# Diurnal Forest Thrush abundance positively covaries with both prey availability and small Indian mongoose abundance

Aurélie Jean-Pierre, Frank Cézilly, Lens Jerry Saint-Louis, Gladys Loranger-Merciris

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Aurélie Jean-Pierre, Frank Cézilly, Lens Jerry Saint-Louis, Gladys Loranger-Merciris. Diurnal Forest Thrush abundance positively covaries with both prey availability and small Indian mongoose abundance. Avian Conservation and Ecology, 2025, 20 (2), pp.art4. 10.5751/ACE-02914-200204 . hal-05219920

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Jean-Pierre, A., F. Cézilly, L. J. Saint-Louis, and G. Loranger-Merciris. 2025. Diurnal Forest Thrush abundance positively covaries with both prey availability and small Indian mongoose abundance. Avian Conservation and Ecology 20(2):4. https://doi.org/10.5751/ACE-02914-200204
Copyright © 2025 by the author(s). Published here under license by the Resilience Alliance. Open Access. CC-BY 4.0

Research Paper

# Diurnal Forest Thrush abundance positively covaries with both prey availability and small Indian mongoose abundance

Aurélie Jean-Pierre¹, Frank Cézilly², Lens Jerry Saint-Louis² and Gladys Loranger-Merciris¹

¹UR ECOTERCA - ÉCOlogie TERrestre CAribéenne, Université des Antilles., ²Caribaea Initiative.

ABSTRACT. Ground-dwelling omnivorous bird species living on tropical islands are particularly vulnerable to deforestation, introduced mammal predators, and declining insect populations. However, little is known about the relative influences of predation risk and invertebrate prey availability on the spatial distribution and abundance of such species in forested habitats. We investigated spatial variation in the diurnal abundance of the near-threatened Forest Thrush (FT), *Turdus lherminieri*, a Caribbean-endemic and secretive ground-dwelling species, in March 2020, corresponding to the dry season, in Guadeloupe, French West Indies. We used camera traps to assess spatial variation in the abundance of FT and that of potential diurnal mammalian predators among three forest types: tropical rainforest (TRF), tropical swamp forest (TSF), and tropical dry forest (TDF). We characterized sampling sites according to temperature, canopy openness, leaf-litter biomass, and estimated local invertebrate abundance and biomass. FT were relatively common, being detected at 21 (35%) of 60 camera-trap locations during the study. FT abundance was significantly higher in TRF compared to the other two habitats, and covaried positively with both invertebrate biomass and the abundance of the small Indian mongoose, *Urva auropunctata*. We discuss our results in relation to previous studies on habitat selection by tropical insectivore, ground-dwelling bird species, and address their overall relevance for FT conservation in the insular Caribbean.

RÉSUMÉ. Les oiseaux omnivores terrestres vivant sur les îles tropicales sont particulièrement vulnérables à la déforestation, à l'introduction de mammifères prédateurs et au déclin des populations d'insectes. Cependant, les influences respectives du risque de prédation et de la disponibilité des proies invertébrés sur la répartition spatiale et l'abondance de ces espèces, dans les habitats forestiers, restent encore peu connues. En mars 2020, pendant la saison sèche, nous avons mené une étude en Guadeloupe (Antilles françaises) sur la Grive à pieds jaunes (*Turdus lherminieri*), une espèce aviaire terrestre, discrète, endémique de la Caraïbe et classée quasi menacée (NT) à l'échelle mondiale. À l'aide de pièges photographiques, nous avons évalué la variation spatiale de son abondance diurne ainsi que celle de ses mammifères prédateurs potentiels, dans trois types de forêts : tropicale ombrophile, marécageuse et sèche. Les sites d'échantillonnage ont été caractérisés en fonction de plusieurs variables environnementales : la température, l'ouverture de la canopée, la biomasse de litière foliaire, ainsi que l'abondance et la biomasse locale estimées des invertébrés. La Grive à pieds jaunes, relativement commune, a été détectée sur 21 des 60 emplacements de pièges photographiques (soit 35 %) au cours de l'étude. Son abondance était significativement plus élevée en forêt tropicale ombrophile que dans les deux autres habitats, et elle était positivement associée à la biomasse d'invertébrés ainsi qu'à l'abondance de la Petite mangouste indienne (*Urva auropunctata*). Nous discutons ces résultats au regard des études précédentes sur la sélection de l'habitat chez les oiseaux tropicaux terrestres, en particulier les espèces à régime majoritairement insectivore, et examinons leur portée pour la conservation de la Grive à pieds jaunes dans la Caraïbe insulaire.

Key Words: avian conservation; camera-trap; invertebrate biomass; tropical forests; *Turdus lherminieri*; *Urva auropunctata*

## INTRODUCTION

Understanding the causes of avian population decline in tropical forests is of crucial importance for conservation (Robinson and Sherry 2012, Tobias et al. 2013). Indeed, a large part of avian diversity, including the most threatened bird species, occurs in this highly vulnerable ecosystem (Sodhi et al. 2011). The decline of tropical forest bird species has been associated with various drivers, such as habitat loss and fragmentation (Feeley and Terborgh 2008, Korfanta et al. 2012), invasive species (Harper and Bunbury 2015, Doherty et al. 2016, Thibault et al. 2018), hunting pressure (Sreekar et al. 2015, Benítez-López et al. 2017), and climate change (Şekercioğlu et al. 2012). However, the relative importance of such drivers and of their interactions may differ between mainland and island species (Doherty et al. 2016), and in relation to diet composition (Curtis et al. 2021).

One avian group of particular concern are tropical, omnivorous ground-dwelling species living on islands, mainly because of their restricted range, habitat loss, high vulnerability to introduced mammal predators, and declining insect populations (Şekercioğlu et al. 2002, Lamarre et al. 2020, Sherry 2021). Many tropical ground-dwelling bird species tend to have omnivorous and generalist food habits, with seasonal variation in their use of fruits, seeds, invertebrates (Herrera et al. 2006, Riehl and Adelson 2008) and, occasionally for some species, small vertebrates (Sandoval et al. 2008). However, avian omnivory is associated with higher extinction rates compared to other specialist diets, possibly constituting a macroevolutionary sink (Burin et al. 2016). In addition, unlike tropical ground-dwelling species living on the mainland (González-Castro et al. 2012), insular ones have limited possibilities to expand their home range in response to reduced
food availability, particularly during the dry season (Williams and Middleton 2008). Ground-dwelling bird species living on tropical islands are also particularly vulnerable to predation on nests, juveniles, and adults by various introduced exotic mammals, such as rats, *Rattus* spp. (Duron et al. 2017b, 2017a), domestic cats, *Felis catus* (Hervías et al. 2014, Medina et al. 2014, Sreekar et al. 2015, Loss et al. 2022) and the small Indian mongoose, *Urva europunctata* (Lewis et al. 2011, Morley and Winder 2013, Berentsen et al. 2020). However, although trade-offs between predation risk and access to food resources can shape habitat use by landbirds (McCabe and Olsen 2015, Johnston-González and Abril 2019), little is known about their joint influence on the spatial distribution and abundance of ground-dwelling tropical bird species.

We investigated the extent to which spatial variation in the abundance of the Caribbean-endemic Forest Thrush (FT), *Turdus lherminieri* (Collar 2020) in Guadeloupe, French West Indies, can be explained by habitat type and variation in both food availability and risk of encounter with mammalian predators, with a focus on the small Indian mongoose. This species was introduced in Guadeloupe, and elsewhere in the West Indies, in the late 19th century mainly to control rodent populations in sugar cane plantations (Lorvelec et al. 2021). However, this introduction did not have the desired effect, as mongooses are diurnal and rodents nocturnal. Instead, the small Indian mongoose has become a ferocious predator of many small terrestrial vertebrates in the West Indies (Nellis and Everard 1983, Hays and Conant 2007).

Forest Thrush is listed as Near Threatened (BirdLife International 2019) as its distribution is restricted to the forested habitats of only four islands in the Lesser Antilles, where it is threatened by habitat destruction and fragmentation, exotic invasive species, and hunting pressure (Arnoux et al. 2014, Collar 2020). The species prefers mature mesic forest with a dense canopy and is thought to feed mainly on the ground, on invertebrates and fallen berries collected in the leaf-litter (Raffaele et al. 1998, Parashuram et al. 2015). The breeding season extends from February to August (Benito-Espinal and Hautcastel 2003, Raffaele et al. 2003), although detailed local studies of the Forest Thrush's breeding phenology are lacking.

A recent study (Jean-Pierre et al. 2023) found that FT was relatively abundant in Guadeloupe with, however, some spatial variation according to forest type, canopy openness, and temperature. We went further by assessing the relative influence of forest type, invertebrate prey availability, and abundance of potential diurnal mammalian predators on spatial variation in FT abundance. We relied on camera traps to assess the abundance of both FT and mammal predators at different stations located in three different forested habitats in Guadeloupe, French West Indies. Because known variation in biotic conditions and vegetation structure within forest types in Guadeloupe (Imbert et al. 1996, 2000, Loranger et al. 2002) may affect habitat selection by FT (Parashuram et al. 2015), we characterized each camera-trap site based on temperature, canopy openness, leaf litter biomass, and mongoose abundance. In addition, we assessed prey availability (both biomass and abundance) by sampling the leaf litter invertebrate community at each sampling site. We conducted our study during the dry season, at which time reduced resource

availability may have a larger influence on foraging decisions and spatial distribution of ground-dwelling birds (Tanaka and Tanaka 1982, Williams and Middleton 2008, Gumede et al. 2022).

# METHODS

## Study area

Our study was conducted in Guadeloupe, an archipelago composed of several islands in the central Lesser Antilles arc. We focused on Basse-Terre and Grande-Terre, the two main islands of the Guadeloupe archipelago, located between the Caribbean Sea and the Atlantic Ocean (16°15′N, 61°35′W). These islands are separated by a narrow strait (Rivière Salée) connected by land bridges (Fig. 1). Although the western island of Basse-Terre is mainly mountainous, with a maximum height of 1467 m (Gadalia et al. 1988), the eastern island of Grande-Terre consists of limestone formation, with a maximum height of 177m (Lasserre 1961). Variation in elevation, rainfall, and temperature result in distinct vegetal formations linked to the bioclimatic sectors (Murphy and Lugo 1986, Myers et al. 2000), mainly including tropical rainforest (TRF), tropical dry forest (TDF), and tropical swamp forest (TSF; Rousteau 1996). TRF is characterized by a rainy and cold climate due to the higher elevation of the southern half of Basse-Terre (Grubb 1971, Van Laere et al. 2016). More precisely, TRF experiences high annual rainfall coupled with low temperatures (NASA Earth Observatory 2023), whereas TDF is subject to a 5-month period of pronounced drought with high temperatures (Holdridge 1967, Bullock et al. 1995). TSF is intermediate in rainfall regime and temperature, and is characterized by the accumulation of partially decayed organic matter at low altitudes (Posa et al. 2011). Canopy openness tends to be higher in TDF than in TRF or TSF on the island of Guadeloupe. Indeed, TDF mainly consists of small trees with poorly developed foliage and little overlap, resulting in open canopy. In contrast, in TRF and, to a lesser extent, in TSF, trees generally have larger leaves and reach higher heights because of competition for light (Carvalho et al. 2021).

