacific, Costa Rica Brown et al., 2012 -84.646 9.688283333 Central Pacific, Costa Rica Brown et al., 2012 -84.64586667 9.688433333 Central Pacific, Costa Rica Brown et al., 2012 -84.64585 9.688416667 Central Pacific, Costa Rica Brown et al., 2012 -84.75388333 10.04471667 Miramar, Costa Rica Records from Rich C. Moore lab -84.7538 10.04746667 Miramar, Costa Rica Records from Rich C. Moore lab -84.75361667 10.04735 Miramar, Costa Rica Records from Rich C. Moore lab -84.75363333 10.04736667 Miramar, Costa Rica Records from Rich C. Moore lab -84.7536 10.04723333 Miramar, Costa Rica Records from Rich C. Moore lab -84.75353333 10.04716667 Miramar, Costa Rica Records from Rich C. Moore lab -84.7531 10.0468 Miramar, Costa Rica Records from Rich C. Moore lab -84.75313333 10.04683333 Miramar, Costa Rica Records from Rich C. Moore lab -84.75311667 10.04683333 Miramar, Costa Rica Records from Rich C. Moore lab -84.75316667 10.04691667 Miramar, Costa Rica Records from Rich C. Moore lab -84.75316667 10.04695 Miramar, Costa Rica Records from Rich C. Moore lab -84.75315 10.04691667 Miramar, Costa Rica Records from Rich C. Moore lab -84.75326667 10.04696667 Miramar, Costa Rica Records from Rich C. Moore lab -84.7533 10.04696667 Miramar, Costa Rica Records from Rich C. Moore lab -84.76035 10.0631 Miramar, Costa Rica Records from Rich C. Moore lab -84.76026667 10.06293333 Miramar, Costa Rica Records from Rich C. Moore lab -84.76018333 10.06313333 Miramar, Costa Rica Records from Rich C. Moore lab -84.7602 10.0631 Miramar, Costa Rica Records from Rich C. Moore lab -84.76026667 10.06323333 Miramar, Costa Rica Records from Rich C. Moore lab -84.76131667 10.06406667 Miramar, Costa Rica Records from Rich C. Moore lab -84.76141667 10.0641 Miramar, Costa Rica Records from Rich C. Moore lab -84.76176667 10.06436667 Miramar, Costa Rica Records from Rich C. Moore lab -84.76183333 10.06443333 Miramar, Costa Rica Records from Rich C. Moore lab -84.77215 10.07075 Miramar, Costa Rica Records from Rich C. Moore lab -84.77908333 10.07698333 Miramar, Costa Rica Records from Rich C. Moore lab -84.77911667 10.07696667 Miramar, Costa Rica Records from Rich C. Moore lab -84.78088333 10.07841667 Miramar, Costa Rica Records from Rich C. Moore lab

-85.0057 10.24638333 South of Canas, Costa Rica Records from Rich C. Moore lab -85.03068333 10.27896667 South of Canas, Costa Rica Records from Rich C. Moore lab -85.03576667 10.29058333 South of Canas, Costa Rica Records from Rich C. Moore lab -85.138 10.46603333 South of Canas, Costa Rica Records from Rich C. Moore lab -85.12158333 10.22575 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.1224 10.22551667 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12236667 10.22546667 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12238333 10.22546667 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12238333 10.22548333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12265 10.22553333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12253333 10.22551667 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12293333 10.22555 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12291667 10.22555 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12745 10.22613333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12853333 10.22581667 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12858333 10.22573333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12871667 10.22583333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.12883333 10.22603333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.22715 10.24885 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.22751667 10.24896667 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.22751667 10.24898333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.22758333 10.2493 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.25703333 10.23616667 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.2572 10.2363 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.2573 10.23625 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.25741667 10.23618333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.2579 10.23566667 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.25871667 10.23525 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.25945 10.2351 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.26053333 10.2348 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.26116667 10.23458333 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.2613 10.23455 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -85.26398333 10.234 Canas to Playa Naranjo, Costa Rica Records from Rich C. Moore lab -84.95686667 9.92385 Paquera, Costa Rica Records from Rich C. Moore lab -84.95901667 9.92215 Paquera, Costa Rica Records from Rich C. Moore lab -84.95905 9.922066667 Paquera, Costa Rica Records from Rich C. Moore lab -84.95911667 9.9218 Paquera, Costa Rica Records from Rich C. Moore lab -84.95903333 9.920733333 Paquera, Costa Rica Records from Rich C. Moore lab -84.9599 9.916966667 Paquera, Costa Rica Records from Rich C. Moore lab -84.95988333 9.916966667 Paquera, Costa Rica Records from Rich C. Moore lab -84.9599 9.909816667 Paquera, Costa Rica Records from Rich C. Moore lab -84.96066667 9.908833333 Paquera, Costa Rica Records from Rich C. Moore lab -84.96095 9.9096 Paquera, Costa Rica Records from Rich C. Moore lab -84.96086667 9.908466667 Paquera, Costa Rica Records from Rich C. Moore lab -84.9497 9.910633333 Paquera, Costa Rica Records from Rich C. Moore lab -84.94418333 9.909433333 Paquera, Costa Rica Records from Rich C. Moore lab -84.94195 9.9071 Paquera, Costa Rica Records from Rich C. Moore lab -84.9423 9.906683333 Paquera, Costa Rica Records from Rich C. Moore lab -84.93785 9.903816667 Paquera, Costa Rica Records from Rich C. Moore lab -84.9378 9.903783333 Paquera, Costa Rica Records from Rich C. Moore lab -84.93606667 9.898066667 Paquera, Costa Rica Records from Rich C. Moore lab -84.93658333 9.898083333 Paquera, Costa Rica Records from Rich C. Moore lab -84.9366 9.898066667 Paquera, Costa Rica Records from Rich C. Moore lab -84.9365 9.898066667 Paquera, Costa Rica Records from Rich C. Moore lab -84.9359 9.896083333 Paquera, Costa Rica Records from Rich C. Moore lab -84.9451 9.888833333 Paquera, Costa Rica Records from Rich C. Moore lab

