Research Paper

INTRODUCTION

Nearctic-Neotropical migratory songbird populations are declining for reasons that remain unclear for most species, but could be attributed to habitat loss or climate change in any phase of the annual cycle, breeding or non-breeding (Rosenberg et al. 2019). A comprehensive understanding of the factors driving migratory bird declines necessitates studies throughout the full annual cycle. However, there has been a historic bias toward studying the breeding phase compared to the non-breeding phase, which includes the migration and the overwintering or stationary non-breeding phase (Marra et al. 2015). The importance of studies in the non-breeding period are exemplified by research that identified it as a critical phase for some migratory songbird species where forest fragmentation and loss are thought to be driving population declines (Rappole and McDonald 1994, Taylor and Stutchbury 2016, La Sorte et al. 2017). This bias hinders understanding of migratory bird ecology needed to inform conservation strategies (Marra et al. 2015).

In the Neotropical non-breeding grounds, habitat (usually forest) loss has been linked to agricultural expansion related to cattle ranching, palm oil, soybeans, cereals, and cash crops, including coffee (Aide et al. 2013, Pendrill et al. 2019, Albert et al. 2020). In Honduras, where our study occurred, a primary driver of habitat loss is conversion of forest habitats to coffee, often as a monoculture system (i.e., sun coffee). In fact, between 2005 to 2017, coffee production increased the deforestation risk estimated at 9636 ha/yr. Two main coffee production systems are used in Central America: sun coffee and shade coffee. Sun coffee farms are effectively monocultures with very simple vegetation structure and do not serve as good habitat for most migratory songbirds that require forest-like conditions (Wunderle and Arendt 2017). Shade coffee, on the other hand, is occupied by many migratory songbird species (Komar 2006, Sherry et al. 2016). Studies that evaluated habitat quality of shade coffee farms reported mixed results that varied by species and region (Johnson et al. 2006, Bakermans et al. 2009, Colorado and Rodewald 2017, Bailey and King 2019, González et al. 2020, 2021). In our study region, an effort is underway to conserve forest under an alternative coffee production system referred to as Integrated Open Canopy (IOC), whereby coffee farmers spare a portion of their property for the conservation of forest or to allow forests to naturally regenerate (i.e., early successional forest), while the adjacent coffee plot is managed either as a sun monoculture or a shaded polyculture/monoculture (Arce et al. 2009, Chandler et al. 2013). IOC farms, as small-scale, land-sparing systems, support forest-dependent species that are scarce or absent from shade coffee farms and sun coffee farms (Chandler et al. 2013, Murillo et al. 2023).

Studies that have evaluated the quality of shade coffee or IOC for individual and population level responses have mostly focused on site rather than landscape-level predictors (Bailey and King 2019, González et al. 2021). Landscape-level predictors have been successfully used to evaluate avian community level responses (e.g., species richness) and population level responses (e.g., abundance). For example, in a Guatemalan working landscape when forest cover increased in the local landscape, migratory songbird species richness increased while abundance decreased within a 1 km radius (Bennett et al. 2018). In working landscapes of Brazil, abundance of forest-dependent bird species and abundance and species richness of frugivorous species were positively influenced by forest cover within a 300 and 600 m radius (Perez Cabral et al. 2021). Resident species richness in a working landscape in Costa Rica was high at intermediate values of percent tree cover at the site scale (50 m radius), whereas at a local landscape scale (600 m radius) the pattern was reversed (Echeverri et al. 2019). Lastly, landscape configuration metrics, such as forest edge density, positively influenced forest specialist bird diversity in Mexico (Carrara et al. 2015). Fewer studies have focused on individual-level performance metrics that reflect habitat quality of migratory songbirds in relation to landscape metrics.

Performance can be measured in different ways. Several studies have used indices of body condition in non-breeding migratory songbirds to infer habitat quality (Johnson et al. 2006, Ruiz-Sánchez et al. 2017, González et al. 2020, Pacheco-Muñoz et al. 2022). Body condition indices are metrics that reflect energy acquisition to meet subsistence needs (Labocha and Hayes 2012). Most body condition metrics are simply body mass by itself or body mass adjusted to account for structural body size. Temporal effects must be considered because body condition may change with time of day on account of thermoregulatory costs and feeding activity (Townsend et al. 2012, Colorado and Rodewald 2017) and fluctuate with date due to seasonality in resource availability (González et al. 2020, 2021). Inferring that higher body condition reflects better performance or higher habitat quality is not straightforward because body condition may reflect a trade-off where birds in poorer condition seek out food-rich habitats (Johnson 2007, McKinnon et al. 2015), whereas birds in better condition avoid predation by selecting safer but less food abundant spaces (Cresswell 2008, McKinnon et al. 2015).

