Introduction
The Latin American and Caribbean (LAC) region is a key area for the preservation of biodiversity, containing approximately one-third of the world’s forests, half of its tropical forests (Blackman et al. Reference Blackman, Epanchin-Niell, Siikamäki and Velez-Lopez2014) and half of the world’s terrestrial species (UNEP 2010). Over the last century, LAC forests have undergone large-scale destruction by anthropogenic activities (FAO 2020), resulting in negative consequences for biodiversity (Wright and Mueller-Landau Reference Wright and Mueller Landau2006) and ecosystem services (FAO 2020).
A main driver of forest loss in the LAC region is conversion of forest to pasture (Willaarts et al. Reference Willaarts, Salmoral, Farinaci and Sanz-Sánchez2014). Conversion of forest to pasture results in major structural and abiotic changes to the habitat. Pastures have higher temperatures (Herrera-Montes and Brokaw Reference Herrera-Montes and Brokaw2010; Nowakowski et al. Reference Nowakowski, Watling, Whitfield, Todd, Kurz and Donnelly2017), more variation in temperature (Herrera-Montes and Brokaw Reference Herrera-Montes and Brokaw2010), lower leaf-litter cover (Díaz-García et al. Reference Díaz-García, Pineda, López-Barrera and Moreno2017), and reduced humidity (Díaz-García et al. Reference Díaz-García, Pineda, López-Barrera and Moreno2017) and soil moisture compared to forest habitats (Holl Reference Holl1999). Amphibians may be particularly vulnerable to forest-to-pasture conversion because they are small-bodied, have limited vagility, and are susceptible to desiccation, which can affect dispersal and reduce survival in open-canopy habitats (Nowakowski et al. Reference Nowakowski, Otero Jiménez, Allen, Diaz-Escobar and Donnelly2013; Rittenhouse et al. Reference Rittenhouse, Harper, Rehard and Semlitsch2008, Reference Rittenhouse, Semlitsch and Thompson2009; Rittenhouse and Semlitsch Reference Rittenhouse and Semlitsch2006).
There is mounting evidence of the negative consequences of deforestation on amphibians (Beebee and Griffiths Reference Beebee and Griffiths2005; Brook et al. Reference Brook, Sodhi and Ng2003; Nowakowski et al. Reference Nowakowski, Frishkoff, Thompson, Smith and Todd2018; Silvano and Segalla Reference Silvano and Segalla2005). There are key gaps in understanding and escalating concerns over declines of many common species and the resulting broad consequences these declines might have on ecosystems (Gaston Reference Gaston2010, Reference Gaston2011; Whitfield et al. Reference Whitfield, Bell, Philippi, Sasa, Bolaños, Chaves, Savage and Donnelly2007). However, there is also growing recognition of the potential of large-scale tropical forest restoration to mitigate some of these negative effects (Chazdon et al. Reference Chazdon, Peres, Dent, Sheil, Lugo, Lamb, Stork and Miller2009; Gillespie et al. Reference Gillespie, Ahmad, Elahan, Evans, Ancrenaz, Goossens and Scroggie2012; Hernández-Ordóñez et al. Reference Hernández-Ordóñez, Urbina-Cardona and Martínez-Ramos2015; Herrera-Montes and Brokaw Reference Herrera-Montes and Brokaw2010; Thompson et al. Reference Thompson, Halstead and Donnelly2018). Some parts of the LAC region have seen shifting social, political, and economic trends in forest and conservation policy (Barbieri and Carr Reference Barbieri and Carr2005; Grau et al. Reference Grau, Aide, Zimmerman, Thomlinson, Helmer and Zou2003; Kull et al. Reference Kull, Ibrahim and Meredith2007; McDonald Reference McDonald2008; Southworth and Tucker Reference Southworth and Tucker2001) that are driving reduction in forest cover loss and secondary forest gain (Aide et al. Reference Aide, Clark, Grau, López-Carr, Levy, Redo, Bonilla-Moheno, Riner, Andrade-Núñez and Muñiz2012; Aide and Grau Reference Aide and Grau2004).