-84.94508333 9.888666667 Paquera, Costa Rica Records from Rich C. Moore lab -84.949 9.834766667 Paquera, Costa Rica Records from Rich C. Moore lab -84.94118333 9.790783333 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.94125 9.79085 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.94131667 9.790633333 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.96968333 9.7735 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.99316667 9.754666667 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.99343333 9.754333333 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.99346667 9.75435 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.99528333 9.753933333 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.99536667 9.754 West of Paquera, Costa Rica Records from Rich C. Moore lab -84.99548333 9.754033333 West of Paquera, Costa Rica Records from Rich C. Moore lab -85.00896667 9.749383333 West of Paquera, Costa Rica Records from Rich C. Moore lab -85.03068333 9.728183333 West of Paquera, Costa Rica Records from Rich C. Moore lab -85.11745 9.71435 West of Paquera, Costa Rica Records from Rich C. Moore lab -85.3402 9.842833333 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.44728333 9.868916667 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.44728333 9.8689 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.52981667 9.8935 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.5298 9.8936 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.52976667 9.8936 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.52976667 9.893833333 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.52978333 9.893916667 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.52973333 9.89395 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.86306667 9.893916667 Playa Carrillo, Costa Rica Records from Rich C. Moore lab -85.6717 10.36371667 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.67116667 10.36388333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.67083333 10.36455 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.6708 10.36428333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.66935 10.36435 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.66851667 10.36433333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.66848333 10.3643 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.13738333 10.46946667 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.13726667 10.47001667 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.13051667 10.45391667 South of Canas, Costa Rica Records from Rich C. Moore lab -85.1306 10.45405 South of Canas, Costa Rica Records from Rich C. Moore lab -85.1306 10.45406667 South of Canas, Costa Rica Records from Rich C. Moore lab -85.1306 10.45408333 South of Canas, Costa Rica Records from Rich C. Moore lab -85.1306 10.454 South of Canas, Costa Rica Records from Rich C. Moore lab -85.13056667 10.45401667 South of Canas, Costa Rica Records from Rich C. Moore lab -85.13096667 10.45443333 South of Canas, Costa Rica Records from Rich C. Moore lab -85.68103333 10.94055 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68105 10.94055 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68118333 10.9405 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68126667 #VALUE! Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68131667 10.94048333 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68126667 10.94045 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68128333 10.94046667 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.6813 10.94045 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68123333 10.9404 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68088333 10.93995 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68086667 10.93993333 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68095 10.93991667 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68081667 10.94015 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68081667 10.94011667 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.68081667 10.94038333 Cuajiniquil, Costa Rica Records from Rich C. Moore lab

-85.68078333 10.94025 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.65598333 10.94601667 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.65596667 10.946 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.65596667 10.946 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.65571667 10.946 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.6575 10.94671667 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.65536667 10.9492 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.45376667 10.64256667 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.45383333 10.64253333 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.51361667 10.75221667 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.5136 10.75223333 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.53281667 10.78365 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.53276667 10.78366667 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.54375 10.81618333 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.54371667 10.81628333 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.65263333 10.58693333 Cuajiniquil, Costa Rica Records from Rich C. Moore lab -85.6527 10.58695 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.65261667 10.58693333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.65246667 10.58695 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.65218333 10.58693333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.65216667 10.58685 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.6519 10.58678333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.65188333 10.58683333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.65171667 10.58681667 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.65096667 10.58681667 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.65043333 10.58675 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.59361667 10.5688 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.59361667 10.56883333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.5937 10.56878333 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.59375 10.56886667 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -85.59373333 10.56886667 From Playa Carillo to Canas, Costa Rica Records from Rich C. Moore lab -87.431005 20.909335 Cancún, Quintana Roo, México Chávez-Pesqueira & Núñez-Farfán, 2016 -88.140450 21.517404 Río Lagartos, Yucatán, México Chávez-Pesqueira & Núñez-Farfán, 2016 -88.568482 20.682268 Chichén Itzá, Yucatán, México Chávez-Pesqueira & Núñez-Farfán, 2016 -89.597500 21.091205 Dzibichaltún, Yucatán, México Chávez-Pesqueira & Núñez-Farfán, 2016 -88.244462 18.582888 Oxtankah, Quintana Roo, México Chávez-Pesqueira & Núñez-Farfán, 2016 -89.470891 18.513235 Caobas, Campeche, México Chávez-Pesqueira & Núñez-Farfán, 2016 -91.085833 18.547106 Mamantel, Campeche, México Chávez-Pesqueira & Núñez-Farfán, 2016 -92.019203 17.494439 Palenque, Chiapas, México Chávez-Pesqueira & Núñez-Farfán, 2016 -93.608474 17.362537 Villa Guadalupe, Tabasco, México Chávez-Pesqueira & Núñez-Farfán, 2016 -95.090427 17.511457 Acayucan, Veracruz, México Chávez-Pesqueira & Núñez-Farfán, 2016 -95.009269 16.905176 Matias Romero, Oaxaca, México Chávez-Pesqueira & Núñez-Farfán, 2016 -95.648294 15.983185 Santiago Astata, Oaxaca, México Chávez-Pesqueira & Núñez-Farfán, 2016 -96.569680 15.678595 Ventanilla, Oaxaca, México Chávez-Pesqueira & Núñez-Farfán, 2016 -98.788123 16.580874 Marquelia, Guerrero, México Chávez-Pesqueira & Núñez-Farfán, 2016 -97.656410 20.479689 Poza Rica, Puebla, México Chávez-Pesqueira & Núñez-Farfán, 2016 -98.743606 21.243556 Tamazunchale, San Luis Potosí, México Chávez-Pesqueira & Núñez-Farfán, 2016 -99.151811 21.843133 Huasteca, San Luis Potosí, México Chávez-Pesqueira & Núñez-Farfán, 2016 -99.125423 23.017133 El Cielo, Tamaulipas, México Chávez-Pesqueira & Núñez-Farfán, 2016 -89.766813 20.360332 Uxmal, Yucatán, México Colecta ejemplar herbario 2015 -95.089119 18.619867 Montepío, Veracruz, México Colecta ejemplar herbario 2015 -95.096811 18.609440 Los Tuxtlas, Veracruz, México Chávez Pesqueira et al., 2014 -95.076820 18.585885 Los Tuxtlas, Veracruz, México Chávez Pesqueira et al., 2014 -95.080036 18.578114 Los Tuxtlas, Veracruz, México Chávez Pesqueira et al., 2014 -95.050521 18.592191 Los Tuxtlas, Veracruz, México Chávez Pesqueira et al., 2014 -95.060625 18.486146 Los Tuxtlas, Veracruz, México Chávez Pesqueira et al., 2014

