Ecosystem Architects: how herbivory and interaction networks shape reef communities in the Caribbean

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Quimbayo, Sergio R. Floeter, Mariana G. Bender This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6330935/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fish are the most diverse and abundant vertebrate group on Earth and special reef fishes take part in numerous interactions, such as cleaning, feeding, and agonistic interactions. Despite the growing literature on the patterns of fish interactions in reef environments, few studies have assessed the role of abundance in modulating reef fish interactions. This study examines how the local abundance, other biological traits of reef fish and benthic coverage influence the feeding and agonistic interactions networks in Curaçao an island located in the Caribbean. We used 109 video-plots at seven reef sites around the island in October of 2013 to estimate fish species abundance (MaxN), feeding and agonistic interactions rates, and benthic coverage. Considering these assemblages metrics we calculated network metrics, including centrality, nestedness, and modularity. Results indicate that fish abundance had a weak positive effect on feeding interactions, with herbivores, sessile, and mobile invertivores engaging in most interactions. Turf and rubble cover negatively influenced feeding interactions. For agonistic interactions, less abundant species initiated more aggressive encounters, with diet, mobility, and body size influencing interactions patterns. Herbivores showed the highest out-degree centrality, while sedentary and larger species engaged more frequently in agonistic interactions. Additionally, higher turf, macroalgae, and coral cover were associated with increased agonistic interactions. These findings highlight the role of herbivorous reef fish in structuring interactions networks and suggest that factors beyond local abundance, such as species traits and habitat characteristics, shape these interactions in Curaçao’s reefs. Abundance. benthic community. ecological networks. fish interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Understanding the processes that modulate species interactions is a persistent challenge in ecology (Paine 1966 ; Bascompte and Jordano 2007 ; Duran-Sala et al. 2025 ). Studies have shown that interactions can be modulated by processes that take place at regional and local scales (Cornell and Lawton 1992 ; Fontoura et al. 2020 ). At the regional scale, environmental and geographical filters, such as temperature and habitat composition can help determine the dynamics of species interactions (Gonzalez-Varo et al. 2010). For instance, temperature exerts a significant influence on biological interactions across multiple taxa since high temperature can determine the timing of life cycle events and therefore the relationships among species (Jiang et al. 2004; Kordas et al. 2011 ). Reef fishes, for example, present more intense plant-herbivore interactions in the tropics, rather than in colder regions (Barneche et al. 2009 ; Longo et al. 2019 ). At the local scale, resource availability (e.g. food and refuge) influences ecological interactions such as trophic interactions and dispersion (Nathan et al. 2000; Valeix et al. 2012 ; Wang et al. 2016 ). In plant-pollinator systems, potential shortages in resource availability inferred from habitat loss alter network architecture, interaction rates, and pollination success (Harrison and Winfree 2015 ; Spiesman and Inouye 2015). Additional studies indicate that various factors, including species richness, environmental conditions such as seasonality, and ecological processes like primary production, can also influence biological interactions (Gonzalez-Varo et al. 2010; Lázaro and Gómez-Martínez 2022 ). Nevertheless, few studies investigate the role of species abundance in interaction networks, especially in marine ecosystems (Webster et al. 2002; Quimbayo et al. 2018 ). The interactions among organisms rely on their presence and potential encounters in nature (Holyoak et al. 2005 ; Blanchet et al. 2020 ). Ecological interactions are often density-dependent, with species abundance often serving as a key driver of interaction frequency and strength (Kokko and Rankin 2006 ; Holland and DeAngelis 2010 ; Muñoz-Gallego et al. 2023 ). When species are more abundant, their interaction opportunities increase, potentially leading to stronger ecological network structures (Vazquez et al. 2005). For example, in reef fish cleaning interactions, the abundance of client species can determine the frequency of interactions and which species are attended to by cleaner fishes (Quimbayo et al. 2018 ). Additionally, more abundant fish species can have a more pronounced impact on the benthic reef community, contributing to changes in community composition and benthic habitat structure (Longo et al. 2014 ; Canterle et al. 2020 ). However, the relationship between abundance and interaction frequency is not linear or a universal rule (Vazquez et al. 2004, 2007). Species interactions are often context-dependent, various factors such as environmental conditions, species behavior, and the spatial-temporal context, can modify this relationship (Schweiger et al. 2010 ; Keith et al. 2023 ). For instance, ecosystems naturally fluctuate in resource availability, meaning that high species abundance does not always translate into higher interaction rates when other factors, such as habitat complexity or competition, mediate these interactions (Adler and Lauenroth 2003 ; Gravel et al. 2011 ). Therefore, while abundance can play a significant role in shaping interactions, it is just one piece of a complex puzzle. In the marine ecosystem, reef fishes constitute one of the most diverse vertebrate assemblages on Earth, with over 6,000 species distributed across tropical and subtropical oceans (Kulbicki et al. 2013 ; Parravicini et al. 2013 ). Their notable species richness, morphological diversity, and extensive distribution contribute to a wide array of interactions, including feeding and agonistic behaviors (Longo et al. 2014 ; Cantor et al. 2018 ; Canterle et al. 2020 ; Fontoura et al. 2020 ). Within this plethora of interactions, feeding interactions are well known, studies show that these interactions are associated with morphological and ecological adaptations to obtain food, as well as particular behaviors (Horn and Ferry-Graham 2006 ; Grant 2017 ). In contrast, agonistic interactions are not so studied and are characterized as defensive and aggressive behaviors, mostly driven by the reproductive and feeding behaviors of species (Hausfater 1975 ; Osorio et al. 2006). Within the coral reef ecosystem, agonistic interactions are commonly observed in damselfishes, one of the most conspicuous fish families in global reefs (McCord et al. 2020). Species belongin Pomacentridae family are herbivorous and cultivate their own algae ‘farm’ where they feed and defend from intruders very eagerly through agonistic interactions (Lobel 1980 ; Ferreira et al. 1998 ). Feeding and agonistic interactions are associated with trophic groups, as the need to obtain and defend food resources drives the species' behaviors within their ecological niches (Longo et al. 2014 ; Fontoura et al. 2020 ; Inagaki et al. 2020 ). Beyond the trophic group, other biological traits have been recognized as drivers of ecological interactions in coral reefs (Robertson 1998 ). In trophic interactions, body size is usually associated with predation success, i.e. the bigger the predator, the higher the predation