Cattle pasture management filters functional traits and alters trait–role relationships in plant–butterfly interaction networks

Cattle pasture management filters functional traits and alters trait–role relationships in plant–butterfly interaction networks | 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 Cattle pasture management filters functional traits and alters trait–role relationships in plant–butterfly interaction networks Roger Ayazo-Berrocal, Wesley Dáttilo, Camilo Correa-Ayram This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8919271/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Grazing intensity and tree cover has potential repercussions for plant–butterfly interactions. Here, we evaluated how three cattle management types—open pastures (OP), woody pastures (WP), and pastures adjacent to forest remnants (FAP)—are associated with species roles within plant–butterfly networks in a deforested tropical dry forest (TDF) region of northern Colombia. Across 24 pastures, we quantified butterfly and plant traits, constructed interaction networks, and estimated species roles using centrality-based indices from a principal component analysis. Our results show that cattle management filtered both plant and butterfly traits, modifying the identity of interacting species even though overall centrality did not differ among management types. Trait–role relationships shifted across management regimes: corolla width influenced plant centrality in FAP (β = 0.036, p < 0.03) and WP (β = 0.041, p < 0.03), whereas butterfly wing length (β = – 0.028, p = 0.049) and body length (β = – 0.073, p = 0.011) were the main traits associated with their interactive roles only in WP. These patterns indicate that environmental filtering reorganizes trait distributions and the mechanisms by which traits shape species importance within plant–butterfly networks. Overall, we show that cattle management does not directly modify species’ interactive roles, but instead acts primarily as a trait-based filter, indirectly shaping plant–butterfly networks by altering trait distributions and trait–role relationships rather than the overall network structure. Promoting tree cover and maintaining forest adjacency within cattle landscapes may enhance the ecological functionality of mutualistic networks in TDF ecosystems. Mutualistic networks Functional trait variation Tropical dry forest Flower-visitor interactions Agroecosystem biodiversity Figures Figure 1 Figure 2 Figure 3 Introduction Tropical dry forests (TDF) are among the most threatened terrestrial biomes, characterized by marked seasonality, high levels of endemism, and long historical exposure to human transformation (Powers et al. 2018 ; Tabarelli et al. 2024 ; Teixeira et al. 2025 ). Across the Neotropics, extensive conversion of TDF to agricultural systems—especially cattle production—has resulted in highly fragmented landscapes where remnant forest patches persist within a matrix of pastures (Dupin et al. 2018 ; Rocha-Filho et al. 2025 ). These changes influence ecological processes at multiple scales, including the dynamics of plant–pollinator interactions that underpin ecosystem functioning (Bustamante-Castillo, Hernández-Baños, and Arizmendi 2020 ). Understanding how mutualistic interactions persist or reconfigure under land-use change is therefore critical for conserving biodiversity and sustaining ecosystem services in these degraded yet ecologically valuable regions (Proesmans et al. 2024 ; Buono et al. 2023 ). Within modified landscapes, species interactions often respond more strongly to trait-based mechanisms than to species identity alone (Zakharova, Meyer, and Seifan 2019 ; Litchman, Edwards, and Boyd 2021 ). Functional traits modulate how species perceive, exploit, and contribute to ecological networks. In pollination systems, traits such as floral morphology or pollinator body size shape partner accessibility, interaction frequency, and specialization (Gómez, Perfectti, and Klingenberg 2014 ; Muchhala 2007 ). Because traits determine how species cope with habitat alteration, management practices that restructure vegetation can induce non-random filtering of traits (Liu, Liu, and Zheng 2015 ; Gilhaus et al. 2017 ; Swift and Bell 2011 ), ultimately affecting the distribution of interactive roles within mutualistic networks (Dáttilo, Guimarães, and Izzo 2013 ; Guimarães et al. 2017 ; Fuzessy and Pizo 2025 ). Empirical research has identified species that play a highly interactive role, characterized by significant network importance and a major influence on co-occurring species. This framework has proven useful for understanding responses of plant–pollinator systems to disturbance yet remains understudied for plant–butterfly assemblages in highly transformed TDF landscapes. Livestock systems provide a valuable context for investigating trait-mediated mechanisms, as pasture management strongly modify vegetation structure and floral resource availability (Vanbergen et al. 2014 ; Lázaro and Santamaría 2016). Forest-adjacent pastures (FAP) and Woody pastures (WP) retain higher greater structural complexity and microhabitat heterogeneity than open pastures (OP), potentially influencing both plant assemblages and butterfly communities. Such differences can influence both the abundance of weed plants and the assemblage of butterfly species visiting flowers (de Araújo Silva et al. 2025 ; Neff, Fettig, and VanOverbeke 2007; Marini et al. 2009 ; Mitja and Miranda 2010 ). Importantly, weedy plants exhibit phenological asynchrony relative to TDF trees. While forest species reproduce around rainfall cues, pasture weeds often flower opportunistically, extending resource availability into periods when the surrounding matrix offers limited or no floral resources (Cortés-Flores et al. 2017 ; McLaren and McDonald 2005). These asynchronous blooms may help sustain pollinator activity and thereby maintain interaction flow across the broader landscape (Shrotri et al. 2025 ; Genini et al. 2021 ). Because vegetation structure and the phenology of resource-providing plants differ among management regimes, livestock systems in TDF landscapes have the potential to filter functional traits of both plants and butterflies. In such systems, floral traits strongly influence the accessibility of nectar resources and subsequently determine whether plant species become central or peripheral within plant–butterfly networks. Floral size, density, and corolla morphology affect interaction frequency and can increase the likelihood that certain species act as local hubs (Xiang et al. 2023 ), while morphological matching between corolla architecture and pollinator morphology further shapes the emergence of modular structures and interaction clusters (Maglianesi et al. 2024 ; Izquierdo-Palma et al. 2021 ). The integration of floral traits has been shown to drive network roles in multiple systems (Lázaro and Santamaría 2016), underscoring the importance of trait-mediated filters in determining the functional contributions of plant species within mutualistic assemblages. Similarly, butterfly traits influence their position as key connectors or peripheral elements in mutualistic networks. Morphological and phenological characteristics often determine interaction frequency, with trait-matching between pollinator morphology and floral architecture being a primary predictor of interaction probability (Guo et al. 2022 ; Mertens et al. 2021 ). In particular, body size and wing dimensions are closely linked to mobility, energetic demands, and foraging range, thereby constraining the number and diversity of potential interaction partners. Larger-bodied butterflies, often characterized by broader wings and higher flight capacity, tend to forage over wider spatial scales but may interact less frequently with individual plant species due to higher energetic costs and lower maneuverability in structurally complex habitats (Hagen et al. 2012 ; Dehling et al. 2016 ). Conversely, smaller butterflies with reduced wing spans often exhibit higher visitation rates and tighter coupling with specific floral resources, which can promote more frequent but spatially constrained interactions (Bartomeus et al. 2016 ; Junker et al. 2013 ). Species with extended activity periods tend to accrue more connections across modules (Guzman, Chamberlain, and Elle 2021 ), whereas generalist foraging strategies often lead to more central network roles (Coux et al. 2016 ; Pires et al. 2022 ). Environmental variation and microclimatic conditions can further shape butterfly centrality (Álamo et al. 2025 ), suggesting that both internal (trait-based) and external (environmental) filters govern the functional contributions of butterflies in plant–pollinator systems. Cattle management introduces an additional environmental filtering in TDF landscapes. Grazing, browsing, and trampling modify microhabitats and plant assemblages (Maza-Villalobos et al. 2022 ), influencing the functional traits of ground-layer vegetation, which in turn structures the foraging opportunities available to butterflies. These management-driven shifts can cascade through local interaction networks by altering which traits are favored (Maza-Villalobos et al. 2022 ). Exclusion of cattle has been shown to increase woody plant density and species richness (Quisehuatl-Medina et al. 2020 ), yet working pastoral landscapes—such as those characteristics of northern Colombia—must balance conservation objectives and the realities of food production. Given that trait distributions shape partner availability and interaction patterns (Santos and Ribeiro 2023 ), understanding how cattle management regimes filter traits are fundamental for interpreting functional variation in butterfly–plant networks. These dynamics unfold within multifunctional landscapes, where biodiversity conservation must coexist with productive activities such as cattle ranching. Multifunctional systems emphasize reconciling agricultural production with ecological processes by maintaining heterogeneous landscapes, enhancing ecosystem multifunctionality, and supporting services such as pollination (McGranahan 2014 ; Leroy et al. 2025 ). Trade-offs are unavoidable—biodiversity-friendly practices may reduce short-term production (Burian et al. 2023 ) or generate complex landscape responses (van der Plas et al. 2019 )—yet adaptive management frameworks and collaborative stewardship can facilitate sustainable outcomes (Chirwa et al. 2024 ; Garibaldi et al. 2023 ; Cockburn et al. 2019 ). In TDF regions of northern Colombia, where cattle ranching dominates land use, developing management strategies that support pollinator communities and maintain interaction networks is essential for sustaining both biodiversity and agricultural resilience. Against this backdrop, WP, FAP, and OP represent distinct management regimes that may impose different environmental filters on plants and butterflies, potentially altering trait distributions and, consequently, the interactive roles species assume. Nevertheless, despite growing evidence that functional traits structure mutualistic networks under land-use change, we still lack empirical tests of how cattle management regimes influence trait–role relationships in plant–butterfly networks in TDF. Addressing this gap is essential for understanding how biodiversity persists and how pollination processes can be enhanced in multifunctional tropical landscapes. Specifically, we ask: (1) Do different pasture management types influence the interactive roles of plants and butterflies? and (2) To what extent do functional traits explain variation in these roles? We hypothesize that (i) management indirectly shapes interactive roles by filtering traits of both plants and butterflies; (ii) plants with wider corollas attain higher centrality due to enhanced accessibility; and (iii) butterfly morphological traits—particularly wing size and overall body size—predict interactive roles. Specifically, smaller butterflies are expected to interact with a greater diversity of plant species and to exhibit higher centrality, reflecting enhanced mobility, broader resource use, and reduced energetic constraints under simplified pasture conditions. Collectively, these hypotheses reflect the expectation that trait-based mechanisms, rather than management per se, are the primary drivers of functional positioning within plant–butterfly networks in livestock-dominated TDF landscapes. Methods Study area We conducted this study in the highly transformed landscape of the Sinú medio and San Jorge subregions (< 200 m asl), in the Córdoba department on the northern Caribbean coast of Colombia. The Caribbean region is characterized by some of the highest human footprint values in the country and a low proportion of remaining natural áreas (Correa Ayram et al. 2020). Córdoba was originally dominated by TDF; however, agricultural expansion and extensive cattle ranching have resulted in the loss of approximately 85% of its original forest cover (Ramos et al. 2016 ). The current landscape is a mosaic dominated by cattle pastures, interspersed with agricultural fields, remnant forest patches, and human settlements (Ruiz 2022 ). The climate is characterized by a marked dry season between December and March, and a rainy season from May to November which strongly constrains plant phenology and floral resources for pollinators. Relative humidity above 80%, mean annual temperature ranges from 26.9°C to 27.8°C, and annual precipitation ranges from 1500 to 2000 mm (Meisel and Pérez 2006 ; Galvis 2009 ; IAvH 1998 ). We conducted fieldwork during the peak flowering season of pasture herbaceous plants, from October to December 2024, which corresponds to the regional rainy season (IGAC 2009 ; Meisel and Pérez 2006 ). We studied three common pasture management types in Córdoba's cattle ranches: (1) FAP: forest-adjacent pastures, (2) WP: woody pastures (high tree density), and (3) OP: open pastures (low tree density). We selected 24 cattle pastures representing three management types (eight per type) within the same biogeographic region to minimize climatic and historical variation. Pasture size ranged from 5 to 35 ha. To ensure spatial independence between sampling sites, we maintained a minimum separation distance of 2 km between pastures; where possible, we selected one pasture of each type within the same farm (Fig. 1 ). The herbaceous layer in FAP was dominated by Bothriochloa pertusa , Brachiaria brizantha cv. Toledo, Echinochloa polystachya , and Brachiaria humidicola . WP featured Dichanthium aristatum , Megathyrsus maximus cv Mombasa, Brachiaria humidicola , and Bothriochloa pertusa , while OP were dominated by Brachiaria humidicola , Brachiaria mutica , and Bothriochloa pertusa . The woody component of WP pastures showed high native tree diversity, including Pachira quinata , Simarouba amara , Cavanillesia platanifolia , Bursera simaruba , Samanea saman , Lecythis minor , Pterocarpus acapulcensis , Ficus carica , and Tabebuia rosea . FAP and OP shared common trees such as Tabebuia rosea , Samanea saman , Gliricidia sepium , Guazuma ulmifolia , Crescentia cujete , and Pachira quinata. Poor soil drainage—a characteristic feature of the alluvial soils with high clay and silt content in the Sinú River valley (IGAC 2009 )—was observed in 37.5% (3/8) of FAP sites and 62.5% (5/8) of WP and OP sites. All farms had over 50 years of cattle production history. Farm size varied from 79.5 to 2,380 ha, with herd sizes ranging from 130 to 4,200 head. Pasture covered 50–98% of the land area across farms, with stocking rates of 0.87–8.5 animals/ha under rotational grazing management. Sampling plant-butterfly interaction To record plant-butterfly interactions, we employed linear transects within each of the 24 selected cattle pastures. In each pasture, we established three 300 m long by 5 m wide linear transects (recording area: 2.5 m on each side of the observer). We surveyed each transect for 30 minutes per day over four consecutive days, resulting in a total sampling effort of six hours per pasture. On each sampling day, we visited at least three different pastures. We conducted all sampling between 7:30 am and 3:30 pm on days with favorable weather conditions (sunny or partly cloudy with minimal wind). To control potential time-of-day effects on butterfly activity and flower visitation, we randomized the order in which we visited the pastures each day. We recorded a flower’s visitation event only upon observing a butterfly's proboscis making direct contact with the reproductive structures of a flower. Butterflies were identified either by sight (for species that could be reliably recognized in flight or perched) or by capture with an entomological net for closer examination. We used visitation frequency as a metric for pollinator functional performance (Vázquez, Morris, and Jordano 2005). For captured specimens, following standard entomological practices and minimizing specimen removal to the extent possible we sacrificed a voucher series of 5–10 individuals per species; all others were photographed and released on site. Sacrificed specimens were stored in plastic containers for transport. Butterfly identification was completed by comparing collected vouchers and photographs to authoritative illustrated guides for Colombian species (Garwood 2017 ). For plant identification, we photographed the habit, leaves (including their arrangement), flowers, and fruits (when available). We provisionally identified plants in the field using common names provided by local farm workers. Subsequently, we assigned scientific names by comparing photographs to specimens in digital herbarium collections (STRI 2025; WFO 2025; Vibrans 2012 ). All butterfly vouchers were deposited in the entomological collection of the Pontificia Universidad Javeriana in Bogotá. Specimen collection was conducted under the permit for collection and mobilization of biological specimens, Resolution ANLA No. 1255 (June 24, 2024), and was certified by the Vice-Rectory for Research of the Pontificia Universidad Javeriana-Bogotá (Official Communication VI-0472). Plant and butterfly traits For butterfly species, we measured three morphological traits: proboscis length, body length, and forewing length (Table S1 ). Proboscis length was estimated for one to ten specimens per species (typically five). Heads were separated from the body, antennae removed and subsequently softened in 70% ethanol for 20 minutes. Each head was pinned onto a polystyrene surface, and labial palps were removed to expose the proboscis insertion. To measure the proboscis, it was then fully extended and fixed using entomological pins. A segmented reference scale (1-mm precision) was placed beside each preparation, and the structures were photographed, following (Lehnert et al. ( 2016 ) and Bauder et al. ( 2015 ). Body and forewing lengths were obtained from scaled photographs taken with a 1-mm precision reference. Body length was defined as the distance from the most distal point of the head to the abdomen, and forewing length as the distance from the anterior wing insertion to the wing apex. Forewing length was averaged from one to seven specimens, and body length from one to five (Table S1 ). When only one specimen was available, its measurement served as the species-level estimate. Because specimen collection was limited to avoid disturbing