## Data collection

Based on prior information (Jean-Pierre et al. 2023), we collected data on FT abundance and that of potential predators (diurnal exotic mammals) at 12 different stations (i.e., 6 TRF stations, 3 TDF stations, and 3 TSF stations; Fig. 1). Stations were set along an altitudinal gradient, in order to cover the diversity of forest habitats in relation to local topography (Smith 2004, Talvitie et al. 2006, Louppe et al. 2021, Jean-Pierre et al. 2022). Five trap locations were established per station, each equipped with a single passive infrared camera (Moultrie© M-40i, 125° field of view). Camera-trap locations within a station were spaced 200 meters apart along a straight line to ensure spatial independence of detections. Each camera-trap location was treated as an independent sampling unit, resulting in a total of 60 locations across all stations. We attached each single camera to a robust tree about 20–30 cm above the ground, to maximize the probability of detection of the relatively small-sized FT (O’Connell et al. 2011). We selected trees with little surrounding vegetation, to allow for an optimal range of camera sensor in the closed habitat. We applied a 30-s delay between pictures to avoid multiple photographs of the same individual over short periods
**Fig. 1.** Locations of the 12 surveyed stations in Guadeloupe (French West Indies) investigated during the dry season (March 2020). Sampling included three tropical dry forest stations (green squares), three tropical swamp forest stations (yellow circles), and six tropical rainforest stations (blue crosses). The Soufrière volcano (triangle) indicates the highest elevation point in Guadeloupe (1467 m). The distance between stations ranged from 3.6 km to 23.7 km.

<figure><img src="image_2.png" /></figure>

of time (Smith et al. 2017). We conducted our study over three weeks, from 2 March to 22 March 2020. Because we only had 20 camera traps, the different stations were sampled on a random rotation basis. At each station, each camera trap remained active for 24 h to document the presence of FT and mammalian species for seven consecutive days, resulting in 420 trapping days. Images collected from camera traps were manually reviewed by a single trained observer (AJP) to ensure consistency. Each independent detection event was defined as a sequence of photographs separated by a minimum of 30 min between consecutive records of the same species at the same camera-trap location (O'Connell et al. 2011). Forest Thrush and mongoose abundance at each camera-trap location was estimated as the total number of independent detection events during the sampling period.

At each camera-trap location, we assessed surrounding biotic and abiotic factors as putative drivers of FT abundance. Following Cook et al. (2020) and Jean-Pierre et al. (2023), we used an ordinal categorical scale with three categories, to characterize canopy openness (*CO*) as closed (0–1/3), partially open (1/3–2/3), or open (2/3–1). We obtained ambient temperature (*TEMP*) data from the metadata associated with the camera-trap recordings. For each camera-trap location, the mean temperature was calculated from all detection events over the seven-day sampling period. Because the leaf-litter is considered as a prey reservoir (Sánchez et al. 2014, Smith et al. 2017, Mansor et al. 2018, 2019, Martay and Pearce-Higgins 2020, Gumede et al. 2022), we estimated leaf-litter biomass (*LLB*) at each camera-trap location. To that end, we used 1-m² quadrat frames to sample the ground leaf-litter on

the four cardinal points around each camera trap, at a distance of 5 m. After collection of invertebrates (see below), we dried the collected leaf litter in an oven for one week at 60 °C, and then weighed it with a balance (with a 0.1 g accuracy, BH 3000 A). We estimated prey availability from both the abundance and total biomass of invertebrates. Although exhaustive sample sorting is a reliable method for fully describing the structure and composition of a macroinvertebrate community (Courtemanch 1996, Cao et al. 1998), it requires considerable time and effort (Ciborowski 1991, Vlek et al. 2006). Among the less time-consuming techniques developed, we chose the sieving method to study the ground leaf-litter arthropod communities (Belshaw and Bolton 1993, Dietl et al. 2009, Vasconcelos et al. 2009, Cole et al. 2016, Araújo and Souza 2022, Aponte Rolón and Perfecto 2023). Because we were only interested in macroinvertebrates (> 2 mm), we manually collected all arthropods from each sieve during a standardized time of 30 minutes, using fine tweezers (Kattan et al. 2006, Niedbala and Ermilov 2022). More precisely, after pooling the four litter samples from each camera-trap location, we used a succession of sieves stacked in decreasing order (4 mm, 3 mm, and 2 mm mesh). We considered invertebrate abundance (*IA*) per camera-trap location as the number of individuals recorded per camera-trap location. We estimated invertebrate biomass (*IB*) per camera-trap location by weighing the individuals collected using a precision balance (with a 0.001 g accuracy, PIONEER™ PRÉCISION PX323M).

## Data analysis

### Comparison of biotic and abiotic factors between forest types

We first investigated to what extent our sampling stations located in the three recognized forest types differed according to biotic and abiotic factors. A Shapiro-Wilk test (Mohd Razali and Bee Wah 2011) indicated that only *TEMP* followed a normal distribution ($W_{TEMP} = 0.98, p = 0.52$). Therefore, we relied on generalized linear model (Nelder and Wedderburn 1972), to assess variation in *TEMP* between forest types. GLM were fitted with a Gaussian distribution using the R package *Stats* v3.6.2. Because *IB*, *IA*, and *LLB* did not follow a normal distribution ($W_{IB} = 0.69, p < 0.001; W_{IA} = 0.85, p < 0.001; W_{LLB} = 0.92, p < 0.001$), we transformed these variables into ordinal variables based on quantile distributions and relied on ordinal logistic regression to assess variation in *CO*, *IB*, *IA*, and *LLB* between forest types, using the R package Ordinal v.2022.11-16 (sensu Gorczynski and Beaudrot 2021). For all models, we included station as a random effect nested in forest type, because each camera-trap location belonged to one of the 12 different stations, themselves distributed in three different forest types. We used the R software 3.6.2 (R Core Team 2019) to perform all analyses and figures. Results were considered significant at the 5% level.

### Modeling FT abundance

We relied on random effect models, i.e., generalized linear mixed models (Bolker et al. 2009), to assess the influence of forest type (included as the categorical variable FOREST with three levels: TRF, TDF, TSF), prey availability (i.e., *IB* and *IA*) and potential predator (i.e., mongoose abundance *MONA* and mongoose presence *MONP*, see results) on FT abundance. Highly correlated variables (Spearman rank-correlation coefficient, $r_s \ge 0.7$) were not included together in the same model (Dormann et al. 2013).
For all models, we included nested random effect parameters (i.e., stations nested within forest type). The dataset included 60 camera-trap locations, of which 39 failed to detect any Forest Thrush during the sampling period, resulting in a substantial proportion of zero counts. Given this data structure, characterized by excess zeros and overdispersion, we considered Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial distributions to model FT abundance (Lapin et al. 2013). The zero-inflated negative binomial distribution (log link) provided the best fit to the data (White and Bennetts 1996). Models were built using the R package *glmmTMB* 1.0.0 (Brooks et al. 2017). All possible combinations of covariates were investigated using the R package *MuMln* 1.43.6, resulting in 18 models without interactions (Barton 2019). We ranked models using the Akaike information criterion corrected for small sample size, AICc, using the R package *AICcmodavg* 2.3-1 (Mazerolle 2017) and considered models with ΔAICc < 2 as equivalent for explaining variation in FT local abundance (Burnham et al. 2011). We checked for goodness-of-fit for all valid models using the R package *DHARMa* 0.2.7 (Hartig 2020). We relied on the R package *glmmTMB* 1.0.0 (Brooks et al. 2017) to obtain coefficient (βi) of model parameters and their associated *p* values.

## RESULTS

### Comparison of biotic and abiotic factors between forest types

Temperature was significantly lower at camera-trap locations dominated by TRF than at those dominated by TDF or TSF (β = -3.23, *p* < 0.001; Appendix 1). On the other hand, canopy openness was higher at camera trap locations dominated by TDF than those dominated by TSF or TRF (β = 4.00, *p* < 0.01; Table S1). Conversely, leaf-litter biomass was lower at camera-trap locations dominated by TDF than those dominated by TSF or TRF (β = -7.81, *p* < 0.01; Appendix 1).

### Factors affecting FT abundance

We obtained 158 captures of FTs at 21 different camera-trap locations (35% of all camera-trap locations), 103 captures of mongooses at 28 different camera-trap locations (46.7%), compared to only 16 captures of domestic cats at 4 different camera-trap locations (6.7%). Given their rare occurrence, the abundance of domestic cats was not included in the analyses. In terms of habitat type, FT were detected at 16 camera-trap locations in TRF (141 captures), 2 camera-trap locations in TDF (13 captures), and 4 camera-trap locations in TSF (4 captures). Detection rates of FT varied significantly among forest types (Fisher's exact test, *p* = 0.03), with FT being more frequently detected in tropical rainforest than in tropical dry or swamp forests.

When comparing models based on abundance at all camera-trap locations, the best model (Table 1, Appendix 2) included *IB*, *MONA*, and *FOREST* as meaningful parameters. More precisely, *IB* and *MONA* were positively associated with FT abundance (βᵢₗ = 0.98, *p* < 0.001, Fig. 2a; βᵣₘₐₙ = 0.91, *p* < 0.001, Fig. 2b). Moreover, FT abundance was higher at camera-trap locations dominated by TRF than at those dominated by TSF or TDF (β = 3.71, *p* < 0.001; Fig. 2c). Analysis of residuals detected no deviation from the expected distributions (see Supplementary Information Appendix 3).