rate (Woodward et al. 2005 ; Brose et al. 2006; Thompson et al. 2012 ). However, prevailing assumptions about this association, the inherent context-dependency of ecological processes means it is not a fixed rule. For instance, recent research has revealed that small fish can play a dominant predatory role on coral reefs, challenging traditional views. (Mihalitsis et al. 2022 ). Along with body size, the ability to move within the water column provides a greater range of feeding interactions. Sergeant-major damselfish, for example, may swim along the water column, this allows this omnivorous species to pursue different types of forage (Nunes et al. 2023 ). Also, there is a relationship between the position in the water column and body size, as the individuals that fed on plankton were usually small, while larger individuals fed on all substrates, including benthos (Nunes et al. 2023 ). Beyond assessing different depths, the capacity to move within and outside the reef may influence the rate and how interactions occur (Fox and Bellwood 2007). Herbivorous-detritivorous fishes, such as surgeonfishes and parrotfishes, are examples of highly mobile species, allowing a higher potential to forage (Lawson et al. 1999 ; Basford et al. 2015 ). For reef fishes, agonistic interactions can serve as a proxy for resource competition among co-occurring species, while patterns of feeding interactions provide insights into potential niche partitioning (Cantor et al. 2018 ; Fontoura et al. 2020 ; Tebbett et al. 2020 ). These interactions at the individual level can scale up to influence broader ecological processes, such as excluding competitors, enhancing species coexistence, or facilitating survival. Consequently, they play a crucial role in modulating the local species pool alongside other ecological factors, including environmental and other biological components (Ferreira et al. 2004 ; Vázquez et al. 2007 ; Coker et al. 2014 ). Understanding these interaction networks is essential for predicting how reef fish communities respond to environmental changes, such as habitat degradation and climate change, which can alter interaction dynamics and, ultimately, ecosystem functioning. In this study, we investigated the role of reef fish species abundances in structuring agonistic and feeding interaction networks in the coral reefs of Curaçao Island, in the Caribbean. Additionally, we explored the influence of biological traits ( trophic group, body size, and mobility), and environmental traits (proportion of benthic coverage) on shaping these interaction networks. These traits are fundamental to understanding the functional roles that species play within the ecosystem and how they navigate the trade-offs associated with resource use and competition (Quimbayo et al. 2021 ; Waechter et al. 2022 ). Accordingly, we expected species abundance to influence interactions differently depending on the type of interaction. Abundance would be positively related to feeding interactions, meaning that more abundant species would engage in more feeding interactions. In contrast, we expected an opposite pattern for agonistic interactions, which could be linked to the agonistic behavior and ecological strategy of certain species, such as territorial damselfish from the Pomacentridae family. These species are known to be central in agonistic interaction networks. Due to their territorial behavior, they tend to live individually, occurring at low local densities, yet they frequently engage in agonistic interactions when their territory is invaded. Regarding biological traits, we expected trophic group to be the most important predictor of both agonistic and feeding interactions. A similar pattern was expected for environmental traits, such as the coverage of benthic organisms. By elucidating the dynamics of these interactions and their underlying determinants, our study contributes to a more comprehensive understanding of local ecological processes within coral reefs. Furthermore, it provides insights into the broader patterns of how biological traits shape interactions in marine ecosystems, which is crucial for conservation strategies and predicting ecosystem responses to environmental change. Materials and Methods Study area Feeding and agonistic interactions among fish were documented at seven reef sites at Curaçao Island in the Caribbean (12°10'10.4520'' N, 68°59' 24.0756''W). These sites were Westpunt, Porto Marie, Oostpunt, Playa Largo, Marie Pampoen, Snake Bay, and Water Factory (Fig. 1). We used the remote underwater video methodology (RUVs), which consists of video-plots, to sample fish interaction in each site. This RUV method is recognized for its ability to minimize the direct influence of divers on fish behavior, which is essential when studying organisms’ behavior and interactions. In addition, this approach allows us to collect data and rewatch it at any time, proving to be a valuable tool (Harvey et al. 2004; Longo and Floeter 2012). Data collection We analyzed 109 video-plots along seven sites, the sampling area was limited to 2 m 2 and to the 10 central minutes of each video to avoid any potential influence from the diver’s presence over the fish behavior. During this time, we identified all observed fish species and recorded all feeding and agonistic interactions. A feeding interaction was considered when a fish took a bite on the reef substratum (Longo et al. 2014). We considered agonistic interactions whenever a species was chasing another individual, one swimming toward the other with no intentions of preying on it (Canterle et al. 2020; Fontoura et al. 2020). Each fish species recorded in the videos was categorized into three biological traits according to Quimbayo et al. (2021): 1) Trophic group (herbivore-detritivores, macroalgae feeders, mobile invertebrate feeders, sessile invertebrate feeders, omnivores, piscivores and planktivores); 2) mobility (sedentary species and species mobile within a reef); and 3) body size classes (0–7 cm, 7.1–15 cm, 15.1–30 cm, 30.1–50 cm, 50.1–80 cm and >80 cm). Fish Abundance and Benthic Cover We estimated fish abundance in each video-plot using the MaxN (Maximum Abundance) method (Harvey et al. 2012). This approach calculates the maximum number of individuals of each species observed together within the entire video-plot (2 m²) during the central 10 minutes of recording (Unsworth et al. 2014). In addition to fish abundance, we estimated the abundance of benthic organisms (benthic cover). To calculate benthic cover, we used photographs taken at the sites where the videos were recorded. These photos were analyzed using the Photoquad software. For each photo, a transect area of 30 cm × 30 cm was delimited, and 50 random points were marked. To minimize potential bias due to the low resolution of some photographs, we categorized the benthic organisms observed at each point into broad functional groups: coral, turf, algae, rubble, sand, or others (e.g., sponges, worms, and ascidians). This process resulted in a percentage of cover for each category in every photo. Out of the 60 videos where feeding interactions were recorded, only 29 had associated photographs for benthic analysis. To address this limitation, we calculated an overall mean benthic cover for each category using data from the available photos. For the videos without photos, these mean values were used as a proxy for benthic cover, ensuring that all observations could be included in the analysis. To ensure the robustness of our approach, we initially fitted two separate models: one using only the benthic cover data derived from photos (29 