Stepping stones sustain bird connectivity at intermediate forest cover: implications for tropical forest conservation

Stepping stones sustain bird connectivity at intermediate forest cover: implications for tropical forest conservation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stepping stones sustain bird connectivity at intermediate forest cover: implications for tropical forest conservation Caio Tavolaro Melo, Milena Fiuza Diniz, Leonardo Silva Lino, Matheus Canova, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8395368/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Context. Theoretical frameworks propose that animals make movement decisions by balancing resource acquisition and movement costs. However, there remains a shortage of empirical studies testing these trade-offs in the context of functional movement thresholds—particularly for tropical forest birds. Objectives. We investigated how functional connectivity influences gap-crossing behavior of forest-dependent birds in fragmented landscapes, aiming to identify forest cover thresholds that balance movement costs and benefits. Methods. The study was conducted in 180 landscapes within the Atlantic Forest–Cerrado transition in southeastern Brazil, distributed across 15 regions spanning gradients of forest cover, stepping-stone density, and patch isolation. Using a modified fixed-point method, we recorded gap-crossing movements of birds and manually digitized trajectories in Google Earth. Landscape metrics were calculated at multiple spatial scales (50, 100, and 200 m). Results. We recorded 2,424 gap-crossing events from 110 species, 45.8% of which were forest dependent. Movements rarely exceeded 100 m from the nearest forest patch, though distances varied among trophic niches and lifestyles. Edge and patch density emerged as key predictors, with shorter and straighter trajectories in more fragmented landscapes. For frugivores, we detected an ecological threshold, with peak gap-crossing at ~14% forest cover (50 m scale). This pattern highlights the role of stepping stones in sustaining connectivity at intermediate forest cover, where movement costs and resource availability are balanced. Conclusions. Our findings provide a practical basis for conservation and restoration planning in fragmented tropical landscapes, emphasizing the importance of fine-scale structural elements to maintain functional connectivity and sustain key ecological functions such as seed dispersal. Atlantic forest edge density forest birds movement path trajectory tropical fragmented landscapes Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The ability of birds to move across landscapes depends on both landscape structure and species-specific traits (Aben et al. 2012 ; Astudillo et al. 2019 ; Sultaire et al. 2023 ). In mountainous regions, landscape complexity arises from topographic variation, microclimatic gradients, and vegetation structure and composition (Aben et al. 2021 ; Laundré et al. 2001 ). These features affect habitat distribution and, consequently, influence how animals move through the landscape. Various landscape elements can either facilitate or hinder movement depending on how each species perceives and interacts with them, as they reflect different degrees of risk and opportunity (Antongiovanni & Metzger 2005 ; Ries & Debinski 2001 ; Rodríguez et al. 2001 ). For example, forest specialist birds tend to restrict their movement to within forest patches rather than venturing across edges and matrix (Bélisle et al. 2001 ; Cosset et al. 2019 ), while generalist species may use riparian corridors to access distant habitats but still avoid direct paths across open matrix due to increased predation risk (Rodríguez et al. 2001 ; Turcotte & Desrochers Turcotte 2003 ). Highly mobile species can cross open areas toward isolated patches to acquire complementary or supplementary resources or seek mates (Bélisle et al. 2001 ). Species make movement decisions based on a range of intrinsic and extrinsic factors, including (1) motivation to move, (2) destination, (3) movement trajectory, (4) energy expenditure, and (5) predation risk (Nathan et al. 2008 ). However, it remains unclear how landscape structure modulates these decisions, particularly in topographically complex environments or those with dense hydrological networks, such as the Atlantic Forest (Sultaire et al. 2023 ). In homogeneous landscapes dominated by forest cover, forest-dependent birds tend to move within continuous forest. As forest is converted to pasture or agriculture, movement becomes more restricted, since many species avoid entering open matrix (Bélisle & Desrochers 2002 ; Hadley & Betts, 2009 ; Villard et al. 1999 ). As the distance between patches increases, so do the energetic costs and predation risks associated with gap-crossing (i.e., the movement of an individual across a non-habitat area to reach another suitable habitat patch), eventually making movement unviable beyond a certain threshold (Bélisle & Desrochers 2002 ; Robertson & Radford 2009 ; Rodríguez et al. 2001 ; Silva et al. 2020 ). While theoretical frameworks have long proposed that animals make movement decisions based on a balance between resource acquisition and movement costs (i.e., energetic costs and predation risk, Dunning et al. 1992 ; Fahrig 2007 ), there remains a critical shortage of empirical studies that test these trade-offs in the context of functional movement thresholds—particularly for tropical forest birds. Most research on landscape fragmentation has emphasized population-level patterns (Awade et al. 2017 ; Cornelius et al. 2017 ; Ramos et al. 2020 ), whereas studies explicitly addressing community-level behavioral responses to variation in forest amount and configuration remain rare (Marini et al. 2020). This gap is especially evident in tropical regions, where high biodiversity and complex landscapes call for refined approaches that link habitat structure to actual movement behavior. Addressing this knowledge gap is essential to improve our understanding of how species perceive and move in fragmented landscapes, and to inform connectivity-based conservation strategies. Although gap-crossing movements may be constrained by limited habitat amount, certain structural elements within the matrix—such as riparian corridors, live fences, and scattered trees acting as stepping stones—can enhance movement by improving habitat configuration and functional connectivity (Dunning et al. 1992 ; Hinsley & Bellamy 2000 ; Levey et al. 2005 , Silva et al. 2020 ). Stepping stones are small habitat elements that reduce the effective distance to suitable habitat and may offer shelter or visual cues that help birds select safer routes through the matrix (Baum et al. 2004 ; Gilpin 1980 ; Sauria et al. 2014). Their spatial distribution is therefore critical to maintaining functional connectivity, especially when forest cover is low (Silva et al. 2020 ). Here, we investigate how landscape structure affects the gap-crossing movements of forest-dependent birds in fragmented landscapes and aim to identify forest cover thresholds that regulate this behavior. We conducted our study in 180 Atlantic Forest-Cerrado landscapes in the southern state of Minas Gerais, Brazil, distributed across 15 regions along gradients of forest cover and stepping stone density. We quantified landscape metrics at multiple spatial scales and assessed how landscape structure influences bird movements across different trophic guilds. The movement was described using direct observations of distance and trajectory complexity. Specifically, we test the hypothesis that movement decisions result from a trade-off between habitat availability and movement costs, leading to peak gap-crossing activity at intermediate levels of forest cover. We also evaluate whether the spatial configuration of forest patches and stepping stones facilitates such movements, thereby enabling functional connectivity in human-modified tropical landscapes. 2. Material and methods 2.1. Study area We conducted this study in 180 landscapes located in the southern state of Minas Gerais, southeastern Brazil (Fig. 1 ). According to the Köppen-Geiger climate classification (Köppen 1936 ), indicating a humid subtropical climate with dry winters and wet summers. The mean annual temperature is 20°C, and average annual precipitation reaches 1,520 mm (available at https://en.climate-data.org/info/sources/ ). Elevation ranges from 720 to 1,350 m, and the topography is predominantly mountainous, comprising hills, ridges, and escarpments. Forest remnants in the study area are situated within a transitional zone between the Atlantic Forest and the Cerrado, two of the most biodiverse and threatened biomes in the Neotropics (Frank E. Zachos & Jan Christian Habel 2011). Extensive deforestation has drastically reduced native vegetation to small and isolated forest patches. Currently, 99% of forest patches are smaller than 20 ha in the study area (Maure et al. 2018 ). Pasture, coffee, and sugarcane plantations dominate the regional land use. Several studies have documented the impacts of habitat loss, fragmentation, and land use on bird communities in the region (Coelho et al. 2016 ; Molina et al. 2023 ; Silva et al. 2020 ). 2.2. Sampling design We sampled 12 regions distributed along a gradient of forest cover and pasture dominance (Online Resource Table S1 ). In each region, we established 15 landscapes, totaling 180 landscapes. Each landscape was centered on a sampling point positioned in the pasture matrix, with a minimum distance of 200 m between points to ensure spatial independence and reduce the chance of recording the same individuals at multiple sites (Bibby et al. 2000 ; Marsden et al. 1999; Ralph et al. 1995 ). This stratified random design was based on variation in forest cover, stepping stone density (i.e., isolate forest patches ≤ 0.005 ha), and distance to the nearest forest patch, providing a balanced representation of the regional heterogeneity. Although the sampling was conducted in open pasture matrices where visibility and sound propagation are relatively high, our observations were restricted to birds actively crossing the matrix, often in short, direct flights. Given that most recorded movements did not exceed 100 m from forest edges, this spacing is adequate to avoid double counting and ensure accurate detection of gap-crossing events (Silva et al. 2020 ). We delineated each region using a circular area with a 2 km radius centered on the largest forest patch in the region. We manually classified land cover within each landscape using Google Earth satellite imagery of 2016 at a 1:6000 scale. The classification included the following categories: forest, coffee plantations, sugarcane fields, water bodies, and pasture. 