<table><caption>**Table 1.** Model selection to assess the influence of invertebrate biomass (IB), invertebrate abundance (IA), mongoose (*Urva europunctata*) abundance (MONA), mongoose presence (MONP), and forest type (FOREST) on Forest Thrush (*Turdus lherminieri*) abundance in Guadeloupe (March 2020). Station identity was included as a random effect nested within forest type. Only models with ΔAICc < 2 were considered to explain variation in the local abundance of Forest Thrush. Variables are as follows: (a) Df = Degrees of freedom; (b) AICc = corrected Akaike Information Criterion; (c) ΔAICc = Difference in AICc between the best model and the compared model; (d) Wi = Akaike weight; (e) LogLik = Log-likelihood value.</caption><thead><tr><th>Model</th><th>Df (a)</th><th>AICc (b)</th><th>ΔAICc (c)</th><th>Ŵ (d)</th><th>LogLik (e)</th></tr></thead><tbody><tr><td>IB + MONA + FOREST</td><td>7</td><td>162.57</td><td>0.00</td><td>0.98</td><td>-73.21</td></tr><tr><td>MONA + IA + FOREST</td><td>7</td><td>171.22</td><td>8.65</td><td>0.01</td><td>-77.54</td></tr><tr><td>IB + MONP + FOREST</td><td>7</td><td>174.42</td><td>11.84</td><td>0.00</td><td>-79.13</td></tr><tr><td>IB + FOREST</td><td>6</td><td>175.80</td><td>13.23</td><td>0.00</td><td>-81.11</td></tr><tr><td>MONA + FOREST</td><td>6</td><td>175.84</td><td>13.27</td><td>0.00</td><td>-81.13</td></tr><tr><td>MONP + IA + FOREST</td><td>7</td><td>176.67</td><td>14.10</td><td>0.00</td><td>-80.26</td></tr><tr><td>MONA + IA</td><td>5</td><td>179.50</td><td>16.93</td><td>0.00</td><td>-84.20</td></tr><tr><td>MONP + IA</td><td>5</td><td>181.10</td><td>18.53</td><td>0.00</td><td>-85.00</td></tr><tr><td>IA + FOREST</td><td>6</td><td>181.74</td><td>19.17</td><td>0.00</td><td>-84.08</td></tr><tr><td>IA</td><td>4</td><td>183.11</td><td>20.54</td><td>0.00</td><td>-87.19</td></tr><tr><td>IB + MONA</td><td>5</td><td>183.69</td><td>21.12</td><td>0.00</td><td>-86.29</td></tr><tr><td>MONP + FOREST</td><td>6</td><td>185.23</td><td>22.66</td><td>0.00</td><td>-85.82</td></tr><tr><td>IB</td><td>4</td><td>186.42</td><td>23.85</td><td>0.00</td><td>-88.85</td></tr><tr><td>MONA</td><td>4</td><td>186.50</td><td>23.93</td><td>0.00</td><td>-88.89</td></tr><tr><td>IB + MONP</td><td>5</td><td>186.72</td><td>24.15</td><td>0.00</td><td>-87.81</td></tr><tr><td>MONP</td><td>4</td><td>191.70</td><td>29.13</td><td>0.00</td><td>-91.49</td></tr><tr><td>FOREST</td><td>5</td><td>195.49</td><td>32.92</td><td>0.00</td><td>-92.19</td></tr><tr><td>NULL</td><td>2</td><td>201.60</td><td>39.03</td><td>0.00</td><td>-98.69</td></tr></tbody></table>

## DISCUSSION

Our results suggest that the Forest Thrush may be relatively common in the tropical rainforests of Guadeloupe, as shown by previous studies (Jean-Pierre et al. 2023). More importantly, our results revealed a positive influence of spatial variation in leaf-litter invertebrate biomass on spatial variation in FT abundance, as well as a positive association between FT abundance and that of one of its potential exotic predators, the small Indian mongoose. Such findings have direct relevance for the conservation and management of this near-threatened species.

### Influence of invertebrate biomass and forest type on the abundance of the Forest Thrush

Spatial variation in invertebrate biomass was the most influential factor explaining spatial variation in FT abundance across forest types. Although FT can feed on both invertebrates and fruits, its diet has not been documented though quantitative data. Future research, possibly based on fecal content and stable isotope analysis (Bosenbecker and Bugoni 2020), would therefore be helpful to assess the extent of FT dietary plasticity and seasonal variation, particularly in the global context of invertebrate loss (Şekercioğlu et al. 2002, Lamarre et al. 2020). However, McKinnon et al. (2017) found that the ecologically similar and omnivorous Wood Thrush, *Hylocichla mustelina*, mainly consumed arthropods, particularly during the driest times of the non-breeding season. In addition, our results are consistent with previous studies of both temperate and tropical forest passerine bird species. For instance, Brown et al. (2011) found that both the taxonomic richness and abundance of large arthropods were significantly greater at sites occupied by
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Predicted abundance of the Forest Thrush (*Turdus lherminieri*) in relation to (A) standardized invertebrate biomass (IB), (B) standardized mongoose (*Urva europunctata*) abundance (MONA), and (C) forest type (Tropical Rainforest, Tropical Dry Forest, Tropical Swamp Forest) based on camera-trap data collected during the dry season in Guadeloupe (March 2020). Shaded areas (A, B) and error bars (C) represent 95% confidence intervals.</figcaption></figure>

Swainson's Warblers, *Limnothlypis swainsonii*, than at unoccupied sites in the southeastern U.S. Consistent with this, Mizuta (2014) found that earthworm abundance associated with forest maturity was a critical factor influencing habitat selection by the Amami

Thrush (*Zoothera major*). Similarly, arthropod abundance was the best predictor of the abundance of the White-breasted Wood-Wren, *Henicorhina leucosticta*, in Caribbean lowland forests of Costa Rica (Sánchez et al. 2014).

However, the overall evidence for species of the genus *Turdus* is less conclusive. On the one hand, Martay and Pearce-Higgins (2020) found a positive association between counts of several thrush species and earthworm abundance in the UK. On the other hand, Newmark and Stanley (2016) found that invertebrate abundance did not differ between territories of the Usambara Thrush, *T. roehli*, whereas habitat structure, in terms of tree density and ground cover, did. Similarly, Jirinec et al. (2016) found that habitat structure was more important than prey availability to explain the relative space use of 37 radiotracked male Wood Thrushes on their breeding grounds in coastal Virginia.

Although we did not include measures of habitat structure in our models, forest type significantly influenced FT abundance, with higher abundance in TRF compared to both TDF and TSF, in accordance with previous studies (Arnoux et al. 2013, Parashuram et al. 2015). However, invertebrate abundance as well as invertebrate biomass did not differ between forest types, such that the influence of forest type and prey availability were not confounded in our analyses. At a broad scale, the three forest types differ markedly in terms of vegetation structure in the Caribbean islands (Areces-Mallea et al. 1999, Banda-Rodríguez et al. 2016), and, more specifically, in Guadeloupe (Imbert et al. 1996, 2000, Imbert and Portecop 2008). In the present study, stations differed significantly according to forest type in terms of temperature, canopy openness, and leaf-litter biomass. Temperature was significantly lower in rainforest stations, as a direct consequence of their higher elevation compared to both dry forest and swamp forest stations. On the other hand, canopy was significantly more open in dry forest stations compared to stations located in the two other forest types. Our results thus suggest that FT may prefer specific light and thermal niches, and favor dim and cool microclimates when foraging for food on the ground as reported for other tropical forest insectivorous bird species (Jirinec et al. 2022). In addition to the observed relationships between FT abundance and biotic predictors, several other factors may influence the long-term persistence of the species in Guadeloupe. Given its preference for mature, shady forests, FT may benefit from the protection of large forested areas, particularly within the boundaries of the Parc National de la Guadeloupe, where habitat conservation and hunting regulations are enforced. However, future threats such as climate change may affect the species both directly, through heat stress and reduced fitness (Pollock et al. 2021), and indirectly, by altering canopy structure, soil moisture (Scholl et al. 2021), and invertebrate prey availability (Kaspari et al. 2017). Furthermore, the recent colonization of Guadeloupe by the Spectacled Thrush (*T. nudigenis*), known for its aggressive behavior toward other thrushes, may lead to competitive interactions that are potentially detrimental to forest thrush populations (Levesque et al. 2005).

These potential threats highlight the importance of implementing long-term monitoring programs to document population trends and to better inform conservation strategies for the species, especially given the current lack of comprehensive abundance data in Guadeloupe. However, because studies on turdids and other ground-dwelling forest birds differ in several respects,
including methodology, geographical area, and time of the year, a meta-analysis would be of interest to provide an overall assessment of the relative influence of food availability and habitat structure on spatial variation in occupancy and abundance.

## Spatial covariation in abundance with the small Indian mongoose

We found a positive spatial covariation between the abundance of FT and that of the small Indian mongoose in Guadeloupe. Our results thus confirm previous reports of spatial co-occurrence between the two species in Guadeloupe based on camera-trap data (Louppe et al. 2021). Still, the causes and consequences of this association deserve particular attention.

First, expectations about spatial covariation between prey and predators are not straightforward. On the one hand, positive spatial co-occurrence can be expected if predators concentrate where their prey are mostly abundant in order to increase encounter rate (Kittle et al. 2017). On the other hand, active avoidance of predators by prey (Gaynor et al. 2019) or strong prey depletion by predators (Amar et al. 2008, Holt et al. 2008) may result in negative spatial co-occurrence in abundance (Murphy et al. 2019). Accordingly, the few results documenting the influence of mongoose presence on the presence or abundance of avian species have provided mixed evidence. Morley and Winder (2013) concluded that three ground bird species were negatively influenced by the presence of mongoose, as they were observed only on mongoose-free islands among 16 small islands within Fiji. On the other hand, Louppe et al. (2021) recorded the presence of the small Indian mongoose in Martinique in sites known to be restricted territories of two threatened endemic bird species, the Martinique Oriole, *Icterus bonana*, and the ground-dwelling, White-breasted Thrasher, *Ramphocinclus brachyurus*. Similarly, Mizuta et al. (2017) found that the distribution and recovery of the Amami Thrush in the Ryukyu Archipelago was strongly influenced by the density of the small Indian mongoose, with thrush populations recovering following mongoose eradication efforts. This finding highlights the potential for significant predator-prey interactions even where spatial co-occurrence is observed. Therefore, the observed positive association between FT and mongoose abundance is in itself poorly informative about their possible interaction as prey and predator.

An alternative explanation for the observed correlation between FT and the small Indian mongoose is that their co-occurrence is simply due to similar habitat preferences associated to a third variable. Although mongoose may prey on birds, they are largely omnivorous and the largest part of their diet on Caribbean islands generally consists of invertebrates, reptiles, small mammals, and fruits (Pimentel 1955, Seaman and Randall 1962, Nellis and Everard 1983). According to a more recent study (Lewis et al. 2011), invertebrates and lizards accounted for 93% of identified prey items in the stomach content of 217 mongooses in Jamaica. Mongooses in the forests of Guadeloupe may therefore concentrate in areas that provide favorable conditions for invertebrates and reptiles. For instance, leaf-litter moisture is crucial for microhabitat choice in both invertebrates and lizards, which tend to seek out shaded areas during dry periods in order to minimize water loss and thermal stress (Vonesh 2001, McGee et al. 2020).