Generally, secondary forest has higher amphibian species richness and abundance than human-modified landscapes (e.g., pasture, agriculture) and lower species richness and abundance than mature forest (Thompson and Donnelly Reference Thompson and Donnelly2018). However, there is variation in species-specific response (Thompson and Donnelly Reference Thompson and Donnelly2018). Some trends in interspecific differences are thought to be attributed to particular ecological traits such as thermal tolerance, desiccation tolerance, breeding requirements, and specialised habitat associations (Ash Reference Ash1997; Gardner et al. Reference Gardner, Ribeiro- Júnior, Barlow, Ávila Pires, Hoogmoed and Peres2007; Rios-López & Aide Reference Rios-López and Aide2007; Vallan Reference Vallan2002). Amphibian response to succession and land-use change can be affected by presence of specific habitat features. For example, riparian buffers can be an important management strategy to maintain amphibian abundance in logged forest (Guzy et al. Reference Guzy, Halloran, Homyack, Thornton-Frost and Willson2019; Vesely and McComb Reference Vesely and McComb2002). Riparian habitats are common features in lowland rainforests and species composition and habitat structure is known to vary between riparian and non-riparian habitat (Bolt et al. Reference Bolt, Schreier, Voss, Sheehan and Barrickman2020; Drucker et al. Reference Drucker, Costa and Magnusson2008; Sabo et al. Reference Sabo, Sponseller, Dixon, Gade, Harms, Heffernan, Jani, Katz, Soykan, Watts and Welter2005). However, past studies on forest succession and amphibians in the tropics are primarily focused only on upland habitat or do not distinguish riparian zones as a different habitat (e.g., Hernández-Ordóñez et al. Reference Hernández-Ordóñez, Urbina-Cardona and Martínez-Ramos2015; Hilje and Aide Reference Hilje and Aide2012).
The objective of our study was to compare the abundance of a direct-developing terrestrial frog, Crausagtor stejnegerianus, among pasture, secondary forest, and mature forest sites to determine if (1) conversion of forest habitat to pasture represents a threat to this species, and if so, (2) does regrowth of secondary forest mitigate negative effects on the abundance of C. stejnegerianus, and (3) does response differ by habitat type (upland, riparian). Although C. stejnegerianus is common throughout its range (Savage Reference Savage2002), there is little available information regarding its ecology and the impacts of land management on this species (Twining and Cossel Reference Twining and Cossel2017).
Materials and methods
Study species
Stejneger’s Robber Frog (Craugastor stejnegerianus) is a small, directly developing, leaf-litter frog that is distributed from northwestern Costa Rica to Panama in the western humid lowlands and premontane slopes. In Costa Rica, the distribution also extends into the western central valley and the periphery of the Atlantic lowlands in proximity to Laguna Arenal. It is considered a diurnal species (Savage Reference Savage2002); however, it has also been reported to be active at night, especially on rainy nights during breeding (Gómez-Hoyos et al. Reference Gómez-Hoyos, Gil-Fernández and Escobar-Lasso2016; Twining and Cossel Reference Twining and Cossel2017). Craugastor stejnegerianus has been observed in mature forest, secondary forest, coffee plantations, and pasture (Santos-Barrera et al. Reference Santos-Barrera, Pacheco, Mendoza-Quijano, Bolaños, Cháves, Daily, Ehrlich and Ceballos2008; Scott Reference Scott1976).
Study sites
The Osa Peninsula (southwestern Costa Rica, 8º25’29.0”N 83º21’23.7”W) is dominated by tropical lowland wet forest (Holdridge et al. Reference Holdridge, Grenke, Hatheway, Liang and Tosi1971) and characterised by a large contiguous plot of forest (Corcovado National Park) surrounded by forest fragments of varying size and age embedded in a matrix of agriculture and pasture land. The Osa Peninsula has two distinct seasons, dry and wet. A marked dry season occurs from January to March when monthly precipitation averages <200 mm. The wet season occurs from April to December, with a several-week period of little rainfall usually occurring in late July and /or early August (veranillo). Rainfall peaks in October and starts to decrease in December nearing the dry season (McDiarmid and Savage Reference McDiarmid, Savage, Donnelly, Crother, Guyer and Wake2005).
We surveyed a chronosequence of secondary forest sites regenerating from pasture in the Osa Peninsula, Costa Rica (Figure 1). Sites consisted of replicates of each of five forest stages: pasture (P, three replicates), secondary forest < 17 years old (S1, two replicates), secondary forest 17−27 years old (S2, four replicates), secondary forest > 27 years old (S3, three replicates), and mature forest (MF, three replicates) for a total of 15 field sites all located under 300 masl. We defined mature forest as forest with a history of minimal human disturbance and containing large-diameter old trees. However, it is possible that these forests could have had some historic selective logging. We calculated forest ages and land-use history by using a combination of aerial photographs and interviews with landowners and binned forest into age groups following a previous study that focused on vegetation succession in the Sarapiquí region of Costa Rica (Letcher & Chazdon Reference Letcher and Chazdon2009). Pasture and secondary forest sites were located adjacent to or as close to mature forest as possible.