-90.313341 19.011110 Yohaltun, Campeche, México Ejemplar 005185 Herbario CICY -89.713889 20.608333 Abalá, Yucatán, México Ejemplar 051821 Herbario CICY -90.230556 19.595833 Edzná, Campeche, México Ejemplar 005211 Herbario CICY -89.263056 21.340556 Telchac, Yucatán, México Ejemplar 005199 Herbario CICY -87.7611 18.5 Othón P. Blanco, Quintana Roo, México Ejemplar 23827 Herbario CICY -97.966667 20.316667 El Salto, Puebla, México Ejemplar 1351628 Herbario MEXU -96.096111 15.987222 Pochutla, Oaxaca, México Ejemplar 1045220 Herbario MEXU -94.405556 18.012500 Ixhuatlán, Veracruz, México Ejemplar 1364555 Herbario MEXU -89.433333 18.666667 Zoh Laguna, Campeche, México Ejemplar 788032 Herbario MEXU -93.966667 16.383333 La Alianza, Chiapas, México Ejemplar 1141906 Herbario MEXU -91.108333 16.789167 Ocosingo, Chiapas, México Ejemplar 1063616 Herbario MEXU -88.011667 19.875556 Carrillo Puerto, Quintana Roo, México Ejemplar 1028716 Herbario MEXU -91.248611 16.934722 Ocosingo, Chiapas, México Ejemplar 1028719 Herbario MEXU -88.000000 14.750000 Río Olancho, Honduras Ejemplar 1411165 Herbario MEXU -88.916667 17.350000 Cayo District, Belize Ejemplar 895402 Herbario MEXU -88.914167 19.625000 José Ma. Morelos, Quintana Roo, México Ejemplar 1329770 Herbario MEXU -89.266667 21.333333 Telchac, Yucatán, México Ejemplar 591310 Herbario MEXU -88.626389 18.473333 Palmar, Quintana Roo, México Ejemplar 1292401 Herbario MEXU -85.266667 12.916667 Matagalpa, Nicaragua Ejemplar 804817 Herbario MEXU -86.466667 12.350000 Isla Momotombito, Nicaragua Ejemplar 414978 Herbario MEXU -86.516667 12.033333 Managua, Nicaragua Ejemplar 409937 Herbario MEXU -90.333 18.883 Yohaltun, Campeche, México Ejemplar 871347 Herbario MEXU -85.383333 12.283333 Chontales, Nicaragua Ejemplar ENCB_47 Herbario ENCB Poli -97.833333 21.700000 Ozuluama, Veracruz, México Ejemplar ENCB_23 Herbario ENCB Poli -98.116667 21.650000 Pánuco, Veracruz, México Ejemplar ENCB_64 Herbario ENCB Poli -94.516667 17.266667 Hidalgotitlán, Veracruz, México Ejemplar V072557 Herbario XAL INECOL -97.441667 20.400000 Papantla, Veracruz, México Ejemplar V110875 Herbario XAL INECOL -96.683333 19.766667 Alto Lucero, Veracruz, México Ejemplar V076956 Herbario XAL INECOL -95.066667 18.566667 Los Tuxtlas, Veracruz, México Ejemplar V031644 Herbario XAL INECOL -95.016667 18.450000 Coyame, Veracruz, México Ejemplar V031747 Herbario XAL INECOL -87.700000 20.500000 Coba, Quintana Roo, México Ejemplar 91 Herbario XAL INECOL -89.266667 21.333333 Telchac, Yucatán, México Ejemplar 12763 Herbario XAL INECOL -90.250000 19.583333 Edzná, Campeche, México Ejemplar Y000595 Herbario XAL INECOL -97.833 21.7 Ozuluama, Veracruz, México Ejemplar V007939 Herbario XAL INECOL -86.75 21.25 Cozumel, Quintana Roo, México Ejemplar 1581 Herbario XAL INECOL -89.713889 20.608333 Abalá, Yucatán, México Ejemplar “1” Herbario NYBG -85.53836667 11.22351667 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.53838333 11.2235 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.53853333 11.2236 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.53828333 11.22343333 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.5382 11.22353333 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.53913333 11.22403333 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.53906667 11.22403333 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.53915 11.22403333 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.53933333 11.22448333 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.53945 11.22461667 Southwest of Highway 1, Nicaragua Records from Rich C. Moore lab -85.96196667 11.39228333 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.96183333 11.39233333 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.96181667 11.39233333 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.9618 11.39231667 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.96176667 11.39241667 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.96176667 11.3924 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.96176667 11.3924 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.9618 11.39236667 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.95165 11.3926 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.96163333 11.39261667 North of Rivas, Nicaragua Records from Rich C. Moore lab

-85.96163333 11.3926 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.96165 11.39263333 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.96165 11.3926 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.97508333 11.39148333 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.97535 11.39171667 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.98463333 11.39136667 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.98456667 11.39146667 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.98461667 11.39143333 North of Rivas, Nicaragua Records from Rich C. Moore lab -85.9849 11.39135 North of Rivas, Nicaragua Records from Rich C. Moore lab -86.05726667 11.6111 North of Rivas, Nicaragua Records from Rich C. Moore lab -86.05733333 11.61108333 North of Rivas, Nicaragua Records from Rich C. Moore lab -86.03075 11.61528333 North of Rivas, Nicaragua Records from Rich C. Moore lab -86.14965 11.89373333 Grenada, Nicaragua Records from Rich C. Moore lab -86.14885 11.89546667 Grenada, Nicaragua Records from Rich C. Moore lab -86.17576667 11.90496667 Grenada, Nicaragua Records from Rich C. Moore lab -85.17661667 11.90501667 Grenada, Nicaragua Records from Rich C. Moore lab -86.19631667 11.87543333 Grenada, Nicaragua Records from Rich C. Moore lab -86.19638333 11.87543333 Grenada, Nicaragua Records from Rich C. Moore lab -86.19646667 11.87541667 Grenada, Nicaragua Records from Rich C. Moore lab -86.19636667 11.8754 Grenada, Nicaragua Records from Rich C. Moore lab -86.19643333 11.87536667 Grenada, Nicaragua Records from Rich C. Moore lab -86.20031667 11.8595 Grenada, Nicaragua Records from Rich C. Moore lab -86.20033333 11.85953333 Grenada, Nicaragua Records from Rich C. Moore lab -86.20023333 11.95943333 Grenada, Nicaragua Records from Rich C. Moore lab -86.5868 12.28953333 Grenada, Nicaragua Records from Rich C. Moore lab -86.58683333 12.28956667 Leon, Nicaragua Records from Rich C. Moore lab -86.5868 12.28983333 Leon, Nicaragua Records from Rich C. Moore lab -86.5868 12.28983333 Leon, Nicaragua Records from Rich C. Moore lab -86.5868 12.28986667 Leon, Nicaragua Records from Rich C. Moore lab -86.58688333 12.28986667 Leon, Nicaragua Records from Rich C. Moore lab -86.58691667 12.28991667 Leon, Nicaragua Records from Rich C. Moore lab -86.58676667 12.29058333 Leon, Nicaragua Records from Rich C. Moore lab -86.5868 12.29053333 Leon, Nicaragua Records from Rich C. Moore lab -86.5868 12.29058333 Leon, Nicaragua Records from Rich C. Moore lab -86.58688333 12.29053333 Leon, Nicaragua Records from Rich C. Moore lab -86.58675 12.29055 Leon, Nicaragua Records from Rich C. Moore lab -87.00408333 12.58233333 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.0041 12.58235 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.00411667 12.58228333 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.00411667 12.5823 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18256667 12.72695 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18298333 12.72755 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18383333 12.729 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18435 12.72998333 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18431667 12.73 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18496667 12.73078333 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.1849 12.73078333 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18483333 12.7309 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18495 12.73118333 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18503333 12.73143333 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.18531667 12.73176667 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.1854 12.73195 North West of Leon, Nicaragua Records from Rich C. Moore lab -87.1856 12.7319 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.92026667 12.83456667 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.92025 12.8345 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.9203 12.83463333 North West of Leon, Nicaragua Records from Rich C. Moore lab