Regardless of its ultimate drivers, spatial co-occurrence may expose FT to predation by mongoose. Direct evidence, based on diet or remains analysis, indicates that the small Indian mongoose can prey upon various bird species (Engeman et al. 2006, Lewis et al. 2011, Akrim et al. 2019). The importance of mongoose predation on birds is also supported by indirect evidence based on bird extinctions and population trends (Hays and Conant 2007, Berentsen et al. 2020, Yagihashi et al. 2021), or by morphological changes in birds following mongoose introduction, such as increased body size, longer wings (Black-faced Grassquit, *Melanospiza bicolor*) and longer tarsi (Common Ground Dove, *Columbina passerina*), which may reflect improved flight performance for predator avoidance, as suggested by Heathcote et al. (2021). If such predation pressure is confirmed, it may have detrimental consequences for Forest Thrush populations, especially if reproductive adults or juveniles are preferentially targeted. The introduction of the small Indian mongoose has historically contributed to the decline and extinction of several ground-nesting bird species in Caribbean and Pacific islands, including the Jamaican Petrel (*Pterodroma caribbaea*; Hays and Conant 2007, Berentsen et al. 2020, Yagihashi et al. 2021). Assessing the precise impact of predation by mongooses on FT and other species in various habitats in Guadeloupe, possibly through fecal or stomach content analysis (Mahmood and Adil 2017, Dabholkar and Devkar 2020, Subrata et al. 2021), would allow a precise estimation of its impact on FT and the local avifauna.

## Limitations and perspectives

Although our results provide new and valuable insights about the factors potentially affecting the spatial distribution of FT in Guadeloupe, some limitations must be acknowledged. First, the present study was relatively limited in time and space, such that it is not possible to generalize the results. In particular, because of the predominance of tropical rainforest among the sampled sites, it might be necessary to increase the number of camera-trap stations at dry and swamp forest in order to increase the representativeness of these habitat types. However, our sampling design reflects the natural distribution of forest habitats in Guadeloupe, where tropical rainforest covers more than 40% of the forested landscape. Second, our study took place over three weeks in March, such that the observed pattern of spatial association between FT and mammal predators might not be representative of what happens at other times of the year, particularly during the wet season when food availability is likely to differ. Third, the choice of camera-trap placement may have influenced detection rates. To optimize sensor performance, cameras were installed on trees with relatively sparse surrounding vegetation, potentially favoring more open microhabitats and possibly introducing a bias in Forest Thrush detectability. Although this method minimized technical errors such as false triggers, standardization of vegetation structure around camera-trap locations is recommended in future studies to limit microhabitat-related biases. Finally, although several associations were statistically significant, the wide confidence intervals around parameter estimates indicate a considerable degree of uncertainty, likely because of the relatively short sampling period and limited sample size. Therefore, these results should be interpreted with caution. Long-term studies with extended temporal and spatial replication are needed to confirm these patterns and to improve our understanding of the seasonal and spatial dynamics of Forest Thrush populations.
## CONCLUSION

Our study confirms the efficiency of camera traps to monitor ground-dwelling bird species, such as FT, in tropical forest and document their spatial occurrence with other species, particularly potential predators. A long-term and extended survey of the FT population in Guadeloupe relying on this technique may therefore prove quite useful in documenting spatial variation in presence and abundance, assessing local conservation status, and informing management decisions for this species of high patrimonial and hunting value.

## Author Contributions:

The conceptualization was carried out by A.J.-P., G.L.-M., and F. C.; the methodology was developed by A.J.-P., G.L.-M., L.J.S.-L., and F.C.; the software for the analysis was provided by A.J.-P. and F.C. Data collection was carried out by A.J.-P. and L.J.S.-L. Data curation was managed by A.J.-P. A.J.-P. wrote the original draft, which was subsequently reviewed and edited by A.J.-P., F.C., and G. L.-M. Project administration was carried out by G.L.-M. and F.C.

## Acknowledgments:

We would like to thank M. Dhénin and M. Pedurand for their assistance in data collection. We would also like to express our gratitude to the Editor-in-Chief and the anonymous reviewer, whose valuable feedback has significantly improved the quality of this manuscript.

## Data Availability:

The data are available upon reasonable request.

## LITERATURE CITED

Akrim, F., T. Mahmood, M. S. Nadeem, S. Qasim, S. Andleeb, and H. Fatima. 2019. Distribution, dietary breadth and niche overlap between two sympatric mongoose species inhabiting Pir Lasura National Park, Azad Jammu and Kashmir, Pakistan. Pakistan Journal of Zoology 51:1497-1507. https://doi.org/10.17582/journal.pjz/2019.51.4.1497.1507

Amar, A., S. Thirgood, J. Pearce-Higgins, and S. Redpath. 2008. The impact of raptors on the abundance of upland passerines and waders. Oikos 117:1143-1152. https://doi.org/10.1111/j.0030-1299.2008.16769.x

Aponte Rolón, B., and I. Perfecto. 2023. Between two trees: environmental effects of *I. micheliana* and *A. latifolia* on leaf litter ants in a coffee agroecosystem. Ecosphere 14:e4442. https://doi.org/10.1002/ecs2.4442

Araüjo, J. S., and J. L. P. Souza. 2022. Sampling techniques and environmental variables influence the distribution of pseudoscorpions in urban forest fragments in the central Amazon. Acta Amazonica 52:199-207. https://doi.org/10.1590/1809-4392202200441

Areces-Mallea, A. E., A. S. Weakley, X. Li, R. G. Sayre, J. D. Parrish, C. V. Tipton, and T. Boucher. 1999. A guide to Caribbean

vegetation types: preliminary classification system and descriptions. The Nature Conservancy, Arlington, Virginia, USA.

Arnoux, E., C. Eraud, N. Navarro, C. Tougard, A. Thomas, F. Cavallo, N. Vetter, B. Faivre, and S. Garnier. 2014. Morphology and genetics reveal an intriguing pattern of differentiation at a very small geographic scale in a bird species, the Forest Thrush *Turdus lherminieri*. Heredity 113:514-525. https://doi.org/10.1038/hdy.2014.56

Arnoux, E., C. Eraud, A. Thomas, F. Cavallo, S. Garnier, and B. Faivre. 2013. Phenotypic variation of Forest Thrushes *Turdus lherminieri* in Guadeloupe: evidence for geographic differentiation at fine spatial scale. Journal of Ornithology 154:977-985. https://doi.org/10.1007/s10336-013-0965-0

Banda-Rodríguez, K., J. Weintritt, and R. T. Pennington. 2016. Caribbean dry forest networking: an opportunity for conservation. Caribbean Naturalist 1:63-72.

Bartoń, K. 2019. MuMIn: multi-model inference. R package version 1.47.5. https://cran.r-project.org/web/packages/MuMIn/index.html https://doi.org/10.32614/CRAN/package.MuMIn

Belshaw, R., and B. Bolton. 1993. The effect of forest disturbance on the leaf litter ant fauna in Ghana. Biodiversity and Conservation 2:656-666. https://doi.org/10.1007/BF00051965

Benítez-López, A., R. Alkemade, A. M. Schipper, D. J. Ingram, P. A. Verweij, J. A. J. Eikelboom, and M. A. J. Huijbregts. 2017. The impact of hunting on tropical mammal and bird populations. Science 356:180-183. https://doi.org/10.1126/science.aaj1891

Benito-Espinal, E., and P. Haucastel. 2003. Les oiseaux des Antilles et leurs nids. PLB Editions, Guadeloupe.

Berentsen, A. R., M. J. Rivera-Rodríguez, K. M. McClure, F. B. Torres-Toledo, J. G. Garcia-Cancel, and A. T. Gilbert. 2020. Home range estimates for small Indian mongooses (*Urva europunctata*) in Southwestern Puerto Rico. Caribbean Journal of Science 50:225-235. https://doi.org/10.18475/cjos.v50i2.a4

BirdLife International. 2019. *Turdus lherminieri*. The IUCN Red List of Threatened Species 2019: e.T22708544A154897073. https://doi.org/10.2305/IUCN.UK.2019-3.RLTS.T22708544A154897073. en

Bolker, B. M., M. E. Brooks, C. J. Clark, S. W. Geange, J. R. Poulsen, M. H. H. Stevens, and J.-S. S. White. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution 24:127-135. https://doi.org/10.1016/j.tree.2008.10.008

Bosenbecker, C., and L. Bugoni. 2020. Trophic niche similarities of sympatric *Turdus* thrushes determined by fecal contents, stable isotopes, and bipartite network approaches. Ecology and Evolution 10:9073-9084. https://doi.org/10.1002/ece3.6485

Brooks, M. E., K. Kristensen, K. J. van Benthem, A. Magnusson, C. W. Berg, A. Nielsen, H. J. Skaug, M. Mächler, and B. M. Bolker. 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R Journal 9:378-400. https://doi.org/10.32614/RJ-2017-066
Brown, J. D., T. J. Benson, and J. C. Bednarz. 2011. Arthropod communities associated with habitats occupied by breeding Swainson's Warblers. Condor 113:890-898. https://doi.org/10.1525/cond.2011.100184

Bullock, S. H., H. A. Mooney, and E. Medina. 1995. Seasonally dry tropical forests. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511753398

Burin, G., W. D. Kissling, P. R. Guimarães, C. H. Şekercioğlu, and T. B. Quental. 2016. Omnivory in birds is a macroevolutionary sink. Nature Communications 7:11250. https://doi.org/10.1038/ncomms11250

Burnham, K. P., D. R. Anderson, and K. P. Huyvaert. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral Ecology and Sociobiology 65:23-35. https://doi.org/10.1007/s00265-010-1029-6

Cao, Y., D. D. Williams, and N. E. Williams. 1998. How important are rare species in aquatic community ecology and bioassessment? Limnology and Oceanography 43:1403-1409. https://doi.org/10.4319/lo.1998.43.7.1403

Carvalho, M. R., C. Jaramillo, F. de la Parra, D. Caballero-Rodríguez, F. Herrera, S. Wing, B. L. Turner, C. D'Apolito, M. Romero-Báez, P. Narváez, C. Martínez, M. Gutierrez, C. Labandeira, G. Bayona, M. Rueda, M. Paez-Reyes, D. Cárdenas, Á. Duque, J. L. Crowley, C. Santos, and D. Silvestro. 2021. Extinction at the end-Cretaceous and the origin of modern Neotropical rainforests. Science 372:63-68. https://doi.org/10.1126/science.abf1969