2.3. Bird Sampling Bird movement sampling . We used a modified fixed-point count method to record bird movements. This method was adapted from standardized fixed-radius point counts commonly used in bird surveys (Bibby et al. 2000 ; Ralph et al. 1995 ; Marsden 1999 ). Unlike traditional point counts, which focus on recording all individuals detected within a given radius, our protocol specifically targeted gap-crossing events. Observers positioned at fixed sampling points recorded only birds that departed from a visible perch and crossed the surrounding open matrix, thereby excluding within-patch movements or local repositioning. To our knowledge, no standardized protocol exists for quantifying gap-crossing at the community level; therefore, our adaptation provides a pragmatic approach for systematically recording such movements across multiple landscapes. Although developed for this study, this adapted protocol offers a replicable and low-cost tool for quantifying gap-crossing at the community level in fragmented tropical landscapes. At each sampling point, observers recorded all bird movements within a 200 m radius during a 10-minute period, using binoculars to identify species and count individuals (Online Resource Table S2). The 200 m radius reflects the maximum distance at which observers could reliably detect and identify birds crossing the matrix (Silva et al. 2020 ), especially species moving briefly or low over the landscape. This distance also minimizes the risk of duplicate detections across sampling points, which were spaced at least 200 m apart. The selection of this radius directly informed the landscape-scale analyses: spatial predictors were calculated within buffers of 50, 100, and 200 m to match the observer’s detection field, the typical scale of gap-crossing behavior, and the presumed perceptual range of forest birds (Marsden 1999 ). This alignment ensured that landscape metrics were ecologically relevant and spatially consistent with both the sampling protocol and the movement behavior being studied. The 10-minute sampling duration follows standard protocols for fixed-radius point counts, which offer a balance between detection efficiency and reduced risk of recording the same individuals multiple times. While relatively brief, this time window is sufficient for capturing gap-crossing events, which typically involve rapid and directional movements across open areas. Importantly, our aim was not to track full movement trajectories, but to register the occurrence and structure of discrete crossing events in a consistent and replicable way across landscapes. The 10-minute duration also helped standardize observer effort, maintain concentration, and reduce variation due to temporal changes in bird activity. We conducted the surveys between May 24 and June 26, 2017. We visited each region two or three times and sampled each point only once. We restricted observations to the morning period, from 6:30 AM to 9:30 AM, which corresponds to the peak of bird activity. We identified bird species using field guides (Ridgely et al., 2015, Sigrist 2009 ) and extracted their ecological traits from the AVONET database (Tobias et al. 2022 ). Specifically, we classified species according to their habitat preference (forest, grassland, human modified), trophic niche (frugivore, invertivore, omnivore), and primary lifestyle (terrestrial, insessorial, aerial, generalist). Spatial movement analysis . During the surveys at each sampling point, we manually traced the trajectories of observed birds using Google Earth. Although GPS or radio telemetry provides higher spatial precision, these methods are limited by cost, logistics, and sample size, usually restricting inference to a few individuals or species. Our approach, while less precise at the fine scale, enabled the recording of thousands of movement events across more than one hundred species, allowing us to capture broad community-level patterns of gap-crossing. This trade-off between precision and representativeness has also been acknowledged in studies of bird movements across fragmented landscapes (Awade et al. 2017 ; Bélisle & Desrochers 2002 ; Silva et al. 2020 ). We defined a movement trajectory as an uninterrupted, directional displacement across the matrix, beginning when a bird departed from a visible perch and ending either at its next landing point—such as a forest patch, stepping stone, or isolated tree—or at the point where it was no longer visible to the observer. Short flights within patches or brief local repositioning movements were not considered. For birds moving in groups, a single trajectory was recorded per event. We did not impose a minimum size threshold to classify starting or ending structures as valid endpoints. Instead, we used the location where the bird was first visually detected in flight and where it subsequently stopped or disappeared from view. As a result, it is possible that some movements began from small habitat elements (e.g., stepping stones or scattered trees) prior to detection. However, all recorded trajectories represent functionally meaningful movements across the matrix between visible landscape elements. Each observed trajectory was digitized as a LINESTRING feature and imported into a GIS environment for spatial analysis using the sf and tidyverse packages in R (Wickham et al. 2019 ). For each trajectory, we calculated (Online Resource Table S3): Total distance traveled: the full length of the path followed by the bird during a gap-crossing event, as observed in real time, and Sinuosity index: measures movement tortuosity and is defined as the ratio between the total distance traveled considering the observed cumulative path and its equivalent straight-line distance, which represents the shortest distance traveled to reach the same displacement as observed. This is a measure of how much an observed path deviates from a straight line and values closer to 1 indicate straighter movements, whereas higher values indicate more convoluted paths, possibly reflecting exploratory or evasive behavior. We interpreted the sinuosity index as a proxy for movement efficiency across the matrix: bird trajectories with low sinuosity likely indicate movement with directional intent—such as targeted forest-to-forest movements—while birds with high sinuosity may have explored the matrix more extensively or responded to structural barriers within the landscape. 2.4. Landscape predictors Landscape metrics were derived from manually classified high-resolution satellite imagery (Google Earth 2016, 1:6000 scale). We mapped land cover into five categories: forest, pasture, coffee plantation, sugarcane field, and water bodies. From these maps, we calculated forest cover (%), number of patches (NP), patch density, and edge density at multiple spatial scales (50, 100, and 200 m buffers), centered on each sampling point, as well as around each movement trajectory to capture the actual area used by birds during matrix crossings. While buffers around the observer’s location (sampling points, located in a pasture matrix) provide a general landscape context, buffers around the movement trajectories better reflect the habitat that birds interacted with during movement. This approach avoids the misattribution of landscape features, especially when trajectories extend beyond the immediate area surrounding the sampling point and intersect structurally distinct patches. By calculating landscape metrics along both types of buffers, we improved the ecological relevance and spatial accuracy of the predictor variables. For each buffer size and type, we computed the following metrics for forest patches (McGarigal et al. 2012 , Online Resource Table S2 e S3): Distance from the nearest forest patch: this metric was calculated once for each sampling point, representing the Euclidean (straight-line) distance between the point and the edge of the closest forest patch. It provides a measure of isolation and quantifies how far bird movements occurred within the matrix relative to available forest habitat (ranging from 0 to 509.54 m). Number of patches (NP, lsm_c_np): quantifies the total number of discrete forest patches within each buffer. For point-centered buffers, we used the raw NP value. For trajectory buffers—where the area varies depending on the shape and length of each movement—we standardized NP by dividing it by the total buffer area, thus defining Patch density. Edge density (ED, lsm_c_ed): measures the total length of patch edges (in meters) per unit area of the buffer (usually in m/ha). Higher edge density reflects a greater degree of fragmentation and exposure to edge effects. Percentage of landscape (PLAND, lsm_c_pland): reflects the proportion of forest within the buffer, serving as a proxy for habitat availability. We applied the procedure using the terra (Hijmans 2023 ), sf (Pebesma 2018 ), and landscapemetrics (Hesselbarth et al. 2019 ) packages in R. 2.5. Statistical analysis To evaluate differences in number of bird movements and distances to the nearest forest patch (i.e., gap-crossing distance) among ecological groups, we compared distance values across trophic niches and primary lifestyles, using only forest-dependent species—those classified as “Forest” or “Woodland” in the AVONET database. These distances, as well as the total number of recorded movements per landscape, were calculated using buffers centered on each sampling point, which reflect the landscape context available to birds at the observation site. To account for observation frequency, all analyses were weighted by the abundance of observed movements, replicating each distance value according to the number of individuals recorded. We used non-parametric Kruskal–Wallis tests to assess overall group differences, followed by pairwise Dunn tests with Bonferroni correction for multiple comparisons. These analyses were implemented using the FSA (Ogle et al. 2023 ) and ggpubr (Kassambara 2023 ) packages in R. Group means were visualized using violin and boxplots, with the number of movements incorporated into the visual distribution. To assess how landscape composition and configuration influence movement patterns, we fitted generalized linear models to two response variables: total trajectory length and sinuosity index. Unless otherwise stated, all landscape metrics used in the movement models were extracted from buffers centered along each individual trajectory. This approach allowed us to capture the actual structural context birds interacted with during gap-crossing events. The analytical unit in these models was each individual movement trajectory recorded during field observations (Online Resource Table S3). Each trajectory was treated as a single movement event, regardless of the number of individuals involved in the group movement, as defined during data collection. Prior to modeling, we filtered the dataset to retain only trajectories performed by forest-dependent species, defined as those associated with “Forest” or “Woodland” habitat types in the AVONET database. We also excluded ecological groups with low sample sizes, specifically birds classified as “Aerial” in primary lifestyle or as “Nectarivore” or “Vertivore” in trophic niche. Prior to modeling, we tested pairwise correlations among predictors and removed variables with |r| ≥ 0.7 to minimize collinearity. All predictors were standardized (mean = 0, SD = 1). We compared error distributions (normal, log-normal, gamma, and Weibull) using maximum likelihood estimation and selected the best-fitting distribution based on AIC. For both movement distance and sinuosity, the gamma distribution with log link provided the best fit. We assessed spatial scale effects by fitting univariate models for each landscape predictor at the three buffer sizes tested (50, 100, and 200 m). The best scale for each predictor was selected based on the model with the lowest AIC and subsequently used in multivariate modeling. We used generalized linear mixed models (GLMMs) fitted via the glmmTMB package (Brooks et al. 2017 ) to evaluate the combined effects of landscape metrics and ecological traits. The full global model included fixed effects for edge and patch density, forest cover, trophic niche, and primary lifestyle. Species identity, landscape, and region were included as random intercepts. We performed model selection using the dredge() function from the MuMIn package (Bartoń 2023 ), limiting the maximum number of fixed predictors to two (m.lim = c(0, 2)), and retaining only models with ΔAICc 0.1. Interaction terms were only considered when their corresponding main effects were also included (hierarchical modeling rule). The best-supported models were assessed for residual diagnostics using the DHARMa package (Hartig 2022 ), which allowed us to evaluate dispersion, zero-inflation, and potential outliers. We also calculated variance inflation factors (VIFs) using the performance package to assess multicollinearity. Only models with acceptable residual patterns and VIF < 5 were retained for interpretation. To explore the existence of thresholds in bird movement in response to forest cover, we performed segmented regression using forest cover (%) as the predictor of bird movement abundance. In this analysis, the sampling unit was each landscape, defined as the buffer area surrounding a sampling point at a given spatial scale (Online Resource Table S2). The response variable was the total number of forest bird movements recorded within each landscape. Analyses were conducted separately for each trophic niche (omnivores, invertivores, and frugivores), considering only forest-dependent species. For each group, we tested segmented models at 50, 100, and 200 m spatial scales, using a grid of 30 candidate breakpoints along the observed forest cover gradient. Each segmented model included a linear term for forest cover and a second term representing the slope beyond the breakpoint (pland_seg). Generalized linear mixed models (GLMMs) with Poisson error distribution were fitted using the glmer() function from the lme4 package (Bates et al. 2015 ), with random intercepts for species identity and sampling point. The best model was selected based on AIC, and trajectories above the 99th percentile of movement abundance were excluded to reduce the influence of outliers. We extracted predictions and confidence intervals from the selected models and visualized the estimated breakpoints and response patterns for each guild. 