-86.92063333 12.8343 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.9206 12.83426667 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.92026667 12.8342 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.92053333 12.83456667 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.92061667 12.83415 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.92056667 12.83423333 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.92058333 12.83475 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.92051667 12.83468333 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.93478333 12.84696667 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.93476667 12.84696667 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.93465 12.84706667 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.93456667 12.84705 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.93401667 12.84655 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.96775 12.84671667 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.93436667 12.84678333 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.9332 12.84583333 North West of Leon, Nicaragua Records from Rich C. Moore lab -86.9329 12.84561667 North West of Leon, Nicaragua Records from Rich C. Moore lab -79.87028333 8.7011 South, Panama Mardonovich et al., 2018 -79.87165 8.7017 South, Panama Mardonovich et al., 2018 -79.91286667 8.5446 South, Panama Mardonovich et al., 2018 -80.34146667 8.5098 South, Panama Mardonovich et al., 2018 -80.50246667 8.4238 South, Panama Mardonovich et al., 2018 -80.50291667 8.4233 South, Panama Mardonovich et al., 2018 -79.87678333 8.7617 South, Panama Mardonovich et al., 2018 -80.76345 8.1094 South, Panama Mardonovich et al., 2018 -80.76345 8.1094 South, Panama Mardonovich et al., 2018 -80.76345 8.1094 South, Panama Mardonovich et al., 2018 -80.76345 8.1094 South, Panama Mardonovich et al., 2018 -80.76345 8.1094 South, Panama Mardonovich et al., 2018 -80.76345 8.1094 South, Panama Mardonovich et al., 2018 -82.32143333 8.3985 West, Panama Mardonovich et al., 2018 -82.14253333 8.9442 West, Panama Mardonovich et al., 2018 -82.16436667 8.9439 West, Panama Mardonovich et al., 2018 -82.14526667 8.9453 West, Panama Mardonovich et al., 2018 -82.14526667 8.9453 West, Panama Mardonovich et al., 2018 -82.14338333 8.9430 West, Panama Mardonovich et al., 2018 -81.75063333 8.2213 West, Panama Mardonovich et al., 2018 -82.14338333 8.9430 West, Panama Mardonovich et al., 2018 -82.14446667 8.9422 West, Panama Mardonovich et al., 2018 -82.14286667 8.9440 West, Panama Mardonovich et al., 2018 -79.67386944 9.5384 Northeast, Panama Mardonovich et al., 2018 -79.67386944 9.5384 Northeast, Panama Mardonovich et al., 2018 -79.67386944 9.5384 Northeast, Panama Mardonovich et al., 2018 -79.67386944 9.5384 Northeast, Panama Mardonovich et al., 2018 -79.67386944 9.5384 Northeast, Panama Mardonovich et al., 2018 -79.67386944 9.5384 Northeast, Panama Mardonovich et al., 2018 -79.67386944 9.5384 Northeast, Panama Mardonovich et al., 2018 -79.68985 9.5129 Northeast, Panama Mardonovich et al., 2018 -79.68985 9.5129 Northeast, Panama Mardonovich et al., 2018 -79.68985 9.5129 Northeast, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018 -79.6813 9.0992 Canal, Panama Mardonovich et al., 2018 -79.6813 9.0992 Canal, Panama Mardonovich et al., 2018 -79.6922 9.1120 Canal, Panama Mardonovich et al., 2018 -79.68906667 9.1117 Canal, Panama Mardonovich et al., 2018

-79.65296667 9.0677 Canal, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018 -79.71511667 9.1207 Canal, Panama Mardonovich et al., 2018 -79.61519167 9.1280 Canal, Panama Mardonovich et al., 2018 -79.6247 9.0793 Canal, Panama Mardonovich et al., 2018 -79.61225 9.0196 Canal, Panama Mardonovich et al., 2018 -79.61225 9.0196 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.59351667 9.0062 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.59221667 9.0054 Canal, Panama Mardonovich et al., 2018 -79.59221667 9.0054 Canal, Panama Mardonovich et al., 2018 -79.59221667 9.0054 Canal, Panama Mardonovich et al., 2018 -79.59221667 9.0054 Canal, Panama Mardonovich et al., 2018 -79.59221667 9.0054 Canal, Panama Mardonovich et al., 2018 -79.59221667 9.0054 Canal, Panama Mardonovich et al., 2018 -79.59221667 9.0054 Canal, Panama Mardonovich et al., 2018 -79.59221667 9.0054 Canal, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018 -79.6973 9.1182 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.60998333 9.0184 Canal, Panama Mardonovich et al., 2018 -79.57843333 8.9895 Canal, Panama Mardonovich et al., 2018 -79.59351667 9.0062 Canal, Panama Mardonovich et al., 2018 -79.697 9.1181 Canal, Panama Mardonovich et al., 2018 -79.6973 9.1182 Canal, Panama Mardonovich et al., 2018 -79.6973 9.1182 Canal, Panama Mardonovich et al., 2018 -79.6973 9.1182 Canal, Panama Mardonovich et al., 2018 -79.6973 9.1182 Canal, Panama Mardonovich et al., 2018 -79.6813 9.0992 Canal, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018 -79.66271667 9.0790 Canal, Panama Mardonovich et al., 2018

Appendix B R script: converting bioclim rasters to ASCII format. Using the raster package in R (Hijmans, 2019).