videos) and another incorporating both photo-derived data and the overall mean benthic cover (60 videos). The results of these models were consistent, showing no significant differences in the key patterns observed. Based on this comparison, we chose to use the second model (with both photo-derived and mean values) to maximize the sample size and include all observations in the analysis. We fitted a nested linear model to investigate the factors influencing the number of feeding interactions. The predictor variables included fish abundance, trophic group, benthic cover proportions, and coverage origin (a categorical variable indicating whether benthic cover data were derived from photos or mean values). Interaction terms between the trophic group and benthic cover proportions were also included. The nested model was constructed incrementally as follows: Model 1: Fish abundance as the sole predictor. Model 2: Fish abundance and trophic group. Model 3: Fish abundance, trophic group, and benthic cover proportions. Model 4: Fish abundance, trophic group, benthic cover proportions, and coverage origin. To evaluate the fit of the models and identify the best-fitting model, we conducted likelihood ratio tests comparing successive models in the nested sequence. Additionally, the Akaike Information Criterion (AIC) was calculated for each model to assess model parsimony and balance between fit and complexity. The most complete model was the one with the lowest AIC, which also showed the best results in the likelihood ratio tests (LRT). Hence this model was selected as the final model, as it provided the best trade-off between explanatory power and simplicity while incorporating all relevant predictors and interactions. All analyses were performed using the R statistical software. Network structure and statistical analysis We described the feeding interaction using two network metrics i.e., nestedness and modularity. A feeding network was defined as a matrix M, in which the element mij corresponds to the number of bites that a fish species i invested in the reef substratum j (i.e., type of substrate bitten). Nestedness describes an asymmetric, hierarchical distribution of interactions among species (Bascompte et al. 2003), whereas modularity describes a compartmentalized distribution of interactions among species (Olesen et al. 2007). We calculated nestedness using the NODF (Nestedness metric based on Overlap and Decreasing Fill) metric as described by Almeida-Neto and Ulrich (2011). This metric quantifies nestedness for the entire matrix and also determines the contribution of individual species and sites (i.e., rows and columns). The NODF nestedness score ranges from 0 (non-nested) to 100 (perfectly nested). For our analysis, we utilized the ‘nestednodf’ function from the Bipartite package in R (Dormann and Strauss 2014). For network modularity, we applied the Q metric that measures the difference between the observed fraction of links connecting species within the same module and the fraction expected by chance (Newman 2006) using an algorithm modified for two‐mode networks (Dormann and Strauss 2014). For this, we used the ‘computeModules’ function from the Bipartite package with the ‘DormannStrauss’ method. We assessed nestedness and modularity significance through a null model where nestedness and modularity values were contrasted with those obtained from null models. This null model was built using the ‘oecosimu’ function from the Vegan package and 1,000 iterations (Jonsson 2001). The observed nested or modular structure of the feeding network was considered significant when its observed NODF and Q‐value, respectively, lay outside of the 95% confidence intervals of their corresponding null distributions. The inclination of each species to engage in agonistic interactions was assessed by computing the network’s out-degree centrality. This metric reflects the total number of other species that each fish species was observed interacting with. Species with a greater number of outward links demonstrate higher out-degree centrality (Fontoura et al. 2020). We tested the influence of fish abundance on the number of feeding and agonistic interactions using a null model implemented through the ‘permatswap’ function under the ‘quasiswap’ method, from the Vegan R package (Oksanen et al. 2007). For the agonistic network analysis, we explored the relationship between interaction frequency and fish trophic groups. To achieve this, we employed a linear model to investigate how all species traits influenced the contribution to out-degree centrality within the agonistic network. We used the 'lm' function from ‘the lme4’ package, treating the seven sampled sites in Curaçao as random effects while considering traits as fixed effects. All three networks were built using the ‘Igraph’ package in R Software (Csardi and Nepusz 2006). Results Feeding interactions A total of 34 reef fish species were observed in the video-plots of Curaçao. Among these, 25 fish species interacted with the benthos through feeding interactions, and 33 species interacted agonistically. The fish-benthos interaction network structure was not influenced by fish abundance, i.e. , the species that interact more frequently with benthic organisms are not the most abundant ones in video-plots (Fig. 2). The majority of feeding interactions were performed by Scarus iseri (25%) , Acanthurus tractus (17%), Sparisoma viride (10.5%) , S. aurofrenatum (7.5%) , A. coeruleus (7%) and Scarus taeniopterus (4%), respectively. The food items with the highest bite rates were turf algae and coral. The reef fish-benthos feeding interaction network exhibited a high NODF value (NODF = 69.17), suggesting a nested pattern. However, when compared to the null model, the observed nestedness was not statistically significant (p = 0.402). Further details are provided in the supporting information (Table S2). We identified five modules where species interact more frequently (Q = 0.196). However, modularity was not significant and not influenced by fish traits, as species belonging to different trophic groups appear together in three out of five modules. The nested model analysis provided insights into the drivers of feeding interactions of reef fishes on Curaçao Island. The model's likelihood and AIC improved progressively as additional predictors were incorporated (table S3), ultimately leading to the final model with the best fit (Chisq = 18.238, p = 0.001, AIC = 4077.866). The maximum abundance had a light positive effect on the feeding interactions, meaning that the greater abundance leads to higher feeding interactions (estimate = 0.702, p < 0.005, Fig. 3a). Additionally, within the trophic groups, herbivores (estimate = 9.197, p < 0.05), sessile (estimate = 6.268, p < 0.05), and mobile invertivores (estimate = 6.006, p < 0.05) positively influenced the feeding interactions (Fig. 3b). Regarding the benthos coverage, turf (estimate = -0.146, p < 0.05) and rubble (estimate =-0.151, p < 0.05) were the only benthos categories to affect the interactions (Fig. 3c). Both categories had an inverse relationship with interactions, i.e. the higher the coverage proportion, the lower the frequency of feeding interactions. Lastly, the coverage origin (whether derived from photos or mean values) was not significant (p = 0.07), this indicates that there was no statistically significant difference between using proportions derived solely from the photos and the proportions imputed with the mean values. Overall, the findings highlight that the trophic group mediates the effect of fish abundance on feeding