Ciborowski, J. J. H. 1991. Estimating processing time of stream benthic samples. Hydrobiologia 222:101-107. https://doi.org/10.1007/BF00006097

Cole, R. J., K. D. Holl, R. A. Zahawi, P. Wickey, and A. R. Townsend. 2016. Leaf litter arthropod responses to tropical forest restoration. Ecology and Evolution 6:5158-5168. https://doi.org/10.1002/ece3.2220

Collar, N. 2020. Forest Thrush (Turdus lherminieri), version1.0 in J. del Hoyo, A. Elliott, J. Sargatal, D. Christie, and E. de Juana, editors. Birds of the World. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.forthr1.01

Cook, R. N., T. Ramirez-Parada, L. Browne, M. Ellis, and J. Karubian. 2020. Environmental correlates of richness, community composition, and functional traits of terrestrial birds and mammals in a fragmented tropical landscape. Landscape Ecology 35:2825-2841. https://doi.org/10.1007/s10980-020-01123-4

Courtemanch, D. L. 1996. Commentary on the subsampling procedures used for rapid bioassessments. Journal of the North American Benthological Society 15:381-385. https://doi.org/10.2307/1467284

Curtis, J. R., W. D. Robinson, G. Rompré, R. P. Moore, and B. McCune. 2021. Erosion of tropical bird diversity over a century is influenced by abundance, diet and subtle climatic tolerances. Scientific Reports 11:10045. https://doi.org/10.1038/s41598-021-89496-7

Dabholkar, Y., and R. V. Devkar. 2020. Diurnal activity and diet of small Indian mongoose *Urva auropunctata* on the outskirts of Vadodara, Gujarat, India. Small Carnivore Conservation 58:58002.

Dietl, J., W. Engels, and M. Solé. 2009. Diet and feeding behaviour of the leaf-litter frog *Ischnocnema henselii* (Anura: Brachycephalidae) in *Araucaria* rain forests on the Serra Geral of Rio Grande do Sul, Brazil. Journal of Natural History 43:1473-1483. https://doi.org/10.1080/00222930902898709

Doherty, T. S., A. S. Glen, D. G. Nimmo, E. G. Ritchie, and C. R. Dickman. 2016. Invasive predators and global biodiversity loss. Proceedings of the National Academy of Sciences 113:11261-11265. https://doi.org/10.1073/pnas.1602480113

Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carré, J. R. G. Marquéz, B. Gruber, B. Lafourcade, P. J. Leitão, T. Münkemüller, C. McClean, P. E. Osborne, B. Reineking, B. Schröder, A. K. Skidmore, D. Zurell, and S. Lautenbach. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27-46. https://doi.org/10.1111/j.1600-0587.2012.07348.x

Duron, Q., E. Bourguet, H. De Meringo, A. Millon, and E. Vidal. 2017a. Invasive rats strengthen predation pressure on bird eggs in a South Pacific island rainforest. Current Zoology 63:583-590. https://doi.org/10.1093/cz/zox009

Duron, Q., A. B. Shiels, and E. Vidal. 2017b. Control of invasive rats on islands and priorities for future action. Conservation Biology 31:761-771. https://doi.org/10.1111/cobi.12885

Engeman, R., D. Whisson, J. Quinn, F. Cano, P. Quiñones, and T. H. White Jr. 2006. Monitoring invasive mammalian predator populations sharing habitat with the critically endangered Puerto Rican parrot *Amazona vittata*. Oryx 40:95-102. https://doi.org/10.1017/S0030605305001286

Feeley, K. J., and J. W. Terborgh. 2008. Direct versus indirect effects of habitat reduction on the loss of avian species from tropical forest fragments. Animal Conservation 11:353-360. https://doi.org/10.1111/j.1469-1795.2008.00182.x

Gadalia, A., N. Gstalter, and D. Westercamp. 1988. La chaîne volcanique de Bouillante, Basse-Terre de Guadeloupe, (Petites Antilles). Identité pétrographique, volcanologique et géodynamique. Géologie de la France 2:101-130.

Gaynor, K. M., J. S. Brown, A. D. Middleton, M. E. Power, and J. S. Brashares. 2019. Landscapes of fear: spatial patterns of risk perception and response. Trends in Ecology & Evolution 34:355-368. https://doi.org/10.1016/j.tree.2019.01.004

González-Castro, A., A. Traveset, and M. Nogales. 2012. Seed dispersal interactions in the Mediterranean Region: contrasting patterns between islands and mainland. Journal of Biogeography 39:1938-1947. https://doi.org/10.1111/j.1365-2699.2012.02693.x

Gorczynski, D., and L. Beaudrot. 2021. Functional diversity and redundancy of tropical forest mammals over time. Biotropica 53:51-62. https://doi.org/10.1111/btp.12844

Grubb, P. J. 1971. Interpretation of the 'Massenerhebung' effect on tropical mountains. Nature 229:44-45. https://doi.org/10.1038/229044a0
Gumede, S. T., D. A. Ehlers Smith, Y. C. Ehlers Smith, S. P. Ngcobo, M. T. Sosibo, M. S. T. Maseko, and C. T. Downs. 2022. Occupancy of two forest specialist birds in the Southern Mistbelt Forests of KwaZulu-Natal and Eastern Cape, South Africa. Bird Conservation International 32:27-42. https://doi.org/10.1017/S0959270920000544

Harper, G. A., and N. Bunbury. 2015. Invasive rats on tropical islands: their population biology and impacts on native species. Global Ecology and Conservation 3:607-627. https://doi.org/10.1016/j.gecco.2015.02.010

Hartig, F. 2020. DHARMA: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3.3.0. https://doi.org/10.32614/CRAN/package.DHARMA

Hays, W. S. T., and S. Conant. 2007. Biology and impacts of Pacific Island invasive species. 1. A worldwide review of effects of the small Indian mongoose, *Herpestes javanicus* (Carnivora: Herpestidae). Pacific Science 61:3-16. https://doi.org/10.1353/psc.2007.0006

Heathcote, A., C. De Ruyck, P. Des Brisay, P. Grieef, and N. Koper. 2021. Ecological release and insular shifts in avian morphological traits in the Caribbean. Ornithology 138:ukab026. https://doi.org/10.1093/ornithology/ukab026

Herrera M., L. G., K. A. Hobson, J. C. Martínez, and G. Méndez C. 2006. Tracing the origin of dietary protein in tropical dry forest birds. Biotropica 38:735-742. https://doi.org/10.1111/j.1744-7429.2006.00201.x

Hervías, S., S. Oppel, F. M. Medina, T. Pipa, A. Díez, J. A. Ramos, R. Ruiz de Ybáñez, and M. Nogales. 2014. Assessing the impact of introduced cats on island biodiversity by combining dietary and movement analysis. Journal of Zoology 292:39-47. https://doi.org/10.1111/jzo.12082

Holdridge, L. R. 1967. Life zone ecology. Tropical Science Center, San José, Costa Rica.

Holt, A. R., Z. G. Davies, C. Tyler, and S. Staddon. 2008. Meta-analysis of the effects of predation on animal prey abundance: evidence from UK vertebrates. PLoS ONE 3:e2400. https://doi.org/10.1371/journal.pone.0002400

Imbert, D., I. Bonhême, E. Saur, and C. Bouchon. 2000. Floristics and structure of the *Pterocarpus officinalis* swamp forest in Guadeloupe, Lesser Antilles. Journal of Tropical Ecology 16:55-68. https://doi.org/10.1017/S0266467400001267

Imbert, D., P. Labbé, and A. Rousteau. 1996. Hurricane damage and forest structure in Guadeloupe, French West Indies. Journal of Tropical Ecology 12:663-680. https://doi.org/10.1017/S026646740000986X

Imbert, D., and J. Portecop. 2008. Hurricane disturbance and forest resilience: assessing structural vs. functional changes in a Caribbean dry forest. Forest Ecology and Management 255:3494-3501. https://doi.org/10.1016/j.foreco.2008.02.030

Jean-Pierre, A., G. Loranger-Merciris, and F. Cézilly. 2022. Spatial occupancy, local abundance and activity rhythm of three ground dwelling columbid species in the forests of Guadeloupe in relation to environmental factors. Diversity 14:480. https://doi.org/10.3390/d14060480

Jean-Pierre, A., G. Loranger-Merciris, L. J. Saint-Louis, and F. Cézilly. 2023. Factors affecting spatial occupancy and local abundance of the Forest Thrush, *Turdus lherminieri*, in Guadeloupe forests. European Journal of Wildlife Research 69:76. https://doi.org/10.1007/s10344-023-01698-8

Jirinec, V., R. E. Isdell, and M. Leu. 2016. Prey availability and habitat structure explain breeding space use of a migratory songbird. Condor 118:309-328. https://doi.org/10.1650/CONDOR-15-140.1

Jirinec, V., P. F. Rodrigues, B. R. Amaral, and P. C. Stouffer. 2022. Light and thermal niches of ground-foraging Amazonian insectivorous birds. Ecology 103:e3645. https://doi.org/10.1002/ecy.3645

Johnston-González, R., and E. Abril. 2019. Predation risk and resource availability explain roost locations of Whimbrel *Numenius phaeopus* in a tropical mangrove delta. *Ibis* 161:839-853. https://doi.org/10.1111/ibi.12678

Kaspari, M., J. Bujan, M. D. Weiser, D. Ning, S. T. Michaletz, H. Zhili, B. J. Enquist, R. B. Waide, J. Zhou, B. L. Turner, and S. J. Wright. 2017. Biogeochemistry drives diversity in the prokaryotes, fungi, and invertebrates of a Panama forest. Ecology 98:2019-2028. https://doi.org/10.1002/ecy.1895

Kattan, G. H., D. Correa, F. Escobar, and C. Medina. 2006. Leaf-litter arthropods in restored forests in the Colombian Andes: a comparison between secondary forest and tree plantations. Restoration Ecology 14:95-102. https://doi.org/10.1111/j.1526-100X.2006.00109.x

Kittle, A. M., M. Anderson, T. Avgar, J. A. Baker, G. S. Brown, J. Hagens, E. Iwachewski, S. Moffatt, A. Mosser, B. R. Patterson, D. E. B. Reid, A. R. Rodgers, J. Shuter, G. M. Street, I. D. Thompson, L. M. Vander Vennen, and J. M. Fryxell. 2017. Landscape-level wolf space use is correlated with prey abundance, ease of mobility, and the distribution of prey habitat. Ecosphere 8:e01783. https://doi.org/10.1002/ecs2.1783

Korfanta, N. M., W. D. Newmark, and M. J. Kauffman. 2012. Long-term demographic consequences of habitat fragmentation to a tropical understory bird community. Ecology 93:2548-2559. https://doi.org/10.1890/11-1345.1

Lamarre, G. P. A., T. M. Fayle, S. T. Segar, B. C. Laird-Hopkins, A. Nakamura, D. Souto-Vilarós, S. Watanabe, and Y. Basset. 2020. Monitoring tropical insects in the 21st century. Advances in Ecological Research 62:295-330. https://doi.org/10.1016/bs.aecr.2020.01.004

Lapin, C. N., M. A. Etterson, and G. J. Niemi. 2013. Occurrence of the Connecticut Warbler increases with size of patches of coniferous forest. Condor 115:168-177. https://doi.org/10.1525/cond.2013.110202

Lasserre, G. 1961. La Guadeloupe, étude géographique. Union française d'impression, Bordeaux, France.