# load packages library(raster)

# set working directory setwd('C:/Users/Owner/Documents/Folder')

# read .tif file ("bio1.tif") to object "bio1" bio1 <- (filename="bio1.tif")

# rasterize and assign to object bio1 <- raster(bio1)

# write raster in .ascii format onto working directory writeRaster(bio1, filename='bio1.asc', format='ascii', overwrite=TRUE)

Appendix C R script: cropping bioclim rasters. Rasters cropped by 1 degree around the widest occurrence points using the raster package in R (Hijmans, 2019).

# load packages library(raster)

# Set working directory setwd('C:/Users/Owner/Documents/Folder')

# Read environmental layers bio1 <- raster('bio1.asc') crs(bio1) <-CRS('+init=EPSG:4326') bio2 <- raster('bio2.asc') crs(bio2) <- CRS('+init=EPSG:4326')

# Check that both environmental layers match compareRaster(bio1, bio2)

# Get occurrences and make them spatial points oc <- read.csv('Occurrences.csv') oc <- SpatialPoints(data.frame(lon = oc$longitude, lat = oc$latitude)) crs(oc) <- CRS('+init=EPSG:4326')

# Initialize a polygon expanded 1 degree outside of minimum occurrence bounds px <- Polygon(rbind(c(matrix(bbox(oc), 2, 2)[1,1] - 1, matrix(bbox(oc), 2, 2)[2,2] + 1), c(matrix(bbox(oc), 2, 2)[1,1] - 1, matrix(bbox(oc), 2, 2)[2,1] - 1), c(matrix(bbox(oc), 2, 2)[1,2] + 1, matrix(bbox(oc), 2, 2)[2,1] - 1), c(matrix(bbox(oc), 2, 2)[1,2] + 1, matrix(bbox(oc), 2, 2)[2,2] + 1), c(matrix(bbox(oc), 2, 2)[1,1] - 1, matrix(bbox(oc), 2, 2)[2,2] + 1)))

# Create the single masking polygon px <- Polygons(list(px), 1)

# Assign spatial information spat.mask <- SpatialPolygons(list(px), proj4string = CRS("+init=EPSG:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))

# Extract new rasters by spatial mask bio1a <- crop(bio1, spat.mask) bio2a <- crop(bio2, spat.mask) if(compareRaster(bio1a, bio2a)){ # Write new environmental raster information with modified names writeRaster(bio1a, "bio1a.asc", overwrite = T) writeRaster(bio2a, "bio2a.asc", overwrite = T)}

Appendix D R script: spatially thinning occurrence records. Using spThin package in R (Aiello-Lammens et al., 2019).

# Load package library(spThin)

# Set working directory setwd('C:/Users/Owner/Documents/Folder’)

# Assign occurrences file to object locdata <- read.csv("Occurrences.csv")

# Define output directory outdir <- ('C:/Users/Owner/Documents/Folder')

# Spatially thin by 10 kilometers thin( loc.data = locdata, lat.col = "latitude", long.col = "longitude", spec.col = "species", thin.par = 10, reps = 100, locs.thinned.list.return = TRUE, write.files = TRUE, max.files = 5, out.dir = outdir, out.base = "coord_thinned", write.log.file = TRUE, log.file = "coord_thinned_full_log_file.txt" )

Appendix E R script: creating binary bias grids. Grids for use in the “Bias file” option in Maxent. Script performed using the here, raster, rgdal, rgeos, sp, and spatstat packages in R (Müller 2017; Hijmans, 2019; Bivand et al., 2019; Bivand and Rundel, 2019; Pebesma and Bivand, 2005; Bivand et al., 2013; Baddeley et al., 2015). # Load packages library(raster) library(sp) library(rgdal) library(spatstat) library(here) library(tools) library(rgeos)

# Set working directory setwd('C:/Users/Owner/Documents/Folder')

# Setup the buffer distance buff_distance <- 70000 # Distance in meters

# Read environmental layers bio1 <- raster('bio1.asc') crs(bio1) <-CRS('+init=EPSG:4326') bio2 <- raster('bio2.asc') crs(bio2) <- CRS('+init=EPSG:4326')

# Check that both environmental layers match compareRaster(bio1, bio2)

# Get occurrences and make them spatial points oc <- read.csv('Occurrences.csv') oc <- SpatialPoints(data.frame(lon = oc$longitude, lat = oc$latitude)) crs(oc) <- CRS('+init=EPSG:4326')

# Initialize a blank raster for binary bias blank <- raster(bio1) crs(blank) <- CRS('+init=EPSG:4326')

# Get count of occurrence points in each cell oc.ras.bin <- rasterize(oc, blank, fun = 'count', na.rm = F) # All cells with count >= 1 get assigned a 1 value oc.ras.bin[oc.ras.bin >= 1] <- 1 # Redundancy to enforce consitent NA values oc.ras.bin[is.na(oc.ras.bin)] <- 0.00001

#### TEST #### oc.ras.bin <- mask(oc.ras.bin, bio1) # Match bias NA values with env. layers NAvalue(oc.ras.bin) <- NAvalue(bio1)

# Check for consistency before masking compareRaster(oc.ras.bin, bio1)

# Initialize a polygon expanded 1 degree outside of minimum occurence bounds px <- Polygon(rbind(c(matrix(bbox(oc), 2, 2)[1,1] - 1, matrix(bbox(oc), 2, 2)[2,2] + 1), c(matrix(bbox(oc), 2, 2)[1,1] - 1, matrix(bbox(oc), 2, 2)[2,1] - 1), c(matrix(bbox(oc), 2, 2)[1,2] + 1, matrix(bbox(oc), 2, 2)[2,1] - 1), c(matrix(bbox(oc), 2, 2)[1,2] + 1, matrix(bbox(oc), 2, 2)[2,2] + 1), c(matrix(bbox(oc), 2, 2)[1,1] - 1, matrix(bbox(oc), 2, 2)[2,2] + 1) ) )