Survival is another performance metric used as an indicator of habitat quality (Johnson 2007). In the absence of known-fate estimates, apparent survival is arguably the closest metric to fitness (Saracco et al. 2008, Bakermans et al. 2009, Bulluck et al. 2019). However, apparent survival is inextricably confounded with permanent emigration within the extent of the study period, such that low values of apparent non-breeding survival could reflect movement away from the site rather than mortality (Schaub and Royle 2014). In fact, songbirds during the nonbreeding season can adopt nomadic space-use strategies leading to large-scale movements within a season (Rappole et al. 1989, Ruiz-Gutierrez et al. 2016, Knight et al. 2019). Nevertheless, because both low persistence and low survival are indicators of poor habitat quality, the potential confounding between these two metrics does not preclude meaningful assertions about habitat quality (Bailey and King 2019). To understand the coffee production landscape’s role on sustaining birds, performance metrics may be informative to show the value of forest and coffee farms, especially when both habitats can be used by these avian species (Sánchez-Clavijo et al. 2019, González et al. 2020, 2021).

Performance metrics such as body condition and apparent survival may vary by age and sex, with evidence of immatures and females experiencing lower performance compared to adult male counterparts (Calvert et al. 2010, Smith et al. 2010, Kresnik and Stutchbury 2014, Wunderle et al. 2014, Rockwell et al. 2017, Cooper et al. 2021, Ritterson et al. 2021a). Differential performance is expected among age and sex groups if either habitat selection or despotic relationships operate in the non-breeding grounds and cause these groups to use different habitats or space-use strategies (Lynch et al. 1985, Morton et al. 1987, Catry et al. 2005). Overlooking differential habitat use across the non-breeding range can have negative implications on conservation strategies of songbirds (Bennett et al. 2019).

In this study we evaluated the influence of site and landscape context on songbird performance metrics (body condition and apparent non-breeding survival) and age and sex ratios for non-breeding migratory songbirds in a working landscape dominated by coffee production with shade, IOC, and sun farms. By studying these relationships, we aimed to characterize the sampled populations and understand how they are responding to this environment. Our hypothesis was that forest loss and degradation reflected in the landscape lower performance of individuals during the non-breeding season and could therefore be a limiting factor of populations. We predicted that performance metrics of body condition and apparent non-breeding survival would be lower in shade coffee farms compared to IOC farms. Concordantly, we predicted body condition and apparent non-breeding survival would be lower in local landscapes with higher shade farm cover or open habitat cover types, and higher in local landscapes with more forest cover.

METHODS

Study area

Our study was conducted in the Yoro Department of Honduras (Fig. 1) within 18 coffee farms ranging in elevation from 825 to 1423 m.a.s.l (mean = 1156.8, SD = 173.4 m.a.s.l.). All farms were dedicated to the cultivation of Coffea arabica, were privately owned, ranged in area from 1.4 to 41.2 hectares (mean = 11.1, SD = 11.3 ha), and were on average separated from each other by 5.90 km (SD = 2.92). The study sites varied in their coffee management systems and included: (1) sun monocultures (N = 2; elevation range = 1260–1296 m.a.s.l.), where the coffee crop is grown with no tree cover (Moguel and Toledo, 1999); (2) shade coffee farms (N = 5; elevation range = 825–1186 m.a.s.l.), represented by shaded polycultures or shaded monocultures with an overstory generally composed of trees from the Fabaceae family and occasional fruiting trees or banana plants (Moguel and Toledo 1999); and (3) integrated open canopy farms (IOC farms; N = 11; elevation range = 870–1423 m.a.s.l.), where coffee is mostly grown as a sun monoculture and sometimes under shade of trees, and adjacent to conserved forest.

Study species

We focused on two Nearctic-Neotropical migratory bird species: the Wood Thrush (Hylocichla mustelina) and Wilson’s Warbler (Cardellina pusilla). The Avian Conservation Assessment Database includes Wood Thrush in its “Yellow Watch List” category because of persistent declines and major threats identified to its populations, whereas the Wilson’s Warbler is considered a “Common Species in Steep Decline” as the global population has decreased by at least 50% since 1970 (Partners in Flight 2021). The Wood Thrush is a frugivorous/insectivorous species that has a non-breeding range from Mexico to Panama, where it inhabits primarily tropical broad-leaved forests, both disturbed and undisturbed, found within the lowland elevations ranging from 50 to 1000 m.a.s.l. (Evans et al. 2020). However, Wood Thrushes also use agroforestry systems such as shade coffee farms at middle to high elevations ranging from ca. 850 to 1300 m.a.s.l. (Bailey and King 2019). The Wilson’s Warbler is an insectivorous species that has a non-breeding range from the Gulf Coast of the United States to Panama, where it inhabits broad-leaved forests, edge habitat, and agroforestry systems (Ammon and Gilbert 2020).