interactions, and that coral cover has a negative influence. The consistency of results across models supports the robustness of these conclusions (table 1). Table 1 Linear model results with the effect of biological traits (MaxN, and trophic group) and environmental traits (benthic coverage proportion) on species feeding interactions Biological and environmental traits Estimate t-value p-value Maximum abundance 0.702 4.233 < 0.001*** Trophic groups Herbivore 9.197 5.328 < 0.001*** Mobile invertivore 6.006 2.753 0.006 ** Sessile invertivore 6.268 2.666 0.007** Omnivore 7.115 1.519 0.129 Benthic coverage proportion Turf proportion -0.146 -2.607 0.009** Coral proportion -0.088 -1.576 0.115 Rubble proportion -0.151 -2.683 0.007** Algae proportion -0.081 -1.158 0.247 Coverage origin 3.258 1.800 0.072 Agonistic interactions In total, 33 reef fish species were observed interacting agonistically, either chasing other fish species or being chased. The most relevant families to agonistic interaction networks were Pomacentridae, Labridae, and Acanthuridae. The agonistic network structure revealed that fish species that engage in agonistic interactions are not necessarily the most abundant, this result was corroborated by the nested model (Fig. 4a). The fish species that interact more frequently belong to the Stegastes genus: S. partitus (68% each), followed by S. adustus (45%), and S. planifrons (24%) (Fig. 5b). While these are central species in Curaçao agonistic networks, Bodianus rufus, Caranx bartholomei, Cephalopholis fulva and Chaetodon striatus were peripheral species, for which only one agonistic interaction was registered. When trophic groups were shown in network graphs, these revealed that the central species are herbivorous species from the Stegastes genus, which were chasing more frequently other herbivorous species (Fig. 4b). The nested model with the best fit and AIC value was the third and most complete model (Table S4). Our results revealed that the maximum abundance negatively influenced the out-degree centrality of agonistic interactions (estimate = -0.087, p < 0.05). This result represents that the species responsible for initiating the agonistic interactions are the least abundant in the network (table 2). Considering other biological traits, the diet was positively correlated to the out-degree centrality throughout all the different trophic groups (p < 0.05). Although different trophic groups had higher effects, herbivores (estimate = 16.854) and planktivores (estimate = 16.983) had the highest effect, followed by omnivores (estimate = 12.220), mobile (estimate = 12.219), and sessile invertivores (estimate = 6.127). Additionally, mobility (estimate = -7.18836, p < 0.05) and body size (estimate = 4.342, p < 0.05) were the other biological traits to influence the out-degree centrality. Sedentary (territorial) species were the ones to engage in more agonistic interactions compared to species that move within the reef or between reefs. Regarding body size, larger fishes represented a higher out-degree centrality than small ones. Lastly, the proportion of benthos coverage, three of the four benthic categories lightly influenced species centrality. The higher coverage proportion of turf (estimate = 0.082, p < 0.05), macroalgae (estimate= 0.085, p < 0.05), and corals (estimate = 0.060, p < 0.05) indicated a higher out-degree centrality. Table 2 Linear model results with the effect of biological traits (MaxN, trophic group, mobility, and body size) and environmental traits (benthic coverage proportion) on species out-degree centrality Biological and environmental traits Estimate t-value p-value Maximum abundance -0.087 -2.896 0.003** Trophic groups Herbivore 16.854 5.483 < 0.001*** Mobile invertivore 12.119 3.908 <0.0001*** Sessile invertivore 6.157 2.006 0.0453* Planktivore 16.983 5.407 < 0.001*** Omnivore -14.241 -3.588 0.0003*** Mobility -7.188 -8.706 < 0.001*** Body size 4.342 8.663 < 0.001*** Benthic coverage proportion Turf proportion 0.082 4.477 < 0.001*** Coral proportion 0.085 3.484 0.0005*** Rubble proportion 0.052 1.740 0.0003 Algae proportion 0.085 3.731 0.0002*** Discussion Our study investigated the factors shaping feeding and agonistic interactions within a coral reef fish community on Curaçao Island. We found that these two interaction types are driven by distinct sets of biological attributes of the fish species. The feeding interaction network, unlike the agonistic network, did not exhibit a clear structure. Null model analyses revealed no significant deviations from random expectations in terms of nestedness or modularity. This suggests that feeding interactions among these reef fishes are not strongly influenced by factors like competitive hierarchies or specialized feeding guilds, i.g. specialization, but probably for opportunistic reasons. This may reflect a high behavioral plasticity of reef fish species, allowing them to adapt flexibly to available resources, especially in dynamic environments or those subject to anthropogenic and natural disturbances (Thompson et al. 2012 ). For example, less structured networks are often observed in systems with lower biodiversity or in locations where environmental conditions are unstable, leading species to feed on a wide range of resources to ensure survival (Bascompte et al. 2003 ; Olesen et al. 2007 ). However, despite the lack of a discernible overall structure, certain species traits significantly influenced feeding interactions. Notably, we observed a positive correlation between the local maximum abundance of a fish species and the frequency of its feeding interactions. This finding supports the idea of ​​density-dependent interactions, where a greater number of individuals increases the probability of encounters and, consequently, interactions (Dingle and Caldwell, 1975 ; Quimbayo et al. 2020 ). However, while greater abundance can promote feeding interactions, it can also intensify competition for resources, especially in communities where benthic resources are limited (Freeman and Byers, 2006). For example, high population densities can lead to changes in feeding behavior, such as an increase in opportunistic foraging, corroborating the argument that the lack of structure in the network also leads to opportunistic foraging (Hixon and Beets, 1993). This dynamic highlights the role of abundance not only as a promoter of interactions but also as a regulatory factor that influences the quality and nature of these interactions. While abundance plays a critical role in shaping feeding interactions, the feeding interactions of reef fishes in Curaçao Island are further influenced by other species' attributes. The trophic groups of reef fish species significantly shaped feeding interactions, with herbivores, sessile, and mobile invertivores being the most prominent contributors. Among these, herbivores emerged as the primary drivers of feeding interactions, a pattern commonly observed in Caribbean reef systems (Longo et al. 2019 ). This finding aligns with the observed interaction network, where herbivore species occupy the highest position, representing a high frequency of interactions. Within the herbivore species from the Curaçao community, some families distinguish themselves by the frequency of feeding interactions, such as parrotfishes (Labridae), surgeonfishes (Acanthuridae), and damselfishes (Pomacentridae). Species such as Scarus iseri , Acanthurus tractus , and Stegastes adustus demonstrated intense foraging activity on algae, detritus, and other organisms within the epilithic algal matrix (EAM), corals, and rubble. The coexistence of