# Create the single masking polygon px <- Polygons(list(px), 1)

# Assign spatial information spat.mask <- SpatialPolygons(list(px), proj4string = CRS("+init=EPSG:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))

# Function to align rasters reduce.rasters <- function(bio.clim){ bc <- raster(here('bioclim', bio.clim)) out <- raster::crop(bc, spat.mask) writeRaster(out, paste(file_path_sans_ext(bio.clim), 'mod.asc', sep = '_'), overwrite = T) }

# Extract new rasters by spatial mask bio1a <- crop(bio1, spat.mask) bio2a <- crop(bio2, spat.mask) oc.bin.a <- crop(oc.ras.bin, spat.mask)

# Double-check alignment between environmental layers and bias raster if(compareRaster(bio1a, oc.bin.a)){ # Write new environmental raster information with modified names writeRaster(bio1a, "bio1a.asc", overwrite = T) writeRaster(bio2a, "bio2a.asc", overwrite = T) # Export bias layer writeRaster(oc.bin.a, "bias_mask_bin_modified.asc", overwrite = T) }

# Convert Bioclim files in.files <- list.files(here('bioclim'), pattern = 'bio_.*\\.asc$') lapply(in.files, reduce.rasters)

# Set buffer size (meters) oc.buff <- buffer(oc, buff_distance)

# Get count of occurrence points in each cell oc.ras.buff <- rasterize(oc.buff, blank, fun = 'count', na.rm = F) # All cells with count >= 1 get assigned a 1 value oc.ras.buff[oc.ras.buff >= 1] <- 1 # Redundancy to enforce consitent NA values oc.ras.buff[is.na(oc.ras.buff)] <- 0.00001 oc.ras.buff <- mask(oc.ras.buff, bio1) oc.bin.buff <- crop(oc.ras.buff, spat.mask)

62 if(compareRaster(bio1a, oc.bin.buff)){ writeRaster(oc.bin.buff, paste0("bias_mask_bin_", buff_distance/1000, "km.asc"), overwrite = TRUE) zip('archive.zip', paste0('bias_mask_bin_', buff_distance/1000, 'km.asc'))}

Appendix F R script: creating pairwise correlation matrix of bioclim variable values extracted from occurrence localities. Performed using R packages dismo, raster, and rgdal (Hijmans et al., 2017; Hijmans 2019; Bivand et al., 2019).

# Load packages library(raster) library(rgdal) library(dismo) library(stats)

# Set working directory setwd('C:/Users/Owner/Documents/Folder’)

# Assign occurrence file to object # Occurrence file must be two columns with no headings LONGLAT <- read.csv("Occurrences_lonlat.csv")

# Set path to environmental layers VARIABLES <- list.files(path="C:/Users/Owner/Documents/WorldClim/cropped_ascii",pattern='asc',full.names= TRUE)

# Create raster stack of environmental layers bio_stack<-stack(VARIABLES)

# Extract variable values at occurrence localities extract<-extract(bio_stack,LONGLAT)

# Run cor test on those values COR <- cor(extract,use="pairwise.complete.obs")

# Write pairwise matrix to file write.csv(COR, file="Correlations.csv")

Appendix G R script: calculating AICc. Using the dismo, ENMeval, and rgdal packages (Hijmans et al., 2017; Muscarella et al., 2014; Bivand et al., 2019).

# For multiple replicates

# Load packages library(rgdal) library(dismo) library(ENMeval)

# Set working directory setwd('C:/Users/Owner/Documents/Maxent_results’)

# Set prediction raster to object pred.raw <-raster('papaya_0.asc')

# Set occurrences to object occ <-read.csv('ThinOccurrences_forAICc.csv')

# Check raster if(is.character(pred.raw)) pred.raw <- raster(pred.raw)

# Set lambdas file to object lambdas0<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_0.lambdas'

# Check lambdas file if(is(lambdas0, 'MaxEnt')) lambdas0 <- textConnection(lambdas0@lambdas0)

# Read lambdas file to object lambdas0 <- read.csv(lambdas0, header=FALSE, stringsAsFactors=FALSE)

# Find k k0 <- sum(as.numeric(lambdas0[, 2]) != 0) - 4

# AICC function from “ENMeval” aicc0 <- calc.aicc(k0, occ, pred.raw)

# The following blocks are for the replicates pred.raw <-raster('papaya_1.asc') occ <-read.csv('ThinOccurrences_forAICc.csv') if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas1<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_1.lambdas' if(is(lambdas1, 'MaxEnt')) lambdas1 <- textConnection(lambdas1@lambdas1) lambdas1 <- read.csv(lambdas1, header=FALSE, stringsAsFactors=FALSE) k1 <- sum(as.numeric(lambdas1[, 2]) != 0) - 4 aicc1 <- calc.aicc(k1, occ, pred.raw) pred.raw <-raster('papaya_2.asc') occ <-read.csv('ThinOccurrences_forAICc.csv') if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas2<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_2.lambdas' if(is(lambdas2, 'MaxEnt')) lambdas2 <- textConnection(lambdas2@lambdas2) lambdas2 <- read.csv(lambdas2, header=FALSE, stringsAsFactors=FALSE) k2 <- sum(as.numeric(lambdas2[, 2]) != 0) - 4 aicc2 <- calc.aicc(k2, occ, pred.raw) pred.raw <-raster('papaya_3.asc') occ <-read.csv('ThinOccurrences_forAICc.csv') if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas3<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_3.lambdas' if(is(lambdas3, 'MaxEnt')) lambdas3 <- textConnection(lambdas3@lambdas3) lambdas3 <- read.csv(lambdas3, header=FALSE, stringsAsFactors=FALSE) k3 <- sum(as.numeric(lambdas3[, 2]) != 0) - 4 aicc3 <- calc.aicc(k3, occ, pred.raw) pred.raw <-raster('papaya_4.asc') occ <-read.csv('ThinOccurrences_forAICc.csv') if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas4<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_4.lambdas'