Avian capture and resighting surveys

We operated mist nets during two field seasons between November and March (2018–2019/2019–2020). Mist net operation details per field season can be found in Appendix 1. Sampling in IOC farms took place in both coffee and forested plots within a property. Following the Institute for Bird Populations “Monitoring for Overwintering Survival-MoSI” protocol (DeSante et al. 2009) for bird captures, we recorded the following measurements: wing chord (mm), tarsus length (mm), mass (g), and age. Age was divided into two categories, adult (also termed After Hatch Year, After Second Year birds) and immature (Hatch Year or Second Year; Pyle 1997, Froehlich 2007). When there was lack of confidence in the age category of an individual because of ambiguity in features used for ageing, we left the age as not-determined and excluded these individuals from models that considered age as a response variable. Sex was determined genetically from a rectrix sample taken from each individual. Individuals for which it was not possible to determine sex from the genetic analysis were excluded from the sex models. Each captured individual was banded with an aluminum band provided by the United States Geological Survey. Individuals caught in the second field season were additionally fitted with a unique combination of three color bands for the resighting surveys and quantification of apparent non-breeding survival. We began resighting surveys at the next visit after color-banding individuals in a site, which was based on an iterative resighting method conducted in a similar working landscape in Costa Rica between 2011 and 2013 (Ritterson et al. 2021a; Appendix 1).

Quantifying landscape composition and configuration

To explore the influence of the landscape on the performance metrics, we delineated local landscapes of 250 m radii (19.6 ha in area) for each farm study site. The landscape was based on a centroid estimated at the midpoint among all points for which we had GPS coordinates, which included resighting surveys and mist netting points. We deemed the 250 m radius adequate because this area (19.6 ha) encompasses the expected home ranges of both Wood Thrush and other non-breeding warblers (Bailey and King 2019, Ritterson et al. 2021a). We conducted a segmentation and cover class assignment process for each of the sites, which resulted in seven cover classes: shade coffee, sun coffee, early successional cover type, pastureland/cropland, other (roads and housing), advanced second growth forest, and forest. The segmentation process was conducted from images obtained from Google Earth (See Appendix 2 for detailed methods). We quantified all cover classes’ area (ha) and edge density (m²/ha) within the local landscape in the R environment using “landscapemetrics” and “sf” packages (Pebesma 2018, Hesselbarth et al. 2019, R Core Team 2023).

We grouped the cover classes into composite categories of forest (including advanced second growth forest and forest classes), shade coffee, and open habitat (including sun coffee, pastureland/cropland, early successional cover type, and other classes). We found that shade coffee cover was negatively correlated with forest cover (r_(df) = −0.31₍₁₆₎, P = 0.21) and that edge density was positively correlated with forest cover (r_(df) = 0.32₍₁₆₎, P = 0.19). We conducted a Principal Component Analysis (PCA) to reduce the predictor variable set and to overcome this observed collinearity among landscape variables (Hotelling 1933, Rao 1964, Buelow et al. 2017). Our PCA included four variables: edge density (m²/ha), open habitats (ha), shade coffee cover (ha) and forest cover (ha). We conducted the analysis using the package “vegan” in R (Oksanen et al. 2018, R Core Team 2023) and obtained two principal components that explained 81.2% of the variation in the data (Appendix 3). The first component (PC1) explained 43.4% of the variation and landscapes with high values of PC1 were dominated by open habitat (sun coffee, pastureland/cropland, early successional cover types, and other features such as houses), as opposed to shade coffee cover. The second component (PC2) explained 37.8% of the variation and landscapes with higher values of PC2 had greater forest cover and more complex edge density (i.e., forest patches with a “jagged” appearance versus patches with abrupt clean edges) and less shade coffee cover (Fig. 2). Conversely, lower values of PC2 represent landscapes with lower forest cover with complex edge density and higher shade coffee cover.

Statistical analyses

To evaluate what influenced the performance of non-breeding songbirds in the working landscape of Yoro, we tested the influence of farm management system (sun, shade, IOC) as a site-level variable and the two landscape principal components as landscape-level variables on age, sex, body condition, and apparent non-breeding survival. Site context and landscape context models for each response variable were run separately because the site and landscape context variables were highly correlated. We fitted a linear model that indicated that sun coffee was significantly positively collinear with PC1–open habitats (β Sun coffee = 1.27, SE = 0.49, t-value = 2.61, P-value = 0.02), whereas the shade coffee was significantly negatively collinear with PC2–increasing forest/edge density (β Shade coffee = −0.75, SE = 0.34, t-value = −2.24, P-value = 0.04). Additionally, for two IOC sites sampling occurred only in the forest portion of the IOC farm and not in both the forest and coffee portion that best represents these composite farms, and for this reason were only included in the landscape context models.

In our study area, elevation could influence the type of farm management system because coffee producers at lower elevations maintain more shade within coffee plantations as a safeguard against warmer conditions. We evaluated if elevation warranted inclusion in the models by exploring its relationship to farm management system and the two landscape principal components. We found that shade coffee farms were more likely to occur at lower elevations (β Shade coffee = −205.89, SE = 76.81, t-value = −2.68, P-value = 0.02), but elevation did not significantly influence PC1 gradient from open habitats to shade nor PC2 gradient from forest with high edge density to shade coffee. We tested two preliminary linear models of body condition: one with elevation and farm management system on body condition and another with elevation and the two landscape principal components. From these, we determined that elevation could be included without multicollinearity issues in all subsequent models because all variance inflation factors were lower than five (Zuur et al. 2007). Finally, we tested and found no evidence for spatial autocorrelation of the PC1–increasing open habitats (Moran’s I = −0.12, P-value = 0.24) and PC2–increasing forest and edge density (Moran’s I = 0.04, P-value = 0.10) with the site locations.