plant–butterfly interactions, some traits were measured from regional museum specimens or from calibrated images available through Butterflies of America (Warren et al. 2024 ). For each species and trait, we calculated a mean and its standard deviation (Table S2). For plant species, we compiled six traits, including five floral attributes: corolla width, corolla length, corolla shape (achlamydeous; bell-shaped; bell- to urn-shaped; obovate; papilionaceous; petaloid calyx; rotate; salverform; spatulate; tubular; two-lipped or zygomorphic), corolla type (dialipetalous, gamopetalous, or achlamydeous), and corolla color (blue, green, lilac, orange, pink, purple, red, white, yellow). We additionally recorded species’ geographic origin (native/exotic). Quantitative floral traits were measured from field photographs taken with a 1-mm precision scale, using measurements from 2–17 flowers collected per species across multiple sites when available. Categorical traits were assigned using botanical literature and digital herbaria, including World Flora Online (WFO 2025), the STRI Research Portal (STRI 2025), and Fichas de Malezas de México (Vibrans 2012 ). When field specimens were unavailable, trait measurements were extracted from literature or from scaled photographs of herbarium specimens in these digital repositories (Table S3). All butterfly and plant trait measurements were extracted from scaled images using ImageJ (Schneider, Rasband, and Eliceiri 2012). Interactive role of plants and butterflies To quantify the importance of plant and butterfly species within interaction networks, we estimated the interactive role of every species occurring in each network constructed for each of the three pasture management types (FAP, WP and OP). For each management type, we built a plant–butterfly meta-network by pooling all interactions recorded across the eight pastures belonging to that category (Table S4; Fig S1 ). This approach was adopted to characterize regime-level interaction patterns and emergent species roles associated with each management type, rather than site-specific network variation, thereby emphasizing the structural and functional properties that consistently arise under shared management conditions and reducing the influence of local stochasticity in species interactions. Each meta-network was weighted, where a ₍ i ⱼ₎ represents the number of interactions between plant i and butterfly j , and zero indicates absence of interaction. Weighted networks that combine interaction frequencies and abundances are appropriate for estimating species roles in ecological interaction systems (Miranda et al. 2019). For each meta-network, we quantified the interactive role of plant and butterfly species using three complementary centrality metrics: degree centrality, betweenness centrality, and closeness centrality (Martín González, Dalsgaard, and Olesen 2010; Cagua, Wootton, and Stouffer 2019). Degree centrality reflects a species’ importance based on the number of interaction partners— with generalist species exhibiting high degree values and specialists exhibiting low values (Bascompte and Jordano 2013). Betweenness centrality identifies species that act as connectors between otherwise separated portions of the network; species with BC > 0 bridge structural gaps (Freeman 1979 ; Martín González, Dalsgaard, and Olesen 2010). Closeness centrality measures the overall proximity of each species to all others in the network, meaning that species with high closeness can influence, and be influenced by, other species more rapidly (Sazima et al. 2010 ). All centrality metrics were computed using the bipartite package in R (Dormann 2025 ). Because centrality metrics tend to be moderately to strongly correlated (Sazima et al. 2010 ; Estrada 2007 ), we summarized them into a single generalized centrality index using principal component analysis (PCA). This PCA was conducted separately for plants and butterflies within each meta-network to account for taxon-specific differences in interaction patterns. In all taxon–management combinations (n = 6), the first principal component (PC1) captured the largest proportion of variation (≥ 68.3%; Table S5), confirming that the three centrality descriptors carry complementary information and supporting the use of PC1 as an integrative measure of species centrality (Sazima et al. 2010 ; Dáttilo et al. 2022 ; Cruz et al. 2024 ; Ratoni et al. 2025 ). We added the minimum negative value among the PC1 data to obtain positive interactive role values, a linear transformation that does not alter the structure of the data but improves numerical handling and interpretability of the composite index. Given the positive correlations between PC1 and the original centrality metrics, high PC1 scores indicate species with high interactive roles (i.e. generalists), whereas low PC1 scores correspond to species with lower interactive roles (i.e. specialists). Statistical analysis Comparing interactive roles across management types To assess whether species’ interactive roles differed among pasture management types (FAP, WP, OP), we first estimated a centrality index. Because only one meta-network was available per management type, classical inferential procedures requiring replicated networks could not be applied. To address this limitation and obtain inference on mean interactive roles for each management category, we implemented a non-parametric bootstrap procedure (Efron and Tibshirani 1986). For butterflies and plants separately, we generated 1000 bootstrap resamples from the empirical distribution of PC1 scores within each management type, calculating mean values and 95% confidence intervals for each resample set. This approach provides robust distribution-free estimates of uncertainty and allows comparisons across management types based on the degree of overlap among confidence intervals. Overlapping intervals indicate that differences among management categories are not reliably distinguishable given the available data structure. All bootstrap procedures were implemented using the boot() function from the boot package (Canty A and Ripley B 2025). Functional traits on species interactive roles We evaluated whether interspecific variation in traits predicted species’ interactive roles within each management type. Analyses were conducted separately for butterflies and plants. For butterflies, explanatory variables included wing length, body length, and proboscis length. For plants, we considered corolla width, corolla length, corolla type, corolla shape, corolla color, and geographic origin (native/exotic). For each meta-network (i.e. taxon × management combination), we fitted separate generalized linear models (GLMs) using PC1 scores as the response variable. Error distributions and link functions were selected according to the nature of the response variable and predictors. Models with continuous predictors were fitted using Gaussian distributions with identity link functions, whereas models with categorical floral traits employed appropriate error structures ensuring correct residual behavior. All models were fitted using the glm() function from base R. Before model fitting, we evaluated normality and homoscedasticity of residuals using Shapiro–Wilk and Breusch–Pagan tests, respectively, and assessed overdispersion for non-Gaussian models. Residual diagnostics were conducted using simulation-based procedures with the DHARMa package (Hartig F 2024 ), allowing detection of non-uniformity, dispersion issues, and potential outliers. Multicollinearity among predictors was evaluated through Variance Inflation Factors (VIF) computed with the vif() function from the car package (Fox J and Weisberg S 2019). We initially explored an information-theoretic model selection approach; however, due to sample size constraints and model singularities, this approach did not yield stable model subsets across all taxon × management combinations. Therefore, we adopted a stepwise model simplification strategy using the step() function from base R, applying both backward and forward selection based on AIC. For each retained model, we extracted coefficients, standard errors, confidence intervals, and significance values, and computed marginal R² using the r.squaredGLM() function from MuMIn . All analyses were conducted in R version 4.3.1. (R Core Team 2023 ). Results Across the 24 surveyed pastures, we recorded 33 butterfly species and 67 plant species. Because networks from individual pastures were pooled by management type, we constructed three plant–butterfly meta-networks corresponding to FAP, WP and OP (Table S4). A total of 3,617 interaction events were documented across the study, ranging from 816 (FAP) to 1,706 (OP). In FAP, we recorded 816 interactions involving 43 plant species and 27 butterfly species. Sida acuta (Malvaceae) was the most frequently visited plant (200 visits; 24.5%), whereas Hedone vibex was the butterfly species interacting for the highest number of plants (228 visits; 27.9%). In WP, we documented 1,095 interactions, comprising 38 plant species and 24 butterfly species. Malachra alceifolia (Malvaceae) was the most frequently visited plant (295 visits; 27%), while Spicauda procne accounted for the highest number of butterfly interactions (382 visits; 35%). In OP, 1,706 interactions were recorded. Lippia dulcis (Verbenaceae) received the highest visitation frequency (421 visits; 24.7%), and Spicauda procne again emerged as the butterfly accounting for the highest number of interaction events (467 visits; 27.4%). Regarding floral traits, dialipetalous (66.5%), yellow (37%), rotaceous (41.3%) and native (92.9%) flowers were the most frequently visited across the entire study. Corolla width ranged from 1.5 mm in Cissus sp. to 50 mm in Limnocharis flava . Corolla length varied between 0.4 mm in Murdannia keisak and 27.9 ± 2.79 mm in Isertia haenkeana . Trait–visitation patterns remained generally consistent across management types, with the exception of OP, where white flowers received comparatively more visits (Table S6). Across the entire dataset, butterfly traits exhibited substantial interspecific variation. Wing length ranged from 8.53 ± 0.09 mm in Hemiargus hanno to 48.52 ± 1.28 mm in Heraclides thoas . Body size varied between 7.05 ± 0.34 mm ( H. hanno ) and 27.15 ± 2.23 mm ( Dryadula phaetusa ). Proboscis length spanned a wide gradient, from 4.42 ± 0.22 mm in Hermeuptychia hermes to 27.04 ± 4.10 mm in Phoebis sennae . These trait ranges were largely consistent across management types, although the species contributing to the extremes varied (Table S2). Effect of pasture management on the interactive role of butterflies and plants Bootstrap estimates showed largely overlapping 95% confidence intervals among management categories for both taxa (Table S7), indicating that variation in interactive roles across management types was not strong enough to be statistically distinguished. Although WP butterflies exhibited the highest mean centrality (1.606), their 95% CI overlapped extensively with those from FAP (0.942–1.639) and OP (1.037–1.691). A similar pattern emerged for plants: despite OP plants having the highest mean (1.394), their confidence interval overlapped broadly with those of FAP (1.013–1.590) and WP (0.806–1.518) (Fig. 2 ). Functional traits on species interactive roles When evaluating whether functional traits predicted the interactive roles of plants and butterflies within the meta-networks for each pasture management type, we found that species’ interactive roles were partially explained by their traits, indicating trait-dependent mechanisms influencing species roles within interaction networks. In tree-rich pastures (WP), butterfly wing length (β = − 0.028, p = 0.049) and body length (β = − 0.073, p = 0.011) were negatively related to centrality (R² = 0.16–0.26; Fig. 3 ; Table S8). In contrast, plant traits showed a consistent positive association with their interactive role. In both forest-adjacent (FAP) and tree-rich (WP) pastures, corolla width was positively related to centrality (β = 0.036–0.041, p < 0.03; R² = 0.13–0.15; Fig. 3 ; Table S8). Discussion Understanding how livestock management filters species’ functional roles within mutualistic networks is essential in TDF landscapes, where habitat transformation is extensive, and biodiversity must operate under strong climatic seasonality. In this study, we evaluated how pasture management types in a cattle-production region of northern Colombia influence the interactive roles of butterfly and plant species within plant–butterfly networks, and whether functional traits predict these roles. In contrast to studies showing that landscape configuration and management intensity often alter pollinator assemblages and interaction patterns (Proesmans et al. 2024 ; Cano et al. 2025 ), we found no evidence that management type (woody pasture, pastures adjacent to forest, or open pasture) significantly influenced species’ interactive roles. Instead, variation in interaction roles was primarily associated with functional traits, suggesting trait-based filtering rather than direct management-driven effects. The absence of management-driven differences likely reflects the ecological characteristics of TDF pastures in the region. First, despite differing in tree cover, the studied pasture types share a similar herbaceous layer dominated by weedy plants capable of thriving under grazing pressure. These herbaceous communities may buffer potential management effects by providing relatively uniform nectar availability across sites—particularly relevant in TDF, where rainfall seasonality produces strong environmental filters (Le Bagousse-Pinguet et al. 2017 ; Oloumane et al. 2025 ). Additionally, the moderate spatial scale of our sampling, combined with the limited functional differentiation of the herb layer across management types, may constrain the degree to which management practices translate into contrasting interaction patterns. In similar livestock landscapes, structural vegetation differences influence interaction roles only when management produces clear contrasts in resource availability, vegetation architecture, or disturbance regimes (Adedoja and Mallinger 2024 ; Peralta et al. 2023 ). Our findings suggest that, in this TDF region, such contrasts were insufficient to generate detectable differences in species-level interaction roles. Functional traits emerged as the primary predictors of interaction roles. For plants, corolla width was positively associated with the number of butterfly partners, consistent with trait-matching mechanisms frequently reported in pollination networks (Xiang et al. 2023 ; de Sousa Perugini et al. 2025 ; Maglianesi et al. 2024 ). Wider corollas often facilitate access for a broader range of visitors, thereby enhancing opportunities for interactions largely independent of management type. In our TDF landscape, this trait effect must also be interpreted through the lens of phenological dynamics in herbaceous weeds. Unlike canopy trees in TDF, which exhibit highly synchronized flowering tied to rainfall pulses (Astegiano et al. 2024 ; Günter et al. 2008 ; Silveira, Martins, and Araújo 2013 ), herbaceous weeds display asynchronous and flexible flowering, responding opportunistically to micro-environmental conditions (Cortés-Flores et al. 2017 ; McLaren and McDonald 2005). This asynchrony may produce sustained nectar availability across extended portions of the year, including periods in which forest trees are not flowering (Cortés-Flores et al. 2023 ). Such off-season floral resources may help sustain butterfly populations during phenological gaps, enabling their persistence within livestock systems and facilitating spillover to nearby habitats—including remnant TDF patches and fruit crop plantations that depend on pollination services (Rocha et al. 2018 ). Under this scenario, plants with wider and more accessible corollas may play a disproportionate role in maintaining interaction flow throughout the year, thereby stabilizing butterfly presence in an otherwise strongly seasonal environment. For butterflies, body size and wingspan were negatively associated with interactive role: larger butterflies interacted with fewer plant species, while smaller species were more generalized. This pattern contrasts with classical expectations linking larger pollinators to higher generalization (Peralta et al. 2023 ), yet it aligns with ecological mechanisms expected in herbaceous, disturbance-prone systems. Smaller butterflies are better suited to exploit fine-grained floral resources, maneuver efficiently within dense low vegetation, and maintain lower energetic requirements, all of which may confer broader realized diets in weedy pastures. The absence of an effect of proboscis length further supports the idea that morphological matching played a secondary role in this system, consistent with findings from other simplified or disturbed Neotropical habitats (Sõber et al. 2024 ). Instead, mobility- and size-related traits appear to mediate access to heterogeneous nectar resources in TDF pastures. Our findings align with broader evidence that functional traits mediate species responses and interaction roles under land-use change (Le Bagousse-Pinguet et al. 2017 ; Adedoja and Mallinger 2024 ; Cano et al. 2025 ). Land-use intensification often imposes strong environmental filters that disproportionately affect species with particular traits, altering interaction patterns and functional overlap. However, when local resource conditions are homogeneous—as in the herb-dominated pastures we studied—trait-based processes may override management effects, generating comparable interactive roles across pasture types. Woody pastures, despite offering shade and structural heterogeneity, did not modify interactive roles, highlighting the resilience of these weedy plant–butterfly interaction networks under the tested gradients. In heavily transformed TDF regions, where > 90% of native cover has been lost, understanding how functional traits shape interaction roles is essential for conservation planning. Our results indicate that trait-based mechanisms, rather than management type, determine the degree to which butterflies and plants contribute to interaction networks in livestock landscapes. This suggests that conservation strategies should prioritize (i) maintaining diverse herbaceous communities that provide continuous nectar resources, (ii) preserving or restoring plant species with accessible floral architectures, and (iii) safeguarding butterfly species with traits that promote broad interaction potential. Moreover, by providing continuous or complementary resources within highly modified landscapes, well-managed livestock pastures may contribute to sustaining pollinator communities beyond their boundaries. When structural and compositional heterogeneity is maintained, these systems can function as supplementary habitats that enhance landscape-level connectivity and support pollination services across the broader tropical dry forest matrix, including remnant forest patches and surrounding agroecosystems. Conclusion This study shows that livestock management regimes in TDF landscapes do not have a strong direct effect on the overall centrality of species within plant–butterfly networks. Instead, management appears to act primarily as a trait-based filter, influencing which plant and