3. Results We recorded a total of 2,424 bird movements through the matrix, involving 110 species, of which 45.8% (36 species) were classified as forest-dependent (Online Resource, Table S4). In general, the gap-crossing movements by forest birds rarely exceeded 100 meters from the nearest forest patch (Fig. 2 ). However, movement distances varied significantly across trophic niches (Kruskal-Wallis χ² = 26.95, df = 2, p < 0.001) and primary lifestyles (Kruskal-Wallis χ² = 14.17, df = 3, p = 0.0027). Invertivores exhibited the shortest average distances (29 m), followed by frugivores (53 m) and omnivores (56 m). Among lifestyle groups, terrestrial species moved the least (23.3 m), while generalists moved the farthest (64 m). The total distance traveled by forest birds was significantly influenced by edge density and varied across trophic niches (Fig. 3 A, Table 1). These results are based on landscape metrics extracted from buffers surrounding each observed movement trajectory, which better represent the structural features birds encountered during their crossings. In general, distance traveled declined with increasing edge density at the 50 m scale (β = − 0.135, p = 0.0028). Among trophic groups, omnivores exhibited significantly longer trajectories than frugivores (β = 0.627, p = 0.001), whereas invertivores did not differ significantly from frugivores. Path sinuosity was also shaped by landscape configuration (Fig. 3 B and C, Table 1, Online Resource TS5). Sinuosity decreased with increasing forest patch density (β = − 0.221, p < 0.001), indicating straighter trajectories in more connected landscapes. Conversely, it increased with edge density (β = 0.231, p < 0.001), suggesting more convoluted routes in highly fragmented areas. Despite the strong effect sizes and model selection support (Online Resource Table S5), residual diagnostics revealed poor fit across all tested models for sinuosity, including the best-supported one. Therefore, we present these results with caution and recommend further investigation of alternative modeling approaches or additional predictors that may better capture the variability in movement path complexity. The number of movements was not linearly related to forest cover and varied consistently across trophic niches (Fig. 4 , Online Resource Table S6). The best-supported models identified clear thresholds beyond which bird movement declined. For frugivores, a distinct peak in gap-crossing occurred at 13.8% forest cover (p = 0.0496, scale: 50 m), with reduced movement at both lower and higher cover levels. This pattern is clearly represented in the predicted spatial maps (Fig. 4 A–D), where the intensity of predicted frugivore movements varies across the forest cover gradient, with darker shades indicating higher predicted values. For omnivores, a marginally significant decline was detected beyond 45.7% forest cover (p = 0.0716, scale: 100 m), whereas invertivores exhibited a peak gap-crossing at 7.2%, although this change was not statistically significant (p > 0.1, scale: 100 m). These results highlight consistent but niche-specific responses across trophic groups. Models based on the number of forest patches showed weak support and inconsistent patterns, reinforcing the decision to focus on forest cover as the primary predictor of movement behavior. 4. Discussion Our study aimed to understand how forest-dependent birds navigate anthropogenic landscapes and how their gap-crossing movements respond to variation in forest amount and spatial configuration. By focusing on these movements rather than long-distance dispersal (Baguette et al. 2012 ), we provide new insights into functional connectivity in fragmented landscapes. Our findings demonstrate that both landscape structure and species-specific traits shape gap-crossing behavior, revealing ecological thresholds that influence movement probability and efficiency. Edge and patch density emerged as the primary predictors influencing both total trajectory length and path sinuosity. In general, birds exhibited shorter and straighter movements in landscapes with lower edge density but higher patch density, likely reflecting greater availability and proximity of access points into forest fragments and stepping stones. These structural features reduce the distances birds must traverse across open matrix and provide visual or physical cues that facilitate movement. In contrast, the observed increase in path sinuosity in areas with higher edge density suggests that birds may follow forest boundaries or adopt more circuitous routes to minimize exposure to open areas—a behavior consistent with previous findings (e.g., Bélisle & St. Clair 2002 ; Gillies & St. Clair 2008 ; Schtickzelle et al. 2006 ). This behavioral adjustment aligns with the concepts of the landscape of fear and the energetic-predation risk trade-off. Spatial variation in predation risk prompts animals to balance resource access against predation risk, leading to modified movement strategies (Brown 1998 ; Laundré et al. 2001 ). Increased sinuosity near forest edges likely reflects heightened perceived risk. Although convoluted, safer paths provide protective cover, they incur higher energetic costs, whereas more direct movements across open matrices, while energetically efficient, may increase stress and predation exposure (Zollner & Lima 2005 ). Among trophic niches, frugivores exhibited a significant movement threshold at 13.8% forest cover, suggesting an ecological optimum for gap-crossing behavior. Although thresholds were also estimated for omnivores and insectivores, these lacked statistical support and were not further interpreted. The response observed for frugivores is consistent with the idea that animals weigh movement costs and benefits when deciding to cross habitat gaps in fragmented landscapes (Dunning et al. 1992 ; Fahrig 2007 ). The spatial predictions derived from our models further illustrate this threshold, showing that gap-crossing intensity peaks at intermediate forest cover and declines at both lower and higher levels. This reinforces the ecological relevance of the observed pattern. Notably, the threshold occurred at a spatial scale of 50 meters, which likely corresponds to the perceptual range of frugivorous birds during movement (Aben et al. 2021 ). At this fine scale, higher forest cover may reduce the visual detection of gaps, diminishing the need for gap-crossing. As a result, birds may favor within-patch movements when habitat continuity is high. Conversely, in highly deforested landscapes, the scarcity or isolation of stepping stones may limit opportunities for safe crossing. Interestingly, the spatial predictions (Fig. 4 A–D) suggest that the observed threshold may also reflect the spatial configuration of stepping stones along the forest cover gradient. At intermediate forest cover (~ 14%), the landscape may provide a favorable balance: fragmentation creates the need for gap-crossing, while the presence and arrangement of stepping stones still support movement. This spatial coincidence reinforces the interpretation that gap-crossing behavior is shaped not only by habitat amount, but also by the structural arrangement of matrix elements that facilitate movement. This finding aligns with the hypothesis that, below a certain habitat amount, landscape configuration becomes a critical determinant of species responses to fragmentation (Fahrig 2002 , 2013 , 2017 ). In such conditions, the spatial arrangement of small habitat elements may compensate for low habitat quantity by enabling movement and resource access, thus sustaining connectivity. Empirical evidence further supports this view, showing that forest and connectivity loss alter bird movement behavior across species, with individuals adjusting trajectory length and direction in response to structural changes in the landscape (Ramos et al. 2020 ). These insights emphasize the need to account for both habitat amount and configuration—especially at fine spatial scales—when evaluating ecological thresholds and planning conservation strategies in human-modified landscapes. One limitation of our study is that all observations were conducted from points located in pasture matrices. Although this ensured uniform visibility and comparability among sampling points, it restricts our ability to evaluate how different matrix types (e.g., coffee or sugarcane plantations) may influence gap-crossing behavior. A further limitation concerns the manual recording of bird trajectories. Some positional uncertainty is expected, especially for movements observed near the 200 m detection limit and for trajectories digitized in Google Earth. However, this approach provided two important advantages: (i) it allowed us to sample a large number of movement events (2,424) across 110 species, representing a wide spectrum of ecological traits, and (ii) it enabled community-level inferences that would not be feasible with GPS tracking of a limited number of tagged individuals. While our method is less precise than automated tracking technologies, it offered a cost-effective and replicable way to detect functional connectivity patterns at the spatial scales most relevant for gap-crossing behavior in tropical fragmented landscapes. Future studies could integrate direct observation with GPS or drone-based tracking to validate and refine the accuracy of manual trajectory data (Aben et al. 2021 ; Awade et al. 2017 ; Da Silva et al. 2015 ). In addition, it should expand sampling to multiple matrix types and incorporate perceptual proxies to refine predictions of functional connectivity. From an applied perspective, our findings suggest that maintaining or restoring functional connectivity should prioritize spatial configurations where habitat patches are separated by less than 65 meters in open matrices. The most influential predictors of gap-crossing behavior operated at spatial scales between 50 and 100 meters, likely representing the perceptual and operational scales for these