Age and sex models

We constructed generalized linear models (GLMs) to model the response of age (adult versus immature) and sex (male versus female) to site context, i.e., farm management system (sun coffee farm, shade coffee farm, or IOC), elevation, and season, and to model the response of age and sex to landscape context (i.e., PC1 = increasing open habitats, PC2 = increasing forest/edge density, elevation, and season). We fitted models with the Bernoulli distribution that is adequate for response variables such as the probability of catching an individual belonging to a given age or sex class (Zuur et al. 2007).

Body condition models

To test the influence of site and landscape on body condition, we constructed GLMs with gaussian responses for Wilson’s Warblers and Wood Thrushes. We used mass scaled against the wing chord as the body condition metric (Peig and Green 2009).

(1)

Where:

M_i is the mass of each individual; L_i is the wing chord of each individual; bSMA is the scaling component resulting from the slope of a linear model of the natural log of wing chord as a predictor of natural log of mass, divided by the Pearson correlation coefficient of natural log of mass and natural log of wing; and L₀ is the mean of the wing chord of all individuals.

Because birds within IOC farms were caught in both coffee and forest portions of the farm, we first evaluated if body condition was significantly different for individuals captured within coffee or forest within IOC farms. We fit two models with mist net cover type (coffee or forest) as a predictor of body condition. The first model represents the variation in an IOC farm management system, where the levels of mist net cover type were IOC–Shade coffee, IOC–Sun coffee and IOC–Forest. The second model consisted of two levels of mist net cover type (IOC-Forest and IOC-Coffee), which lumped either shade or sun management types. We found no evidence for an effect of cover type within IOC farms on body condition (Appendix 4). We continued the modeling of body condition without these mist net-level predictors.

Previous research has shown the need to account for possible changes in body condition with time of day or day of season (Colorado and Rodewald 2017, González et al. 2020, 2021). Following these studies we first built two simple site context models: one where body condition was a quadratic function of ordinal date, and another where body condition was a linear function of capture time of day. If ordinal date and/or capture time had clear effects on body condition, these were included in a full site context model as additive effects to farm management system, elevation, and season, as well as the landscape context models.

GLM procedures

GLMs were fitted in the Bayesian framework by using the package “brms” in the R environment (Bürkner 2017, 2018, R Core Team 2023). General model specifications included the use of generic weak informative priors for the intercept and the slope coefficients and default priors for other parameters (e.g., variance), three Hamiltonian Montecarlo chains, 10,000 iterations, and 2,000 discarded iterations (“warm-up”). Posterior predictive checks were used to observe that the modeled posterior draws were adequate and comparable to the data. Convergence of a model was assessed by visual inspection of posterior HMC chains and the Gelman-Rubin statistic, R-hat (Brooks and Gelman 1998). An effect was considered a clear effect when the 95% credible interval of a predictor’s posterior distribution did not contain zero.

Apparent non-breeding survival models

Apparent non-breeding survival of Wilson’s Warblers in season two was evaluated with Cormack-Jolly Seber (CJS) models (Cormack 1964, Lebreton et al. 1992). It was not possible to estimate apparent non-breeding survival for Wood Thrushes because we had only three individual resights throughout the study period. Resight records were organized in an encounter history of 18 occasions; each occasion was one week. For the site context models we built grouped fixed effect models where the coffee farm management system was the grouping category. We used the grouping category to evaluate its influence on resighting probability and apparent non-breeding survival. Elevation was added as a predictor of apparent non-breeding survival and week number as a covariate to evaluate if the progression of the season had an influence on resighting probability. For landscape-based models we used the state-space formulation of CJS to estimate probability of resighting as well as probability of apparent non-breeding survival for each individual at a weekly time interval (Gimenez et al. 2007). Resighting probability included the predictor of PC2 (increasing forest/edge density) and week number, and apparent non-breeding survival was modeled as a function of the landscape variables of PC1 (increasing open habitats), PC2 (increasing forest/edge density), and elevation. CJS models were built with the language “jags” with package “rjags” and its wrapper package “jagsUI” in the R environment (Plummer 2003, Kéry and Schaub 2012, Kellner 2018). Model specifications included 20,000 iterations, 5,000 burn-in discards, and 3 Markov Chain Montecarlo Chains. We used uninformative priors for all parameters. We conducted convergence and clear effect checks as described in the GLM Procedures.