butterfly species attain more influential interactive roles. Larger-bodied butterflies tended to exhibit lower centrality, whereas plant species with broader corollas consistently acted as interaction hubs. These patterns reveal that functional traits, rather than management per se, are the main determinants of species’ positions within networks. A second key mechanism emerges from the phenology of the weed species common in grazed pastures. Unlike the strongly seasonal flowering of dry forest trees, these weedy species display asynchronous and prolonged flowering, providing floral resources during periods when the surrounding forest matrix offers few or none. Such temporally decoupled resource availability may help maintain pollinator activity across seasonal bottlenecks, thereby supporting butterfly populations that later interact with both forest plant communities and pollinator-dependent fruit crops cultivated in the region. Together, these findings underscore that biodiversity-friendly pasture management can contribute to sustaining mutualistic processes not by altering network structure directly, but by conserving trait diversity and maintaining continuous floral resource availability. Protecting woody elements and diverse ruderal assemblages within livestock systems may thus enhance functional linkages among agricultural lands, pollinator communities, and tropical dry forest remnants, strengthening ecosystem resilience in one of the world’s most threatened biomes. Declarations Acknowledgments The authors wish to thank the biologists Brayan Pérez, Sergio Torres, and Sebastián Mogrovejo for their support during the fieldwork. We are grateful to Agropecuaria Tabaidá S.A.S. and the other cattle farms for granting us access to their properties to conduct this research. Funding This work was supported by the Colombian Ministry of Science, Technology, and Innovation (Minciencias) through the "Bicentenario" Scholarship (Cohort II) and by the Pontificia Universidad Javeriana through the "Academic/Research Assistantship" Scholarship, both funding the doctoral training of the first author, Roger Ayazo Berrocal. The first author also received research-phase support from the Doctoral Thesis Project Support Scholarship (proposal code PPTA_20937) awarded by the University's Vice-Rectory for Research. Competing intereses The first author received logistical support from Agropecuaria Tabaidá S.A.S. during fieldwork on their properties. The remaining authors have no relevant financial or non-financial interests to disclose. Author contributions Study conception, design, material preparation and data collection were performed by R-AB and C-CA. Analysis were performed by R-AB and W-D. The first draft of the manuscript was written by R-AB and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Adedoja OA, Mallinger RE (2024) Can Trait Matching Inform the Design of Pollinator-Friendly Urban Green Spaces? A Review and Synthesis of the Literature. Ecosphere 15(1). https://doi.org/10.1002/ecs2.4734 Álamo M, Mingarro M, Wilson RJ, Álvarez HA (2025) Effects of Elevation and Microclimatic Temperatures on Butterfly–Flower Interaction Networks in a Mediterranean Mountain Range. Insect Conserv Divers. https://doi.org/10.1111/icad.70008 de Araújo Silva G, Nascimento BSD, U de Jesus Araújo Pinto, A L C dos Santos, M do Vale Beirão, and, de Oliveira J, Silva (2025) Changes in Vegetation Cover and Their Effects on the Diversity of Fruit-Feeding Butterflies. Austral Ecology 50 (7). https://doi.org/10.1111/aec.70095 Astegiano J, Carbone L, Zamudio F, Tavella J, Ashworth L, Aguilar R, Beccacece HernánM et al (2024) Diversifying Agroecological Systems: Plant-Pollinator Network Organisation and Landscape Heterogeneity Matter. Agric Ecosyst Environ 361. https://doi.org/10.1016/j.agee.2023.108816 . (February). Elsevier B.V. Bagousse-Pinguet Y, Le N, Gross FT, Maestre V, Maire F, de Bello CR, Fonseca J, Kattge E, Valencia J, Leps, Liancourt P (2017) Testing the Environmental Filtering Concept in Global Drylands. J Ecol 105(4):1058–1069. https://doi.org/10.1111/1365-2745.12735 Bartomeus I, Gravel D, Tylianakis JM, Aizen MA, Dickie IA, and Maud Bernard-Verdier (2016) A Common Framework for Identifying Linkage Rules across Different Types of Interactions. Funct Ecol 30(12):1894–1903. https://doi.org/10.1111/1365-2435.12666 Bascompte J, and Pedro Jordano (2013) Mutualistic Networks. Princeton University Press Bauder JAS, Morawetz L, Warren AD, Krenn HW (2015) Functional Constraints on the Evolution of Long Butterfly Proboscides: Lessons from Neotropical Skippers (Lepidoptera: Hesperiidae). J Evol Biol 28(3):678–687. https://doi.org/10.1111/jeb.12601 Buono CM, Lofaso J, Smisko W, Gerth C, Santare J, Prior KM (2023) Historical Forest Disturbance Results in Variation in Functional Resilience of Seed Dispersal Mutualisms. Ecology 104(4). https://doi.org/10.1002/ecy.3978 Burian A, Norton BA, Alston D, Willmot A, Reynolds S, Meynell G, Lynch P, and M Bulling (2023) Low-Cost Management Interventions and Their Impact on Multilevel Trade-Offs in Agricultural Grasslands. J Appl Ecol 60(10):2079–2090. https://doi.org/10.1111/1365-2664.14492 Bustamante-Castillo M, Hernández-Baños BE, Arizmendi MC (2020) Hummingbird-Plant Visitation Networks in Agricultural and Forested Areas in a Tropical Dry Forest Region of Guatemala. J Ornithol 161(1):189–201. https://doi.org/10.1007/s10336-019-01712-4 Cagua E, Fernando KL, Wootton, Stouffer DB (2019) Keystoneness, Centrality, and the Structural Controllability of Ecological Networks. Journal of Ecology 107 (4). Blackwell Publishing Ltd: 1779–90. https://doi.org/10.1111/1365-2745.13147 Cano D, Martínez-Núñez C, Pérez AJ, Alcántara JM, Moretti M, Salido T, Rey PJ (2025) Agricultural Intensification Indirectly Reshapes Bee–Plant Interaction Networks through Shifts in Bee Functional Traits. Ecol Appl 35(6). https://doi.org/10.1002/eap.70105 Canty A, and Ripley B (2025) Boot: Bootstrap Funct doi. 10.32614/CRAN.package.boot Chirwa PW, Kozanayi W, Uisso AJ, Tshidzumba RP, Babalola FD, Amusa TO (2024) Socio-Economic Factors, Policy and Governance Systems Influencing Multifunctional Landscapes. In Trees in a Sub-Saharan Multi-Functional Landscape: Research, Management, and Policy , 305–27. https://doi.org/10.1007/978-3-031-69812-5_13 Cockburn J, Cundill G, Shackleton S, Rouget M, Zwinkels M, Cornelius SA, Metcalfe L, van den D, Broeck (2019) Collaborative Stewardship in Multifunctional Landscapes: Toward Relational, Pluralistic Approaches. Ecol Soc 24(4). https://doi.org/10.5751/ES-11085-240432 Correa Ayram C, Andrés Andrés Etter, Julián Díaz-Timoté, Susana Rodríguez Buriticá, Wilson Ramírez, and Germán Corzo. 2020. Spatiotemporal Evaluation of the Human Footprint in Colombia: Four Decades of Anthropic Impact in Highly Biodiverse Ecosystems. Ecological Indicators 117 (April). Elsevier: 106630. https://doi.org/10.1016/j.ecolind.2020.106630 Cortés-Flores J, K Hernández-Esquivel A, González-Rodríguez, Ibarra-Manríquez G (2017) Flowering Phenology, Growth Forms, and Pollination Syndromes in Tropical Dry Forest Species: Influence of Phylogeny and Abiotic Factors. Am J Bot 104(1):39–49. https://doi.org/10.3732/ajb.1600305 Cortés-Flores J, Lopezaraiza-Mikel M, de Santiago-Hernández MH, Martén-Rodríguez S, Cristóbal-Pérez EJ, Aguilar-Aguilar MJ, Balvino-Olvera FJ et al (2023) Successional and Phenological Effects on Plant-Floral Visitor Interaction Networks of a Tropical Dry Forest. J Ecol 111(4):927–942. https://doi.org/10.1111/1365-2745.14072 Coux C, Rader R, Bartomeus I, Tylianakis JM (2016) Linking Species Functional Roles to Their Network Roles. Ecol Lett 19(7):762–770. https://doi.org/10.1111/ele.12612 Cruz CP, Luna P, Villalobos F, Guevara R, Hinojoza-Díaz I, and Wesley Dáttilo (2024) The Central Importance of the Honeybee (Apis Mellifera L.) within Plant-Bee Interaction Networks Decreases along a Neotropical Elevational Gradient. Ecol Complex 60. https://doi.org/10.1016/j.ecocom.2024.101105 . (December). Elsevier B.V Dáttilo W, Cruz CP, Luna P, Ratoni B, Ismael A, Hinojosa-Díaz FS, Neves M, Leponce F, Villalobos, and Roger Guevara (2022) The Impact of the Honeybee Apis Mellifera on the Organization of Pollination Networks Is Positively Related with Its Interactive Role throughout Its Geographic Range. Diversity 14(11). https://doi.org/10.3390/d14110917 . MDPI Dáttilo W, Guimarães PR, Izzo TJ (2013) Spatial Structure of Ant-Plant Mutualistic Networks. Oikos 122(11):1643–1648. https://doi.org/10.1111/j.1600-0706.2013.00562.x Dehling D, Matthias P, Jordano HM, Schaefer K, Böhning-Gaese (2016) and Matthias Schleuning. Morphology Predicts Species’ Functional Roles and Their Degree of Specialization in Plant–Frugivore Interactions. Proceedings of the Royal Society B: Biological Sciences 283 (1823). Royal Society of London. https://doi.org/10.1098/rspb.2015.2444 Dormann C (2025) Package bipartite Type Package Title Visualising Bipartite Networks and Calculating Some (Ecological) Indices . https://orcid.org/0000-0002-7079-3478 Dupin MG, V MM, Espirito-Santo ME, Leite JO, Silva AM, Rocha RS, Barbosa, Anaya FC (2018) Land Use Policies and Deforestation in Brazilian Tropical Dry Forests between 2000 and 2015. Environmental Research Letters 13 (3). https://doi.org/10.1088/1748-9326/aaadea Efron B, and R Tibshirani (1986) Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Stat Sci 1(1):54–77 Estrada E (2007) Characterization of Topological Keystone Species. Local, Global and ‘Meso-Scale’ Centralities in Food Webs. Ecol Complex 4(1–2):48–57. https://doi.org/10.1016/j.ecocom.2007.02.018 Fox J, Weisberg S (2019) An R Companion to Applied Regression . Third. Thousand Oaks CA: Sage. https://www.john-fox.ca/Companion/ Freeman LC (1979) Centrality in Social Networks Conceptual Clarification. Social Networks 1:215–239. http://dx.doi.org/10.1016/0378-8733(78)90021-7 Fuzessy LF, Pizo MA (2025) Navigating a Changing World: On the Significance of Rewiring for Mutualistic Interactions, Caveats and Future Directions. Oikos 2025 (10). https://doi.org/10.1002/oik.11230 Galvis L (2009) Geografía Económica Del Caribe Continental . Cartagena Garibaldi LA, Zermoglio PF, Jobbágy EG, Andreoni L, Ortiz de Urbina A, Grass I, Oddi FJ (2023) How to Design Multifunctional Landscapes? J Appl Ecol 60(12):2521–2527. https://doi.org/10.1111/1365-2664.14517 Garwood K (2017) Butterfly Catalogs. BioButterfly Database . < http://www.butterflycatalogs.com/ Genini J, Guimarães PR, Sazima M, Sazima I, Morellato LPC (2021) Temporal Organization among Pollination Systems in a Tropical Seasonal Forest. Sci Nat 108(4). https://doi.org/10.1007/s00114-021-01744-y Gilhaus K, Boch S, Fischer M, Hölzel N, Kleinebecker T, Prati D, Rupprecht D, Schmitt B, Klaus VH (2017) Grassland Management in Germany: Effects on Plant Diversity and Vegetation Composition. Tuexenia 37(1):379–397. https://doi.org/10.14471/2017.37.010 Gómez JM, Perfectti F, Klingenberg CP (2014) The Role of Pollinator Diversity in the Evolution of Corolla-Shape Integration in a Pollination-Generalist Plant Clade. Philosophical Trans Royal Soc B: Biol Sci 369(1649). https://doi.org/10.1098/rstb.2013.0257 Guimarães PR, Pires MM, Jordano P, Bascompte J, Thompson JN (2017) Indirect Effects Drive Coevolution in Mutualistic Networks. Nature 550(7677):511–514. https://doi.org/10.1038/nature24273 Günter S, Stimm B, Cabrera M, Diaz ML, Lojan M, Ordoñez E, Richter M, Weber M (2008) Tree Phenology in Montane Forests of Southern Ecuador Can Be Explained by Precipitation, Radiation and Photoperiodic Control. J Trop Ecol 24(3):247–258. https://doi.org/10.1017/S0266467408005063 Guo L, Zhao LF, Ye JL, Ji ZJ, Tang JJ, Bai KY, Zheng SJ, Hu LL, Chen X (2022) Using Aquatic Animals as Partners to Increase Yield and Maintain Soil Nitrogen in the Paddy Ecosystems. ELIFE 11. Zhejiang Univ, Coll Life Sci, Hangzhou, Peoples R China. https://doi.org/10.7554/eLife.73869 Guzman LM, Chamberlain SA, Elle E (2021) Network Robustness and Structure Depend on the Phenological Characteristics of Plants and Pollinators. Ecol Evol 11(19):13321–13334. https://doi.org/10.1002/ece3.8055 Hagen M, Kissling WD, Rasmussen C, Marcus AM, De Aguiar LE, Brown DW, Carstensen I, Alves-Dos-Santos et al (2012) 2 - Biodiversity, Species Interactions and Ecological Networks in a Fragmented World. In Advances in Ecological Research , edited by Ute Jacob and Guy Woodward, 46:89–210. Academic Press. https://doi.org/10.1016/B978-0-12-396992-7.00002-2 Hartig F (2024) DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. https://doi.org/ 10.32614/CRAN.package.DHARMa%3Chttps: 10.32614="cran.package.dharma%3C=" doi.org="urlz="%3E.%3C/https:%3E IAvH (1998) El Bosque Seco Tropical (Bs-T) En Colombia. Programa de Inventario de la Biodiversidad Grupo de Exploraciones y Monitoreo Ambiental GEMA, Bogotá IGAC (2009) Descripción Del Medio Biofísico. Estudio General de Suelos y Zonificación de Tierras: Departamento de Córdoba . Montería Izquierdo-Palma J, Del Coro Arizmendi M, Lara C, Ornelas JF (2021) Forbidden Links, Trait Matching and Modularity in Plant-Hummingbird Networks: Are Specialized Modules Characterized by Higher Phenotypic Floral Integration? PeerJ 9. https://doi.org/10.7717/peerj.10974 Junker RR, Blüthgen N, Brehm T, Binkenstein J, Paulus J, Schaefer HM, and Martina Stang (2013) Specialization on Traits as Basis for the Niche-Breadth of Flower Visitors and as Structuring Mechanism of Ecological Networks. Funct Ecol 27(2):329–341. 10.1111/1365-2435.12005 Lázaro A, and L Santamaría (2016) Flower-Visitor Selection on Floral Integration in Three Contrasting Populations of Lonicera Implexa. Am J Bot 103(2):325–336. https://doi.org/10.3732/ajb.1500336 Lehnert MS, Beard CE, Gerard PD, Kornev KG (2016) and Peter H. Adler. Structure of the Lepidopteran Proboscis in Relation to Feeding Guild. Journal of Morphology 277 (2). John Wiley and Sons Inc.: 167–82. https://doi.org/10.1002/jmor.20487 Leroy A, Martin O, Gautier J-L, Bretagnolle V, Gaba S (2025) The Surrounding Landscape Affects Ecosystem Multifunctionality in Oilseed Rape Fields. J Appl Ecol 62(9):2283–2295. https://doi.org/10.1111/1365-2664.70105 Litchman E, Edwards KF, Boyd PW (2021) Toward Trait-Based Food Webs: Universal Traits and Trait Matching in Planktonic Predator–Prey and Host–Parasite Relationships. Limnol Oceanogr 66(11):3857–3872. https://doi.org/10.1002/lno.11924 Liu M, Liu G, Zheng X (2015) Spatial Pattern Changes of Biomass, Litterfall and Coverage with Environmental Factors across Temperate Grassland Subjected to Various Management Practices. Landscape Ecol 30(3):477–486. https://doi.org/10.1007/s10980-014-0120-1 Maglianesi MA, Brenes E, Chaves-Elizondo N, Zuniga K, Castro Jiménez A, Barreto E, Duchenne F, Graham CH (2024) Species Morphology Better Predicts Plant-Hummingbird Interactions across Elevations than Nectar Traits. Proceedings of the Royal Society B: Biological Sciences 291 (2031). https://doi.org/10.1098/rspb.2024.1279 Marini L, Fontana P, Battisti A, Gaston KJ (2009) Agricultural Management, Vegetation Traits and Landscape Drive Orthopteran and Butterfly Diversity in a Grassland-Forest Mosaic: A Multi-Scale Approach. Insect Conserv Divers 2(3):213–220. https://doi.org/10.1111/j.1752-4598.2009.00053.x Martín González AM, Dalsgaard B, Jens M, Olesen (2010) Centrality Measures and the Importance of Generalist Species in Pollination Networks. Ecol Complex 7(1):36–43. https://doi.org/10.1016/j.ecocom.2009.03.008 Maza-Villalobos S, García-Ramírez P, Endress BA, Lopez-Toledo L (2022) Plant Functional Traits under Cattle Grazing and Fallow Age Scenarios in a Tropical Dry Forest of Northwestern Mexico. Basic Appl Ecol 64:30–44. https://doi.org/10.1016/j.baae.2022.06.006 McGranahan DA (2014) Ecologies of Scale: Multifunctionality Connects Conservation and Agriculture across Fields, Farms, and Landscapes. Land 3(3):739–769. https://doi.org/10.3390/land3030739 McLaren KP, and M A McDonald (2005) Seasonal Patterns of Flowering and Fruiting in a Dry Tropical Forest in Jamaica. Biotropica 37(4):584–590. https://doi.org/10.1111/j.1744-7429.2005.00075.x Meisel A, Pérez G (2006) Geografía Física y Poblamiento En La Costa Caribe Colombiana. Documentos de Trabajo Sobre Economía Regional . Cartagena. http://www.banrep.gov.co/publicaciones/pub_ec_reg4.htm Mertens JEJ, Brisson L, Janeček Š, Klomberg Y, Maicher V, Sáfián S, Delabye S et al (2021) Elevational and Seasonal Patterns of Butterflies and Hawkmoths in Plant-Pollinator Networks in Tropical Rainforests of Mount Cameroon. Sci Rep 11(1). https://doi.org/10.1038/s41598-021-89012-x Miranda PatríciaN José Eduardo Lahoz da Silva Ribeiro, Pedro Luna, Izaias Brasil, Jacques Hubert Charles Delabie, and Wesley Dáttilo. 2019. The Dilemma of Binary or Weighted Data in Interaction Networks. Ecological Complexity 38 (April). Elsevier B.V.: 1–10. https://doi.org/10.1016/j.ecocom.2018.12.006 Mitja D, Miranda IS (2010) Weed Community Dynamics in Two Pastures Grown after Clearing Brazilian Amazonian Rainforest. Weed Res 50(2):163–173. https://doi.org/10.1111/j.1365-3180.2010.00767.x Muchhala N (2007) Adaptive Trade-off in Floral Morphology Mediates Specialization for Flowers Pollinated by Bats and Hummingbirds. Am Nat 169(4):494–504. https://doi.org/10.1086/512047 Neff PK, K SM, Fettig, and D R VanOverbeke (2007) Variable Response of Butterflies and Vegetation to Elk Herbivory: An Exclosure Experiment in Ponderosa Pine and Aspen-Mixed Conifer Forests. Southwest Naturalist 52(1):1–14. https://doi.org/10.1894/0038-4909(2007)52[1:VROBAV]2.0.CO;2 Oloumane P-N, Previl C, Zerey WE, El Zerey-Belaskri A (2025) Applying Joint Species Distribution Modelling to Assess the Relative Influence of Ecological Filters on Community Assembly in the El Bayadh Steppe, Algeria. J Arid Land 17(7):979–996. https://doi.org/10.1007/s40333-025-0082-y Peralta G, Webber CJ, Perry GLW, Stouffer DB, Vázquez DP, Tylianakis JM (2023) Scale-Dependent Effects of Landscape Structure on Pollinator Traits, Species Interactions and Pollination Success. Ecography 2023 (5). https://doi.org/10.1111/ecog.06453 Pires EP, Faria LDB, Monteiro AB, Domingos DQ, Mansanares ME, Hermes MG (2022) Insect Sociality Plays a Major Role in a Highly Complex Flower-Visiting Network in the Neotropical Savanna. Apidologie 53(1). https://doi.org/10.1007/s13592-022-00923-8 van der Plas F, Allan E, Fischer M, Alt F, Arndt H, Binkenstein J, Blaser S et al (2019) Towards the Development of General Rules Describing Landscape Heterogeneity–Multifunctionality Relationships. J Appl Ecol 56(1):168–179. https://doi.org/10.1111/1365-2664.13260 Powers JS, Feng X, Sanchez-Azofeifa A, and D Medvigy (2018) Focus on Tropical Dry Forest Ecosystems and Ecosystem Services in the Face of Global Change. Environ Res Lett 13(9). https://doi.org/10.1088/1748-9326/aadeec Proesmans W, Felten E, Laurent E, Albrecht M, Cyrille N, Labonté A, Maurer C et al (2024) Urbanisation and Agricultural Intensification Modulate Plant–Pollinator Network Structure and Robustness. Funct Ecol 38(3):628–641. https://doi.org/10.1111/1365-2435.14503 Quisehuatl-Medina A, Averett JP, Endress BA, Lopez-Toledo L (2020) Removal of Cattle Accelerates Tropical Dry Forest Succession in Northwestern Mexico. Biotropica 52(3):457–469. https://doi.org/10.1111/btp.12748 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria Ramos A, Ospino J, Serrano D, Linares J (2016) Análisis Geográfico de La Perdida y/o Fragmentación Del Bosque Seco Tropical En Córdoba Mediante Imagines Landsat Para El Período 1985–2003. EVODIA 1(1):15–26 Ratoni B, Cruz CP, Roger Guevara, Daniel González-Tokman, Ricardo Ayala, Fernanda Baena, and Wesley Dáttilo. 