Levesque, A., F. Duzont, and A. Ramshaï. 2005. Précisions sur 5 espèces d'oiseaux dont la nidification a été découverte en Guadeloupe (Antilles Françaises) depuis 1997. Journal of Caribbean Ornithology 18:45-47.
Lewis, D. S., R. van Veen, and B. S. Wilson. 2011. Conservation implications of small Indian mongoose (*Herpestes auropunctatus*) predation in a hotspot within a hotspot: the Hellshire Hills, Jamaica. Biological Invasions 13:25-33. https://doi.org/10.1007/s10530-010-9781-0

Loranger, G., J.-F. Ponge, D. Imbert, and P. Lavelle. 2002. Leaf decomposition in two semi-evergreen tropical forests: influence of litter quality. Biology and Fertility of Soils 35:247-252. https://doi.org/10.1007/s00374-002-0467-3

Lorvelec O., T. Yvon, and A. Lenoble. 2021. Histoire de la petite mangouste indienne *Urva auropunctata* (Hodgson, 1836) dans les Antilles: une introduction aux conséquences sociétales et écologiques majeures. Anthropozoologica 56:1-22. https://doi.org/10.5252/anthropozoologica2021v56a1

Loss, S. R., B. Boughton, S. M. Cady, D. W. Londe, C. McKinney, T. J. O'Connell, G. J. Riggs, and E. P. Robertson. 2022. Review and synthesis of the global literature on domestic cat impacts on wildlife. Journal of Animal Ecology 91:1361-1372. https://doi.org/10.1111/1365-2656.13745

Louppe, V., A. Herrel, B. Pisanu, S. Grouard, and G. Veron. 2021. Assessing occupancy and activity of two invasive carnivores in two Caribbean islands: implications for insular ecosystems. Journal of Zoology 313:182-194. https://doi.org/10.1111/jzo.12845

Mahmood, T., and A. Adil. 2017. Diet composition of small Indian mongoose (*Herpestes javanicus*) varies seasonally in its native range. Animal Biology 67:69-80. https://doi.org/10.1163/15707563-00002516

Mansor, M. S., N. A. Abdullah, M. R. Abdullah Halim, S. Md. Nor, and R. Ramli. 2018. Diet of tropical insectivorous birds in lowland Malaysian rainforest. Journal of Natural History 52:2301-2316. https://doi.org/10.1080/00222933.2018.1534015

Mansor, M. S., F. Z. Rozali, N. A. Abdullah, S. M. Nor, and R. Ramli. 2019. How important is aerial leaf litter for insectivorous birds foraging in a Malaysian tropical forest? Global Ecology and Conservation 20:e00722. https://doi.org/10.1016/j.gecco.2019.e00722

Martay, B., and J. W. Pearce-Higgins. 2020. Opening a can of worms: Can the availability of soil invertebrates be indicated by birds? Ecological Indicators 113:106222. https://doi.org/10.1016/j.ecolind.2020.106222

Mazerolle, M. J. 2017. AICcmodavg: model selection and multimodel inference based on (Q)AIC(c). R package version 2.3-2. https://doi.org/10.32614/CRAN/package.AICcmodavg

McCabe, J. D., and B. J. Olsen. 2015. Tradeoffs between predation risk and fruit resources shape habitat use of landbirds during autumn migration. Auk 132:903-913. https://doi.org/10.1642/AUK-14-213.1

McGee, K. M., T. M. Porter, M. Wright, and M. Hajibabaei. 2020. Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics. Scientific Reports 10:18429. https://doi.org/10.1038/s41598-020-75452-4

McKinnon, E. A., T. K. Kyser, and B. J. M. Stutchbury. 2017. Does the proportion of arthropods versus fruit in the diet influence overwintering condition of an omnivorous songbird? Journal of Field Ornithology 88:65-79. https://doi.org/10.1111/jofo.12187

Medina, F. M., E. Bonnaud, E. Vidal, and M. Nogales. 2014. Underlying impacts of invasive cats on islands: not only a question of predation. Biodiversity and Conservation 23:327-342. https://doi.org/10.1007/s10531-013-0603-4

Mizuta, T. 2014. Habitat requirements of the endangered Amami Thrush (*Zoothera dauma major*), endemic to Amami-Oshima Island, southwestern Japan. Wilson Journal of Ornithology 126:298-304. https://doi.org/10.1676/13-102.1

Mizuta, T., M. Takashi, H. Torikai, T. Watanbe, and K. Fukasawa. 2017. Song-count surveys and population estimates reveal the recovery of the endangered Amami Thrush *Zoothera dauma major*, which is endemic to Amami-Oshima Island in south-western Japan. Bird Conservation International 27:470-482. https://doi.org/10.1017/S095927091600023X

Mohd Razali, N., and Y. Bee Wah. 2011. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics 2:13-14.

Morley, C. G., and L. Winder. 2013. The effect of the small Indian mongoose (*Urva auropunctatus*), island quality and habitat on the distribution of native and endemic birds on small islands within Fiji. PLoS ONE 8:e53842. https://doi.org/10.1371/journal.pone.0053842

Murphy, A., M. J. Kelly, S. M. Karpanty, V. Andrianjakarivelo, and Z. J. Farris. 2019. Using camera traps to investigate spatial co-occurrence between exotic predators and native prey species: a case study from northeastern Madagascar. Journal of Zoology 307:264-273. https://doi.org/10.1111/jzo.12645

Murphy, P. G., and A. E. Lugo. 1986. Ecology of tropical dry forest. Annual Review of Ecology and Systematics 17:67-88. https://doi.org/10.1146/annurev.es.17.110186.000435

Myers, N., R. A. Mittermeler, C. G. Mittermeler, G. A. B. Da Fonseca, and J. Kent. 2000. Biodiversity hotspots for conservation priorities. Nature 403:853-858. https://doi.org/10.1038/35002501

NASA Earth Observatory. 2023. Rainforest. https://earthobservatory.nasa.gov/biome/biorainforest.php

Nelder, J. A., and R. W. M. Wedderburn. 1972. Generalized linear models. Journal of the Royal Statistical Society Series A 135:370-384. https://doi.org/10.2307/2344614

Nellis, D. W., and C. O. R. Everard. 1983. The biology of the mongoose in the Caribbean. Studies on the Fauna of Curaçao and other Caribbean Islands 64:1-162.

Newmark, W. D., and T. R. Stanley. 2016. The influence of food abundance, food dispersion and habitat structure on territory selection and size of an Afrotropical terrestrial insectivore. Ostrich 87:199-207. https://doi.org/10.2989/00306525.2016.1216903
Niedbala, W., and S. G. Ermilov. 2022. Additions to the ptyctimous mite fauna (Acarina, Oribatida) of Nepal, with description of two new species of the family Steganacaridae. Systematic and Applied Acarology 27:442-449. https://doi.org/10.11158/saa.27.3.3

O'Connell, A. F., J. D. Nichols, and K. U. Karanth. 2011. Camera traps in animal ecology: methods and analyses. Springer, Tokyo, Japan. https://doi.org/10.1007/978-4-431-99495-4

Parashuram, D., S. Oppel, C. Fenton, G. James, J. Daley, G. Gray, N. J. Collar, and P. M. Dolman. 2015. The Forest Thrush *Turdus lherminieri* prefers mature mesic forest with dense canopy. Bird Conservation International 25:503-513. https://doi.org/10.1017/S0959270914000495

Pimentel, D. 1955. Biology of the Indian mongoose in Puerto Rico. Journal of Mammalogy 36:62-68. https://doi.org/10.2307/1375723

Pollock, H. S., J. D. Brawn, and Z. A. Cheviron. 2021. Heat tolerances of temperate and tropical birds and their implications for susceptibility to climate warming. Functional Ecology 35:93-104. https://doi.org/10.1111/1365-2435.13693

Posa, M. R. C., L. S. Wijedasa, and R. T. Corlett. 2011. Biodiversity and conservation of tropical peat swamp forests. BioScience 61:49-57. https://doi.org/10.1525/bio.2011.61.1.10

R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Raffaele, H. A., J. W. Wiley, O. H. Garrido, A. R. Keith, and J. I. Raffaele. 1998. A guide to the birds of the West Indies. Princeton University Press, Princeton, New Jersey, USA.

Raffaele, H., J. Wiley, O. Garrido, A. Keith, and J. Raffaele. 2003. Birds of the West Indies. Princeton University Press, Princeton, New Jersey, USA.

Riehl, C., and G. S. Adelson. 2008. Seasonal insectivory by Black-headed Trogons, a tropical dry forest frugivore. Journal of Field Ornithology 79:371-380. https://doi.org/10.1111/j.1557-9263.2008.00196.x

Robinson, W. D., and T. W. Sherry. 2012. Mechanisms of avian population decline and species loss in tropical forest fragments. Journal of Ornithology 153:141-152. https://doi.org/10.1007/s10336-011-0806-y

Rousteau, A. 1996. Ecological map of Guadeloupe. National Forestry Service, University of Antilles Guyane, National Park of Guadeloupe, General Board of Guadeloupe, Guadeloupe.