species. Increasing patch and edge density within this range could enhance movement frequency and efficiency, facilitating access to complementary resources. This threshold aligns with empirical findings from the Atlantic Forest, where forest bird communities were shown to experience functional isolation when native patches were separated by more than 50 meters of open habitat (Zurita et al. 2012 ). Such convergence across studies underscores the importance of fine-scale spatial structure in promoting movement across fragmented landscapes. Recognizing the identified ecological optimum as a critical threshold can guide the establishment of buffer zones between native vegetation and anthropogenic areas. These transitional areas would provide safer conditions for gap-crossing and optimize the delivery of key ecosystem services, such as seed dispersal and pollination, which are more efficiently performed within this optimal range of forest cover and movement distance. Moreover, the spatial configuration of forest patches and stepping stones within this perceptual range emerges as a key element for facilitating gap-crossing by frugivores. Ensuring that small forested elements are strategically distributed across fragmented landscapes can help maintain functional connectivity even in areas with intermediate forest cover. Therefore, conservation and restoration strategies should not only increase forest amount, but also enhance the spatial arrangement of stepping stones to support movement across the matrix. Declarations Author Contribution Caio Tavolaro Melo, Leonardo da Silva Lino, Matheus Canova, Milton Cezar Ribeiro and Érica Hasui originally formulated the idea. Leonardo da Silva Lino, Matheus Canova and Érica Hasui conceived and designed the experiments. Leonardo da Silva Lino and Matheus Canova conducted fieldwork. Caio Tavolaro Melo, Leonardo da Silva Lino, Matheus Canova, Carla Evangelista Pereira Gonçalves and Érica Hasui analyzed the data and performed statistical analyses. All author wrote the manuscript. Other authors provided review advice. All authors read and approved the final manuscript. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Aben J, Adriaensen F, Thijs KW, Pellikka P, Siljander M, Lens L, Matthysen, E (2012). Effects of matrix composition and configuration on forest bird movements in a fragmented Afromontane biodiversity hot spot. 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Birds’ gap-crossing in open matrices depends on landscape structure, tree size, and predation risk. Perspectives in Ecology and Conservation, 18(2), 73–82. https://doi.org/10.1016/j.pecon.2020.02.001 Sultaire SM, Millspaugh JJ, Jackson PJ, Montgomery RA (2023). The influence of fine-scale topography on detection of a mammal assemblage at camera traps in a mountainous landscape. Wildlife Biology, 2023(2), e01026. https://doi.org/10.1002/wlb3.01026 Tobias JA, Sheard C, Pigot AL, Devenish AJM., Yang J, Sayol F, Neate-Clegg MHC, Alioravainen N, Weeks TL, Barber RA, Walkden PA, MacGregor HEA, Jones SE I, Vincent C, Phillips AG, Marples NM, Montaño‐Centellas FA, Leandro‐Silva V, Claramunt S, … Schleuning M. (2022). AVONET: Morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898 Turcotte Y, Desrochers A (2003). Landscape-dependent response to predation risk by forest birds. Oikos, 100(3), 614–618. Villard M-A, Trzcinski MK, Merriam G (1999). Fragmentation effects on forest birds: Relative influence of woodland cover and configuration on landscape occupancy. Conservation Biology, 13(4), 774–783. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, … Yutani H (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686 Zachos FE, Habel JC (Eds.). (2011). Biodiversity hotspots. Springer. https://doi.org/10.1007/978-3-642-20992-5 Zollner PA, Lima SL (2005). Behavioral tradeoffs when dispersing across a patchy landscape. Oikos, 108(2), 219–230. https://doi.org/10.1111/j.0030-1299.2005.13711.x Zurita G, Pe’er G, Bellocq MI, Hansbauer MM (2012). Edge effects and their influence on habitat suitability calculations: A continuous approach applied to birds of the Atlantic forest. Journal of Applied Ecology, 49(2), 503–512. https://doi.org/10.1111/j.1365-2664.2011.02104.x Additional Declarations No competing interests reported. Supplementary Files OnlineResource1.xlsx floatimage5.jpeg Graphical abstract Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviews received at journal 11 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor assigned by journal 18 Dec, 2025 Submission checks completed at journal 18 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8395368","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580079809,"identity":"f0043090-71a9-4269-b5c6-27d38a7c9a46","order_by":0,"name":"Caio Tavolaro Melo","email":"data:image/png;base64,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","orcid":"","institution":"Universidade Federal De Alfenas - Instituto de Ciências da Natureza","correspondingAuthor":true,"prefix":"","firstName":"Caio","middleName":"Tavolaro","lastName":"Melo","suffix":""},{"id":580079810,"identity":"22c87f18-9c55-4d4b-aeeb-ea2842dd40cc","order_by":1,"name":"Milena Fiuza Diniz","email":"","orcid":"","institution":"Smithsonian Conservation Biology Institute","correspondingAuthor":false,"prefix":"","firstName":"Milena","middleName":"Fiuza","lastName":"Diniz","suffix":""},{"id":580079811,"identity":"ba8b72cb-47fc-407b-80f0-3f32365f5a0a","order_by":2,"name":"Leonardo Silva Lino","email":"","orcid":"","institution":"Universidade Federal De Alfenas - Instituto de Ciências da Natureza","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"Silva","lastName":"Lino","suffix":""},{"id":580079812,"identity":"b77ddb5c-683b-482e-92d0-92a4846e0954","order_by":3,"name":"Matheus Canova","email":"","orcid":"","institution":"Universidade Federal De Alfenas - Instituto de Ciências da Natureza","correspondingAuthor":false,"prefix":"","firstName":"Matheus","middleName":"","lastName":"Canova","suffix":""},{"id":580079813,"identity":"c74d17f2-7d48-41ac-b472-c50214b491d8","order_by":4,"name":"Carla Evangelista Pereira Gonçalves","email":"","orcid":"","institution":"Universidade Federal De Alfenas - Instituto de Ciências da Natureza","correspondingAuthor":false,"prefix":"","firstName":"Carla","middleName":"Evangelista Pereira","lastName":"Gonçalves","suffix":""},{"id":580079814,"identity":"6c1edc7d-c2aa-4478-81b6-3259f7972fc2","order_by":5,"name":"Milton Cezar Ribeiro","email":"","orcid":"","institution":"Universidade Estadual Paulista - UNESP","correspondingAuthor":false,"prefix":"","firstName":"Milton","middleName":"Cezar","lastName":"Ribeiro","suffix":""},{"id":580079815,"identity":"3242eef3-acf2-49b0-96f6-b0abb32f5328","order_by":6,"name":"Érica Hasui","email":"","orcid":"","institution":"Universidade Federal De Alfenas - Instituto de Ciências da Natureza","correspondingAuthor":false,"prefix":"","firstName":"Érica","middleName":"","lastName":"Hasui","suffix":""}],"badges":[],"createdAt":"2025-12-18 12:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8395368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8395368/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101355505,"identity":"edfe3096-8c86-48a7-a298-e4781b38e4f4","added_by":"auto","created_at":"2026-01-28 20:15:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108771,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area within the Atlantic Forest domain, located in the southern portion of Minas Gerais, southeastern Brazil. The central panel shows a land-use map of the 12 sampled regions distributed along gradients of forest cover and pasture dominance. Each red circle represents one of the 15 sampling points per region, where bird movements across the matrix were recorded. Circular insets display selected landscapes with detailed land cover and observed bird movement trajectories, highlighting variation in forest configuration and stepping stone availability across landscapes.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8395368/v1/ffc87e06c06b6454cbd968b0.jpg"},{"id":101355495,"identity":"fe1a226b-3c1a-4e21-b018-42419b038cc2","added_by":"auto","created_at":"2026-01-28 20:15:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21072,"visible":true,"origin":"","legend":"\u003cp\u003eDistance to the nearest forest patch for forest birds according to (A) trophic niche and (B) primary lifestyle. Violin plots represent the distribution of distances, with boxplots indicating medians and interquartile ranges. Mean distances (in meters) are shown above each group and may differ from the boxplot lines due to skewed distributions and the presence of outliers. Different letters above groups indicate significant differences (Kruskal–Wallis followed by Dunn tests, p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8395368/v1/f7b4fba2303ed02f564d52bf.jpg"},{"id":101355497,"identity":"f1a03083-f772-43f1-89ad-8bb4da78413a","added_by":"auto","created_at":"2026-01-28 20:15:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189817,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of landscape metrics and ecological traits on bird movement behavior. (A) Total distance traveled decreased significantly with edge density at the 50 m scale (β = –0.135, p = 0.0028), with responses varying across trophic niches. Omnivores showed significantly longer trajectories than frugivores (β = 0.627, p = 0.001), while invertivores showed no significant difference. (B) Movement sinuosity decreased with patch density (β = –0.222, p \u0026lt; 0.001), and (C) increased with edge density (β = 0.231, p \u0026lt; 0.001). Lines represent GLMM predictions with 95% confidence intervals (shaded areas). Random effects account for variation among sampling points, species, and landscapes.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8395368/v1/3c2437e4c734906a0987b56f.jpg"},{"id":101355512,"identity":"0741644d-6385-4eb4-9b4b-7f3f55ab98ca","added_by":"auto","created_at":"2026-01-28 20:15:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46630,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted abundance of frugivorous bird movement in relation to forest cover (%). Predicted maps of frugivorous bird movements based on gradients of forest cover (A–D); black points represent the sampling points used in the analysis. Panel E shows model predictions from generalized linear mixed models with segmented regression; shaded bands indicate 95% confidence intervals. The optimal spatial scale (50 m, selected based on AIC comparison) and the vertical dashed line (13.8% forest cover, 50 m scale) represents the estimated ecological threshold for frugivores, where gap-crossing activity peaks. This threshold provides a reference point for designing buffer zones and restoration actions. Grey points represent observed movement frequencies across the forest cover gradient. The color gradient indicates predicted bird gap-crossing intensity, with darker shades representing higher predicted movements.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8395368/v1/409f952b670928c408988aa1.jpg"},{"id":101398667,"identity":"955ca07c-cb11-4697-9471-cee4cf2939d1","added_by":"auto","created_at":"2026-01-29 09:43:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":899616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8395368/v1/a45e94d8-9b65-421d-96f3-afbdcd39059c.pdf"},{"id":101355506,"identity":"0c1aaf6d-d2e3-4fba-a330-5bfb9dc9681e","added_by":"auto","created_at":"2026-01-28 20:15:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":204159,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8395368/v1/f3f98c17554b36dd5177c551.xlsx"},{"id":101355496,"identity":"682c7f9f-6dc5-4d36-99a1-d5219a372277","added_by":"auto","created_at":"2026-01-28 20:15:40","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":761687,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8395368/v1/6ff8542216d029232b2b02e4.