RESULTS

Wilson’s Warbler

We captured a total of 111 Wilson’s Warblers in 17 of our 18 sites across seasons: 79 in IOC farms, 27 in shade farms, and 5 in sun coffee farms. From the birds caught within IOC farms for which we had net-level cover data (N = 69 individuals), 27 were caught in forest and 42 in coffee. Wilson’s Warblers were mostly immature (N = 62 of 88 individuals for which age was determined) and male (N = 82 of 99 for which sex was determined). Because for some individuals a given variable could not be collected on site (i.e., age was collected, but sex was not determined), models are fit to variable sample sizes.

Farm management system did not influence the age classes present (β Shade coffee relative to IOC = 0.18, SD = 0.56, 95% CrI = −0.92 to 1.29; β Sun coffee relative to IOC = 0.73, SD = 0.80, 95% CrI = −0.80 to 2.37). The landscape context also did not influence the age classes present (β PC1–increasing open habitat = −0.23, SD = 0.38, 95% CrI = −0.97 to 0.51; β PC2–increasing forest and edge density = −0.10, SD = 0.33, 95% CrI = −0.76 to 0.54). Sex was not influenced by farm management system (β Shade coffee relative to IOC = −0.2, SD = 0.54, 95% CrI = −1.26 to 0.88; β Sun coffee relative to IOC = 0.51, SD = 0.84, 95% CrI = −1.09 to 2.18) or landscape context (β PC1–increasing open habitat = 0.07, SD = 0.42, 95% CrI = −0.74 to 0.88; β PC2–increasing forest and edge density = 0.06, SD = 0.34, 95% CrI = −0.59 to 0.74). Season and elevation did not influence age or sex proportions (See Appendix 4 for all coefficient estimates across models).

Body condition index values for Wilson’s Warblers increased with ordinal date linearly (β = 0.17 SD = 0.07, 95% CrI = 0.03 to 0.30; Table 1) according to the models that only included date. Subsequent models only included the linear effect for ordinal date. Although the effect was not clear in the full site and context level models (Table 2), 97% and 93% of the posterior distribution samples are greater than zero, respectively, providing some evidence for the linear positive effect of ordinal date on body condition models (Fig. 3a-b, Fig. 4a). Body condition was higher in shade coffee compared to IOC (β Shade coffee relative to IOC = 0.47, SD = 0.18, 95% CrI = 0.10 to 0.83; β Sun coffee relative to IOC = −0.04, SD = 0.33, 95% CrI = −0.68 to 0.62; Table 2; Fig. 4b). In shade coffee, the average mass was 0.47 g higher than in IOC, which accounts for 6.74% of the estimated average mass in IOC (6.87 g). In the landscape context model the only clear effect indicated that body condition of Wilson's Warblers decreased with PC2–increasing forest/edge density (β = −0.31, SD = 0.10, 95% CrI = −0.50 to −0.11; Table 2, Fig. 4c). Elevation and season did not influence body condition in either site or landscape models.

Apparent non-breeding weekly survival of Wilson’s Warblers did not differ between IOC farms (0.973, SD = 0.013, 95% CrI = 0.943–0.993) and shade coffee farms (0.944, SD = 0.04, 95% CrI = 0.832–0.994; Fig. 5a) as indicated by overlap of the 95% credible intervals. The mean weekly probability of resighting did not differ between IOC (0.563, SD = 0.125, 95% CrI = 0.310–0.794) and shade coffee (0.697, SD = 0.127, 95% CrI = 0.419–0.902). Week number and elevation did not influence apparent survival (Appendix 4). The landscape context model indicated that apparent non-breeding survival increased and resighting probability decreased with PC2–increasing forest/edge density (Table 3; Fig. 5b,c). We found no evidence within the landscape context model for the influence of week number on resighting probability or elevation on apparent survival (Table 3). Mean apparent non-breeding weekly survival estimated with the landscape context model was 0.989 (SD = 0.011, 95% CrI = 0.958–0.999) and mean weekly probability of resighting was 0.684 (SD = 0.12, 95% CrI = 0.432–0.875).

Wood Thrush

We captured a total of 78 Wood Thrush in 13 of our 18 sites, across both seasons, with 48 caught in IOC, 30 caught in shade coffee, and none in sun coffee farms. Individuals were mostly immature across seasons (N = 43 immatures out of 57 individuals for which age could be determined) and mostly male (N = 54 males of 71 individuals for which sex could be determined). Out of 48 individuals caught in IOC farms, 36 were caught in forest and 12 in the coffee plot. Age was not influenced by farm management system (β Shade coffee relative to IOC = 0.55, SD = 0.63, 95% CrI = −0.67 to 1.82) or landscape context (β PC1-Increasing open habitat = 0.67, SD = 0.55, 95% CrI = −0.39 to 1.77; β PC2-Increasing forest and edge density = −0.33, SD = 0.44, 95% CrI = −1.20 to 0.51). Sex was also not influenced by farm management system (β Shade coffee relative to IOC = 0.27, SD = 0.61, 95% CrI = −0.93 to 1.49) or landscape context (β PC1-Increasing open habitat = −0.14, SD = 0.52, 95% CrI = −1.18 to 0.88; β PC2-Increasing forest and edge density = −0.20, SD = 0.44, 95% CrI = −1.07 to 0.66). According to both site and landscape context models there were fewer males caught in season two (β site context model = −1.82, SD = 0.64, 95% CrI = −3.12 to −0.61; β landscape context model = −1.84, SD = 0.63, 95% CrI = −3.12 to −0.65). Whereas the landscape context model indicated more immatures caught in season two (β = 1.33, SD = 0.57, 95% CrI = 0.23–2.47). Elevation did not have a clear effect on age or sex (Appendix 4).