Sánchez, N. V., L. E. Vargas-Castro, G. Avalos, and F. Paniagua. 2014. Effect of prey availability on the abundance of White-breasted Wood-Wrens, insectivorous birds of tropical lowland forests. Journal of Field Ornithology 85:347-354. https://doi.org/10.1111/jofo.12074

Sandoval, L., E. Biamonte, and A. Solano-Ugalde. 2008. Previously unknown food items in the diet of six Neotropical bird species. Wilson Journal of Ornithology 120:214-216. https://doi.org/10.1676/07-012.1

Scholl, M. A., M. Bassiouni, and A. J. Torres-Sánchez. 2021. Drought stress and hurricane defoliation influence mountain

clouds and moisture recycling in a tropical forest. Proceedings of the National Academy of Sciences 118:e2021646118. https://doi.org/10.1073/pnas.2021646118

Seaman, G. A., J. E. Randall. 1962. The mongoose as a predator in the Virgin Islands. Journal of Mammalogy 43:544-546. https://doi.org/10.2307/1376922

Şekercioğlu, Ç. H., P. R. Ehrlich, G. C. Daily, D. Aygen, D. Goehring, and R. F. Sandí. 2002. Disappearance of insectivorous birds from tropical forest fragments. Proceedings of the National Academy of Sciences 99:263-267. https://doi.org/10.1073/pnas.012616199

Şekercioğlu, Ç. H., R. B. Primack, and J. Wormworth. 2012. The effects of climate change on tropical birds. Biological Conservation 148:1-18. https://doi.org/10.1016/j.biocon.2011.10.019

Sherry, T. W. 2021. Sensitivity of tropical insectivorous birds to the Anthropocene: a review of multiple mechanisms and conservation implications. Frontiers in Ecology and Evolution 9:662873. https://doi.org/10.3389/fevo.2021.662873

Smith, D. A. E., Y. C. E. Smith, and C. T. Downs. 2017. Seasonal habitat requirements of Lemon Dove (*Alopelia larvata*) in coastal forest: camera-trap surveys of a reclusive species. African Zoology 52:199-207. https://doi.org/10.1080/15627020.2017.1388193

Smith, D. R. 2004. Application of adaptive cluster sampling to biological populations. Pages 75-122 in W. L. Thompson and K. P. Burnham, editors. Sampling rare or elusive species. Island, Washington, D.C., USA.

Sodhi, N. S., Ç. H. Şekercioğlu, J. Barlow, and S. K. Robinson. 2011. Conservation of tropical birds. John Wiley & Sons, Chichester, UK. https://doi.org/10.1002/9781444342611

Sreekar, R., G. Huang, J. B. Zhao, B. O. Pasion, M. Yasuda, K. Zhang, I. Peabotuwage, X. Wang, R. C. Quan, J. W. Ferry Slik, R. T. Corlett, E. Goodale, and R. D. Harrison. 2015. The use of species-area relationships to partition the effects of hunting and deforestation on bird extirpations in a fragmented landscape. Diversity and Distributions 21:441-450. https://doi.org/10.1111/ddi.12292

Subrata, S. A., S. R. T. Siregar, A. André, and J. R. Michaux. 2021. Identifying prey of the Javan mongoose (*Urva javanica*) in Java from fecal samples using next-generation sequencing. Mammalian Biology 101:63-70. https://doi.org/10.1007/s42991-020-00086-y

Talvitie, M., O. Leino, and M. Holopainen. 2006. Inventory of sparse forest populations using adaptive cluster sampling. Silva Fennica 40:354. https://doi.org/10.14214/sf.354

Tanaka, L. K., and S. K. Tanaka. 1982. Rainfall and seasonal changes in arthropod abundance on a tropical oceanic island. Biotropica 14:114-123. https://doi.org/10.2307/2387740

Thibault, M., E. Vidal, M. A. Potter, T. Sanchez, and F. Brescia. 2018. The invasive Red-vented bulbul (*Pycnonotus cafer*) outcompetes native birds in a tropical biodiversity hotspot. PLoS ONE 13:e0192249. https://doi.org/10.1371/journal.pone.0192249

Tobias, J. A., Ç. H. Şekercioğlu, and H. F. Vargas. 2013. Bird conservation in tropical ecosystems. Pages 258-276 in D. W.
Macdonald and K. J. Willis, editors. Key topics in conservation biology 2. John Wiley & Sons, Chichester, UK. https://doi.org/10.1002/9781118520178.ch15

Van Laere, G., Y. Gall, and A. Rousteau. 2016. The Forest Ecosystems Observatory in Guadeloupe (FWI). Caribbean Naturalist Special Issue 1:108-115.

Vasconcelos, H. L., R. Pacheco, R. C. Silva, P. B. Vasconcelos, C. T. Lopes, A. N. Costa, and E. M. Bruna. 2009. Dynamics of the leaf-litter arthropod fauna following fire in a Neotropical woodland savanna. PLoS ONE 4:e7762. https://doi.org/10.1371/journal.pone.0007762

Vlek, H. E., F. Šporka, and I. Krno. 2006. Influence of macroinvertebrate sample size on bioassessment of streams. Hydrobiologia 566:523-542. https://doi.org/10.1007/s10750-006-0074-7

Vonesh, J. R. 2001. Patterns of richness and abundance in a tropical African leaf-litter herpetofauna. Biotropica 33:502-510. https://doi.org/10.1111/j.1744-7429.2001.tb00204.x

White, G. C., and R. E. Bennetts. 1996. Analysis of frequency count data using the negative binomial distribution. Ecology 77:2549-2557. https://doi.org/10.2307/2265753

Williams, S. E., and J. Middleton. 2008. Climatic seasonality, resource bottlenecks, and abundance of rainforest birds: implications for global climate change. Diversity and Distributions 14:69-77. https://doi.org/10.1111/j.1472-4642.2007.00418.x

Yagihashi, T., S. I. Seki, T. Nakaya, K. Nakata, and N. Kotaka. 2021. Eradication of the mongoose is crucial for the conservation of three endemic bird species in Yambaru, Okinawa Island, Japan. Biological Invasions 23:2249-2260. https://doi.org/10.1007/s10530-021-02503-w
**Appendix 1.** Raw values of Forest Thrush (*Turdus lherminieri*) abundance, Forest Thrush presence, ambient temperature (Temperature, in °C), canopy openness (closed [0-1/3] = -1, partially open [1/3-2/3] = 0 or open [2/3-1] = 1), leaf-litter biomass (in grams), invertebrate abundance (number of individuals), invertebrate biomass (in grams), mongoose abundance and mongoose presence (0 = absence, 1 = presence) recorded at 60 camera-trap locations across tropical rainforest (TRF), tropical dry forest (TDF), and tropical swamp forest (TSF) sites in Guadeloupe (French West Indies) during the dry season (March 2020). Each camera-trap location corresponds to an individual site, with five sites surveyed per station and 12 stations sampled overall. Variables were collected over a 7-day sampling period at each location.