66 if(is(lambdas4, 'MaxEnt')) lambdas4 <- textConnection(lambdas4@lambdas4) lambdas4 <- read.csv(lambdas4, header=FALSE, stringsAsFactors=FALSE) k4 <- sum(as.numeric(lambdas4[, 2]) != 0) - 4 aicc4 <- calc.aicc(k4, occ, pred.raw) pred.raw <-raster('papaya_5.asc') occ <-read.csv('ThinOccurrences_forAICc.csv') if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas5<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_5.lambdas' if(is(lambdas5, 'MaxEnt')) lambdas5 <- textConnection(lambdas5@lambdas5) lambdas5 <- read.csv(lambdas5, header=FALSE, stringsAsFactors=FALSE) k5 <- sum(as.numeric(lambdas5[, 2]) != 0) - 4 aicc5 <- calc.aicc(k5, occ, pred.raw) pred.raw <-raster('papaya_6.asc') occ <-read.csv('ThinOccurrences_forAICc.csv') if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas6<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_6.lambdas' if(is(lambdas6, 'MaxEnt')) lambdas6 <- textConnection(lambdas6@lambdas6) lambdas6 <- read.csv(lambdas6, header=FALSE, stringsAsFactors=FALSE) k6 <- sum(as.numeric(lambdas6[, 2]) != 0) - 4 aicc6 <- calc.aicc(k6, occ, pred.raw) pred.raw <-raster('papaya_7.asc') occ <-read.csv('ThinOccurrences_forAICc.csv') if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas7<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_7.lambdas' if(is(lambdas7, 'MaxEnt')) lambdas7 <- textConnection(lambdas7@lambdas7) lambdas7 <- read.csv(lambdas7, header=FALSE, stringsAsFactors=FALSE) k7 <- sum(as.numeric(lambdas7[, 2]) != 0) - 4 aicc7 <- calc.aicc(k7, occ, pred.raw) pred.raw <-raster('papaya_8.asc') occ <-read.csv('ThinOccurrences_forAICc.csv')

67 if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas8<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_8.lambdas' if(is(lambdas8, 'MaxEnt')) lambdas8 <- textConnection(lambdas8@lambdas8) lambdas8 <- read.csv(lambdas8, header=FALSE, stringsAsFactors=FALSE) k8 <- sum(as.numeric(lambdas8[, 2]) != 0) - 4 aicc8 <- calc.aicc(k8, occ, pred.raw) pred.raw <-raster('papaya_9.asc') occ <-read.csv('ThinOccurrences_forAICc.csv') if(is.character(pred.raw)) pred.raw <- raster(pred.raw) lambdas9<- 'C:/Users/Owner/Documents/Maxent_results/FinalModels/FinalModels_Raw/top5_SS_RM15/pa paya_9.lambdas' if(is(lambdas9, 'MaxEnt')) lambdas9 <- textConnection(lambdas9@lambdas9) lambdas9 <- read.csv(lambdas9, header=FALSE, stringsAsFactors=FALSE) k9 <- sum(as.numeric(lambdas9[, 2]) != 0) - 4 aicc9 <- calc.aicc(k9, occ, pred.raw)

# AICc for replicates were averaged in Excel

Appendix H R script: extracting suitability values at occurrence localities. Using the raster package (Hijmans, 2019).

# Extract raster values at spatial points # library(raster) setwd('C:/Users/Owner/Documents/ExtractFromPoints')

# set files to objects raster <- raster("AllVar_BS_RM15.asc") spatialpoints <- read.csv("AllOccurrences.csv") extract <- extract(raster, spatialpoints, method='simple', buffer=NULL, small=FALSE, cellnumbers=FALSE, fun=NULL, na.rm=TRUE, layer, nl, df=FALSE, factors=FALSE) write.csv(extract, file="ExtractSuit.csv")

Appendix I

R script: combining yield and suitability values into one table. Using the raster package (Hijmans, 2019).

# Retrieving a matrix of high quality farm yield values by suitability from Maxent model

# 1. Modify yield raster to include grid cells of high quality (quality = 1) # for Mexico and Brazil # medium quality (quality = 0.8 or above) for India, since there are no =1 # cells in India

# 2. Resample the coarser yield raster to the finer suitability raster # do this for global rasters

# 3. Crop each raster to Mexico, Brazil, and India, separately

# 4. Combine values based on spatial location to create a matrix of yield x suit

#### Step 1 ####

library(raster) library(RStoolbox)

quality <- raster("papaya_DataQuality_Yield.tif") yield <- raster("Yield.tif")

# visualize rasters ggR(quality) ggR(yield)

# assign mask based on quality quality_mask_med <- quality <= 0.5 quality_mask_high <- quality < 1

# visualize mask ggR(quality_mask_med, geom_raster = TRUE) ggR(quality_mask_high, geom_raster = TRUE)

# create medium quality yield raster object yield_medquality <- mask(yield, quality_mask_med, maskvalue=quality_mask_med, updatevalue=NA)

# visualize ggR(yield_medquality) # write file writeRaster(yield_medquality, filename="yield_medquality.asc", format="ascii", overwrite=FALSE)

# create high quality yield raster object yield_highquality <- mask(yield, quality_mask_high, maskvalue=quality_mask_high, updatevalue=NA) # visualize ggR(yield_highquality) # write file writeRaster(yield_highquality, filename="yield_highquality.asc", format="ascii", overwrite=FALSE)

#### Step 2 ####

# load files yield_medquality <- raster("yield_medquality.asc") yield_highquality <- raster("yield_highquality.asc") suitability <- raster("suitability.asc")

# check raster details yield_medquality yield_highquality suitability

# resample and write rasters simultaneously resample(yield_medquality, suitability, method="bilinear", filename="yield_MQ_resamp.asc", overwrite=TRUE) resample(yield_highquality, suitability, method="bilinear", filename="yield_HQ_resamp.asc", overwrite=TRUE)

#### Step 3 #### library(maptools) library(rgeos) library(rgdal)

# load world shapefile getinfo.shape("world.shp") # read shapefile world.map <- readOGR("world.shp")

# Investigate attribute table to know what polygon to extract # List spatial object and the first 4 attribute records head(world.map, n=4)

# Subset area of interest: # Field name for countries in gadm2.shp is "NAME_0" Mexico <- world.map[world.map$NAME_0 == "Mexico",] plot(Mexico) # check CRS crs(Mexico) # export area of interest writeOGR(Mexico, dsn = '.', layer = 'Mexico', driver = "ESRI Shapefile")

# double check the details of exported shapefile Mexico_file <- readOGR("Mexico.shp") head(Mexico_file, n=10) levels(Mexico@data$NAME_0) plot(Mexico_file)

# repeat for Brazil and India

# now open raster layers and set CRS yield <- raster("yield_HQ_resamp.asc") # check/set CS crs(yield) crs(yield) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0" suitability <- raster("suitability.asc") # check/set CS crs(suitability) crs(suitability) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