We did not find evidence for a landscape (β PC1-Increasing open habitat = 0.00, SD = 0.99, 95% CrI = −1.94 to 1.98; β PC2-Increasing forest and edge density = 0.02, SD = 0.99, 95% CrI = −1.94 to 1.97) or farm management system influence on Wood Thrush body condition (β Shade coffee relative to IOC = −0.002, SD = 0.99, 95% CrI = −1.94 to 1.95). Similarly, ordinal date, capture time, season, and elevation did not influence body condition (Appendix 4). We could not quantify resighting probability nor apparent non-breeding survival because, in 18 occasions, we only recorded three resights for 49 color banded individuals.

DISCUSSION

Landscape context influenced the non-breeding performance of one of the two Nearctic-Neotropical migratory songbird species we studied. For Wilson’s Warblers, we found contrasting results in which body condition was lower but apparent non-breeding survival higher in complex, forested landscapes compared to shade coffee–dominated landscapes. For Wood Thrushes, we did not find evidence for the landscape or site context’s influence on body condition, and we were not able to evaluate the apparent non-breeding survival of Wood Thrush. Elevation did not have a detectable influence on any of the studied metrics, and age and sex were not influenced by either farm management system or the landscape context. It should be noted, however, that most of the individuals at all sites were immature males. We consider that a higher male capture for both species was not influenced by the use of playback in our methods because, during season one, when passive mist netting was used as well (Appendix 1), males still represented the majority of individuals captured. Research conducted in Belize on one of our study species, the Wood Thrush, also found no significant difference between sexes captured with playback and passive netting treatment (Chin et al. 2014).

The lack of support for any effect of site or landscape characteristics on Wood Thrushes could be due to several reasons. We could not evaluate apparent non-breeding survival because our methodology was based on resighting color marked individuals and Wood Thrushes were rarely resighted. Some failure to resight Wood Thrushes could be due to low territoriality, large home ranges, or wandering space use strategy. The high variation in body condition of Wood Thrushes across our sites may contribute to why we could not find signals for the influence of farm management system or the landscape context. Previous research in our study area found that radio-tagged Wood Thrushes’ use of heavily shaded coffee, secondary vegetation, and coffee-forest edge negatively influenced non-breeding survival rates and positively influenced transience (Bailey and King 2019). Although the same study did not model body condition as a response to habitat type, they found no relationship between body condition and survival rates except that individuals with higher body condition had smaller home ranges and were more sedentary (Bailey and King 2019). The lack of movement data for individual Wood Thrushes during our research did not allow for comparisons between the two migratory species. As such, we continue our discussion focusing on Wilson’s Warblers.

Our findings on body condition of Wilson’s Warblers contrast with other non-breeding studies that suggest that forests may positively influence body condition. A study in Mexico found that Wilson’s Warblers’ body condition was less variable in conserved cloud forests across years compared to that in moderately or highly disturbed forests (Ruiz-Sánchez et al. 2017). Another study reported that across the non-breeding range and during five non-breeding seasons, Wilson’s Warblers’ body condition decreased as canopy structure and primary productivity decreased from the early to late non-breeding period (Saracco et al. 2008). Declines in canopy structure and primary productivity reflect the shift in season in which the final months of the non-breeding phase (February and March) overlap with the onset of the dry season, which may result in a reduction in resources for the species (Saracco et al. 2008). Thus, for Wilson’s Warblers forests can provide consistent body condition across years but seasonality may reduce the habitat quality of these forests as measured by body condition (Saracco et al. 2008, Ruiz-Sánchez et al. 2017). The caveat to the comparison between the findings of Saracco et al. (2008) and Ruiz-Sánchez et al. (2017) is that they did not compare body condition within forests to body condition in other habitat types. But another study showed that Canada Warblers (Cardellina canadensis) non-breeding in the Colombian Andes were found to have comparable body condition between shade coffee farms and forest, except for one year in which forest sustained individuals with higher body condition (González et al. 2020). Our findings, in contrast, suggest that body condition is higher in shade coffee farms and in landscapes where there is a higher dominance of shade coffee.