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stepping stones sustain bird connectivity at intermediate forest cover: implications for tropical forest conservation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe ability of birds to move across landscapes depends on both landscape structure and species-specific traits (Aben et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Astudillo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sultaire et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In mountainous regions, landscape complexity arises from topographic variation, microclimatic gradients, and vegetation structure and composition (Aben et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Laundr\u0026eacute; et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These features affect habitat distribution and, consequently, influence how animals move through the landscape. Various landscape elements can either facilitate or hinder movement depending on how each species perceives and interacts with them, as they reflect different degrees of risk and opportunity (Antongiovanni \u0026amp; Metzger \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ries \u0026amp; Debinski \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rodr\u0026iacute;guez et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For example, forest specialist birds tend to restrict their movement to within forest patches rather than venturing across edges and matrix (B\u0026eacute;lisle et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Cosset et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while generalist species may use riparian corridors to access distant habitats but still avoid direct paths across open matrix due to increased predation risk (Rodr\u0026iacute;guez et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Turcotte \u0026amp; Desrochers Turcotte \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Highly mobile species can cross open areas toward isolated patches to acquire complementary or supplementary resources or seek mates (B\u0026eacute;lisle et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecies make movement decisions based on a range of intrinsic and extrinsic factors, including (1) motivation to move, (2) destination, (3) movement trajectory, (4) energy expenditure, and (5) predation risk (Nathan et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, it remains unclear how landscape structure modulates these decisions, particularly in topographically complex environments or those with dense hydrological networks, such as the Atlantic Forest (Sultaire et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In homogeneous landscapes dominated by forest cover, forest-dependent birds tend to move within continuous forest. As forest is converted to pasture or agriculture, movement becomes more restricted, since many species avoid entering open matrix (B\u0026eacute;lisle \u0026amp; Desrochers \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hadley \u0026amp; Betts, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Villard et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). As the distance between patches increases, so do the energetic costs and predation risks associated with gap-crossing (i.e., the movement of an individual across a non-habitat area to reach another suitable habitat patch), eventually making movement unviable beyond a certain threshold (B\u0026eacute;lisle \u0026amp; Desrochers \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Robertson \u0026amp; Radford \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rodr\u0026iacute;guez et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile theoretical frameworks have long proposed that animals make movement decisions based on a balance between resource acquisition and movement costs (i.e., energetic costs and predation risk, Dunning et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Fahrig \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), there remains a critical shortage of empirical studies that test these trade-offs in the context of functional movement thresholds\u0026mdash;particularly for tropical forest birds. Most research on landscape fragmentation has emphasized population-level patterns (Awade et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cornelius et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), whereas studies explicitly addressing community-level behavioral responses to variation in forest amount and configuration remain rare (Marini et al. 2020). This gap is especially evident in tropical regions, where high biodiversity and complex landscapes call for refined approaches that link habitat structure to actual movement behavior. Addressing this knowledge gap is essential to improve our understanding of how species perceive and move in fragmented landscapes, and to inform connectivity-based conservation strategies.\u003c/p\u003e \u003cp\u003eAlthough gap-crossing movements may be constrained by limited habitat amount, certain structural elements within the matrix\u0026mdash;such as riparian corridors, live fences, and scattered trees acting as stepping stones\u0026mdash;can enhance movement by improving habitat configuration and functional connectivity (Dunning et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Hinsley \u0026amp; Bellamy \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Levey et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Silva et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Stepping stones are small habitat elements that reduce the effective distance to suitable habitat and may offer shelter or visual cues that help birds select safer routes through the matrix (Baum et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gilpin \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Sauria et al. 2014). Their spatial distribution is therefore critical to maintaining functional connectivity, especially when forest cover is low (Silva et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we investigate how landscape structure affects the gap-crossing movements of forest-dependent birds in fragmented landscapes and aim to identify forest cover thresholds that regulate this behavior. We conducted our study in 180 Atlantic Forest-Cerrado landscapes in the southern state of Minas Gerais, Brazil, distributed across 15 regions along gradients of forest cover and stepping stone density. We quantified landscape metrics at multiple spatial scales and assessed how landscape structure influences bird movements across different trophic guilds. The movement was described using direct observations of distance and trajectory complexity. Specifically, we test the hypothesis that movement decisions result from a trade-off between habitat availability and movement costs, leading to peak gap-crossing activity at intermediate levels of forest cover. We also evaluate whether the spatial configuration of forest patches and stepping stones facilitates such movements, thereby enabling functional connectivity in human-modified tropical landscapes.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eWe conducted this study in 180 landscapes located in the southern state of Minas Gerais, southeastern Brazil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the K\u0026ouml;ppen-Geiger climate classification (K\u0026ouml;ppen \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1936\u003c/span\u003e), indicating a humid subtropical climate with dry winters and wet summers. The mean annual temperature is 20\u0026deg;C, and average annual precipitation reaches 1,520 mm (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://en.climate-data.org/info/sources/\u003c/span\u003e\u003cspan address=\"https://en.climate-data.org/info/sources/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Elevation ranges from 720 to 1,350 m, and the topography is predominantly mountainous, comprising hills, ridges, and escarpments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eForest remnants in the study area are situated within a transitional zone between the Atlantic Forest and the Cerrado, two of the most biodiverse and threatened biomes in the Neotropics (Frank E. Zachos \u0026amp; Jan Christian Habel 2011). Extensive deforestation has drastically reduced native vegetation to small and isolated forest patches. Currently, 99% of forest patches are smaller than 20 ha in the study area (Maure et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Pasture, coffee, and sugarcane plantations dominate the regional land use. Several studies have documented the impacts of habitat loss, fragmentation, and land use on bird communities in the region (Coelho et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Molina et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling design\u003c/h2\u003e \u003cp\u003eWe sampled 12 regions distributed along a gradient of forest cover and pasture dominance (Online Resource Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In each region, we established 15 landscapes, totaling 180 landscapes. Each landscape was centered on a sampling point positioned in the pasture matrix, with a minimum distance of 200 m between points to ensure spatial independence and reduce the chance of recording the same individuals at multiple sites (Bibby et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Marsden et al. 1999; Ralph et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). This stratified random design was based on variation in forest cover, stepping stone density (i.e., isolate forest patches \u0026le; 0.005 ha), and distance to the nearest forest patch, providing a balanced representation of the regional heterogeneity. Although the sampling was conducted in open pasture matrices where visibility and sound propagation are relatively high, our observations were restricted to birds actively crossing the matrix, often in short, direct flights. Given that most recorded movements did not exceed 100 m from forest edges, this spacing is adequate to avoid double counting and ensure accurate detection of gap-crossing events (Silva et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe delineated each region using a circular area with a 2 km radius centered on the largest forest patch in the region. We manually classified land cover within each landscape using Google Earth satellite imagery of 2016 at a 1:6000 scale. The classification included the following categories: forest, coffee plantations, sugarcane fields, water bodies, and pasture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Bird Sampling\u003c/h2\u003e \u003cp\u003e \u003cem\u003eBird movement sampling\u003c/em\u003e. We used a modified fixed-point count method to record bird movements. This method was adapted from standardized fixed-radius point counts commonly used in bird surveys (Bibby et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ralph et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Marsden \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Unlike traditional point counts, which focus on recording all individuals detected within a given radius, our protocol specifically targeted gap-crossing events. Observers positioned at fixed sampling points recorded only birds that departed from a visible perch and crossed the surrounding open matrix, thereby excluding within-patch movements or local repositioning. To our knowledge, no standardized protocol exists for quantifying gap-crossing at the community level; therefore, our adaptation provides a pragmatic approach for systematically recording such movements across multiple landscapes. Although developed for this study, this adapted protocol offers a replicable and low-cost tool for quantifying gap-crossing at the community level in fragmented tropical landscapes.