<table><thead><tr><th>Site</th><th>Station</th><th>Forest Thrush abundance</th><th>Forest Thrush presence</th><th>Temperature</th><th>Canopy openness</th><th>Leaf-litter biomass</th><th>Invertebrate abundance</th><th>Invertebrate biomass</th><th>Mongoose abundance</th><th>Mongoose presence</th><th>Forest type</th></tr></thead><tbody><tr><td>S1</td><td>Z1</td><td>0</td><td>0</td><td>28.59</td><td>0</td><td>36.88</td><td>60</td><td>1.22</td><td>1</td><td>1</td><td>TDF</td></tr><tr><td>S2</td><td>Z1</td><td>0</td><td>0</td><td>30.26</td><td>0</td><td>40.99</td><td>30</td><td>0.75</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S3</td><td>Z1</td><td>0</td><td>0</td><td>28.88</td><td>1</td><td>79.54</td><td>87</td><td>2.17</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S4</td><td>Z1</td><td>0</td><td>0</td><td>29.15</td><td>0</td><td>46.37</td><td>39</td><td>1.18</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S5</td><td>Z1</td><td>0</td><td>0</td><td>29.66</td><td>1</td><td>57.84</td><td>56</td><td>1.71</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S6</td><td>Z2</td><td>1</td><td>1</td><td>25.39</td><td>0</td><td>394.50</td><td>80</td><td>3.44</td><td>8</td><td>1</td><td>TSF</td></tr><tr><td>S7</td><td>Z2</td><td>0</td><td>0</td><td>24.69</td><td>0</td><td>478.60</td><td>19</td><td>0.74</td><td>0</td><td>0</td><td>TSF</td></tr><tr><td>S8</td><td>Z2</td><td>0</td><td>0</td><td>26.36</td><td>0</td><td>182.31</td><td>18</td><td>0.30</td><td>6</td><td>1</td><td>TSF</td></tr><tr><td>S9</td><td>Z2</td><td>0</td><td>0</td><td>28.4</td><td>0</td><td>304.76</td><td>24</td><td>0.31</td><td>0</td><td>0</td><td>TSF</td></tr><tr><td>S10</td><td>Z2</td><td>0</td><td>0</td><td>24.84</td><td>-1</td><td>207.85</td><td>41</td><td>0.51</td><td>0</td><td>0</td><td>TSF</td></tr><tr><td>S11</td><td>Z3</td><td>0</td><td>0</td><td>20.15</td><td>0</td><td>339.65</td><td>20</td><td>1.42</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S12</td><td>Z3</td><td>0</td><td>0</td><td>19.29</td><td>0</td><td>509.53</td><td>45</td><td>1.98</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S13</td><td>Z3</td><td>9</td><td>1</td><td>19.18</td><td>0</td><td>560.89</td><td>60</td><td>3.09</td><td>2</td><td>1</td><td>TRF</td></tr><tr><td>S14</td><td>Z3</td><td>4</td><td>1</td><td>19.62</td><td>0</td><td>546.78</td><td>49</td><td>2.52</td><td>3</td><td>1</td><td>TRF</td></tr><tr><td>S15</td><td>Z3</td><td>29</td><td>1</td><td>19.44</td><td>-1</td><td>762.94</td><td>159</td><td>5.71</td><td>8</td><td>1</td><td>TRF</td></tr><tr><td>S16</td><td>Z4</td><td>2</td><td>1</td><td>24.71</td><td>0</td><td>506.55</td><td>54</td><td>1.22</td><td>2</td><td>1</td><td>TRF</td></tr></tbody></table>
<table><thead><tr><th>Site</th><th>Station</th><th>Forest Thrush abundance</th><th>Forest Thrush presence</th><th>Temperature</th><th>Canopy openness</th><th>Leaf-litter biomass</th><th>Invertebrate abundance</th><th>Invertebrate biomass</th><th>Mongoose abundance</th><th>Mongoose presence</th><th>Forest type</th></tr></thead><tbody><tr><td>S17</td><td>Z4</td><td>0</td><td>0</td><td>22.66</td><td>0</td><td>392.55</td><td>10</td><td>0.09</td><td>1</td><td>1</td><td>TRF</td></tr><tr><td>S18</td><td>Z4</td><td>0</td><td>0</td><td>23.03</td><td>0</td><td>460.31</td><td>80</td><td>0.74</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S19</td><td>Z4</td><td>0</td><td>0</td><td>22.78</td><td>-1</td><td>259.90</td><td>28</td><td>0.77</td><td>5</td><td>1</td><td>TRF</td></tr><tr><td>S20</td><td>Z4</td><td>46</td><td>1</td><td>24.55</td><td>0</td><td>600.53</td><td>126</td><td>1.60</td><td>7</td><td>1</td><td>TRF</td></tr><tr><td>S21</td><td>Z5</td><td>3</td><td>1</td><td>24.33</td><td>0</td><td>230.94</td><td>133</td><td>0.99</td><td>2</td><td>1</td><td>TRF</td></tr><tr><td>S22</td><td>Z5</td><td>16</td><td>1</td><td>23.87</td><td>0</td><td>361.85</td><td>395</td><td>3.82</td><td>6</td><td>1</td><td>TRF</td></tr><tr><td>S23</td><td>Z5</td><td>1</td><td>1</td><td>23.44</td><td>0</td><td>199.93</td><td>151</td><td>1.12</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S24</td><td>Z5</td><td>0</td><td>0</td><td>26.63</td><td>0</td><td>153.74</td><td>48</td><td>0.84</td><td>6</td><td>1</td><td>TRF</td></tr><tr><td>S25</td><td>Z5</td><td>1</td><td>1</td><td>23.04</td><td>0</td><td>71.70</td><td>110</td><td>0.79</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S26</td><td>Z6</td><td>7</td><td>1</td><td>25.16</td><td>0</td><td>154.27</td><td>184</td><td>11.47</td><td>5</td><td>1</td><td>TDF</td></tr><tr><td>S27</td><td>Z6</td><td>0</td><td>0</td><td>28.08</td><td>1</td><td>135.06</td><td>108</td><td>2.30</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S28</td><td>Z6</td><td>0</td><td>0</td><td>27.13</td><td>0</td><td>148.56</td><td>66</td><td>0.66</td><td>2</td><td>1</td><td>TDF</td></tr><tr><td>S29</td><td>Z6</td><td>0</td><td>0</td><td>26.15</td><td>0</td><td>135.24</td><td>151</td><td>1.13</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S30</td><td>Z6</td><td>0</td><td>0</td><td>28.32</td><td>1</td><td>150.52</td><td>82</td><td>1.28</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S31</td><td>Z7</td><td>1</td><td>1</td><td>21.26</td><td>0</td><td>510.41</td><td>31</td><td>1.07</td><td>1</td><td>1</td><td>TRF</td></tr><tr><td>S32</td><td>Z7</td><td>0</td><td>0</td><td>22.53</td><td>0</td><td>381.66</td><td>22</td><td>0.62</td><td>2</td><td>1</td><td>TRF</td></tr><tr><td>S33</td><td>Z7</td><td>1</td><td>1</td><td>21.06</td><td>0</td><td>644.73</td><td>69</td><td>1.64</td><td>2</td><td>1</td><td>TRF</td></tr><tr><td>S34</td><td>Z7</td><td>10</td><td>1</td><td>19.74</td><td>0</td><td>759.51</td><td>161</td><td>5.64</td><td>4</td><td>1</td><td>TRF</td></tr><tr><td>S35</td><td>Z7</td><td>5</td><td>1</td><td>20.54</td><td>0</td><td>680.25</td><td>92</td><td>1.31</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S36</td><td>Z8</td><td>0</td><td>0</td><td>24.99</td><td>-1</td><td>531.01</td><td>272</td><td>2.72</td><td>1</td><td>1</td><td>TSF</td></tr><tr><td>S37</td><td>Z8</td><td>0</td><td>0</td><td>25.89</td><td>-1</td><td>678.45</td><td>22</td><td>0.64</td><td>3</td><td>1</td><td>TSF</td></tr><tr><td>S38</td><td>Z8</td><td>0</td><td>0</td><td>24.58</td><td>0</td><td>619.87</td><td>68</td><td>2.26</td><td>1</td><td>1</td><td>TSF</td></tr><tr><td>S39</td><td>Z8</td><td>0</td><td>0</td><td>26.88</td><td>0</td><td>541.18</td><td>162</td><td>2.15</td><td>0</td><td>0</td><td>TSF</td></tr><tr><td>S40</td><td>Z8</td><td>0</td><td>0</td><td>26.23</td><td>-1</td><td>587.34</td><td>200</td><td>2.29</td><td>0</td><td>0</td><td>TSF</td></tr></tbody></table>
<table><thead><tr><th>Site</th><th>Station</th><th>Forest Thrush abundance</th><th>Forest Thrush presence</th><th>Temperature</th><th>Canopy openness</th><th>Leaf-litter biomass</th><th>Invertebrate abundance</th><th>Invertebrate biomass</th><th>Mongoose abundance</th><th>Mongoose presence</th><th>Forest type</th></tr></thead><tbody><tr><td>S41</td><td>Z9</td><td>5</td><td>1</td><td>23.12</td><td>0</td><td>597.55</td><td>208</td><td>3.75</td><td>1</td><td>1</td><td>TRF</td></tr><tr><td>S42</td><td>Z9</td><td>1</td><td>1</td><td>23.58</td><td>0</td><td>558.05</td><td>144</td><td>2.17</td><td>3</td><td>1</td><td>TRF</td></tr><tr><td>S43</td><td>Z9</td><td>0</td><td>0</td><td>25.91</td><td>0</td><td>533.95</td><td>46</td><td>2.26</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S44</td><td>Z9</td><td>0</td><td>0</td><td>23.92</td><td>0</td><td>398.82</td><td>66</td><td>0.81</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S45</td><td>Z9</td><td>0</td><td>0</td><td>25.16</td><td>0</td><td>546.67</td><td>97</td><td>1.20</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S46</td><td>Z10</td><td>6</td><td>1</td><td>26.11</td><td>0</td><td>66.97</td><td>184</td><td>6.55</td><td>13</td><td>1</td><td>TDF</td></tr><tr><td>S47</td><td>Z10</td><td>0</td><td>0</td><td>29.98</td><td>1</td><td>56.08</td><td>13</td><td>0.37</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S48</td><td>Z10</td><td>0</td><td>0</td><td>26.92</td><td>0</td><td>44.77</td><td>30</td><td>1.02</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S49</td><td>Z10</td><td>0</td><td>0</td><td>31.11</td><td>0</td><td>53.55</td><td>50</td><td>0.56</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S50</td><td>Z10</td><td>0</td><td>0</td><td>27.16</td><td>0</td><td>44.16</td><td>72</td><td>1.63</td><td>0</td><td>0</td><td>TDF</td></tr><tr><td>S51</td><td>Z11</td><td>0</td><td>0</td><td>21.38</td><td>0</td><td>577.02</td><td>93</td><td>1.78</td><td>3</td><td>1</td><td>TRF</td></tr><tr><td>S52</td><td>Z11</td><td>0</td><td>0</td><td>21.67</td><td>0</td><td>591.90</td><td>16</td><td>0.34</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S53</td><td>Z11</td><td>0</td><td>0</td><td>22.15</td><td>0</td><td>578.45</td><td>123</td><td>3.00</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S54</td><td>Z11</td><td>0</td><td>0</td><td>22.7</td><td>0</td><td>560.72</td><td>47</td><td>0.69</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S55</td><td>Z11</td><td>7</td><td>1</td><td>21.13</td><td>0</td><td>623.08</td><td>149</td><td>3.56</td><td>0</td><td>0</td><td>TRF</td></tr><tr><td>S56</td><td>Z12</td><td>0</td><td>0</td><td>26.15</td><td>0</td><td>234.84</td><td>6</td><td>1.04</td><td>1</td><td>1</td><td>TSF</td></tr><tr><td>S57</td><td>Z12</td><td>2</td><td>1</td><td>25.81</td><td>0</td><td>715.33</td><td>182</td><td>11.90</td><td>4</td><td>1</td><td>TSF</td></tr><tr><td>S58</td><td>Z12</td><td>0</td><td>0</td><td>24.71</td><td>0</td><td>351.55</td><td>40</td><td>3.85</td><td>0</td><td>0</td><td>TSF</td></tr><tr><td>S59</td><td>Z12</td><td>1</td><td>1</td><td>23.91</td><td>0</td><td>379.53</td><td>28</td><td>3.57</td><td>0</td><td>0</td><td>TSF</td></tr><tr><td>S60</td><td>Z12</td><td>0</td><td>0</td><td>25.89</td><td>1</td><td>287.43</td><td>112</td><td>1.74</td><td>0</td><td>0</td><td>TSF</td></tr></tbody></table>
**Appendix 2.** Parameter estimates from the best model explaining relative abundance of the Forest Thrush (*Turdus lherminieri*) at camera-trap locations: significant effects of invertebrate biomass (IB, in grams), mongoose abundance (MONA), and forest type (FOREST, including: tropical rainforest TRF, tropical dry forest TDF, and tropical swamp forest TSF).

<table><thead><tr><th>Predictor</th><th>Estimate (β)</th><th>Standard Error (SE)</th><th>z-value</th><th>p-value</th><th>95% CI lower</th><th>95% CI upper</th><th>Significance level</th></tr></thead><tbody><tr><td>(Intercept)</td><td>-2.954</td><td>0.982</td><td>-3.009</td><td>0.003</td><td>-4.878</td><td>-1.030</td><td>**</td></tr><tr><td>IB</td><td>0.979</td><td>0.285</td><td>3.441</td><td>0.001</td><td>0.421</td><td>1.537</td><td>***</td></tr><tr><td>MONA</td><td>0.914</td><td>0.203</td><td>4.494</td><td>0.000</td><td>0.516</td><td>1.313</td><td>***</td></tr><tr><td>TRF</td><td>3.708</td><td>0.999</td><td>3.711</td><td>0.000</td><td>1.750</td><td>5.667</td><td>***</td></tr><tr><td>TDF</td><td>-0.672</td><td>1.336</td><td>-0.503</td><td>0.615</td><td>-3.291</td><td>1.948</td><td>-</td></tr></tbody></table>
**Appendix 3.** Residual diagnostic plots for the best regression model assessing the influence of forest type, invertebrate biomass, and mongoose abundance on Forest Thrush (*Turdus lherminieri*) abundance. (A) Quantile-quantile (Q-Q) plot of the residuals, showing deviations from the expected distribution (goodness-of-fit tests: Kolmogorov-Smirnov test, p = 0.55; dispersion test, p = 0.96; outlier test, p = 1). (B) Plot of the simulated residuals against the predicted values.

<figure><img src="image_4.png" /></figure>