Labridae, Pomacentridae, and Acanthuridae at the apex of the coral reef food web is probably explained by resource partitioning. For example, while scrapers such as members of the family Labridae, including Scarus iseri , remove encrusting algae attached to hard surfaces, grazers such as Acanthurus tractus and Stegastes adustus consume filamentous algae and macroalgae available in larger quantities. These differences in feeding habits not only diversify the types of resources consumed but also reduce overlapping trophic niches, which minimize direct competition between these families and allow coexistence. While partitioning the resource, these herbivore species continue to play a critical role in shaping benthic community dynamics. By removing encrusting algae and macroalgae, these species prevent these competing plants from outcompeting corals for space and light, allowing corals to maintain their ecological dominance. Furthermore, by feeding on organisms associated with algae and the epilithic mat (EAM), herbivores increase nutrient cycling in the ecosystem, promoting a more balanced and diverse environment. For these reasons, these species can be considered keystone species, whose activity shapes the composition of the benthic community and regulates ecological processes crucial to the health of the reef ecosystem. However, the relationship between trophic groups and feeding interactions can be context-dependent, being influenced by resource disponibility, revealing an even more complex dynamic. Benthic cover significantly influenced feeding and agonistic interactions. While turf algae and rubble had a negative effect on feeding interactions, a higher coverage proportion of turf, macroalgae, and coral led to a higher out-degree centrality. For feeding interactions, this inverse pattern suggests that substrates such as turf and rubble provide fewer nutritional resources or make food access more challenging. Previous studies have shown that herbivores dominate feeding interactions in Caribbean reef environments, including Curaçao (Longo et al. 2019 ). This dominance is often associated with the high availability and nutritional value of benthic food items in tropical regions, as well as the increased metabolic and feeding rates driven by higher temperatures (Demko et al. 2019; Duran et al. 2019 ). However, our results indicate that habitats dominated by substrates like rubble and turf may significantly reduce these interactions, possibly due to the low nutritional quality or lower structural complexity of these benthic components (Barott et al. 2012). Although herbivores frequently consume turf algae, the dominance of these substrates in the habitat may limit the efficiency of feeding interactions, restricting the availability of optimal resources and negatively impacting the reef trophic network (Adam et al. 2015). These findings align with the observed positive impact of benthic cover on agonistic interactions. While turf and rubble cover reduced feeding interactions, greater coverage of these same substrates had a slightly positive effect on agonistic interactions, suggesting that habitat characteristics shape different behavioral dynamics. This result contrasts with our initial hypothesis that lower resource availability (lower coverage proportion) would increase territorial and agonistic interactions due to intensified competition. However, it is possible that even with a higher proportion of turf, its low nutritional quality may still promote agonistic behaviors as individuals compete over suboptimal but spatially concentrated resources. Alternatively, the dominance of turf may alter the spatial distribution of fish by forcing them to forage in areas where resources are scarce or patchily distributed. This can lead to uneven spatial distribution, with individuals competing for limited areas where resources are more abundant or accessible, as a consequence, the frequency of agonistic interactions would increase, as fish compete for these restricted resources. The out-degree centrality of the agonistic interactions network was negatively influenced by the species abundance, meaning that the most central species were the least abundant in the agonistic interactions network. These species at the center of the network were mainly territorial damselfishes from the Stegastes genus ( S. adustus , S. partitus , and S. planifrons ), this result aligns with their well-known territorial behavior (Barneche et al. 2009 ; Fontoura et al. 2020 ). Damselfishes can vary at different levels of territoriality (aggressiveness), more territorial species (i.e. more aggressive) tolerate less other individuals of the same species or other species in their territory. Thus, the abundance of these territorial species is limited, corroborating the inverse effect of abundance on centrality in interactions. Along with abundance, diet positively affected the out-degree centrality, between the different trophic groups, herbivores and planktivores were the ones to have a higher influence in the agonistic interactions. This goes along with damselfishes as the central species in the network since these species are territorial herbivores that cultivate they’re own algae ‘farm’. Through algae farming, these fishes shape the composition of algae and micro-invertebrates on reefs, influencing primary production as well as the flow of energy and nutrients (Klumpp et al. 1987 ; Ferreira et al. 1998 ), an activity that helps maintain reef structure and functioning (Randazzo Eisemann et al. 2019 ). Notably, the interactions mediated by herbivore-detritivores were disproportionately high when considering their local abundances, categorizing them as keystone species in interaction networks (Paine 1969 ; Quimbayo et al. 2017 ). This crucial role of territorial herbivores on agonistic interaction networks has been demonstrated in reef ecosystems worldwide (Fontoura et al. 2020 ). Due to their active territorial defense and agonistic behavior, these fish commonly occupy central positions in these networks, indicating a higher frequency of interactions relative to other species (Fontoura et al. 2020 ). Additionally, other biological traits of the reef fish species had a significant effect on the out-degree centrality of the agonistic network. Mobility had a negative effect on the out-degree centrality, sedentary species engaged in more agonistic interactions when compared to species that are mobile within the reef and between reefs. This could be attributed to the fact that territorial species, such as damselfishes, tend to have a limited home range and typically occupy only a few meters of habitat throughout their lives. In a nutshell, mobility denotes species behavior, which plays a fundamental role in network structuring. Fontoura et al. ( 2020 ) similarly identified that herbivorous species with small habitat ranges often occupied central positions in agonistic networks. Besides mobility, body size was the other biological trait to influence the out-degree centrality, with larger fish displaying higher centrality values than smaller ones. This result contrasts with the expectation that small body size species (e.g. Stegastes spp.) would contribute more to the out-degree centrality. On the other hand, species such as Scarus iseri, Acanthurus coeruleus and Cephalopholis cruentata are usually at least three times larger than damselfishes (Welsh and Bellwood 2012 ) and performed a peripheral role in the agonistic interactions network. The presence of larger species likely influenced out-degree centrality indirectly, while traits like territorial behavior, abundance, and