\u003c/p\u003e \u003cp\u003eAt each sampling point, observers recorded all bird movements within a 200 m radius during a 10-minute period, using binoculars to identify species and count individuals (Online Resource Table S2). The 200 m radius reflects the maximum distance at which observers could reliably detect and identify birds crossing the matrix (Silva et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), especially species moving briefly or low over the landscape. This distance also minimizes the risk of duplicate detections across sampling points, which were spaced at least 200 m apart.\u003c/p\u003e \u003cp\u003eThe selection of this radius directly informed the landscape-scale analyses: spatial predictors were calculated within buffers of 50, 100, and 200 m to match the observer\u0026rsquo;s detection field, the typical scale of gap-crossing behavior, and the presumed perceptual range of forest birds (Marsden \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This alignment ensured that landscape metrics were ecologically relevant and spatially consistent with both the sampling protocol and the movement behavior being studied.\u003c/p\u003e \u003cp\u003eThe 10-minute sampling duration follows standard protocols for fixed-radius point counts, which offer a balance between detection efficiency and reduced risk of recording the same individuals multiple times. While relatively brief, this time window is sufficient for capturing gap-crossing events, which typically involve rapid and directional movements across open areas. Importantly, our aim was not to track full movement trajectories, but to register the occurrence and structure of discrete crossing events in a consistent and replicable way across landscapes. The 10-minute duration also helped standardize observer effort, maintain concentration, and reduce variation due to temporal changes in bird activity. We conducted the surveys between May 24 and June 26, 2017. We visited each region two or three times and sampled each point only once. We restricted observations to the morning period, from 6:30 AM to 9:30 AM, which corresponds to the peak of bird activity.\u003c/p\u003e \u003cp\u003eWe identified bird species using field guides (Ridgely et al., 2015, Sigrist \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and extracted their ecological traits from the AVONET database (Tobias et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Specifically, we classified species according to their habitat preference (forest, grassland, human modified), trophic niche (frugivore, invertivore, omnivore), and primary lifestyle (terrestrial, insessorial, aerial, generalist).\u003c/p\u003e \u003cp\u003e \u003cem\u003eSpatial movement analysis\u003c/em\u003e. During the surveys at each sampling point, we manually traced the trajectories of observed birds using Google Earth. Although GPS or radio telemetry provides higher spatial precision, these methods are limited by cost, logistics, and sample size, usually restricting inference to a few individuals or species. Our approach, while less precise at the fine scale, enabled the recording of thousands of movement events across more than one hundred species, allowing us to capture broad community-level patterns of gap-crossing. This trade-off between precision and representativeness has also been acknowledged in studies of bird movements across fragmented landscapes (Awade et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; B\u0026eacute;lisle \u0026amp; Desrochers \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe defined a movement trajectory as an uninterrupted, directional displacement across the matrix, beginning when a bird departed from a visible perch and ending either at its next landing point\u0026mdash;such as a forest patch, stepping stone, or isolated tree\u0026mdash;or at the point where it was no longer visible to the observer. Short flights within patches or brief local repositioning movements were not considered. For birds moving in groups, a single trajectory was recorded per event.\u003c/p\u003e \u003cp\u003eWe did not impose a minimum size threshold to classify starting or ending structures as valid endpoints. Instead, we used the location where the bird was first visually detected in flight and where it subsequently stopped or disappeared from view. As a result, it is possible that some movements began from small habitat elements (e.g., stepping stones or scattered trees) prior to detection. However, all recorded trajectories represent functionally meaningful movements across the matrix between visible landscape elements.\u003c/p\u003e \u003cp\u003eEach observed trajectory was digitized as a LINESTRING feature and imported into a GIS environment for spatial analysis using the sf and tidyverse packages in R (Wickham et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For each trajectory, we calculated (Online Resource Table S3):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTotal distance traveled: the full length of the path followed by the bird during a gap-crossing event, as observed in real time, and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSinuosity index: measures movement tortuosity and is defined as the ratio between the total distance traveled considering the observed cumulative path and its equivalent straight-line distance, which represents the shortest distance traveled to reach the same displacement as observed. This is a measure of how much an observed path deviates from a straight line and values closer to 1 indicate straighter movements, whereas higher values indicate more convoluted paths, possibly reflecting exploratory or evasive behavior.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWe interpreted the sinuosity index as a proxy for movement efficiency across the matrix: bird trajectories with low sinuosity likely indicate movement with directional intent\u0026mdash;such as targeted forest-to-forest movements\u0026mdash;while birds with high sinuosity may have explored the matrix more extensively or responded to structural barriers within the landscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Landscape predictors\u003c/h2\u003e \u003cp\u003eLandscape metrics were derived from manually classified high-resolution satellite imagery (Google Earth 2016, 1:6000 scale). We mapped land cover into five categories: forest, pasture, coffee plantation, sugarcane field, and water bodies. From these maps, we calculated forest cover (%), number of patches (NP), patch density, and edge density at multiple spatial scales (50, 100, and 200 m buffers), centered on each sampling point, as well as around each movement trajectory to capture the actual area used by birds during matrix crossings.\u003c/p\u003e \u003cp\u003eWhile buffers around the observer\u0026rsquo;s location (sampling points, located in a pasture matrix) provide a general landscape context, buffers around the movement trajectories better reflect the habitat that birds interacted with during movement. This approach avoids the misattribution of landscape features, especially when trajectories extend beyond the immediate area surrounding the sampling point and intersect structurally distinct patches. By calculating landscape metrics along both types of buffers, we improved the ecological relevance and spatial accuracy of the predictor variables.\u003c/p\u003e \u003cp\u003eFor each buffer size and type, we computed the following metrics for forest patches (McGarigal et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Online Resource Table S2 e S3):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDistance from the nearest forest patch: this metric was calculated once for each sampling point, representing the Euclidean (straight-line) distance between the point and the edge of the closest forest patch. It provides a measure of isolation and quantifies how far bird movements occurred within the matrix relative to available forest habitat (ranging from 0 to 509.54 m).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNumber of patches (NP, lsm_c_np): quantifies the total number of discrete forest patches within each buffer. For point-centered buffers, we used the raw NP value. For trajectory buffers\u0026mdash;where the area varies depending on the shape and length of each movement\u0026mdash;we standardized NP by dividing it by the total buffer area, thus defining Patch density.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEdge density (ED, lsm_c_ed): measures the total length of patch edges (in meters) per unit area of the buffer (usually in m/ha). Higher edge density reflects a greater degree of fragmentation and exposure to edge effects.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePercentage of landscape (PLAND, lsm_c_pland): reflects the proportion of forest within the buffer, serving as a proxy for habitat availability.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWe applied the procedure using the terra (Hijmans \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), sf (Pebesma \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and landscapemetrics (Hesselbarth et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) packages in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eTo evaluate differences in number of bird movements and distances to the nearest forest patch (i.e., gap-crossing distance) among ecological groups, we compared distance values across trophic niches and primary lifestyles, using only forest-dependent species\u0026mdash;those classified as \u0026ldquo;Forest\u0026rdquo; or \u0026ldquo;Woodland\u0026rdquo; in the AVONET database. These distances, as well as the total number of recorded movements per landscape, were calculated using buffers centered on each sampling point, which reflect the landscape context available to birds at the observation site.\u003c/p\u003e \u003cp\u003eTo account for observation frequency, all analyses were weighted by the abundance of observed movements, replicating each distance value according to the number of individuals recorded. We used non-parametric Kruskal\u0026ndash;Wallis tests to assess overall group differences, followed by pairwise Dunn tests with Bonferroni correction for multiple comparisons. These analyses were implemented using the FSA (Ogle et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and ggpubr (Kassambara \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) packages in R. Group means were visualized using violin and boxplots, with the number of movements incorporated into the visual distribution.\u003c/p\u003e \u003cp\u003eTo assess how landscape composition and configuration influence movement patterns, we fitted generalized linear models to two response variables: total trajectory length and sinuosity index. Unless otherwise stated, all landscape metrics used in the movement models were extracted from buffers centered along each individual trajectory. This approach allowed us to capture the actual structural context birds interacted with during gap-crossing events. The analytical unit in these models was each individual movement trajectory recorded during field observations (Online Resource Table S3). Each trajectory was treated as a single movement event, regardless of the number of individuals involved in the group movement, as defined during data collection. Prior to modeling, we filtered the dataset to retain only trajectories performed by forest-dependent species, defined as those associated with \u0026ldquo;Forest\u0026rdquo; or \u0026ldquo;Woodland\u0026rdquo; habitat types in the AVONET database. We also excluded ecological groups with low sample sizes, specifically birds classified as \u0026ldquo;Aerial\u0026rdquo; in primary lifestyle or as \u0026ldquo;Nectarivore\u0026rdquo; or \u0026ldquo;Vertivore\u0026rdquo; in trophic niche.