Figure 1. Map of study sites in the Osa Peninsula, Costa Rica in pasture (P), secondary forest < 17 years old (S1), secondary forest 17−27 years old (S2), secondary forest > 27 years old (S3), and mature forest (MF).
Amphibian and reptile surveys
We conducted diurnal and nocturnal visual encounter surveys along linear transects (Crump and Scott Reference Crump, Scott, Heyer, Donnelly, McDiarmid, Hayek and Foster1994). To sample across seasons, we aimed to survey each site three times annually, once during the marked dry season (January to March) and twice during the rainy season. We sampled six sites during a pilot period between September 2014 and December 2014 and all 15 sites annually between January 2015 and December 2016 for a total of six to seven sampling occasions per site. At each site, we established six randomly placed 50 x 2 m transects and sampled them repeatedly during the study; three transects were in riparian habitat and three in upland habitat. We defined upland as habitat at least 35 m from any water features and riparian transects were located along stream banks. There was one S1 secondary forest site (< 17 years old) that was too small to place six transects while maintaining at least 35 m from other transects, streams, and the edge. Therefore, only 4 transects (two in upland and two in riparian habitat) were placed at this site. In total, we conducted 1,128 transect surveys.
N-mixture models
N-mixture models are a class of models that allow for estimation of population size from replicate count data regardless of the identity of the individual, allow for the estimation of abundance, and account for imperfect detection (Royle Reference Royle2004). Additionally, effects of covariates can be incorporated into the abundance and detection model. We selected N-mixture models for analysis because we anticipated detection to be highly variable in our surveys and these models allowed us to account for predicted sources of variation (such as surveying during drastically different seasons).
The N-mixture model is composed of two model parts: 1) the abundance model that estimates the local abundance at a site i, (N i ), with mean local abundance λ, and 2) an observation model that links N i with detection probability p, y ij ∼ binomial (N i , pij ), where y ij represents counts at a site i during replicate survey j. To estimate abundance, N-mixture models use a binomial distribution to model the detection process and a separate distribution to model the dispersion of individuals among sampling units (Royle Reference Royle2004; Royle & Nichols Reference Royle and Nichols2003). We modelled abundance using a Poisson (log link) distribution and a zero-inflated Poisson distribution. The two models had similar results and similar goodness-of-fit but the Poisson distribution had better convergence and so we report results from that model.
We estimated the effect of forest stage and habitat (upland, riparian) on abundance. We estimated survey-specific covariates time of day (TOD) and season for probability of detection. We converted categorical variable forest stage to dummy variables using mature forest as the reference group, and categorical variables TOD (nocturnal: 0, diurnal: 1), season (wet: 0, dry: 1), and habitat (upland: 0, riparian: 1). For abundance, we included the nested effect of transect in site as a random effect to account for multiple transects within sites (η site[i] ). For detection, we included random transect-survey effects (η ij ).
Detection probability was expressed with a logit-linear regression coefficient, formulated as
$${\rm{logit}}\left( {{p_{ij}}} \right) = \alpha 0 + \alpha {\rm{1}}^*{TO{D_j}} + \alpha {\rm{2}}^*{seaso{n_j}} + {\eta _{ij}},$$
$${\eta _{{{ij}}}}{_~}{\rm{Normal}}\left( {0,{\rm{ }}{\sigma ^2}} \right)$$
where p is the detection probability at transect i during survey j, α1 is the model coefficient for TOD, and α2 is the model coefficient for season.