diet positioned herbivorous territorial species at the center of the agonistic interactions network. This highlights that biological traits play a pivotal role in shaping the structure of agonistic interaction networks, with sedentary, territorial, and herbivorous species driving the networks’ structure. However, the unexpected influence of larger body sizes on the network centrality highlights the complexity of ecological interactions, suggesting that both interspecific dynamics and behavioral traits can outweigh traditional size-based expectations. In conclusion, our study reveals that agonistic and feeding interactions in reef environments are shaped by a shared set of biological and environmental traits, though in distinct ways. For instance, local abundance positively influenced feeding interactions, highlighting how resource availability can promote trophic activity. Conversely, species with lower local abundances played central roles in agonistic networks, indicating that these interactions may serve functions beyond resource competition, such as territorial defense (Ceccarelli et al. 2005 ; Fontoura et al. 2020 ). Furthermore, the trophic group of fish species emerged as a fundamental trait influencing both interaction types. Herbivores, in particular, stood out as keystone species, significantly contributing to interaction frequency. This underscores their critical role in controlling algal growth, maintaining ecosystem balance, and reinforcing the urgent need for their conservation. On the other hand, less complex substrates, such as rubble and turf, negatively impacted feeding interactions while fostering agonistic behaviors. This pattern suggests that degraded benthic habitats not only disrupt trophic dynamics but also intensify territorial disputes, likely due to reduced resource availability and consequent competition. Environments dominated by rubble or turf are often associated with disturbances, such as reef degradation, which compromise feeding interactions and overall ecosystem health. This highlights the importance of conservation of reef habitats in a climate change moment Furthermore, the agonistic interactions performed by territorial species, such as Stegastes , are crucial for the maintenance of territorial areas with greater structural complexity, which, in turn, support more diverse and functional communities. Despite their ecological significance, agonistic interactions are still underrepresented in studies when compared to trophic interactions. Given the variability in territorial behavior among species (Pratchett et al. 2016 ), further research is warranted to understand how these behaviors influence ecosystem structure and function. Lastly, our study shows how connected reef species are through interactions, emphasizing the importance of identifying the environmental drivers or biological traits that shape ecological interactions and processes. This knowledge enhances our understanding of reef ecosystem functionality and provides insights into the roles of species within one of the most diverse and vital habitats on Earth. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Ethics approval This study was observational, and the ethical approval was not required. Competing interest The authors have no relevant financial or non-financial interests to disclose. 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Farming damselfishes shape algal turf sediment dynamics on coral reefs. Mar Env Res 160: 104988. https://doi.org/10.1016/j.marenvres.2020.104988 Tebbett SB, Siqueira AC, Bellwood DR (2022). The functional roles of surgeonfishes on coral reefs: past, present and future. Rev Fish Biol Fish 1-53. http://dx.doi.org/10.1007/s11160-021-09692-6 Thompson RM, Brose U, Dunne JA, Hall RO, Hladyz S, Kitching RL, … Tylianakis JM (2012). Food webs: reconciling the structure and function of biodiversity. Trends Ecol Evol 27(12): 689-697. https://doi.org/10.1016/j.tree.2012.08.005 Unsworth RK F, Peters JR, McCloskey RM, Hinder SL (2014). Optimising stereo baited underwater video for sampling fish and invertebrates in temperate coastal habitats. Estuarine Coastal and Shelf Sci 150: 281-287. https://doi.org/10.1016/j.ecss.2014.03.020 Valeix M, Loveridge AJ, Macdonald DW (2012). Influence of prey dispersion on territory and group size of African lions: a test of the resource dispersion hypothesis. Ecology 93(11): 2490-2496. https://doi.org/10.1890/12-0018.1 Vázquez DP, Aizen MA (2004). Asymmetric specialization: a pervasive feature of plant–pollinator interactions. Ecology 85(5): 1251-1257. Vázquez DP, Poulin R, Krasnov BR, Shenbrot GI (2005). Species abundance and the distribution of specialization in host-parasite interaction networks. Journal of Animal Ecology 946-955. Vázquez DP, Melián CJ, Williams NM, Blüthgen N, Krasnov BR, Poulin R (2007). Species abundance and asymmetric interaction strength in ecological networks. Oikos 116(7): 1120-1127. https://doi.org/10.1111/j.0030-1299.2007.15828 Venables WN, Ripley BD (2002). Modern Applied Statistics with S-PLUS, Fourth edition. Springer, New York. ISBN 0-387-95457-0. Vermeij MJ, Van Moorselaar I, Engelhard S, Hörnlein C, Vonk SM, Visser PM (2010). The effects of nutrient enrichment and herbivore abundance on the ability of turf algae to overgrow coral in the Caribbean. PloS One 5(12): e14312. https://doi.org/10.1371/journal.pone.0014312 Waechter LS, Luiz OJ, Leprieur F, Bender MG (2022). Functional biogeography of marine vertebrates in Atlantic Ocean reefs. Diversity and Distributions 28(8): 1680-1693. https://doi.org/10.1111/ddi.13430 Waldie PA, Blomberg SP, Cheney KL, Goldizen AW, Grutter AS (2011). Long-term effects of the cleaner fish Labroides dimidiatus on coral reef fish communities. PLoS One 6(6): e21201. https://doi.org/10.1371/journal.pone.0021201 Wang Y, Xiao X, Yu X, Xu J, Cai Y, Lei G (2016). Resource availability determines food chain length in Chinese subtropical rivers. Aquat Ecology 50: 187-195. https://doi.org/10.1007/s10452-016-9567-2 Webster MS, Hixon MA (2000). Mechanisms and individual consequences of intraspecific competition in a coral-reef fish. Mar Ecol Prog Ser 196: 187-194. https://doi.org/10.3354/meps196187 Webster MS, Almany GR (2002). Positive indirect effects in a coral reef fish community. Ecol Lett 5(4): 549-557. https://doi.org/10.1046/j.1461-0248.2002.00355 Welsh JQ, Bellwood DR (2012). How far do schools of roving herbivores rove? A case study using Scarus rivulatus. Coral Reefs 31: 991-1003. White JSS, O'Donnell JL (2010). Indirect effects of a key ecosystem engineer alter survival and growth of foundation coral species. Ecology 91(12): 3538-3548. https://doi.org/10.1890/09-2322.1 Woodward G, Ebenman B, Emmerson M, Montoya JM, Olesen JM, Valido A, Warren PH (2005). Body size in ecological networks. Trends Ecol Evolution 20(7): 402-409. https://doi.org/10.1016/j.tree.2005.04.005 Supplementary Files SupplementarymaterialNunesPalma.