We also found that Wilson’s Warblers’ body condition increased with ordinal date with a clear effect in the models that only incorporated ordinal date (Table 1) and a marginally clear effect in site and landscape context models (Table 2). Our finding for ordinal date is consistent with other studies in coffee working landscapes for some songbird species (Colorado and Rodewald 2017, González et al. 2020). However, the connection between body condition and ordinal date needs further exploring in our study area. Across our study sites precipitation decreases over the non-breeding period. For the 2018–2019 field season the mean precipitation in November was 59.51 (7.1 mm SD) and in March was 15.69 (1.24 mm SD); in 2019–2020, mean precipitation in November was 73.52 (7.39 mm SD) and in March was 40.05 (4.18 mm SD; Funk et al. 2014). In other non-breeding studies, insects and arthropod food resources decreased with drying habitat conditions or increased with rainfall (Strong and Sherry 2000, Studds and Marra 2007). In those studies, body condition followed the pattern of the food resource. We are unaware of the effect of precipitation on food resources in Yoro, but if precipitation strongly correlates to resources as in the other non-breeding studies, then we are observing an opposite pattern within shade coffee where, when precipitation decreases, body condition increases. Abundance or density estimates between the two farm management systems could have contributed to our understanding of the mechanisms that influence variation in body condition. For example, in a scenario where an ideal-despotic distribution takes place, a habitat with a higher number of individuals results in some individuals experiencing lower suitability than others, whereas in a habitat with lower abundance, suitability is more even among individuals (Rodenhouse et al. 1997). We could not estimate abundance or density adequately with our data because of our capacity and focus on collecting information for body condition and apparent survival, but we caught more Wilson’s Warblers in IOC than in shade coffee sites, and the shade coffee sites had on average a smaller property size (mean = 2.99 hectares) than the IOC farms (mean = 21.33 hectares); shade coffee farms had lower amounts of forest in the surrounding landscape, as well. Future research could evaluate if individuals in the Yoro coffee-producing region follow an ideal-despotic distribution, as this mechanism could suggest that birds in shade coffee are experiencing an increase in body condition because of less competition.

Wilson’s Warblers’ apparent non-breeding survival was higher and resighting probability was lower within complex, forested landscapes. Resighting probability was likely negatively influenced by the forest structural complexity and taller canopy, as the birds were frequently observed using the upper canopy. Apart from our study, we only found one other study that evaluated drivers of apparent non-breeding survival for Wilson’s Warblers (Saracco et al. 2008). The researchers found that across the non-breeding range and a period of five non-breeding seasons, Wilson’s Warblers’ apparent survival was higher where there was higher primary productivity, which supports our findings that forests positively influence apparent survival. For other songbirds non-breeding in coffee producing landscapes, such as American Redstarts (Setophaga ruticilla) in Jamaica, apparent survival within the non-breeding period was comparable between shade coffee farms and natural habitats, such as black mangrove forest (Johnson et al. 2006).

There are several possible interpretations for the opposing response of body condition and apparent non-breeding survival to the landscape context. One interpretation is that shade coffee farms and forests are complementary habitats in a working landscape where shade coffee plantations provide food while forests provide roosting habitat and protection from predation (Jirinec et al. 2011, McElaney 2019, Narango et al. 2019). This is supported by the site-level findings that body condition was higher in shade coffee farms compared to IOC. On the other hand, this pattern could indicate an ecological trap (Schlaepfer et al. 2002, Robertson and Hutto 2006) if birds are attracted to food-rich, shade coffee landscapes where apparent survival is lower because of increased predation risk. Many of the trees used in the Yoro shade coffee farms belong to the Fabaceae family (e.g., Inga sp.) that are known to be preferred foraging sites for migratory insectivore songbirds on coffee farms (Bakermans et al. 2012, Narango et al. 2019). At the same time, Yoro shade coffee farms possess structural vegetation qualities that may increase predation risk by promoting use of these areas by diurnal raptors, such as the Ferruginous Pygmy Owl (Glaucidium brasilianum; Bailey and King 2019; personal observation). These notions of a trade-off between metrics of performance like body condition and predation avoidance are discussed in a study on Wood Thrush in Belize where the individuals with higher body condition were in food-poor habitat (McKinnon et al. 2015). In this scenario, the trade-off is that high-performing individuals settle in areas poorer in food resources if it means avoiding predation (Brown and Kotler 2004). Another interpretation arises from the observation that lower apparent survival could instead indicate higher emigration rather than lower true survival because emigration and mortality cannot be distinguished with the framework of non-spatial CJS models (Schaub and Royle 2014). Under this interpretation, shade coffee habitat has resources that sustain and increase body condition for Wilson’s Warblers but apparent survival is lower in landscapes dominated by shade coffee because of high emigration. In this scenario, forested landscapes have lower emigration because they possess constant food resources, allowing individuals to remain there. The link between non-breeding site persistence and high food availability has been shown in other work on Northern Waterthrush in Puerto Rico (Smith et al. 2011). The consistency of resource availability of forest compared to shade coffee could also explain why individuals do not need to accrue more weight, as only individuals in unfavorable food conditions may need to be heavier as a hedge against food scarcity (Lima 1986). If emigration from shade coffee is higher, it could be local, i.e., individuals may be unavailable for resighting because of movement between daytime activity and roosting sites (McElaney 2019). It could also be regional, if individuals in shade coffee landscapes are more likely to adopt a transient or wandering space-use strategy (Brown and Sherry 2008, Bailey and King 2019), or it could involve long-distance intratropical movements (Knight et al. 2019). However, we did not find an effect of week number in our apparent survival models, which suggests that resighting probability remained relatively constant in relation to the progression of the non-breeding season.