2025. Thermal Tolerance and Sociality Explain the Interactive Role of Bees in a Pollination Network. (2025) (3) John Wiley and Sons Inc. https://doi.org/10.1111/oik.10639 Rocha OJ, Gómez C, Hamrick JL, Trapnell DW, Smouse PE, Macaya G (2018) Reproductive Consequences of Variation in Flowering Phenology in the Dry Forest Tree Enterolobium Cyclocarpum in Guanacaste, Costa Rica. Am J Bot 105(12):2037–2050. https://doi.org/10.1002/ajb2.1205 Rocha-Filho LCD, Cardoso JCF, D Oliveira AC, Araújo TN, Castro-Melo ALDSE, Augusto SC (2025) How Much Biodiversity Do Yellow Passionfruit (Passiflora Edulis) Croplands Harbour? Insights from Trap-Nesting Bees, Wasps and Their Natural Enemies. J Nat Conserv 84. https://doi.org/10.1016/j.jnc.2024.126818 Ruiz R (2022) FLORA RELICTUAL DEL BOSQUE SECO TROPICAL (BST) DEL DEPARTAMENTO DE CÓRDOBA . 1a ed. Montería: Fondo editorial Universidad de Córdoba Santos TD, Ribeiro ADS (2023) Mutualistic Interaction Network Structure between Bird and Plant Species in a Semi-Arid Neotropical Environment. Acta Oecol 118. https://doi.org/10.1016/j.actao.2023.103897 Sazima C, Guimarães PR, Sérgio F, dos Reis, Sazima I (2010) What Makes a Species Central in a Cleaning Mutualism Network? Oikos 119(8):1319–1325. https://doi.org/10.1111/j.1600-0706.2009.18222.x Schneider CA, Wayne S, Rasband, Kevin W, Eliceiri (2012) NIH Image to ImageJ: 25 Years of Image Analysis. Nat Methods 9(7):671–675. https://doi.org/10.1038/nmeth.2089 Shrotri S, Kaur S, Dhargalkar R, Valappil NP, and V Gowda (2025) Mass Flowering and Flowering Asynchrony Characterize a Seasonal Herbaceous Community in the Western Ghats. Biotropica 57(5). https://doi.org/10.1111/btp.70080 Silveira AP, Martins FR, Araújo FS (2013) Do Vegetative and Reproductive Phenophases of Deciduous Tropical Species Respond Similarly to Rainfall Pulses? J Forestry Res 24(4):643–651. https://doi.org/10.1007/s11676-013-0366-5 Sõber V, Aavik T, Kaasik A, Mesipuu M, and T Teder (2024) Insect-Pollinated Plants Are First to Disappear from Overgrowing Grasslands: Implications for Restoring Functional Ecosystems. Biol Conserv 291. https://doi.org/10.1016/j.biocon.2024.110457 Sousa Perugini LG, de LR, Jorge J, Ollerton MA, Milaneze-Gutierre, Rech AR (2025) High Modularity of Plant-Pollinator Interactions in an Urban Garden Is Driven by Phenological Continuity and Flower Morphology. Urban Ecosyst 28(3). https://doi.org/10.1007/s11252-025-01737-z STRI. (2025) STRI. STRI . https://striresearch.si.edu/ Swift KI, Bell FW (2011) What Are the Environmental Consequences of Using Silviculturally Effective Forest Vegetation Management Treatments? For Chron 87(2):201–216. https://doi.org/10.5558/tfc2011-008 Tabarelli M, B K C Filgueiras EMS, Ribeiro AV, Lopes, Leal IR (2024) Tropical Dry Forests. In Encyclopedia of Biodiversity, Third Edition: Volume 1–7 , V1-294. https://doi.org/10.1016/B978-0-12-822562-2.00090-6 Teixeira LP, Pires-Oliveira JC, Portal-Gomes PW, Eisenlohr PV, Santos JDO, Lima SS, Moat J, Nic Lughadha E, Moro MF (2025) Biodiversity at Risk: Climate Change Impacts on Brazil’s Semiarid Caatinga Flora. Earth Syst Environ. https://doi.org/10.1007/s41748-025-00883-w Vanbergen AJ, Ben A, Woodcock A, Gray F, Grant A, Telford P, Lambdon DS, Chapman RF, Pywell MS, Heard, and Stephen Cavers (2014) Grazing Alters Insect Visitation Networks and Plant Mating Systems. Funct Ecol 28(1):178–189. https://doi.org/10.1111/1365-2435.12191 Vázquez DP, William F, Morris, and Pedro Jordano (2005) Interaction Frequency as a Surrogate for the Total Effect of Animal Mutualists on Plants. Ecol Lett 8(10):1088–1094. https://doi.org/10.1111/j.1461-0248.2005.00810.x Vibrans H (2012) Malezas de México. Conabio . http://www.conabio.gob.mx/malezasdemexico/2inicio/paginas/lista-plantas-nov2011.html Warren AD, Davis KJ, Stangeland EM, Pelham JP, Willmott KR, Grishin NV (2024) Illustrated List of Butterflies of America. Butterflies of America . April 9. https://butterfliesofamerica.com/ WFO. (2025) World Flora Online. The World Flora Online . http://www.worldfloraonline.org Xiang G, Jiang Y, Lan J, Huang L, Hao L, Liu Z, and J Xia (2023) Different Influences of Phylogenetically Conserved and Independent Floral Traits on Plant Functional Specialization and Pollination Network Structure. Front Plant Sci 14. https://doi.org/10.3389/fpls.2023.1084995 Zakharova L, Meyer KM, Seifan M (2019) Trait-Based Modelling in Ecology: A Review of Two Decades of Research. Ecol Model 407. https://doi.org/10.1016/j.ecolmodel.2019.05.008 Additional Declarations No competing interests reported. Supplementary Files SuplementarymaterialP2.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 10 Mar, 2026 Editor assigned by journal 01 Mar, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 19 Feb, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8919271","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605899862,"identity":"551a9cd4-3211-4282-8d14-c887e12d67ab","order_by":0,"name":"Roger Ayazo-Berrocal","email":"","orcid":"","institution":"Pontificia Universidad Javeriana","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"","lastName":"Ayazo-Berrocal","suffix":""},{"id":605899863,"identity":"426fed88-5133-4a40-8b71-2c7dfef7b0fa","order_by":1,"name":"Wesley Dáttilo","email":"","orcid":"","institution":"Instituto de Ecología AC","correspondingAuthor":false,"prefix":"","firstName":"Wesley","middleName":"","lastName":"Dáttilo","suffix":""},{"id":605899864,"identity":"18f45963-5ba2-4079-acc1-52e7bfae9b4b","order_by":2,"name":"Camilo Correa-Ayram","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDADfoYDIEoCwuMhRotkA8laDA4g8/Bp4W/vffbhQw2DvPHBw88kPu6xkDdnP8D44G0bgzw/Di0SZ44bz5xxjMFw24FjZpIznkkY7uxJYDac28ZgOLMBh3sk0piZedgYEswOHDA25jkgwbjhQAKbNG8bQwKqU9G0/PnHkGDccPyz8Z8DEvYbzj9g/01QCyNIAcMZw8cMByQSN9xIYGPGp0XizDFmxt4+CcMZB84UPuw5IJG84cbDZsk55yRw+oW/vY2Z4cc3G3n+Gcc3HPhxoM52w/nkgx/elNngDDGYZUAEdwdjAwM8TvECfhzuGAWjYBSMglEAACFhWNCU3uuMAAAAAElFTkSuQmCC","orcid":"","institution":"Pontificia Universidad Javeriana","correspondingAuthor":true,"prefix":"","firstName":"Camilo","middleName":"","lastName":"Correa-Ayram","suffix":""}],"badges":[],"createdAt":"2026-02-19 16:10:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8919271/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8919271/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104754917,"identity":"92a3d4ae-4e0a-4742-ac22-4b1c2203f6c6","added_by":"auto","created_at":"2026-03-16 21:40:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2406767,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of the 24 sampled cattle pastures within the TDF landscape of Córdoba (Colombia). Three pasture management types: forest-adjacent pastures (FAP), wooded pastures (WP), and open pastures (OP) (n = 8 per type), where ecological interaction data were compiled to build plant–butterfly networks\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8919271/v1/084b380b7b83c1b82de6b6f9.png"},{"id":104754915,"identity":"5a8130aa-9706-4f1c-b5b1-1169eab7a1d5","added_by":"auto","created_at":"2026-03-16 21:40:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2417725,"visible":true,"origin":"","legend":"\u003cp\u003eMean interactive role (PC1) of butterfly and plant species across the three pasture management types—forest-adjacent pastures (FAP), wooded pastures (WP), and open pastures (OP). Points represent bootstrap means and vertical lines indicate 95% bootstrap confidence intervals for each management x taxon combination\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8919271/v1/5b1286ccf1d24ce856390baf.png"},{"id":104754919,"identity":"4a629a9f-318f-4ab8-8110-d7f511b4f7af","added_by":"auto","created_at":"2026-03-16 21:40:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6659466,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies’ interactive roles (PC1) and functional traits of butterflies and plants across pasture management types. Panels a and b show butterfly traits in wooded pastures (WP): (a) wing length and (b) body length. Panels c and d show plant traits, with (c) corolla width in forest-adjacent pastures (FAP) and (d) corolla width in wooded pastures (WP). Red lines represent fitted regression slopes (β), with shaded areas indicating 95% confidence intervals\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8919271/v1/5498afff64d6c5bde74a661a.png"},{"id":105033735,"identity":"47897ba3-7b99-481a-ba49-94fe2141c215","added_by":"auto","created_at":"2026-03-20 07:21:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9410112,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919271/v1/1879c30a-394d-4be8-afb9-4b50090899ad.pdf"},{"id":104754916,"identity":"1dfa7c83-0df3-4326-8cde-a095e8858b44","added_by":"auto","created_at":"2026-03-16 21:40:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2429513,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementarymaterialP2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8919271/v1/ad7190832b93ee86854bd013.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cattle pasture management filters functional traits and alters trait–role relationships in plant–butterfly interaction networks","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTropical dry forests (TDF) are among the most threatened terrestrial biomes, characterized by marked seasonality, high levels of endemism, and long historical exposure to human transformation (Powers et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tabarelli et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teixeira et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Across the Neotropics, extensive conversion of TDF to agricultural systems—especially cattle production—has resulted in highly fragmented landscapes where remnant forest patches persist within a matrix of pastures (Dupin et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rocha-Filho et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). These changes influence ecological processes at multiple scales, including the dynamics of plant–pollinator interactions that underpin ecosystem functioning (Bustamante-Castillo, Hernández-Baños, and Arizmendi \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding how mutualistic interactions persist or reconfigure under land-use change is therefore critical for conserving biodiversity and sustaining ecosystem services in these degraded yet ecologically valuable regions (Proesmans et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Buono et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin modified landscapes, species interactions often respond more strongly to trait-based mechanisms than to species identity alone (Zakharova, Meyer, and Seifan \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Litchman, Edwards, and Boyd \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Functional traits modulate how species perceive, exploit, and contribute to ecological networks. In pollination systems, traits such as floral morphology or pollinator body size shape partner accessibility, interaction frequency, and specialization (Gómez, Perfectti, and Klingenberg \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Muchhala \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Because traits determine how species cope with habitat alteration, management practices that restructure vegetation can induce non-random filtering of traits (Liu, Liu, and Zheng \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gilhaus et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Swift and Bell \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), ultimately affecting the distribution of interactive roles within mutualistic networks (Dáttilo, Guimarães, and Izzo \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Guimarães et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fuzessy and Pizo \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Empirical research has identified species that play a highly interactive role, characterized by significant network importance and a major influence on co-occurring species. This framework has proven useful for understanding responses of plant–pollinator systems to disturbance yet remains understudied for plant–butterfly assemblages in highly transformed TDF landscapes.\u003c/p\u003e \u003cp\u003eLivestock systems provide a valuable context for investigating trait-mediated mechanisms, as pasture management strongly modify vegetation structure and floral resource availability (Vanbergen et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lázaro and Santamaría 2016). Forest-adjacent pastures (FAP) and Woody pastures (WP) retain higher greater structural complexity and microhabitat heterogeneity than open pastures (OP), potentially influencing both plant assemblages and butterfly communities. Such differences can influence both the abundance of weed plants and the assemblage of butterfly species visiting flowers (de Araújo Silva et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Neff, Fettig, and VanOverbeke 2007; Marini et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mitja and Miranda \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Importantly, weedy plants exhibit phenological asynchrony relative to TDF trees. While forest species reproduce around rainfall cues, pasture weeds often flower opportunistically, extending resource availability into periods when the surrounding matrix offers limited or no floral resources (Cortés-Flores et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; McLaren and McDonald 2005). These asynchronous blooms may help sustain pollinator activity and thereby maintain interaction flow across the broader landscape (Shrotri et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Genini et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBecause vegetation structure and the phenology of resource-providing plants differ among management regimes, livestock systems in TDF landscapes have the potential to filter functional traits of both plants and butterflies. In such systems, floral traits strongly influence the accessibility of nectar resources and subsequently determine whether plant species become central or peripheral within plant–butterfly networks. Floral size, density, and corolla morphology affect interaction frequency and can increase the likelihood that certain species act as local hubs (Xiang et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), while morphological matching between corolla architecture and pollinator morphology further shapes the emergence of modular structures and interaction clusters (Maglianesi et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Izquierdo-Palma et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The integration of floral traits has been shown to drive network roles in multiple systems (Lázaro and Santamaría 2016), underscoring the importance of trait-mediated filters in determining the functional contributions of plant species within mutualistic assemblages.\u003c/p\u003e \u003cp\u003eSimilarly, butterfly traits influence their position as key connectors or peripheral elements in mutualistic networks. Morphological and phenological characteristics often determine interaction frequency, with trait-matching between pollinator morphology and floral architecture being a primary predictor of interaction probability (Guo et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mertens et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In particular, body size and wing dimensions are closely linked to mobility, energetic demands, and foraging range, thereby constraining the number and diversity of potential interaction partners. Larger-bodied butterflies, often characterized by broader wings and higher flight capacity, tend to forage over wider spatial scales but may interact less frequently with individual plant species due to higher energetic costs and lower maneuverability in structurally complex habitats (Hagen et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dehling et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conversely, smaller butterflies with reduced wing spans often exhibit higher visitation rates and tighter coupling with specific floral resources, which can promote more frequent but spatially constrained interactions (Bartomeus et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Junker et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Species with extended activity periods tend to accrue more connections across modules (Guzman, Chamberlain, and Elle \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), whereas generalist foraging strategies often lead to more central network roles (Coux et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pires et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Environmental variation and microclimatic conditions can further shape butterfly centrality (Álamo et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), suggesting that both internal (trait-based) and external (environmental) filters govern the functional contributions of butterflies in plant–pollinator systems.\u003c/p\u003e \u003cp\u003eCattle management introduces an additional environmental filtering in TDF landscapes. Grazing, browsing, and trampling modify microhabitats and plant assemblages (Maza-Villalobos et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), influencing the functional traits of ground-layer vegetation, which in turn structures the foraging opportunities available to butterflies. These management-driven shifts can cascade through local interaction networks by altering which traits are favored (Maza-Villalobos et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Exclusion of cattle has been shown to increase woody plant density and species richness (Quisehuatl-Medina et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), yet working pastoral landscapes—such as those characteristics of northern Colombia—must balance conservation objectives and the realities of food production. Given that trait distributions shape partner availability and interaction patterns (Santos and Ribeiro \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), understanding how cattle management regimes filter traits are fundamental for interpreting functional variation in butterfly–plant networks. These dynamics unfold within multifunctional landscapes, where biodiversity conservation must coexist with productive activities such as cattle ranching. Multifunctional systems emphasize reconciling agricultural production with ecological processes by maintaining heterogeneous landscapes, enhancing ecosystem multifunctionality, and supporting services such as pollination (McGranahan \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Leroy et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Trade-offs are unavoidable—biodiversity-friendly practices may reduce short-term production (Burian et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) or generate complex landscape responses (van der Plas et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)—yet adaptive management frameworks and collaborative stewardship can facilitate sustainable outcomes (Chirwa et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Garibaldi et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cockburn et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In TDF regions of northern Colombia, where cattle ranching dominates land use, developing management strategies that support pollinator communities and maintain interaction networks is essential for sustaining both biodiversity and agricultural resilience.