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6330935","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437097953,"identity":"9dddc8fa-5e34-4e2a-bcb3-c64888bbca81","order_by":0,"name":"Rafaella Nunes-Palma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYFACxgcMDAUWUE7FfzkQdeABXi3MBgwMBhJQzhlmY7CWBKK1MLYxJzaAGPi0mLcfZvzwwUBC3lwi9+DDn21s6fPDDj8E2mInp9uAXYvMmWRmyRkGEoY7Z+QlG/Oc48ndeDvNAKgl2djsAHYtEgz5B6R5DCQYN9zIMZNmKJPI3Tg7AaTlQOI2XFr4HzP//mMgYQ/UYv7zB5tBuuHs9A/4tUgks0kDvZ8IsoWBpy0hQV46h4AtEo/ZLHsMJJI3nHljLM1z5oDhBumcggMJBnj8wp/MfONHhY3thuM5hh9/VByQl5+dvvnDhwo7OVxaMIEBWKUBscpBQL6BFNWjYBSMglEwEgAAbyde3c+/oI4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0002-1787-994X","institution":"Universidade Federal de Santa Maria","correspondingAuthor":true,"prefix":"","firstName":"Rafaella","middleName":"","lastName":"Nunes-Palma","suffix":""},{"id":437097954,"identity":"068ec7ad-3250-458e-9d82-66dd0147ea2d","order_by":1,"name":"Juan P. Quimbayo","email":"","orcid":"","institution":"University of Miami College of Arts and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"P.","lastName":"Quimbayo","suffix":""},{"id":437097955,"identity":"6c201c25-3ce3-4249-ad27-206972038236","order_by":2,"name":"Sergio R. Floeter","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina - Campus Florianópolis: Universidade Federal de Santa Catarina","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"R.","lastName":"Floeter","suffix":""},{"id":437097956,"identity":"153a6f45-70f5-4df7-b0be-72c850fa72b3","order_by":3,"name":"Mariana G. Bender","email":"","orcid":"","institution":"Universidade Federal de Santa Maria","correspondingAuthor":false,"prefix":"","firstName":"Mariana","middleName":"G.","lastName":"Bender","suffix":""}],"badges":[],"createdAt":"2025-03-28 22:11:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6330935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6330935/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81134376,"identity":"37b72fea-09ee-406e-b4e0-1b131a61f66b","added_by":"auto","created_at":"2025-04-22 15:16:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237882,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of Curaçao island in the Caribbean Sea (A) and the location of sample sites in Curaçao island (B). WP : Westpunt; PL: Playa Largo; PM: Porto Marie; SB : Snake Bay; WF: Water Factory; MP: Marie Pampoen; OP: Oostpunt\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6330935/v1/050c47d449c56539cf08f961.png"},{"id":81134380,"identity":"58b659c5-1d92-4916-8501-48fa6bf12b87","added_by":"auto","created_at":"2025-04-22 15:16:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":526434,"visible":true,"origin":"","legend":"\u003cp\u003eFish-benthos interaction network in Curaçao island. Lines are links that represent fish-benthos interactions and line thickness denotes interaction frequency, i.e., the number of interactions among fish and benthos. Circles on the left are proportional to the local abundance of fish species, colors represent the trophic groups: herbivores (HD) in green, mobile invertivores (IM) in orange, carnivores in red, and omnivores (OM) in pink.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6330935/v1/5f10c1fc69559aff3fe0623c.png"},{"id":81135182,"identity":"5c5de55c-2407-4abb-b6ba-8db11d54706b","added_by":"auto","created_at":"2025-04-22 15:24:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143858,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between fish maximum abundance, benthic proportion, and feeding interactions across trophic groups. 3A) Boxplot showing the relationship between fish mean abundance and feeding interactions. 3B) Distribution of feeding interactions across trophic groups, with violin plots representing density and boxplots showing medians and interquartile ranges, colors represent the trophic groups: herbivores (HD), mobile invertivores (IM), sessile invertivores (IS), and omnivores (OM). 3C) Relationship between benthic proportion and the number of feeding interactions, colors represent the benthic group: algae, coral, rubble, and turf.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6330935/v1/4707e422c179b53fbaca3446.png"},{"id":81135184,"identity":"d84583be-62ce-4827-acca-d9b7b89ab3fa","added_by":"auto","created_at":"2025-04-22 15:24:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":418054,"visible":true,"origin":"","legend":"\u003cp\u003eReef fish agonistic interaction networks in Curaçao island. In 4A, the centrality degree network, where circles are proportional to fish centrality degree and larger circles represent fish species that are more central in the network; lines represent the interaction between pairs of species and line thickness denotes interaction strength. The three central species and their respective centrality degree values were: \u003cem\u003eS. adustus \u003c/em\u003e(10), \u003cem\u003eS. partitus\u003c/em\u003e (9) and \u003cem\u003eS. planifrons \u003c/em\u003e(5.15). In 3B, fish trophic groups are depicted in the agonistic network. Circles are proportional to species abundance; lines represent the interaction between pairs of species and line thickness denotes interaction strength\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6330935/v1/24d33cd184cd0f06c1f26fc7.png"},{"id":81135189,"identity":"8cecd44d-4f7a-40da-bdb7-489fc48e6459","added_by":"auto","created_at":"2025-04-22 15:24:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":207269,"visible":true,"origin":"","legend":"\u003cp\u003eThe contribution of fish traits to the out-degree centrality in the agonistic interactions network in Curaçao, Caribbean: 4A) body size, 4B) trophic group, 4C) mobility, and 4D) mean abundance, and proportion of benthic coverage. The acronyms present in 4A represent the following diets: Herbivore-Detritivore (HD); Invertivorous targeting mobile invertebrate (IM); Omnivore (OM) and Planktivore (PK)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6330935/v1/3b7dc23217a4a86991e01853.png"},{"id":105224501,"identity":"a994999d-079c-43f0-a490-3f13697e8d37","added_by":"auto","created_at":"2026-03-23 16:14:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2090629,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6330935/v1/17ca162a-922e-41d5-932c-af79af4ced51.pdf"},{"id":81134385,"identity":"a31e30be-8087-40df-9779-1741fcd16845","added_by":"auto","created_at":"2025-04-22 15:16:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":450042,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialNunesPalma.docx","url":"https://assets-eu.researchsquare.com/files/rs-6330935/v1/ef461f7f6e53e6ae11ddcd2c.docx"}],"financialInterests":"","formattedTitle":"Ecosystem Architects: how herbivory and interaction networks shape reef communities in the Caribbean","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding the processes that modulate species interactions is a persistent challenge in ecology (Paine \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1966\u003c/span\u003e; Bascompte and Jordano \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Duran-Sala et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Studies have shown that interactions can be modulated by processes that take place at regional and local scales (Cornell and Lawton \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Fontoura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the regional scale, environmental and geographical filters, such as temperature and habitat composition can help determine the dynamics of species interactions (Gonzalez-Varo et al. 2010). For instance, temperature exerts a significant influence on biological interactions across multiple taxa since high temperature can determine the timing of life cycle events and therefore the relationships among species (Jiang et al. 2004; Kordas et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Reef fishes, for example, present more intense plant-herbivore interactions in the tropics, rather than in colder regions (Barneche et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Longo et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At the local scale, resource availability (e.g. food and refuge) influences ecological interactions such as trophic interactions and dispersion (Nathan et al. 2000; Valeix