# plot Yield plot(yield, col = rev(terrain.colors(50)))

# plot Suitability plot(suitability, col = rev(terrain.colors(50)))

# import the vector boundary crop_extent <- readOGR("Mexico.shp") # double check CRS crs(crop_extent)

# plot plot(crop_extent)

# crop rasters to vector boundary

# crop yield yield_crop <- crop(yield, crop_extent) plot(yield_crop, main = "Cropped yield") # plot shapefile outline overtop the cropped raster plot(crop_extent, bg="transparent", add=TRUE)

# crop suitability suit_crop <- crop(suitability, crop_extent) plot(suit_crop, main = "Cropped suit") # plot shapefile outline overtop the cropped raster plot(crop_extent, bg="transparent", add=TRUE)

#### note that crop() causes a rectangular raster result, #### which includes areas of surrounding countries. #### conveniently, the EarthStat yield data is absent for the countries surrounding #### of Mexico, Brazil, and India. #### so, later, when we combine attributes based on spatial location, #### we will eliminate rows containing NA, #### resulting in data only for the specified country.

# write raster objects to files writeRaster(yield_crop, filename="yield_HQ_Mexico.asc", format="ascii", overwrite=TRUE) writeRaster(suit_crop, filename="suitability_Mexico.asc", format="ascii", overwrite=TRUE)

#### Step 4 ####

# assign raster files to raster objects yield <- raster("yield_HQ_Mexico.asc") suitability <- raster("suitability_Mexico.asc") # read extents of rasters to objects yield_extent <- extent(yield) suit_extent <- extent(suitability)

# create a raster stack and extract values for all rasters rs <- stack(yield,suitability) names(rs) <- c("yield","suitability") # view details of raster stack rs

# extract values for all rasters in stack extract.rs <- as.data.frame(extract(rs, yield_extent))

# check dimensions of object before deleting NA rows dim(extract.rs)

# then call na.omit to remove all rows that contain NA NAomit <- na.omit(extract.rs) # double check that number of rows has decreased dim(NAomit)

# write matrix to file write.csv(NAomit,"YieldBySuit_Mexico.csv", row.names = FALSE)

Appendix J

R script: creating graphs from yield by suitability matrices.

### Box and whisker plots ###

# first categorize the yield values into three categories # first third of the range will be "low" # second third of the range will be "medium" # third third of the range will be "high" # make this into three columns in Excel # each column will be a list of suitability values present in each yield category # save as .csv setwd('C:/Users/Owner/Documents/YieldBySuitCalc/Figures') data <- read.csv("YieldBySuit_Brazil_Categ.csv")

# plot without category labels boxplot(data, range = 1.5, width = NULL, varwidth = FALSE, notch = FALSE, outline = FALSE, names=c("","",""), plot = TRUE, border = par("fg"), col = NULL, log = "", pars = list(boxwex = 0.8, staplewex = 0.5, outwex = 0.5), horizontal = FALSE, add = FALSE, at = NULL, ylim=(0:1))

# vector containing category labels nms <- c("Low", "Medium", "High")

# insert category labels mtext("Low", side=1, line= 0.4, at=1, font=2) mtext("Medium", side=1, line= 0.4, at=2, font=2) mtext("High", side=1, line= 0.4, at=3, font=2)

# add titles to plot # title() will add titles in automatic locations # title(main="Brazil", xlab="Papaya Farm Yield", ylab="Suitability")

# instead I used mtext() to modify the location of titles mtext(side=1, "Yield",font=2,line=1.5) mtext(side=2, "Suitability",font=2,line=1.7) mtext(side=3, "Brazil",font=2,line=0.5)

# bolden the tick labels axis(2, font=2)

### Scatter plot with line ###

# here use the .csv with two columns "yield" and "suitability"

# read .csv into data frame matrix data <- read.csv("YieldBySuit_Mexico.csv")

# assign vectors to x and y, respectively x <- data[,1] y <- data[,2]

# first change distance from tick to tick labels # this line must come before plot() function par(mgp = c(0, 0.5, 0))

# arguments in plot(): # xlim needs to use a comma # ylim needs to use a : # pch: numeric values (from 0 to 25) or character symbols (“+”, “.”, “;”, etc) specifying # the point symbols (or shapes). # cex: numeric values indicating the point size. # col: color name for points.

# make labels (xlab and ylab) BLANK because I will add labels later plot(x, y, xlab=" ", ylab=" ", xlim=c(0,100), ylim=c(0:1), pch=".", cex=2, col="grey")

# set label margins # c(bottom,left,top,right) margins <- par(mar = c(5,5,5,5) + 0.1) # officiate par(margins)

# now draw titles within specified space of the margin # specified space is designated by "line" # cex.lab is size

# two ways to add axes labels: # mtext() or title() # mtext() offers option to BOLD the title mtext(side=1, "Yield",font=2,line=1.5) mtext(side=2, "Suitability",font=2,line=1.7) mtext(side=3, "Mexico",font=2,line=0.5)

# bold tick labels axis(1, font=2) axis(2, font=2)

# perform linear regression in order to generate trendline lm(x ~ y)

# draw trendline # lwd is width of line abline(lm(y ~ x), col="black", lwd=5)

Appendix K

R script: Welch’s and Student’s t-test.

# set working directory setwd('C:/Users/Owner/Documents/YieldBySuitCalc/Figures')

# read in file Brazil <- read.csv("YieldBySuit_Brazil_Categ.csv") Brazil <- data.frame(Brazil)

## convert columns to vectors ##

## low yield low <- as.vector(Brazil['Low']) class(low) low <- Brazil[['Low']] class(low) low <- Brazil[,1] class(low) # remove NAs low <- low[!is.na(low)]

## medium yield medium <- as.vector(Brazil['Medium']) class(medium) medium <- Brazil[['Medium']] class(medium) medium <- Brazil[,2] class(medium) # remove NAs medium <- medium[!is.na(medium)]

## high yield high <- as.vector(Brazil['High']) class(high) high <- Brazil[['High']] class(high) high <- Brazil[,3] class(high) # remove NAs high <- high[!is.na(high)]

## Two Sample t-test ## t.test(low,medium,var.equal=TRUE) t.test(medium,high,var.equal=TRUE) t.test(low,high,var.equal=TRUE)

## Welch's Two Sample t-test ## t.test(low,medium,var.equal=FALSE) t.test(medium,high,var.equal=FALSE) t.test(low,high,var.equal=FALSE)