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, WP, FAP, and OP represent distinct management regimes that may impose different environmental filters on plants and butterflies, potentially altering trait distributions and, consequently, the interactive roles species assume. Nevertheless, despite growing evidence that functional traits structure mutualistic networks under land-use change, we still lack empirical tests of how cattle management regimes influence trait–role relationships in plant–butterfly networks in TDF. Addressing this gap is essential for understanding how biodiversity persists and how pollination processes can be enhanced in multifunctional tropical landscapes. Specifically, we ask: (1) \u003cem\u003eDo different pasture management types influence the interactive roles of plants and butterflies? and (2) To what extent do functional traits explain variation in these roles?\u003c/em\u003e We hypothesize that (i) management indirectly shapes interactive roles by filtering traits of both plants and butterflies; (ii) plants with wider corollas attain higher centrality due to enhanced accessibility; and (iii) butterfly morphological traits—particularly wing size and overall body size—predict interactive roles. Specifically, smaller butterflies are expected to interact with a greater diversity of plant species and to exhibit higher centrality, reflecting enhanced mobility, broader resource use, and reduced energetic constraints under simplified pasture conditions. Collectively, these hypotheses reflect the expectation that trait-based mechanisms, rather than management per se, are the primary drivers of functional positioning within plant–butterfly networks in livestock-dominated TDF landscapes.\u003c/p\u003e \n\n \u003cp\u003e \u003c/p\u003e \n\n \n\n \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch3\u003eStudy area\u003c/h3\u003e\u003cp\u003eWe conducted this study in the highly transformed landscape of the Sinú medio and San Jorge subregions (\u0026lt; 200 m asl), in the Córdoba department on the northern Caribbean coast of Colombia. The Caribbean region is characterized by some of the highest human footprint values in the country and a low proportion of remaining natural áreas (Correa Ayram et al. 2020). Córdoba was originally dominated by TDF; however, agricultural expansion and extensive cattle ranching have resulted in the loss of approximately 85% of its original forest cover (Ramos et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The current landscape is a mosaic dominated by cattle pastures, interspersed with agricultural fields, remnant forest patches, and human settlements (Ruiz \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe climate is characterized by a marked dry season between December and March, and a rainy season from May to November which strongly constrains plant phenology and floral resources for pollinators. Relative humidity above 80%, mean annual temperature ranges from 26.9°C to 27.8°C, and annual precipitation ranges from 1500 to 2000 mm (Meisel and Pérez \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Galvis \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; IAvH \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe conducted fieldwork during the peak flowering season of pasture herbaceous plants, from October to December 2024, which corresponds to the regional rainy season (IGAC \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Meisel and Pérez \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). We studied three common pasture management types in Córdoba's cattle ranches: (1) FAP: forest-adjacent pastures, (2) WP: woody pastures (high tree density), and (3) OP: open pastures (low tree density).\u003c/p\u003e\u003cp\u003eWe selected 24 cattle pastures representing three management types (eight per type) within the same biogeographic region to minimize climatic and historical variation. Pasture size ranged from 5 to 35 ha. To ensure spatial independence between sampling sites, we maintained a minimum separation distance of 2 km between pastures; where possible, we selected one pasture of each type within the same farm (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe herbaceous layer in FAP was dominated by \u003cem\u003eBothriochloa pertusa\u003c/em\u003e, \u003cem\u003eBrachiaria brizantha\u003c/em\u003e cv. Toledo, \u003cem\u003eEchinochloa polystachya\u003c/em\u003e, and \u003cem\u003eBrachiaria humidicola\u003c/em\u003e. WP featured \u003cem\u003eDichanthium aristatum\u003c/em\u003e, \u003cem\u003eMegathyrsus maximus\u003c/em\u003e cv Mombasa, \u003cem\u003eBrachiaria humidicola\u003c/em\u003e, and \u003cem\u003eBothriochloa pertusa\u003c/em\u003e, while OP were dominated by \u003cem\u003eBrachiaria humidicola\u003c/em\u003e, \u003cem\u003eBrachiaria mutica\u003c/em\u003e, and \u003cem\u003eBothriochloa pertusa\u003c/em\u003e. The woody component of WP pastures showed high native tree diversity, including \u003cem\u003ePachira quinata\u003c/em\u003e, \u003cem\u003eSimarouba amara\u003c/em\u003e, \u003cem\u003eCavanillesia platanifolia\u003c/em\u003e, \u003cem\u003eBursera simaruba\u003c/em\u003e, \u003cem\u003eSamanea saman\u003c/em\u003e, \u003cem\u003eLecythis minor\u003c/em\u003e, \u003cem\u003ePterocarpus acapulcensis\u003c/em\u003e, \u003cem\u003eFicus carica\u003c/em\u003e, and \u003cem\u003eTabebuia rosea\u003c/em\u003e. FAP and OP shared common trees such as \u003cem\u003eTabebuia rosea\u003c/em\u003e, \u003cem\u003eSamanea saman\u003c/em\u003e, \u003cem\u003eGliricidia sepium\u003c/em\u003e, \u003cem\u003eGuazuma ulmifolia\u003c/em\u003e, \u003cem\u003eCrescentia cujete\u003c/em\u003e, and \u003cem\u003ePachira quinata.\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePoor soil drainage—a characteristic feature of the alluvial soils with high clay and silt content in the Sinú River valley (IGAC \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e)—was observed in 37.5% (3/8) of FAP sites and 62.5% (5/8) of WP and OP sites. All farms had over 50 years of cattle production history. Farm size varied from 79.5 to 2,380 ha, with herd sizes ranging from 130 to 4,200 head. Pasture covered 50–98% of the land area across farms, with stocking rates of 0.87–8.5 animals/ha under rotational grazing management.\u003c/p\u003e\u003ch2\u003eSampling plant-butterfly interaction\u003c/h2\u003e\u003cp\u003eTo record plant-butterfly interactions, we employed linear transects within each of the 24 selected cattle pastures. In each pasture, we established three 300 m long by 5 m wide linear transects (recording area: 2.5 m on each side of the observer). We surveyed each transect for 30 minutes per day over four consecutive days, resulting in a total sampling effort of six hours per pasture.\u003c/p\u003e\u003cp\u003eOn each sampling day, we visited at least three different pastures. We conducted all sampling between 7:30 am and 3:30 pm on days with favorable weather conditions (sunny or partly cloudy with minimal wind). To control potential time-of-day effects on butterfly activity and flower visitation, we randomized the order in which we visited the pastures each day.\u003c/p\u003e\u003cp\u003eWe recorded a flower’s visitation event only upon observing a butterfly's proboscis making direct contact with the reproductive structures of a flower. Butterflies were identified either by sight (for species that could be reliably recognized in flight or perched) or by capture with an entomological net for closer examination. We used visitation frequency as a metric for pollinator functional performance (Vázquez, Morris, and Jordano 2005). For captured specimens, following standard entomological practices and minimizing specimen removal to the extent possible we sacrificed a voucher series of 5–10 individuals per species; all others were photographed and released on site. Sacrificed specimens were stored in plastic containers for transport.\u003c/p\u003e\u003cp\u003eButterfly identification was completed by comparing collected vouchers and photographs to authoritative illustrated guides for Colombian species (Garwood \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). For plant identification, we photographed the habit, leaves (including their arrangement), flowers, and fruits (when available). We provisionally identified plants in the field using common names provided by local farm workers. Subsequently, we assigned scientific names by comparing photographs to specimens in digital herbarium collections (STRI 2025; WFO 2025; Vibrans \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll butterfly vouchers were deposited in the entomological collection of the Pontificia Universidad Javeriana in Bogotá. Specimen collection was conducted under the permit for collection and mobilization of biological specimens, Resolution ANLA No. 1255 (June 24, 2024), and was certified by the Vice-Rectory for Research of the Pontificia Universidad Javeriana-Bogotá (Official Communication VI-0472).\u003c/p\u003e\u003ch3\u003ePlant and butterfly traits\u003c/h3\u003e\u003cp\u003eFor butterfly species, we measured three morphological traits: proboscis length, body length, and forewing length (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Proboscis length was estimated for one to ten specimens per species (typically five). Heads were separated from the body, antennae removed and subsequently softened in 70% ethanol for 20 minutes. Each head was pinned onto a polystyrene surface, and labial palps were removed to expose the proboscis insertion. To measure the proboscis, it was then fully extended and fixed using entomological pins. A segmented reference scale (1-mm precision) was placed beside each preparation, and the structures were photographed, following (Lehnert et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Bauder et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBody and forewing lengths were obtained from scaled photographs taken with a 1-mm precision reference. Body length was defined as the distance from the most distal point of the head to the abdomen, and forewing length as the distance from the anterior wing insertion to the wing apex. Forewing length was averaged from one to seven specimens, and body length from one to five (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). When only one specimen was available, its measurement served as the species-level estimate. Because specimen collection was limited to avoid disturbing plant–butterfly interactions, some traits were measured from regional museum specimens or from calibrated images available through Butterflies of America (Warren et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). For each species and trait, we calculated a mean and its standard deviation (Table S2).\u003c/p\u003e\u003cp\u003eFor plant species, we compiled six traits, including five floral attributes: corolla width, corolla length, corolla shape (achlamydeous; bell-shaped; bell- to urn-shaped; obovate; papilionaceous; petaloid calyx; rotate; salverform; spatulate; tubular; two-lipped or zygomorphic), corolla type (dialipetalous, gamopetalous, or achlamydeous), and corolla color (blue, green, lilac, orange, pink, purple, red, white, yellow). We additionally recorded species’ geographic origin (native/exotic). Quantitative floral traits were measured from field photographs taken with a 1-mm precision scale, using measurements from 2–17 flowers collected per species across multiple sites when available. Categorical traits were assigned using botanical literature and digital herbaria, including World Flora Online (WFO 2025), the STRI Research Portal (STRI 2025), and \u003cem\u003eFichas de Malezas de México\u003c/em\u003e (Vibrans \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). When field specimens were unavailable, trait measurements were extracted from literature or from scaled photographs of herbarium specimens in these digital repositories (Table S3).\u003c/p\u003e\u003cp\u003eAll butterfly and plant trait measurements were extracted from scaled images using ImageJ (Schneider, Rasband, and Eliceiri 2012).\u003c/p\u003e\u003ch3\u003eInteractive role of plants and butterflies\u003c/h3\u003e\u003cp\u003eTo quantify the importance of plant and butterfly species within interaction networks, we estimated the interactive role of every species occurring in each network constructed for each of the three pasture management types (FAP, WP and OP). For each management type, we built a plant–butterfly meta-network by pooling all interactions recorded across the eight pastures belonging to that category (Table S4; Fig \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). This approach was adopted to characterize regime-level interaction patterns and emergent species roles associated with each management type, rather than site-specific network variation, thereby emphasizing the structural and functional properties that consistently arise under shared management conditions and reducing the influence of local stochasticity in species interactions. Each meta-network was weighted, where \u003cem\u003ea\u003c/em\u003e₍\u003csub\u003ei\u003c/sub\u003eⱼ₎ represents the number of interactions between plant \u003cem\u003ei\u003c/em\u003e and butterfly \u003cem\u003ej\u003c/em\u003e, and zero indicates absence of interaction. Weighted networks that combine interaction frequencies and abundances are appropriate for estimating species roles in ecological interaction systems (Miranda et al. 2019).\u003c/p\u003e\u003cp\u003eFor each meta-network, we quantified the interactive role of plant and butterfly species using three complementary centrality metrics: degree centrality, betweenness centrality, and closeness centrality (Martín González, Dalsgaard, and Olesen 2010; Cagua, Wootton, and Stouffer 2019). Degree centrality reflects a species’ importance based on the number of interaction partners— with generalist species exhibiting high degree values and specialists exhibiting low values (Bascompte and Jordano 2013). Betweenness centrality identifies species that act as connectors between otherwise separated portions of the network; species with BC \u0026gt; 0 bridge structural gaps (Freeman \u003cspan class=\"CitationRef\"\u003e1979\u003c/span\u003e; Martín González, Dalsgaard, and Olesen 2010). Closeness centrality measures the overall proximity of each species to all others in the network, meaning that species with high closeness can influence, and be influenced by, other species more rapidly (Sazima et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). All centrality metrics were computed using the \u003cem\u003ebipartite\u003c/em\u003e package in R (Dormann \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause centrality metrics tend to be moderately to strongly correlated (Sazima et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Estrada \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), we summarized them into a single generalized centrality index using principal component analysis (PCA). This PCA was conducted separately for plants and butterflies within each meta-network to account for taxon-specific differences in interaction patterns. In all taxon–management combinations (n = 6), the first principal component (PC1) captured the largest proportion of variation (≥ 68.3%; Table S5), confirming that the three centrality descriptors carry complementary information and supporting the use of PC1 as an integrative measure of species centrality (Sazima et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dáttilo et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cruz et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ratoni et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). We added the minimum negative value among the PC1 data to obtain positive interactive role values, a linear transformation that does not alter the structure of the data but improves numerical handling and interpretability of the composite index. Given the positive correlations between PC1 and the original centrality metrics, high PC1 scores indicate species with high interactive roles (i.e. generalists), whereas low PC1 scores correspond to species with lower interactive roles (i.e. specialists).\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003ch2\u003eComparing interactive roles across management types\u003c/h2\u003e\u003cp\u003eTo assess whether species’ interactive roles differed among pasture management types (FAP, WP, OP), we first estimated a centrality index. Because only one meta-network was available per management type, classical inferential procedures requiring replicated networks could not be applied. To address this limitation and obtain inference on mean interactive roles for each management category, we implemented a non-parametric bootstrap procedure (Efron and Tibshirani 1986).\u003c/p\u003e\u003cp\u003eFor butterflies and plants separately, we generated 1000 bootstrap resamples from the empirical distribution of PC1 scores within each management type, calculating mean values and 95% confidence intervals for each resample set. This approach provides robust distribution-free estimates of uncertainty and allows comparisons across management types based on the degree of overlap among confidence intervals. Overlapping intervals indicate that differences among management categories are not reliably distinguishable given the available data structure. All bootstrap procedures were implemented using the boot() function from the \u003cem\u003eboot\u003c/em\u003e package (Canty A and Ripley B 2025).\u003c/p\u003e\u003ch2\u003eFunctional traits on species interactive roles\u003c/h2\u003e\u003cp\u003eWe evaluated whether interspecific variation in traits predicted species’ interactive roles within each management type. Analyses were conducted separately for butterflies and plants. For butterflies, explanatory variables included wing length, body length, and proboscis length. For plants, we considered corolla width, corolla length, corolla type, corolla shape, corolla color, and geographic origin (native/exotic). For each meta-network (i.e. taxon × management combination), we fitted separate generalized linear models (GLMs) using PC1 scores as the response variable.\u003c/p\u003e\u003cp\u003eError distributions and link functions were selected according to the nature of the response variable and predictors. Models with continuous predictors were fitted using Gaussian distributions with identity link functions, whereas models with categorical floral traits employed appropriate error structures ensuring correct residual behavior. All models were fitted using the glm() function from base R.\u003c/p\u003e\u003cp\u003eBefore model fitting, we evaluated normality and homoscedasticity of residuals using Shapiro–Wilk and Breusch–Pagan tests, respectively, and assessed overdispersion for non-Gaussian models. Residual diagnostics were conducted using simulation-based procedures with the DHARMa package (Hartig F \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), allowing detection of non-uniformity, dispersion issues, and potential outliers. Multicollinearity among predictors was evaluated through Variance Inflation Factors (VIF) computed with the vif() function from the \u003cem\u003ecar\u003c/em\u003e package (Fox J and Weisberg S 2019).