Abundance was expressed as a log-linear regression coefficient, formulated as
$$\eqalign{ {\rm{log}}\left( {{\lambda _{{i}}}} \right) = \,& & \beta 0 + \beta {\rm{1}}{P_{{i}}} + \beta {\rm{2}}S{1_{{i}}} + \beta {\rm{3}}S{{\rm{2}}_{{i}}} + \beta 4S{{\rm{3}}_{{i}}} + \beta {{5Habita}}{{{t}}_{{i}}} \cr & & + \beta {\rm{6}}{P_{{{i}}}}\!*\!{{Habita}}{{{t}}_{{i}}} + \beta {\rm{7}}S{{\rm{1}}_{\rm{i}}}^*{{Habita}}{{{t}}_{{i}}} + \beta {\rm{8S}}{{\rm{2}}_{{i}}} \,^*{{Habita}}{{{t}}_{{i}}} \cr & & + \beta {\rm{9S}}{{\rm{3}}_{{i}}}\,^*{{Habita}}{{{t}}_{{i}}} + {\eta _{{{site}}\left[ {{i}} \right],}} \cr}$$
$${\eta _{{{site}}[{{i}}]}}{_~}{\rm{Normal}}\left( {0,{\rm{ }}{\sigma ^{\rm{2}}}} \right)$$
where λ is the abundance at transect i, βs are the model coefficients for forest stage (P, S1, S2, and S3), habitat, and the interaction between forest stage and habitat.
We used MCMC with 180,000 iterations of three chains each. The first 90,000 were removed as burn-in and then chains were thinned by 30. A total of 9,000 samples across the three chains were used to approximate posterior summary statistics, model coefficients, and credible intervals. We evaluated convergence by visual inspection of chain mixing plots and by the Gelman and Rubin statistic, which was < 1.05 for all monitored parameters (Gelman and Rubin Reference Gelman and Rubin1992). We evaluated goodness-of-fit through a posterior predictive check (Bayesian p-value: 0.51, c-hat: 1.00). We ran models by calling programme JAGS (Plummer Reference Plummer and Hornik2003) from R v4.0.1 (R Core Team 2021) using package jagsUI (Kellner Reference Kellner2021). Data and code are available at: https://github.com/MichelleThompson86/CRASTE_SecForests.
Results
We detected C. stejnegerianus at every site except for one (pasture site). Raw counts of C. stejnegerianus per transect survey ranged from zero to 13 individuals. There were 37 observations in pasture, 49 in Stage 1 secondary forest, 211 in Stage 2 secondary forest, 152 in Stage 3 secondary forest, and 128 in mature forest for a total of 577 observations.
N-mixture models
Mean probability of detection per individual was 0.014 (95% CI 0.006–0.022). Estimated mean λ (local [transect] abundance) was 9.606 (95% CI 2.975–28.268). When upland and riparian habitats are considered together, the mean abundance per transect was 4.06 times higher in mature forest compared to pasture. (Figure 2; pasture mean estimated abundance = 2.19, 95% CI 1.22–5.28, mature forest mean estimated abundance = 8.90, 95% CI: 4.61–21.22). When compared to mature forest, estimated abundance of C. stejnegerianus was significantly lower in upland pasture sites (Table 1, Figure 3). We found a significant positive interaction between pasture and habitat (Table 1). Secondary forest sites had abundances similar to mature forest sites, regardless of forest stage and habitat type (Table 1, Figures 2 and 3). TOD did not have a significant effect on probability of detection (Table 1). There was a higher probability of detection in the dry season compared to the wet season (Table 1).
Figure 2. Mean (white circle) estimated abundance per transect for pasture (P), secondary forest < 17 years old (S1), secondary forest 17−27 years old (S2), secondary forest > 27 years old (S3), and mature forest (MF). Black bars indicate 50% credible intervals (CIs) and error lines represent 95% CIs. Grey shaded areas represent the posterior distribution density curves.
Table 1. Mean effects (α, β) and 95% credible intervals (CIs) for abundance (λ) and probability of detection (p). Abbreviations for forest stage: pasture (P), secondary forest < 17 years old (S1), secondary forest 17−27 years old (S2), and secondary forest > 27 years old (S3).
Bold values indicate a significant effect (95% CI that does not include zero).
Figure 3. Estimated abundance (λ) for each riparian (circle) and upland (triangle) transect in pasture (P), secondary forest < 17 years old (S1), secondary forest 17−27 years old (S2), secondary forest > 27 years old (S3), and mature forest (MF). Error bars represent 95% confidence intervals.
Discussion
Our findings show that small populations of C. stejnegerianus can persist in pastures but that pasture is no substitute for forest. Mean abundance per transect was lower in upland pasture compared to mature forest. However, riparian buffers partially mitigate the negative effect of conversion to pasture and regrowth of secondary forest on pasture habitats restores abundances similar to those in mature forest.