\u003c/p\u003e \u003cp\u003ePrior to modeling, we tested pairwise correlations among predictors and removed variables with |r| \u0026ge; 0.7 to minimize collinearity. All predictors were standardized (mean\u0026thinsp;=\u0026thinsp;0, SD\u0026thinsp;=\u0026thinsp;1). We compared error distributions (normal, log-normal, gamma, and Weibull) using maximum likelihood estimation and selected the best-fitting distribution based on AIC. For both movement distance and sinuosity, the gamma distribution with log link provided the best fit. We assessed spatial scale effects by fitting univariate models for each landscape predictor at the three buffer sizes tested (50, 100, and 200 m). The best scale for each predictor was selected based on the model with the lowest AIC and subsequently used in multivariate modeling.\u003c/p\u003e \u003cp\u003eWe used generalized linear mixed models (GLMMs) fitted via the glmmTMB package (Brooks et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to evaluate the combined effects of landscape metrics and ecological traits. The full global model included fixed effects for edge and patch density, forest cover, trophic niche, and primary lifestyle. Species identity, landscape, and region were included as random intercepts. We performed model selection using the dredge() function from the MuMIn package (Bartoń \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), limiting the maximum number of fixed predictors to two (m.lim\u0026thinsp;=\u0026thinsp;c(0, 2)), and retaining only models with ΔAICc\u0026thinsp;\u0026lt;\u0026thinsp;2 and Akaike weights (wi)\u0026thinsp;\u0026gt;\u0026thinsp;0.1. Interaction terms were only considered when their corresponding main effects were also included (hierarchical modeling rule).\u003c/p\u003e \u003cp\u003eThe best-supported models were assessed for residual diagnostics using the DHARMa package (Hartig \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which allowed us to evaluate dispersion, zero-inflation, and potential outliers. We also calculated variance inflation factors (VIFs) using the performance package to assess multicollinearity. Only models with acceptable residual patterns and VIF\u0026thinsp;\u0026lt;\u0026thinsp;5 were retained for interpretation.\u003c/p\u003e \u003cp\u003eTo explore the existence of thresholds in bird movement in response to forest cover, we performed segmented regression using forest cover (%) as the predictor of bird movement abundance. In this analysis, the sampling unit was each landscape, defined as the buffer area surrounding a sampling point at a given spatial scale (Online Resource Table S2). The response variable was the total number of forest bird movements recorded within each landscape. Analyses were conducted separately for each trophic niche (omnivores, invertivores, and frugivores), considering only forest-dependent species. For each group, we tested segmented models at 50, 100, and 200 m spatial scales, using a grid of 30 candidate breakpoints along the observed forest cover gradient.\u003c/p\u003e \u003cp\u003eEach segmented model included a linear term for forest cover and a second term representing the slope beyond the breakpoint (pland_seg). Generalized linear mixed models (GLMMs) with Poisson error distribution were fitted using the glmer() function from the lme4 package (Bates et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with random intercepts for species identity and sampling point. The best model was selected based on AIC, and trajectories above the 99th percentile of movement abundance were excluded to reduce the influence of outliers. We extracted predictions and confidence intervals from the selected models and visualized the estimated breakpoints and response patterns for each guild.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eWe recorded a total of 2,424 bird movements through the matrix, involving 110 species, of which 45.8% (36 species) were classified as forest-dependent (Online Resource, Table S4). In general, the gap-crossing movements by forest birds rarely exceeded 100 meters from the nearest forest patch (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, movement distances varied significantly across trophic niches (Kruskal-Wallis χ\u0026sup2; = 26.95, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and primary lifestyles (Kruskal-Wallis χ\u0026sup2; = 14.17, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.0027). Invertivores exhibited the shortest average distances (29 m), followed by frugivores (53 m) and omnivores (56 m). Among lifestyle groups, terrestrial species moved the least (23.3 m), while generalists moved the farthest (64 m).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe total distance traveled by forest birds was significantly influenced by edge density and varied across trophic niches (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table\u0026nbsp;1). These results are based on landscape metrics extracted from buffers surrounding each observed movement trajectory, which better represent the structural features birds encountered during their crossings. In general, distance traveled declined with increasing edge density at the 50 m scale (β = \u0026minus;\u0026thinsp;0.135, p\u0026thinsp;=\u0026thinsp;0.0028). Among trophic groups, omnivores exhibited significantly longer trajectories than frugivores (β\u0026thinsp;=\u0026thinsp;0.627, p\u0026thinsp;=\u0026thinsp;0.001), whereas invertivores did not differ significantly from frugivores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePath sinuosity was also shaped by landscape configuration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C, Table\u0026nbsp;1, Online Resource TS5). Sinuosity decreased with increasing forest patch density (β = \u0026minus;\u0026thinsp;0.221, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating straighter trajectories in more connected landscapes. Conversely, it increased with edge density (β\u0026thinsp;=\u0026thinsp;0.231, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting more convoluted routes in highly fragmented areas. Despite the strong effect sizes and model selection support (Online Resource Table S5), residual diagnostics revealed poor fit across all tested models for sinuosity, including the best-supported one. Therefore, we present these results with caution and recommend further investigation of alternative modeling approaches or additional predictors that may better capture the variability in movement path complexity.\u003c/p\u003e \u003cp\u003eThe number of movements was not linearly related to forest cover and varied consistently across trophic niches (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Online Resource Table S6). The best-supported models identified clear thresholds beyond which bird movement declined. For frugivores, a distinct peak in gap-crossing occurred at 13.8% forest cover (p\u0026thinsp;=\u0026thinsp;0.0496, scale: 50 m), with reduced movement at both lower and higher cover levels. This pattern is clearly represented in the predicted spatial maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;D), where the intensity of predicted frugivore movements varies across the forest cover gradient, with darker shades indicating higher predicted values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor omnivores, a marginally significant decline was detected beyond 45.7% forest cover (p\u0026thinsp;=\u0026thinsp;0.0716, scale: 100 m), whereas invertivores exhibited a peak gap-crossing at 7.2%, although this change was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.1, scale: 100 m). These results highlight consistent but niche-specific responses across trophic groups. Models based on the number of forest patches showed weak support and inconsistent patterns, reinforcing the decision to focus on forest cover as the primary predictor of movement behavior.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study aimed to understand how forest-dependent birds navigate anthropogenic landscapes and how their gap-crossing movements respond to variation in forest amount and spatial configuration. By focusing on these movements rather than long-distance dispersal (Baguette et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), we provide new insights into functional connectivity in fragmented landscapes. Our findings demonstrate that both landscape structure and species-specific traits shape gap-crossing behavior, revealing ecological thresholds that influence movement probability and efficiency.\u003c/p\u003e \u003cp\u003eEdge and patch density emerged as the primary predictors influencing both total trajectory length and path sinuosity. In general, birds exhibited shorter and straighter movements in landscapes with lower edge density but higher patch density, likely reflecting greater availability and proximity of access points into forest fragments and stepping stones. These structural features reduce the distances birds must traverse across open matrix and provide visual or physical cues that facilitate movement. In contrast, the observed increase in path sinuosity in areas with higher edge density suggests that birds may follow forest boundaries or adopt more circuitous routes to minimize exposure to open areas\u0026mdash;a behavior consistent with previous findings (e.g., B\u0026eacute;lisle \u0026amp; St. Clair \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gillies \u0026amp; St. Clair \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schtickzelle et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis behavioral adjustment aligns with the concepts of the landscape of fear and the energetic-predation risk trade-off. Spatial variation in predation risk prompts animals to balance resource access against predation risk, leading to modified movement strategies (Brown \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Laundr\u0026eacute; et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Increased sinuosity near forest edges likely reflects heightened perceived risk. Although convoluted, safer paths provide protective cover, they incur higher energetic costs, whereas more direct movements across open matrices, while energetically efficient, may increase stress and predation exposure (Zollner \u0026amp; Lima \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong trophic niches, frugivores exhibited a significant movement threshold at 13.8% forest cover, suggesting an ecological optimum for gap-crossing behavior. Although thresholds were also estimated for omnivores and insectivores, these lacked statistical support and were not further interpreted. The response observed for frugivores is consistent with the idea that animals weigh movement costs and benefits when deciding to cross habitat gaps in fragmented landscapes (Dunning et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Fahrig \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The spatial predictions derived from our models further illustrate this threshold, showing that gap-crossing intensity peaks at intermediate forest cover and declines at both lower and higher levels. This reinforces the ecological relevance of the observed pattern.