\u003c/p\u003e\u003cp\u003eWe initially explored an information-theoretic model selection approach; however, due to sample size constraints and model singularities, this approach did not yield stable model subsets across all taxon × management combinations. Therefore, we adopted a stepwise model simplification strategy using the step() function from base R, applying both backward and forward selection based on AIC. For each retained model, we extracted coefficients, standard errors, confidence intervals, and significance values, and computed marginal R² using the r.squaredGLM() function from \u003cem\u003eMuMIn\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eAll analyses were conducted in R version 4.3.1. (R Core Team \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAcross the 24 surveyed pastures, we recorded 33 butterfly species and 67 plant species. Because networks from individual pastures were pooled by management type, we constructed three plant\u0026ndash;butterfly meta-networks corresponding to FAP, WP and OP (Table S4). A total of 3,617 interaction events were documented across the study, ranging from 816 (FAP) to 1,706 (OP).\u003c/p\u003e \u003cp\u003eIn FAP, we recorded 816 interactions involving 43 plant species and 27 butterfly species. \u003cem\u003eSida acuta\u003c/em\u003e (Malvaceae) was the most frequently visited plant (200 visits; 24.5%), whereas \u003cem\u003eHedone vibex\u003c/em\u003e was the butterfly species interacting for the highest number of plants (228 visits; 27.9%).\u003c/p\u003e \u003cp\u003eIn WP, we documented 1,095 interactions, comprising 38 plant species and 24 butterfly species. \u003cem\u003eMalachra alceifolia\u003c/em\u003e (Malvaceae) was the most frequently visited plant (295 visits; 27%), while \u003cem\u003eSpicauda procne\u003c/em\u003e accounted for the highest number of butterfly interactions (382 visits; 35%).\u003c/p\u003e \u003cp\u003eIn OP, 1,706 interactions were recorded. \u003cem\u003eLippia dulcis\u003c/em\u003e (Verbenaceae) received the highest visitation frequency (421 visits; 24.7%), and \u003cem\u003eSpicauda procne\u003c/em\u003e again emerged as the butterfly accounting for the highest number of interaction events (467 visits; 27.4%).\u003c/p\u003e \u003cp\u003eRegarding floral traits, dialipetalous (66.5%), yellow (37%), rotaceous (41.3%) and native (92.9%) flowers were the most frequently visited across the entire study. Corolla width ranged from 1.5 mm in \u003cem\u003eCissus\u003c/em\u003e sp. to 50 mm in \u003cem\u003eLimnocharis flava\u003c/em\u003e. Corolla length varied between 0.4 mm in \u003cem\u003eMurdannia keisak\u003c/em\u003e and 27.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79 mm in \u003cem\u003eIsertia haenkeana\u003c/em\u003e. Trait\u0026ndash;visitation patterns remained generally consistent across management types, with the exception of OP, where white flowers received comparatively more visits (Table S6).\u003c/p\u003e \u003cp\u003eAcross the entire dataset, butterfly traits exhibited substantial interspecific variation. Wing length ranged from 8.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 mm in \u003cem\u003eHemiargus hanno\u003c/em\u003e to 48.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28 mm in \u003cem\u003eHeraclides thoas\u003c/em\u003e. Body size varied between 7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 mm (\u003cem\u003eH. hanno\u003c/em\u003e) and 27.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23 mm (\u003cem\u003eDryadula phaetusa\u003c/em\u003e). Proboscis length spanned a wide gradient, from 4.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 mm in \u003cem\u003eHermeuptychia hermes\u003c/em\u003e to \u003cem\u003e27.04\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10 mm\u003c/em\u003e in \u003cem\u003ePhoebis sennae\u003c/em\u003e. These trait ranges were largely consistent across management types, although the species contributing to the extremes varied (Table S2).\u003c/p\u003e\n\u003ch3\u003eEffect of pasture management on the interactive role of butterflies and plants\u003c/h3\u003e\n\u003cp\u003eBootstrap estimates showed largely overlapping 95% confidence intervals among management categories for both taxa (Table S7), indicating that variation in interactive roles across management types was not strong enough to be statistically distinguished. Although WP butterflies exhibited the highest mean centrality (1.606), their 95% CI overlapped extensively with those from FAP (0.942\u0026ndash;1.639) and OP (1.037\u0026ndash;1.691). A similar pattern emerged for plants: despite OP plants having the highest mean (1.394), their confidence interval overlapped broadly with those of FAP (1.013\u0026ndash;1.590) and WP (0.806\u0026ndash;1.518) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional traits on species interactive roles\u003c/h2\u003e \u003cp\u003eWhen evaluating whether functional traits predicted the interactive roles of plants and butterflies within the meta-networks for each pasture management type, we found that species\u0026rsquo; interactive roles were partially explained by their traits, indicating trait-dependent mechanisms influencing species roles within interaction networks. In tree-rich pastures (WP), butterfly wing length (β = \u0026minus;\u0026thinsp;0.028, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) and body length (β = \u0026minus;\u0026thinsp;0.073, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) were negatively related to centrality (R\u0026sup2; = 0.16\u0026ndash;0.26; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table S8). In contrast, plant traits showed a consistent positive association with their interactive role. In both forest-adjacent (FAP) and tree-rich (WP) pastures, corolla width was positively related to centrality (β\u0026thinsp;=\u0026thinsp;0.036\u0026ndash;0.041, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.03; R\u0026sup2; = 0.13\u0026ndash;0.15; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table S8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding how livestock management filters species\u0026rsquo; functional roles within mutualistic networks is essential in TDF landscapes, where habitat transformation is extensive, and biodiversity must operate under strong climatic seasonality. In this study, we evaluated how pasture management types in a cattle-production region of northern Colombia influence the interactive roles of butterfly and plant species within plant\u0026ndash;butterfly networks, and whether functional traits predict these roles. In contrast to studies showing that landscape configuration and management intensity often alter pollinator assemblages and interaction patterns (Proesmans et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cano et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we found no evidence that management type (woody pasture, pastures adjacent to forest, or open pasture) significantly influenced species\u0026rsquo; interactive roles. Instead, variation in interaction roles was primarily associated with functional traits, suggesting trait-based filtering rather than direct management-driven effects.\u003c/p\u003e \u003cp\u003eThe absence of management-driven differences likely reflects the ecological characteristics of TDF pastures in the region. First, despite differing in tree cover, the studied pasture types share a similar herbaceous layer dominated by weedy plants capable of thriving under grazing pressure. These herbaceous communities may buffer potential management effects by providing relatively uniform nectar availability across sites\u0026mdash;particularly relevant in TDF, where rainfall seasonality produces strong environmental filters (Le Bagousse-Pinguet et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oloumane et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, the moderate spatial scale of our sampling, combined with the limited functional differentiation of the herb layer across management types, may constrain the degree to which management practices translate into contrasting interaction patterns. In similar livestock landscapes, structural vegetation differences influence interaction roles only when management produces clear contrasts in resource availability, vegetation architecture, or disturbance regimes (Adedoja and Mallinger \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Peralta et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our findings suggest that, in this TDF region, such contrasts were insufficient to generate detectable differences in species-level interaction roles.\u003c/p\u003e \u003cp\u003eFunctional traits emerged as the primary predictors of interaction roles. For plants, corolla width was positively associated with the number of butterfly partners, consistent with trait-matching mechanisms frequently reported in pollination networks (Xiang et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; de Sousa Perugini et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Maglianesi et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Wider corollas often facilitate access for a broader range of visitors, thereby enhancing opportunities for interactions largely independent of management type. In our TDF landscape, this trait effect must also be interpreted through the lens of phenological dynamics in herbaceous weeds.\u003c/p\u003e \u003cp\u003eUnlike canopy trees in TDF, which exhibit highly synchronized flowering tied to rainfall pulses (Astegiano et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026uuml;nter et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Silveira, Martins, and Ara\u0026uacute;jo \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), herbaceous weeds display asynchronous and flexible flowering, responding opportunistically to micro-environmental conditions (Cort\u0026eacute;s-Flores et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McLaren and McDonald 2005). This asynchrony may produce sustained nectar availability across extended portions of the year, including periods in which forest trees are not flowering (Cort\u0026eacute;s-Flores et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such off-season floral resources may help sustain butterfly populations during phenological gaps, enabling their persistence within livestock systems and facilitating spillover to nearby habitats\u0026mdash;including remnant TDF patches and fruit crop plantations that depend on pollination services (Rocha et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Under this scenario, plants with wider and more accessible corollas may play a disproportionate role in maintaining interaction flow throughout the year, thereby stabilizing butterfly presence in an otherwise strongly seasonal environment.\u003c/p\u003e \u003cp\u003eFor butterflies, body size and wingspan were negatively associated with interactive role: larger butterflies interacted with fewer plant species, while smaller species were more generalized. This pattern contrasts with classical expectations linking larger pollinators to higher generalization (Peralta et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), yet it aligns with ecological mechanisms expected in herbaceous, disturbance-prone systems. Smaller butterflies are better suited to exploit fine-grained floral resources, maneuver efficiently within dense low vegetation, and maintain lower energetic requirements, all of which may confer broader realized diets in weedy pastures. The absence of an effect of proboscis length further supports the idea that morphological matching played a secondary role in this system, consistent with findings from other simplified or disturbed Neotropical habitats (S\u0026otilde;ber et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Instead, mobility- and size-related traits appear to mediate access to heterogeneous nectar resources in TDF pastures.\u003c/p\u003e \u003cp\u003eOur findings align with broader evidence that functional traits mediate species responses and interaction roles under land-use change (Le Bagousse-Pinguet et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Adedoja and Mallinger \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cano et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Land-use intensification often imposes strong environmental filters that disproportionately affect species with particular traits, altering interaction patterns and functional overlap. However, when local resource conditions are homogeneous\u0026mdash;as in the herb-dominated pastures we studied\u0026mdash;trait-based processes may override management effects, generating comparable interactive roles across pasture types. Woody pastures, despite offering shade and structural heterogeneity, did not modify interactive roles, highlighting the resilience of these weedy plant\u0026ndash;butterfly interaction networks under the tested gradients.\u003c/p\u003e \u003cp\u003eIn heavily transformed TDF regions, where \u0026gt;\u0026thinsp;90% of native cover has been lost, understanding how functional traits shape interaction roles is essential for conservation planning. Our results indicate that trait-based mechanisms, rather than management type, determine the degree to which butterflies and plants contribute to interaction networks in livestock landscapes. This suggests that conservation strategies should prioritize (i) maintaining diverse herbaceous communities that provide continuous nectar resources, (ii) preserving or restoring plant species with accessible floral architectures, and (iii) safeguarding butterfly species with traits that promote broad interaction potential.\u003c/p\u003e \u003cp\u003eMoreover, by providing continuous or complementary resources within highly modified landscapes, well-managed livestock pastures may contribute to sustaining pollinator communities beyond their boundaries. When structural and compositional heterogeneity is maintained, these systems can function as supplementary habitats that enhance landscape-level connectivity and support pollination services across the broader tropical dry forest matrix, including remnant forest patches and surrounding agroecosystems.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that livestock management regimes in TDF landscapes do not have a strong direct effect on the overall centrality of species within plant\u0026ndash;butterfly networks. Instead, management appears to act primarily as a trait-based filter, influencing which plant and butterfly species attain more influential interactive roles. Larger-bodied butterflies tended to exhibit lower centrality, whereas plant species with broader corollas consistently acted as interaction hubs. These patterns reveal that functional traits, rather than management per se, are the main determinants of species\u0026rsquo; positions within networks.\u003c/p\u003e \u003cp\u003eA second key mechanism emerges from the phenology of the weed species common in grazed pastures. Unlike the strongly seasonal flowering of dry forest trees, these weedy species display asynchronous and prolonged flowering, providing floral resources during periods when the surrounding forest matrix offers few or none. Such temporally decoupled resource availability may help maintain pollinator activity across seasonal bottlenecks, thereby supporting butterfly populations that later interact with both forest plant communities and pollinator-dependent fruit crops cultivated in the region.\u003c/p\u003e \u003cp\u003eTogether, these findings underscore that biodiversity-friendly pasture management can contribute to sustaining mutualistic processes not by altering network structure directly, but by conserving trait diversity and maintaining continuous floral resource availability. Protecting woody elements and diverse ruderal assemblages within livestock systems may thus enhance functional linkages among agricultural lands, pollinator communities, and tropical dry forest remnants, strengthening ecosystem resilience in one of the world\u0026rsquo;s most threatened biomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the biologists Brayan P\u0026eacute;rez, Sergio Torres, and Sebasti\u0026aacute;n Mogrovejo for their support during the fieldwork. We are grateful to Agropecuaria Tabaid\u0026aacute; S.A.S. and the other cattle farms for granting us access to their properties to conduct this research.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Colombian Ministry of Science, Technology, and Innovation (Minciencias) through the \u0026quot;Bicentenario\u0026quot; Scholarship (Cohort II) and by the Pontificia Universidad Javeriana through the \u0026quot;Academic/Research Assistantship\u0026quot; Scholarship, both funding the doctoral training of the first author, Roger Ayazo Berrocal. The first author also received research-phase support from the Doctoral Thesis Project Support Scholarship (proposal code PPTA_20937) awarded by the University\u0026apos;s Vice-Rectory for Research.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eCompeting intereses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe first author received logistical support from Agropecuaria Tabaid\u0026aacute; S.A.S. during fieldwork on their properties. The remaining authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception, design, material preparation and data collection were performed by R-AB and C-CA. Analysis were performed by R-AB and W-D. The first draft of the manuscript was written by R-AB and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdedoja OA, Mallinger RE (2024) Can Trait Matching Inform the Design of Pollinator-Friendly Urban Green Spaces? A Review and Synthesis of the Literature. Ecosphere 15(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecs2.4734\u003c/span\u003e\u003cspan address=\"10.1002/ecs2.4734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aacute;lamo M, Mingarro M, Wilson RJ, \u0026Aacute;lvarez HA (2025) Effects of Elevation and Microclimatic Temperatures on Butterfly\u0026ndash;Flower Interaction Networks in a Mediterranean Mountain Range. Insect Conserv Divers. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/icad.70008\u003c/span\u003e\u003cspan address=\"10.1111/icad.70008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Ara\u0026uacute;jo Silva G, Nascimento BSD, U de Jesus Ara\u0026uacute;jo Pinto, A L C dos Santos, M do Vale Beir\u0026atilde;o, and, de Oliveira J, Silva (2025) Changes in Vegetation Cover and Their Effects on the Diversity of Fruit-Feeding Butterflies. \u003cem\u003eAustral Ecology\u003c/em\u003e 50 (7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/aec.70095\u003c/span\u003e\u003cspan address=\"10.1111/aec.70095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAstegiano J, Carbone L, Zamudio F, Tavella J, Ashworth L, Aguilar R, Beccacece Hern\u0026aacute;nM et al (2024) Diversifying Agroecological Systems: Plant-Pollinator Network Organisation and Landscape Heterogeneity Matter. Agric Ecosyst Environ 361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agee.2023.108816\u003c/span\u003e\u003cspan address=\"10.1016/j.agee.2023.108816\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (February). Elsevier B.V.