Our results are consistent with other research that, in general, craugastorid frogs are sensitive to habitat change. For example, a review on the effects of land-use conversion on amphibians estimated a 9.3-fold decrease in abundance of craugastorid frogs as a result of habitat alteration (Nowakowski et al. Reference Nowakowski, Frishkoff, Thompson, Smith and Todd2018) and Ficetola et al. (Reference Ficetola, Furlani, Colombo and De Bernardi2008) found lower density of calling males of C. fitzingeri in pasture compared to secondary and primary forest. Leaf litter provides a refuge for many direct-developing frogs for all or most of their life stages (Ryan et al. Reference Ryan, Scott, Cook, Willink, Chaves, Bolaños, García-Rodríguez, Latella and Koerner2015; Scott Reference Scott1976). Leaf-litter dwelling frogs with direct development of eggs, such as C. stejnegerianus, require humid conditions for development, and terrestrial-developing species are often small-bodied with high surface-to-volume ratios and low heat tolerances (Nowakowski et al. Reference Nowakowski, Watling, Whitfield, Todd, Kurz and Donnelly2017; Scheffers et al. Reference Scheffers, Brunner, Ramirez, Shoo, Diesmos and Williams2013), which can result in vulnerability to desiccation and thermal stress in open-canopy habitats such as pasture (Duarte et al. Reference Duarte, Tejedo, Katzenberger, Marangoni, Baldo, Beltrán, Martí, Richter-Boix and Gonzalez-Voyer2012; Hoffman et al. 2021; Tracy et al. Reference Tracy, Christian and Tracy2010).
We found a higher probability of detection in the dry season compared to the wet season. This result was opposite of our prediction. We provide two hypotheses for this outcome. First, it was often raining during many of the surveys during the wet season and this can make it hard to see, potentially affecting detection. Second, leaf-dropping events in tropical forests are generally higher in the dry season than in the wet season resulting in high leaf-litter depths in the late dry or early very wet season (Frankie et al. Reference Frankie, Baker and Opler1974; Levings & Windsor Reference Levings and Windsor1984). Leaf-litter dynamics are known to affect herpetofauna densities (Folt Reference Folt2017; Guyer Reference Guyer1988; Whitfield et al. Reference Whitfield, Reider, Greenspan and Donnelly2014). Changes in density can be a result of bottom-up effects of increased arthropod food resources with increasing litter depth (Folt Reference Folt2017; Guyer Reference Guyer1988; Levings & Windsor Reference Levings and Windsor1984; Lieberman & Dock Reference Lieberman and Dock1982; Toft Reference Toft1980) or top-down effects of predator dynamics (Folt Reference Folt2017). Furthermore, Ryan et al. (Reference Ryan, Scott, Cook, Willink, Chaves, Bolaños, García-Rodríguez, Latella and Koerner2015) reported a decrease in leaf-litter amphibians, including C. stejnegerianus, during a high rainfall, La Niña event. We did not test for differences in abundance between seasons but it is possible an increase in frog density and/or activity in the high levels of leaflitter during the dry season affected the probability of detection.
Declines in common, non-threatened species have received less conservation attention than threatened and rare species (Redford et al. Reference Redford, Berger and Zack2013). Declines in common species are particularly alarming because proportionally small declines can result in the loss of a large number of individuals, faunal biomass, interactions, and ecosystem services, and declines in common species can often signal declines in the overall abundance of assemblages (Gaston Reference Gaston2010; Gaston & Fuller Reference Gaston and Fuller2008). Terrestrial leaf-litter amphibians play important roles in ecosystems such as nutrient cycling and energy flow of forest ecosystems because they can be present at high densities and they are efficient at converting invertebrate biomass into usable energy (Beard et al. Reference Beard, Vogt and Kulmatiski2002, Reference Beard, Eschtruth, Vogt, Vogt and Scatena2003; Best & Welsh Reference Best and Welsh2014; Davic & Welsh Reference Davic and Welsh2004). Craugastor stejnegerianus is a common species throughout its range (San Vito: Ryan et al. Reference Ryan, Scott, Cook, Willink, Chaves, Bolaños, García-Rodríguez, Latella and Koerner2015; Santos-Barrera et al. Reference Santos-Barrera, Pacheco, Mendoza-Quijano, Bolaños, Cháves, Daily, Ehrlich and Ceballos2008; Scott Reference Scott1976; Golfito: Barquero Reference Barquero2003; Dehling Reference Dehling2005; San Ramón: Acosta-Chavez et al. Reference Acosta-Chaves, Madrigal-Elizondo, Chaves, Morera-Chacón, García-Rodríguez and Bolaños2019; Rincon: Ryan et al. Reference Ryan, Scott, Cook, Willink, Chaves, Bolaños, García-Rodríguez, Latella and Koerner2015; Scott Reference Scott1976) and can be present at extremely high densities. For example, Scott (Reference Scott1976) estimated a density of 4,586/ha at Las Cruces Biological Station and 431/ha at Rincón de Osa, Costa Rica. Our transect study design did not result in density estimates but the model allowed us to estimate a total abundance of 160.254 (95% CI 83–282) in our mature forest transect sampling area, and 39.374 (95% CI 22–95) in our pasture transect sampling area, which we interpret as our transects crossing the home range or habitat use of this quantity of individuals in the mature forest and pasture transects sampled (a total of 18 transects measuring 50 × 2 m in each habitat type). The estimate of individual probability of detection was low, which can lead to unreliable estimates of abundance (Royle Reference Royle2004). Therefore, abundance estimates should be interpreted with caution. While not directly comparable, considering the estimates of 431–4,586/ha by Scott (Reference Scott1976), our abundance estimates seem plausible. Therefore, conversion of forest to pasture and resulting reduction in abundance of this species likely has significant negative consequences for ecosystem function.