\u003c/p\u003e \u003cp\u003eNotably, the threshold occurred at a spatial scale of 50 meters, which likely corresponds to the perceptual range of frugivorous birds during movement (Aben et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At this fine scale, higher forest cover may reduce the visual detection of gaps, diminishing the need for gap-crossing. As a result, birds may favor within-patch movements when habitat continuity is high. Conversely, in highly deforested landscapes, the scarcity or isolation of stepping stones may limit opportunities for safe crossing.\u003c/p\u003e \u003cp\u003eInterestingly, the spatial predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;D) suggest that the observed threshold may also reflect the spatial configuration of stepping stones along the forest cover gradient. At intermediate forest cover (~\u0026thinsp;14%), the landscape may provide a favorable balance: fragmentation creates the need for gap-crossing, while the presence and arrangement of stepping stones still support movement. This spatial coincidence reinforces the interpretation that gap-crossing behavior is shaped not only by habitat amount, but also by the structural arrangement of matrix elements that facilitate movement.\u003c/p\u003e \u003cp\u003eThis finding aligns with the hypothesis that, below a certain habitat amount, landscape configuration becomes a critical determinant of species responses to fragmentation (Fahrig \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In such conditions, the spatial arrangement of small habitat elements may compensate for low habitat quantity by enabling movement and resource access, thus sustaining connectivity. Empirical evidence further supports this view, showing that forest and connectivity loss alter bird movement behavior across species, with individuals adjusting trajectory length and direction in response to structural changes in the landscape (Ramos et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These insights emphasize the need to account for both habitat amount and configuration\u0026mdash;especially at fine spatial scales\u0026mdash;when evaluating ecological thresholds and planning conservation strategies in human-modified landscapes.\u003c/p\u003e \u003cp\u003eOne limitation of our study is that all observations were conducted from points located in pasture matrices. Although this ensured uniform visibility and comparability among sampling points, it restricts our ability to evaluate how different matrix types (e.g., coffee or sugarcane plantations) may influence gap-crossing behavior. A further limitation concerns the manual recording of bird trajectories. Some positional uncertainty is expected, especially for movements observed near the 200 m detection limit and for trajectories digitized in Google Earth. However, this approach provided two important advantages: (i) it allowed us to sample a large number of movement events (2,424) across 110 species, representing a wide spectrum of ecological traits, and (ii) it enabled community-level inferences that would not be feasible with GPS tracking of a limited number of tagged individuals. While our method is less precise than automated tracking technologies, it offered a cost-effective and replicable way to detect functional connectivity patterns at the spatial scales most relevant for gap-crossing behavior in tropical fragmented landscapes. Future studies could integrate direct observation with GPS or drone-based tracking to validate and refine the accuracy of manual trajectory data (Aben et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Awade et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Da Silva et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, it should expand sampling to multiple matrix types and incorporate perceptual proxies to refine predictions of functional connectivity.\u003c/p\u003e \u003cp\u003eFrom an applied perspective, our findings suggest that maintaining or restoring functional connectivity should prioritize spatial configurations where habitat patches are separated by less than 65 meters in open matrices. The most influential predictors of gap-crossing behavior operated at spatial scales between 50 and 100 meters, likely representing the perceptual and operational scales for these species. Increasing patch and edge density within this range could enhance movement frequency and efficiency, facilitating access to complementary resources.\u003c/p\u003e \u003cp\u003eThis threshold aligns with empirical findings from the Atlantic Forest, where forest bird communities were shown to experience functional isolation when native patches were separated by more than 50 meters of open habitat (Zurita et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Such convergence across studies underscores the importance of fine-scale spatial structure in promoting movement across fragmented landscapes.\u003c/p\u003e \u003cp\u003eRecognizing the identified ecological optimum as a critical threshold can guide the establishment of buffer zones between native vegetation and anthropogenic areas. These transitional areas would provide safer conditions for gap-crossing and optimize the delivery of key ecosystem services, such as seed dispersal and pollination, which are more efficiently performed within this optimal range of forest cover and movement distance.\u003c/p\u003e \u003cp\u003eMoreover, the spatial configuration of forest patches and stepping stones within this perceptual range emerges as a key element for facilitating gap-crossing by frugivores. Ensuring that small forested elements are strategically distributed across fragmented landscapes can help maintain functional connectivity even in areas with intermediate forest cover. Therefore, conservation and restoration strategies should not only increase forest amount, but also enhance the spatial arrangement of stepping stones to support movement across the matrix.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCaio Tavolaro Melo, Leonardo da Silva Lino, Matheus Canova, Milton Cezar Ribeiro and \u0026Eacute;rica Hasui originally formulated the idea. Leonardo da Silva Lino, Matheus Canova and \u0026Eacute;rica Hasui conceived and designed the experiments. Leonardo da Silva Lino and Matheus Canova conducted fieldwork. Caio Tavolaro Melo, Leonardo da Silva Lino, Matheus Canova, Carla Evangelista Pereira Gon\u0026ccedil;alves and \u0026Eacute;rica Hasui analyzed the data and performed statistical analyses. All author wrote the manuscript. Other authors provided review advice. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAben J, Adriaensen F, Thijs KW, Pellikka P, Siljander M, Lens L, Matthysen, E (2012). Effects of matrix composition and configuration on forest bird movements in a fragmented Afromontane biodiversity hot spot. Animal Conservation, 15(6), 658\u0026ndash;668. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-1795.2012.00562.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-1795.2012.00562.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAben J, Signer J, Heiskanen J, Pellikka P, Travis JMJ (2021). 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Journal of Applied Ecology, 49(2), 503\u0026ndash;512. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2664.2011.02104.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2664.2011.02104.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"landscape-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"land","sideBox":"Learn more about [Landscape Ecology](https://www.springer.com/journal/10980)","snPcode":"10980","submissionUrl":"https://submission.nature.com/new-submission/10980/3","title":"Landscape Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Atlantic forest, edge density, forest birds, movement path, trajectory, tropical fragmented landscapes","lastPublishedDoi":"10.21203/rs.3.rs-8395368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8395368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eContext.\u003c/p\u003e\n\u003cp\u003eTheoretical frameworks propose that animals make movement decisions by balancing resource acquisition and movement costs. However, there remains a shortage of empirical studies testing these trade-offs in the context of functional movement thresholds—particularly for tropical forest birds.\u003c/p\u003e\n\u003cp\u003eObjectives.\u003c/p\u003e\n\u003cp\u003eWe investigated how functional connectivity influences gap-crossing behavior of forest-dependent birds in fragmented landscapes, aiming to identify forest cover thresholds that balance movement costs and benefits.\u003c/p\u003e\n\u003cp\u003eMethods.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in 180 landscapes within the Atlantic Forest–Cerrado transition in southeastern Brazil, distributed across 15 regions spanning gradients of forest cover, stepping-stone density, and patch isolation. Using a modified fixed-point method, we recorded gap-crossing movements of birds and manually digitized trajectories in Google Earth. Landscape metrics were calculated at multiple spatial scales (50, 100, and 200 m).\u003c/p\u003e\n\u003cp\u003eResults.\u003c/p\u003e\n\u003cp\u003eWe recorded 2,424 gap-crossing events from 110 species, 45.8% of which were forest dependent. Movements rarely exceeded 100 m from the nearest forest patch, though distances varied among trophic niches and lifestyles. Edge and patch density emerged as key predictors, with shorter and straighter trajectories in more fragmented landscapes. For frugivores, we detected an ecological threshold, with peak gap-crossing at ~14% forest cover (50 m scale). This pattern highlights the role of stepping stones in sustaining connectivity at intermediate forest cover, where movement costs and resource availability are balanced.\u003c/p\u003e\n\u003cp\u003eConclusions.\u003c/p\u003e\n\u003cp\u003eOur findings provide a practical basis for conservation and restoration planning in fragmented tropical landscapes, emphasizing the importance of fine-scale structural elements to maintain functional connectivity and sustain key ecological functions such as seed dispersal.\u003c/p\u003e","manuscriptTitle":"Stepping stones sustain bird connectivity at intermediate forest cover: implications for tropical forest conservation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 20:15:32","doi":"10.21203/rs.3.rs-8395368/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T23:27:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T19:06:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T12:20:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40211706336393918448463512656438783052","date":"2026-02-06T21:14:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11219396006117183840158807042568025799","date":"2026-02-05T16:51:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108885040699017629843204933319965381011","date":"2026-01-25T14:17:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T05:05:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-19T03:12:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-19T03:11:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Landscape Ecology","date":"2025-12-18T12:05:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"landscape-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"land","sideBox":"Learn more about [Landscape Ecology](https://www.springer.com/journal/10980)","snPcode":"10980","submissionUrl":"https://submission.nature.com/new-submission/10980/3","title":"Landscape Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f64f41b9-96e9-4c34-bb66-124002fd2145","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-04-29T23:27:36+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T23:38:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 20:15:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8395368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8395368","identity":"rs-8395368","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}