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagousse-Pinguet Y, Le N, Gross FT, Maestre V, Maire F, de Bello CR, Fonseca J, Kattge E, Valencia J, Leps, Liancourt P (2017) Testing the Environmental Filtering Concept in Global Drylands. J Ecol 105(4):1058\u0026ndash;1069. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.12735\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.12735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartomeus I, Gravel D, Tylianakis JM, Aizen MA, Dickie IA, and Maud Bernard-Verdier (2016) A Common Framework for Identifying Linkage Rules across Different Types of Interactions. Funct Ecol 30(12):1894\u0026ndash;1903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.12666\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.12666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBascompte J, and Pedro Jordano (2013) Mutualistic Networks. Princeton University Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauder JAS, Morawetz L, Warren AD, Krenn HW (2015) Functional Constraints on the Evolution of Long Butterfly Proboscides: Lessons from Neotropical Skippers (Lepidoptera: Hesperiidae). J Evol Biol 28(3):678\u0026ndash;687. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jeb.12601\u003c/span\u003e\u003cspan address=\"10.1111/jeb.12601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuono CM, Lofaso J, Smisko W, Gerth C, Santare J, Prior KM (2023) Historical Forest Disturbance Results in Variation in Functional Resilience of Seed Dispersal Mutualisms. Ecology 104(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecy.3978\u003c/span\u003e\u003cspan address=\"10.1002/ecy.3978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurian A, Norton BA, Alston D, Willmot A, Reynolds S, Meynell G, Lynch P, and M Bulling (2023) Low-Cost Management Interventions and Their Impact on Multilevel Trade-Offs in Agricultural Grasslands. J Appl Ecol 60(10):2079\u0026ndash;2090. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.14492\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.14492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBustamante-Castillo M, Hern\u0026aacute;ndez-Ba\u0026ntilde;os BE, Arizmendi MC (2020) Hummingbird-Plant Visitation Networks in Agricultural and Forested Areas in a Tropical Dry Forest Region of Guatemala. J Ornithol 161(1):189\u0026ndash;201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10336-019-01712-4\u003c/span\u003e\u003cspan address=\"10.1007/s10336-019-01712-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCagua E, Fernando KL, Wootton, Stouffer DB (2019) Keystoneness, Centrality, and the Structural Controllability of Ecological Networks. \u003cem\u003eJournal of Ecology\u003c/em\u003e 107 (4). Blackwell Publishing Ltd: 1779\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.13147\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.13147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCano D, Mart\u0026iacute;nez-N\u0026uacute;\u0026ntilde;ez C, P\u0026eacute;rez AJ, Alc\u0026aacute;ntara JM, Moretti M, Salido T, Rey PJ (2025) Agricultural Intensification Indirectly Reshapes Bee\u0026ndash;Plant Interaction Networks through Shifts in Bee Functional Traits. Ecol Appl 35(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/eap.70105\u003c/span\u003e\u003cspan address=\"10.1002/eap.70105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanty A, and Ripley B (2025) Boot: Bootstrap Funct doi. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.32614/CRAN.package.boot\u003c/span\u003e\u003cspan address=\"10.32614/CRAN.package.boot\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChirwa PW, Kozanayi W, Uisso AJ, Tshidzumba RP, Babalola FD, Amusa TO (2024) Socio-Economic Factors, Policy and Governance Systems Influencing Multifunctional Landscapes. In \u003cem\u003eTrees in a Sub-Saharan Multi-Functional Landscape: Research, Management, and Policy\u003c/em\u003e, 305\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-69812-5_13\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-69812-5_13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCockburn J, Cundill G, Shackleton S, Rouget M, Zwinkels M, Cornelius SA, Metcalfe L, van den D, Broeck (2019) Collaborative Stewardship in Multifunctional Landscapes: Toward Relational, Pluralistic Approaches. Ecol Soc 24(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5751/ES-11085-240432\u003c/span\u003e\u003cspan address=\"10.5751/ES-11085-240432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorrea Ayram C, Andr\u0026eacute;s Andr\u0026eacute;s Etter, Juli\u0026aacute;n D\u0026iacute;az-Timot\u0026eacute;, Susana Rodr\u0026iacute;guez Buritic\u0026aacute;, Wilson Ram\u0026iacute;rez, and Germ\u0026aacute;n Corzo. 2020. Spatiotemporal Evaluation of the Human Footprint in Colombia: Four Decades of Anthropic Impact in Highly Biodiverse Ecosystems. \u003cem\u003eEcological Indicators\u003c/em\u003e 117 (April). Elsevier: 106630. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2020.106630\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2020.106630\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCort\u0026eacute;s-Flores J, K Hern\u0026aacute;ndez-Esquivel A, Gonz\u0026aacute;lez-Rodr\u0026iacute;guez, Ibarra-Manr\u0026iacute;quez G (2017) Flowering Phenology, Growth Forms, and Pollination Syndromes in Tropical Dry Forest Species: Influence of Phylogeny and Abiotic Factors. Am J Bot 104(1):39\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3732/ajb.1600305\u003c/span\u003e\u003cspan address=\"10.3732/ajb.1600305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCort\u0026eacute;s-Flores J, Lopezaraiza-Mikel M, de Santiago-Hern\u0026aacute;ndez MH, Mart\u0026eacute;n-Rodr\u0026iacute;guez S, Crist\u0026oacute;bal-P\u0026eacute;rez EJ, Aguilar-Aguilar MJ, Balvino-Olvera FJ et al (2023) Successional and Phenological Effects on Plant-Floral Visitor Interaction Networks of a Tropical Dry Forest. J Ecol 111(4):927\u0026ndash;942. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.14072\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.14072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoux C, Rader R, Bartomeus I, Tylianakis JM (2016) Linking Species Functional Roles to Their Network Roles. Ecol Lett 19(7):762\u0026ndash;770. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12612\u003c/span\u003e\u003cspan address=\"10.1111/ele.12612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz CP, Luna P, Villalobos F, Guevara R, Hinojoza-D\u0026iacute;az I, and Wesley D\u0026aacute;ttilo (2024) The Central Importance of the Honeybee (Apis Mellifera L.) within Plant-Bee Interaction Networks Decreases along a Neotropical Elevational Gradient. Ecol Complex 60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecocom.2024.101105\u003c/span\u003e\u003cspan address=\"10.1016/j.ecocom.2024.101105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (December). Elsevier B.V\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026aacute;ttilo W, Cruz CP, Luna P, Ratoni B, Ismael A, Hinojosa-D\u0026iacute;az FS, Neves M, Leponce F, Villalobos, and Roger Guevara (2022) The Impact of the Honeybee Apis Mellifera on the Organization of Pollination Networks Is Positively Related with Its Interactive Role throughout Its Geographic Range. Diversity 14(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/d14110917\u003c/span\u003e\u003cspan address=\"10.3390/d14110917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. MDPI\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026aacute;ttilo W, Guimar\u0026atilde;es PR, Izzo TJ (2013) Spatial Structure of Ant-Plant Mutualistic Networks. Oikos 122(11):1643\u0026ndash;1648. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0706.2013.00562.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0706.2013.00562.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDehling D, Matthias P, Jordano HM, Schaefer K, B\u0026ouml;hning-Gaese (2016) and Matthias Schleuning. Morphology Predicts Species\u0026rsquo; Functional Roles and Their Degree of Specialization in Plant\u0026ndash;Frugivore Interactions. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e 283 (1823). Royal Society of London. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.2015.2444\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2015.2444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDormann C (2025) \u003cem\u003ePackage bipartite Type Package Title Visualising Bipartite Networks and Calculating Some (Ecological) Indices\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://orcid.org/0000-0002-7079-3478\u003c/span\u003e\u003cspan address=\"https://orcid.org/0000-0002-7079-3478\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDupin MG, V MM, Espirito-Santo ME, Leite JO, Silva AM, Rocha RS, Barbosa, Anaya FC (2018) Land Use Policies and Deforestation in Brazilian Tropical Dry Forests between 2000 and 2015. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e 13 (3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/aaadea\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/aaadea\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEfron B, and R Tibshirani (1986) Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Stat Sci 1(1):54\u0026ndash;77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEstrada E (2007) Characterization of Topological Keystone Species. Local, Global and \u0026lsquo;Meso-Scale\u0026rsquo; Centralities in Food Webs. Ecol Complex 4(1\u0026ndash;2):48\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecocom.2007.02.018\u003c/span\u003e\u003cspan address=\"10.1016/j.ecocom.2007.02.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox J, Weisberg S (2019) \u003cem\u003eAn R Companion to Applied Regression\u003c/em\u003e. Third. Thousand Oaks CA: Sage. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.john-fox.ca/Companion/\u003c/span\u003e\u003cspan address=\"https://www.john-fox.ca/Companion/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreeman LC (1979) Centrality in Social Networks Conceptual Clarification. Social Networks 1:215\u0026ndash;239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/0378-8733(78)90021-7\u003c/span\u003e\u003cspan address=\"10.1016/0378-8733(78)90021-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuzessy LF, Pizo MA (2025) Navigating a Changing World: On the Significance of Rewiring for Mutualistic Interactions, Caveats and Future Directions. \u003cem\u003eOikos\u003c/em\u003e 2025 (10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/oik.11230\u003c/span\u003e\u003cspan address=\"10.1002/oik.11230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalvis L (2009) \u003cem\u003eGeograf\u0026iacute;a Econ\u0026oacute;mica Del Caribe Continental\u003c/em\u003e. Cartagena\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaribaldi LA, Zermoglio PF, Jobb\u0026aacute;gy EG, Andreoni L, Ortiz de Urbina A, Grass I, Oddi FJ (2023) How to Design Multifunctional Landscapes? J Appl Ecol 60(12):2521\u0026ndash;2527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.14517\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.14517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarwood K (2017) Butterfly Catalogs. \u003cem\u003eBioButterfly Database\u003c/em\u003e. \u0026lt; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.butterflycatalogs.com/\u003c/span\u003e\u003cspan address=\"http://www.butterflycatalogs.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenini J, Guimar\u0026atilde;es PR, Sazima M, Sazima I, Morellato LPC (2021) Temporal Organization among Pollination Systems in a Tropical Seasonal Forest. Sci Nat 108(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00114-021-01744-y\u003c/span\u003e\u003cspan address=\"10.1007/s00114-021-01744-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilhaus K, Boch S, Fischer M, H\u0026ouml;lzel N, Kleinebecker T, Prati D, Rupprecht D, Schmitt B, Klaus VH (2017) Grassland Management in Germany: Effects on Plant Diversity and Vegetation Composition. Tuexenia 37(1):379\u0026ndash;397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14471/2017.37.010\u003c/span\u003e\u003cspan address=\"10.14471/2017.37.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez JM, Perfectti F, Klingenberg CP (2014) The Role of Pollinator Diversity in the Evolution of Corolla-Shape Integration in a Pollination-Generalist Plant Clade. Philosophical Trans Royal Soc B: Biol Sci 369(1649). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rstb.2013.0257\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2013.0257\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuimar\u0026atilde;es PR, Pires MM, Jordano P, Bascompte J, Thompson JN (2017) Indirect Effects Drive Coevolution in Mutualistic Networks. Nature 550(7677):511\u0026ndash;514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature24273\u003c/span\u003e\u003cspan address=\"10.1038/nature24273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026uuml;nter S, Stimm B, Cabrera M, Diaz ML, Lojan M, Ordo\u0026ntilde;ez E, Richter M, Weber M (2008) Tree Phenology in Montane Forests of Southern Ecuador Can Be Explained by Precipitation, Radiation and Photoperiodic Control. J Trop Ecol 24(3):247\u0026ndash;258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0266467408005063\u003c/span\u003e\u003cspan address=\"10.1017/S0266467408005063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo L, Zhao LF, Ye JL, Ji ZJ, Tang JJ, Bai KY, Zheng SJ, Hu LL, Chen X (2022) Using Aquatic Animals as Partners to Increase Yield and Maintain Soil Nitrogen in the Paddy Ecosystems. \u003cem\u003eELIFE\u003c/em\u003e 11. Zhejiang Univ, Coll Life Sci, Hangzhou, Peoples R China. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7554/eLife.73869\u003c/span\u003e\u003cspan address=\"10.7554/eLife.73869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzman LM, Chamberlain SA, Elle E (2021) Network Robustness and Structure Depend on the Phenological Characteristics of Plants and Pollinators. Ecol Evol 11(19):13321\u0026ndash;13334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.8055\u003c/span\u003e\u003cspan address=\"10.1002/ece3.8055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagen M, Kissling WD, Rasmussen C, Marcus AM, De Aguiar LE, Brown DW, Carstensen I, Alves-Dos-Santos et al (2012) 2 - Biodiversity, Species Interactions and Ecological Networks in a Fragmented World. In \u003cem\u003eAdvances in Ecological Research\u003c/em\u003e, edited by Ute Jacob and Guy Woodward, 46:89\u0026ndash;210. Academic Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-396992-7.00002-2\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-396992-7.00002-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartig F (2024) DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. https://doi.org/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.32614/CRAN.package.DHARMa%3Chttps: 10.32614=\"cran.package.dharma%3C=\" doi.org=\"urlz=\"%3E.%3C/https:%3E\u003c/span\u003e\u003cspan address=\"http://10.32614/CRAN.package.DHARMa%3Chttps: 10.32614=\u0026quot;cran.package.dharma%3C=\u0026quot; doi.org=\u0026quot;urlz=\u0026quot;%3E.%3C/https:%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIAvH (1998) El Bosque Seco Tropical (Bs-T) En Colombia. Programa de Inventario de la Biodiversidad Grupo de Exploraciones y Monitoreo Ambiental GEMA, Bogot\u0026aacute;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIGAC (2009) \u003cem\u003eDescripci\u0026oacute;n Del Medio Biof\u0026iacute;sico. Estudio General de Suelos y Zonificaci\u0026oacute;n de Tierras: Departamento de C\u0026oacute;rdoba\u003c/em\u003e. Monter\u0026iacute;a\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIzquierdo-Palma J, Del Coro Arizmendi M, Lara C, Ornelas JF (2021) Forbidden Links, Trait Matching and Modularity in Plant-Hummingbird Networks: Are Specialized Modules Characterized by Higher Phenotypic Floral Integration? PeerJ 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7717/peerj.10974\u003c/span\u003e\u003cspan address=\"10.7717/peerj.10974\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJunker RR, Bl\u0026uuml;thgen N, Brehm T, Binkenstein J, Paulus J, Schaefer HM, and Martina Stang (2013) Specialization on Traits as Basis for the Niche-Breadth of Flower Visitors and as Structuring Mechanism of Ecological Networks. Funct Ecol 27(2):329\u0026ndash;341. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1365-2435.12005\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.12005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026aacute;zaro A, and L Santamar\u0026iacute;a (2016) Flower-Visitor Selection on Floral Integration in Three Contrasting Populations of Lonicera Implexa. Am J Bot 103(2):325\u0026ndash;336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3732/ajb.1500336\u003c/span\u003e\u003cspan address=\"10.3732/ajb.1500336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehnert MS, Beard CE, Gerard PD, Kornev KG (2016) and Peter H. Adler. Structure of the Lepidopteran Proboscis in Relation to Feeding Guild. \u003cem\u003eJournal of Morphology\u003c/em\u003e 277 (2). John Wiley and Sons Inc.: 167\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jmor.20487\u003c/span\u003e\u003cspan address=\"10.1002/jmor.20487\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeroy A, Martin O, Gautier J-L, Bretagnolle V, Gaba S (2025) The Surrounding Landscape Affects Ecosystem Multifunctionality in Oilseed Rape Fields. J Appl Ecol 62(9):2283\u0026ndash;2295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.70105\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.70105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLitchman E, Edwards KF, Boyd PW (2021) Toward Trait-Based Food Webs: Universal Traits and Trait Matching in Planktonic Predator\u0026ndash;Prey and Host\u0026ndash;Parasite Relationships. Limnol Oceanogr 66(11):3857\u0026ndash;3872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/lno.11924\u003c/span\u003e\u003cspan address=\"10.1002/lno.11924\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu M, Liu G, Zheng X (2015) Spatial Pattern Changes of Biomass, Litterfall and Coverage with Environmental Factors across Temperate Grassland Subjected to Various Management Practices. Landscape Ecol 30(3):477\u0026ndash;486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-014-0120-1\u003c/span\u003e\u003cspan address=\"10.1007/s10980-014-0120-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaglianesi MA, Brenes E, Chaves-Elizondo N, Zuniga K, Castro Jim\u0026eacute;nez A, Barreto E, Duchenne F, Graham CH (2024) Species Morphology Better Predicts Plant-Hummingbird Interactions across Elevations than Nectar Traits. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e 291 (2031). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.2024.1279\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2024.1279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarini L, Fontana P, Battisti A, Gaston KJ (2009) Agricultural Management, Vegetation Traits and Landscape Drive Orthopteran and Butterfly Diversity in a Grassland-Forest Mosaic: A Multi-Scale Approach. Insect Conserv Divers 2(3):213\u0026ndash;220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1752-4598.2009.00053.