Even if low abundances of C. stejnegerianus are present in pasture habitats, these small populations are likely to be at heightened risk of stochastic local extinction (Lande Reference Lande1993; Wissel et al. Reference Wissel, Stephan, Zaschke and Remmert1994) and may rely on nearby source forest habitat to persist. Restoration of forests in human-modified habitats is an effective management strategy for conserving this leaf-litter species. The estimated time to recovery is short. Secondary forests less than 17 years of age already had comparable abundances to mature forests. This time to faunal recovery aligns with the timeline of canopy physical structure (Clark et al. Reference Clark, Oberbauer, Clark, Ryan and Dubayah2021) and above-ground biomass (Letcher & Chazdon Reference Letcher and Chazdon2009) during lowland tropical forest regeneration. However, our secondary forest and pasture sites were located close to mature forest remnants, embedded in a landscape that still has considerable forest cover. Therefore, our results showing a rapid increase in the abundance of C. stejnegerianus during secondary forest succession likely represents a best-case scenario.
Some of our pasture riparian sites were buffered by sparse, scattered trees and others were closer to meeting regulations for riparian forest buffers under Costa Rican law (at least 15 m width). The positive effect for the interaction term between pasture and habitat indicates the size of the negative effect of pasture on abundance is mediated by habitat type. The negative effect pasture has on abundance is partially offset by the presence of riparian habitat. Therefore, our results support the strategy of maintaining remnant natural vegetation, such as remnant trees or riparian buffers, for persistence of amphibian populations in modified landscapes, and this is reinforced by the results of other studies (e.g., herpetofauna: Robinson et al. Reference Robinson, Warmsley, Nowakowski, Reider and Donnelly2013; fish: Lorion & Kennedy Reference Lorion and Kennedy2009; birds: Mitchell et al. Reference Mitchell, Edwards, Bernard, Coomes, Jucker, Davies and Struebig2018; Thompson et al. Reference Thompson, Salicetti-Nelson and Donnelly2022). These results highlight the importance of policy such as Costa Rica Forestry Law (no. 7575), which describes restrictions for clearing trees in riparian zones. However, current regulations and enforcement of the protection of riparian buffers in modified landscapes without also protecting surrounding mature forests may not sufficiently protect the habitat that is crucial to amphibian species.
Acknowledgements
We thank landowners and people who facilitated fieldwork: Organization for Tropical Studies, Osa Conservation, MINAE/SINAC, Bosque del Cabo, Rancho Tropical, Danta Corcovado Lodge, D. Solano, J. J. Jiménez, and M. Sánchez. Permission to handle and study animals was given by Florida International University IACUC-14-027-CR02 and SINAC-SE-GASP-PI-R-0045-2014, SINAC-SE-GASP-PI-R-0146-2014, SINAC-SE-CUS-PI-R-105-2015, SINAC-SE-CUS-PI-R-001-2016.
Financial support
This study was funded by the Fulbright US Student Award, OTS Studies Foster and Dole Fellowship (1329), and FIU Doctoral Evidence Acquisition Fellowship awarded to MET.
Competing interests
The authors declare none.
Ethical statement
The authors assert that all procedures contributing to this work comply with applicable national and institutional ethical guidelines on the care and use of laboratory or otherwise regulated animals.
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