Although we found that non-breeding survival was lower in shade coffee–dominated landscapes, other studies have found that annual survival estimates can indicate that coffee and forest are comparable for species performance (González et al. 2020, González et al. 2021). The role of edge density as part of the composite landscape metrics is not clear to us. It may be the result of an inherent quality of the forest remnants of Yoro or it could indicate an unexplored interaction between landscape configuration and composition similar to the findings for arthropod abundance from a study in working landscapes in Europe (Martin et al. 2019). We were interested in evaluating the influence of edge on performance, as previous research in our study area found that the Wood Thrushes using coffee-forest edge had lower survival rates (Bailey and King 2019). However, because forest-dominated landscapes tended to also have high edge density (Fig. 2), both were included in the same principal component predictor variable and we could not evaluate the effects of edge quality separately.

Our study encompasses distinct private properties situated in a montane region of Honduras, necessitating significant logistical and resource efforts to achieve our research objectives. We encountered challenges in obtaining balanced sample sizes across different coffee management systems. Although our sampling sites were fewer in shade coffee compared to IOC sites, the posterior distributions for both models show the evidence supporting the body condition results in our Yoro dataset (see Fig. 3a-b). Furthermore, our modeling framework, which incorporates a landscape context model, demonstrates that variation in areas surrounding farms contributed to variations in body condition across sites. This framework complements our findings, as it considers predictors that are numerically continuous, thereby providing each site with its own distinctive characteristics beyond the categorical three farm management systems we examined.

Despite our study’s aforementioned limitations, we have contributed to a growing body of research on the role of working landscapes on the ecology of migratory songbirds during the non-breeding period. The quality of agricultural habitats on migratory songbird performance has been mostly studied through the lens of shade coffee (as reviewed by Albert et al. 2020). Determining how shade coffee influences performance is not straightforward. In fact, findings are mixed with body condition and apparent survival being comparable between shade coffee and forest, or increasing, or decreasing with shade coffee farm cover (Bakermans et al. 2009, Chandler and King 2011, Colorado and Rodewald 2017, Bailey and King 2019, González et al. 2020, 2021). We know enough from these previous studies to recognize that shade coffee is a viable habitat but that forest still holds intrinsic value (González et al. 2021). Nevertheless, we still need to know how these habitat types support more species, in different regions and in the long-term to account for inherent species ecology, geographic variation, and human practices that shape landscapes, and annual variability, respectively (Faaborg et al. 2010). Additionally, studies in working landscapes are highly relevant for conservation.

Conservation of habitat in working landscapes may follow one of two strategies (Green et al. 2005): one that integrates production systems with native trees (land-sharing, e.g., shade coffee) or another that conserves forest patches within the landscape (land-sparing, e.g., IOC). Land-sparing and land-sharing are both viable strategies for the livelihoods of people as well as sustaining biodiversity, and avian research has been conducted in both (Kremen and Merenlender 2018, Valente et al. 2022). In our study system, shade coffee farms may not be of optimal quality for apparent non-breeding survival, as we observed that most farms were shaded monocultures, sensu Moguel and Toledo (1999). However, there are management practices for shade coffee farms proposed to improve this production system for birds and biodiversity (as reviewed by González-Prieto 2018). In our study area, there are 19 farms implementing the small scale land-sparing strategy of IOC, encompassing ca. 70 hectares of coffee and 150 hectares of forest or early successional habitat, spared for conservation within these IOC farms. Management practices for IOC farms that promote apparent survival for migratory songbirds and other biodiversity have not yet been developed but some best practices have been recommended to support forest-dependent species richness (Ritterson et al. 2021b). For the conservation of migratory songbird species richness and diversity, researchers argue that balancing both land-sparing and land-sharing is advantageous (Valente et al. 2022). We further add that these two frameworks and the landscape context can help us understand what is needed in landscape level management to sustain avian performance metrics.

We have evaluated the landscape’s influence on population and individual performance metrics. Previous research recommended the use of more than one performance metric to evaluate habitat quality with body condition, survival, and density suggested as metrics that may be informative (Johnson et al. 2006, González et al. 2021). We echo these recommendations, especially because we discovered how apparent non-breeding survival and body condition had different responses. We also recommend that studies consider the landscape context and first characterize the landscape of interest to help derive informative landscape metrics as predictors. In the Yoro working landscape, conserving forest for migratory bird performance should be a priority as these positively influence apparent non-breeding survival. Additionally, shade farms can provide valuable habitat for body condition, showing management potential to conserve migratory songbirds.

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