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1752-4598.2009.00053.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;n Gonz\u0026aacute;lez AM, Dalsgaard B, Jens M, Olesen (2010) Centrality Measures and the Importance of Generalist Species in Pollination Networks. Ecol Complex 7(1):36\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecocom.2009.03.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ecocom.2009.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaza-Villalobos S, Garc\u0026iacute;a-Ram\u0026iacute;rez P, Endress BA, Lopez-Toledo L (2022) Plant Functional Traits under Cattle Grazing and Fallow Age Scenarios in a Tropical Dry Forest of Northwestern Mexico. Basic Appl Ecol 64:30\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.baae.2022.06.006\u003c/span\u003e\u003cspan address=\"10.1016/j.baae.2022.06.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGranahan DA (2014) Ecologies of Scale: Multifunctionality Connects Conservation and Agriculture across Fields, Farms, and Landscapes. Land 3(3):739\u0026ndash;769. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/land3030739\u003c/span\u003e\u003cspan address=\"10.3390/land3030739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLaren KP, and M A McDonald (2005) Seasonal Patterns of Flowering and Fruiting in a Dry Tropical Forest in Jamaica. Biotropica 37(4):584\u0026ndash;590. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1744-7429.2005.00075.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1744-7429.2005.00075.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeisel A, P\u0026eacute;rez G (2006) \u003cem\u003eGeograf\u0026iacute;a F\u0026iacute;sica y Poblamiento En La Costa Caribe Colombiana. Documentos de Trabajo Sobre Econom\u0026iacute;a Regional\u003c/em\u003e. Cartagena. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.banrep.gov.co/publicaciones/pub_ec_reg4.htm\u003c/span\u003e\u003cspan address=\"http://www.banrep.gov.co/publicaciones/pub_ec_reg4.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMertens JEJ, Brisson L, Janeček Š, Klomberg Y, Maicher V, S\u0026aacute;fi\u0026aacute;n S, Delabye S et al (2021) Elevational and Seasonal Patterns of Butterflies and Hawkmoths in Plant-Pollinator Networks in Tropical Rainforests of Mount Cameroon. Sci Rep 11(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-89012-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-89012-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiranda Patr\u0026iacute;ciaN Jos\u0026eacute; Eduardo Lahoz da Silva Ribeiro, Pedro Luna, Izaias Brasil, Jacques Hubert Charles Delabie, and Wesley D\u0026aacute;ttilo. 2019. The Dilemma of Binary or Weighted Data in Interaction Networks. \u003cem\u003eEcological Complexity\u003c/em\u003e 38 (April). Elsevier B.V.: 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecocom.2018.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ecocom.2018.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitja D, Miranda IS (2010) Weed Community Dynamics in Two Pastures Grown after Clearing Brazilian Amazonian Rainforest. Weed Res 50(2):163\u0026ndash;173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-3180.2010.00767.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3180.2010.00767.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuchhala N (2007) Adaptive Trade-off in Floral Morphology Mediates Specialization for Flowers Pollinated by Bats and Hummingbirds. Am Nat 169(4):494\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/512047\u003c/span\u003e\u003cspan address=\"10.1086/512047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeff PK, K SM, Fettig, and D R VanOverbeke (2007) Variable Response of Butterflies and Vegetation to Elk Herbivory: An Exclosure Experiment in Ponderosa Pine and Aspen-Mixed Conifer Forests. Southwest Naturalist 52(1):1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1894/0038-4909(2007)52[1:VROBAV]2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.1894/0038-4909(2007)52[1:VROBAV]2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOloumane P-N, Previl C, Zerey WE, El Zerey-Belaskri A (2025) Applying Joint Species Distribution Modelling to Assess the Relative Influence of Ecological Filters on Community Assembly in the El Bayadh Steppe, Algeria. J Arid Land 17(7):979\u0026ndash;996. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40333-025-0082-y\u003c/span\u003e\u003cspan address=\"10.1007/s40333-025-0082-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeralta G, Webber CJ, Perry GLW, Stouffer DB, V\u0026aacute;zquez DP, Tylianakis JM (2023) Scale-Dependent Effects of Landscape Structure on Pollinator Traits, Species Interactions and Pollination Success. \u003cem\u003eEcography\u003c/em\u003e 2023 (5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ecog.06453\u003c/span\u003e\u003cspan address=\"10.1111/ecog.06453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePires EP, Faria LDB, Monteiro AB, Domingos DQ, Mansanares ME, Hermes MG (2022) Insect Sociality Plays a Major Role in a Highly Complex Flower-Visiting Network in the Neotropical Savanna. Apidologie 53(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13592-022-00923-8\u003c/span\u003e\u003cspan address=\"10.1007/s13592-022-00923-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Plas F, Allan E, Fischer M, Alt F, Arndt H, Binkenstein J, Blaser S et al (2019) Towards the Development of General Rules Describing Landscape Heterogeneity\u0026ndash;Multifunctionality Relationships. J Appl Ecol 56(1):168\u0026ndash;179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.13260\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.13260\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowers JS, Feng X, Sanchez-Azofeifa A, and D Medvigy (2018) Focus on Tropical Dry Forest Ecosystems and Ecosystem Services in the Face of Global Change. Environ Res Lett 13(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/aadeec\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/aadeec\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProesmans W, Felten E, Laurent E, Albrecht M, Cyrille N, Labont\u0026eacute; A, Maurer C et al (2024) Urbanisation and Agricultural Intensification Modulate Plant\u0026ndash;Pollinator Network Structure and Robustness. Funct Ecol 38(3):628\u0026ndash;641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.14503\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.14503\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuisehuatl-Medina A, Averett JP, Endress BA, Lopez-Toledo L (2020) Removal of Cattle Accelerates Tropical Dry Forest Succession in Northwestern Mexico. Biotropica 52(3):457\u0026ndash;469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/btp.12748\u003c/span\u003e\u003cspan address=\"10.1111/btp.12748\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos A, Ospino J, Serrano D, Linares J (2016) An\u0026aacute;lisis Geogr\u0026aacute;fico de La Perdida y/o Fragmentaci\u0026oacute;n Del Bosque Seco Tropical En C\u0026oacute;rdoba Mediante Imagines Landsat Para El Per\u0026iacute;odo 1985\u0026ndash;2003. EVODIA 1(1):15\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRatoni B, Cruz CP, Roger Guevara, Daniel Gonz\u0026aacute;lez-Tokman, Ricardo Ayala, Fernanda Baena, and Wesley D\u0026aacute;ttilo. 2025. Thermal Tolerance and Sociality Explain the Interactive Role of Bees in a Pollination Network. (2025) (3) John Wiley and Sons Inc. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/oik.10639\u003c/span\u003e\u003cspan address=\"10.1111/oik.10639\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha OJ, G\u0026oacute;mez C, Hamrick JL, Trapnell DW, Smouse PE, Macaya G (2018) Reproductive Consequences of Variation in Flowering Phenology in the Dry Forest Tree Enterolobium Cyclocarpum in Guanacaste, Costa Rica. Am J Bot 105(12):2037\u0026ndash;2050. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ajb2.1205\u003c/span\u003e\u003cspan address=\"10.1002/ajb2.1205\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha-Filho LCD, Cardoso JCF, D Oliveira AC, Ara\u0026uacute;jo TN, Castro-Melo ALDSE, Augusto SC (2025) How Much Biodiversity Do Yellow Passionfruit (Passiflora Edulis) Croplands Harbour? Insights from Trap-Nesting Bees, Wasps and Their Natural Enemies. J Nat Conserv 84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jnc.2024.126818\u003c/span\u003e\u003cspan address=\"10.1016/j.jnc.2024.126818\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz R (2022) \u003cem\u003eFLORA RELICTUAL DEL BOSQUE SECO TROPICAL (BST) DEL DEPARTAMENTO DE C\u0026Oacute;RDOBA\u003c/em\u003e. 1a ed. Monter\u0026iacute;a: Fondo editorial Universidad de C\u0026oacute;rdoba\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos TD, Ribeiro ADS (2023) Mutualistic Interaction Network Structure between Bird and Plant Species in a Semi-Arid Neotropical Environment. Acta Oecol 118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actao.2023.103897\u003c/span\u003e\u003cspan address=\"10.1016/j.actao.2023.103897\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSazima C, Guimar\u0026atilde;es PR, S\u0026eacute;rgio F, dos Reis, Sazima I (2010) What Makes a Species Central in a Cleaning Mutualism Network? Oikos 119(8):1319\u0026ndash;1325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0706.2009.18222.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0706.2009.18222.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider CA, Wayne S, Rasband, Kevin W, Eliceiri (2012) NIH Image to ImageJ: 25 Years of Image Analysis. Nat Methods 9(7):671\u0026ndash;675. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nmeth.2089\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.2089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrotri S, Kaur S, Dhargalkar R, Valappil NP, and V Gowda (2025) Mass Flowering and Flowering Asynchrony Characterize a Seasonal Herbaceous Community in the Western Ghats. Biotropica 57(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/btp.70080\u003c/span\u003e\u003cspan address=\"10.1111/btp.70080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilveira AP, Martins FR, Ara\u0026uacute;jo FS (2013) Do Vegetative and Reproductive Phenophases of Deciduous Tropical Species Respond Similarly to Rainfall Pulses? J Forestry Res 24(4):643\u0026ndash;651. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11676-013-0366-5\u003c/span\u003e\u003cspan address=\"10.1007/s11676-013-0366-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026otilde;ber V, Aavik T, Kaasik A, Mesipuu M, and T Teder (2024) Insect-Pollinated Plants Are First to Disappear from Overgrowing Grasslands: Implications for Restoring Functional Ecosystems. Biol Conserv 291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2024.110457\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2024.110457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSousa Perugini LG, de LR, Jorge J, Ollerton MA, Milaneze-Gutierre, Rech AR (2025) High Modularity of Plant-Pollinator Interactions in an Urban Garden Is Driven by Phenological Continuity and Flower Morphology. Urban Ecosyst 28(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11252-025-01737-z\u003c/span\u003e\u003cspan address=\"10.1007/s11252-025-01737-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSTRI. (2025) STRI. \u003cem\u003eSTRI\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://striresearch.si.edu/\u003c/span\u003e\u003cspan address=\"https://striresearch.si.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwift KI, Bell FW (2011) What Are the Environmental Consequences of Using Silviculturally Effective Forest Vegetation Management Treatments? For Chron 87(2):201\u0026ndash;216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5558/tfc2011-008\u003c/span\u003e\u003cspan address=\"10.5558/tfc2011-008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabarelli M, B K C Filgueiras EMS, Ribeiro AV, Lopes, Leal IR (2024) Tropical Dry Forests. In \u003cem\u003eEncyclopedia of Biodiversity, Third Edition: Volume 1\u0026ndash;7\u003c/em\u003e, V1-294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-822562-2.00090-6\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-822562-2.00090-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeixeira LP, Pires-Oliveira JC, Portal-Gomes PW, Eisenlohr PV, Santos JDO, Lima SS, Moat J, Nic Lughadha E, Moro MF (2025) Biodiversity at Risk: Climate Change Impacts on Brazil\u0026rsquo;s Semiarid Caatinga Flora. Earth Syst Environ. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s41748-025-00883-w\u003c/span\u003e\u003cspan address=\"10.1007/s41748-025-00883-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanbergen AJ, Ben A, Woodcock A, Gray F, Grant A, Telford P, Lambdon DS, Chapman RF, Pywell MS, Heard, and Stephen Cavers (2014) Grazing Alters Insect Visitation Networks and Plant Mating Systems. Funct Ecol 28(1):178\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.12191\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.12191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026aacute;zquez DP, William F, Morris, and Pedro Jordano (2005) Interaction Frequency as a Surrogate for the Total Effect of Animal Mutualists on Plants. Ecol Lett 8(10):1088\u0026ndash;1094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1461-0248.2005.00810.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1461-0248.2005.00810.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVibrans H (2012) Malezas de M\u0026eacute;xico. \u003cem\u003eConabio\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.conabio.gob.mx/malezasdemexico/2inicio/paginas/lista-plantas-nov2011.html\u003c/span\u003e\u003cspan address=\"http://www.conabio.gob.mx/malezasdemexico/2inicio/paginas/lista-plantas-nov2011.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarren AD, Davis KJ, Stangeland EM, Pelham JP, Willmott KR, Grishin NV (2024) Illustrated List of Butterflies of America. \u003cem\u003eButterflies of America\u003c/em\u003e. April 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://butterfliesofamerica.com/\u003c/span\u003e\u003cspan address=\"https://butterfliesofamerica.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWFO. (2025) World Flora Online. \u003cem\u003eThe World Flora Online\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldfloraonline.org\u003c/span\u003e\u003cspan address=\"http://www.worldfloraonline.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang G, Jiang Y, Lan J, Huang L, Hao L, Liu Z, and J Xia (2023) Different Influences of Phylogenetically Conserved and Independent Floral Traits on Plant Functional Specialization and Pollination Network Structure. Front Plant Sci 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1084995\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1084995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZakharova L, Meyer KM, Seifan M (2019) Trait-Based Modelling in Ecology: A Review of Two Decades of Research. Ecol Model 407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolmodel.2019.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolmodel.2019.05.008\" 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":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mutualistic networks, Functional trait variation, Tropical dry forest, Flower-visitor interactions, Agroecosystem biodiversity","lastPublishedDoi":"10.21203/rs.3.rs-8919271/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8919271/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGrazing intensity and tree cover has potential repercussions for plant\u0026ndash;butterfly interactions. Here, we evaluated how three cattle management types\u0026mdash;open pastures (OP), woody pastures (WP), and pastures adjacent to forest remnants (FAP)\u0026mdash;are associated with species roles within plant\u0026ndash;butterfly networks in a deforested tropical dry forest (TDF) region of northern Colombia. Across 24 pastures, we quantified butterfly and plant traits, constructed interaction networks, and estimated species roles using centrality-based indices from a principal component analysis.\u003c/p\u003e \u003cp\u003eOur results show that cattle management filtered both plant and butterfly traits, modifying the identity of interacting species even though overall centrality did not differ among management types. Trait\u0026ndash;role relationships shifted across management regimes: corolla width influenced plant centrality in FAP (β\u0026thinsp;=\u0026thinsp;0.036, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.03) and WP (β\u0026thinsp;=\u0026thinsp;0.041, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.03), whereas butterfly wing length (β = \u0026ndash; 0.028, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) and body length (β = \u0026ndash; 0.073, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) were the main traits associated with their interactive roles only in WP. These patterns indicate that environmental filtering reorganizes trait distributions and the mechanisms by which traits shape species importance within plant\u0026ndash;butterfly networks.\u003c/p\u003e \u003cp\u003eOverall, we show that cattle management does not directly modify species\u0026rsquo; interactive roles, but instead acts primarily as a trait-based filter, indirectly shaping plant\u0026ndash;butterfly networks by altering trait distributions and trait\u0026ndash;role relationships rather than the overall network structure. Promoting tree cover and maintaining forest adjacency within cattle landscapes may enhance the ecological functionality of mutualistic networks in TDF ecosystems.\u003c/p\u003e","manuscriptTitle":"Cattle pasture management filters functional traits and alters trait–role relationships in plant–butterfly interaction networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 21:40:25","doi":"10.21203/rs.3.rs-8919271/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-05T14:07:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T08:00:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T04:34:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T23:38:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148902630420014450850418621734156900425","date":"2026-03-13T16:44:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114367234525991728889785787375265728560","date":"2026-03-13T13:13:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149820109859297674997712720135260624863","date":"2026-03-13T10:11:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203380674643051814053290952158537808905","date":"2026-03-13T02:55:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188823632698335227882955209841528430608","date":"2026-03-11T03:39:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-11T02:44:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-01T07:59:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-20T05:43:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2026-02-19T15:57:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cccfa524-510b-4998-a297-1bc4bf861df4","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-05T14:09:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 21:40:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8919271","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8919271","identity":"rs-8919271","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}