et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In plant-pollinator systems, potential shortages in resource availability inferred from habitat loss alter network architecture, interaction rates, and pollination success (Harrison and Winfree \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Spiesman and Inouye 2015). Additional studies indicate that various factors, including species richness, environmental conditions such as seasonality, and ecological processes like primary production, can also influence biological interactions (Gonzalez-Varo et al. 2010; L\u0026aacute;zaro and G\u0026oacute;mez-Mart\u0026iacute;nez \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, few studies investigate the role of species abundance in interaction networks, especially in marine ecosystems (Webster et al. 2002; Quimbayo et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interactions among organisms rely on their presence and potential encounters in nature (Holyoak et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Blanchet et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ecological interactions are often density-dependent, with species abundance often serving as a key driver of interaction frequency and strength (Kokko and Rankin \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Holland and DeAngelis \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mu\u0026ntilde;oz-Gallego et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When species are more abundant, their interaction opportunities increase, potentially leading to stronger ecological network structures (Vazquez et al. 2005). For example, in reef fish cleaning interactions, the abundance of client species can determine the frequency of interactions and which species are attended to by cleaner fishes (Quimbayo et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, more abundant fish species can have a more pronounced impact on the benthic reef community, contributing to changes in community composition and benthic habitat structure (Longo et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Canterle et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the relationship between abundance and interaction frequency is not linear or a universal rule (Vazquez et al. 2004, 2007). Species interactions are often context-dependent, various factors such as environmental conditions, species behavior, and the spatial-temporal context, can modify this relationship (Schweiger et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Keith et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, ecosystems naturally fluctuate in resource availability, meaning that high species abundance does not always translate into higher interaction rates when other factors, such as habitat complexity or competition, mediate these interactions (Adler and Lauenroth \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Gravel et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, while abundance can play a significant role in shaping interactions, it is just one piece of a complex puzzle.\u003c/p\u003e \u003cp\u003eIn the marine ecosystem, reef fishes constitute one of the most diverse vertebrate assemblages on Earth, with over 6,000 species distributed across tropical and subtropical oceans (Kulbicki et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Parravicini et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Their notable species richness, morphological diversity, and extensive distribution contribute to a wide array of interactions, including feeding and agonistic behaviors (Longo et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cantor et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Canterle et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fontoura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Within this plethora of interactions, feeding interactions are well known, studies show that these interactions are associated with morphological and ecological adaptations to obtain food, as well as particular behaviors (Horn and Ferry-Graham \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Grant \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, agonistic interactions are not so studied and are characterized as defensive and aggressive behaviors, mostly driven by the reproductive and feeding behaviors of species (Hausfater \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Osorio et al. 2006). Within the coral reef ecosystem, agonistic interactions are commonly observed in damselfishes, one of the most conspicuous fish families in global reefs (McCord et al. 2020). Species belongin Pomacentridae family are herbivorous and cultivate their own algae \u0026lsquo;farm\u0026rsquo; where they feed and defend from intruders very eagerly through agonistic interactions (Lobel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Ferreira et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Feeding and agonistic interactions are associated with trophic groups, as the need to obtain and defend food resources drives the species' behaviors within their ecological niches (Longo et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fontoura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Inagaki et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond the trophic group, other biological traits have been recognized as drivers of ecological interactions in coral reefs (Robertson \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In trophic interactions, body size is usually associated with predation success, i.e. the bigger the predator, the higher the predation rate (Woodward et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Brose et al. 2006; Thompson et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, prevailing assumptions about this association, the inherent context-dependency of ecological processes means it is not a fixed rule. For instance, recent research has revealed that small fish can play a dominant predatory role on coral reefs, challenging traditional views. (Mihalitsis et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Along with body size, the ability to move within the water column provides a greater range of feeding interactions. Sergeant-major damselfish, for example, may swim along the water column, this allows this omnivorous species to pursue different types of forage (Nunes et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Also, there is a relationship between the position in the water column and body size, as the individuals that fed on plankton were usually small, while larger individuals fed on all substrates, including benthos (Nunes et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond assessing different depths, the capacity to move within and outside the reef may influence the rate and how interactions occur (Fox and Bellwood 2007). Herbivorous-detritivorous fishes, such as surgeonfishes and parrotfishes, are examples of highly mobile species, allowing a higher potential to forage (Lawson et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Basford et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor reef fishes, agonistic interactions can serve as a proxy for resource competition among co-occurring species, while patterns of feeding interactions provide insights into potential niche partitioning (Cantor et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fontoura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tebbett et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These interactions at the individual level can scale up to influence broader ecological processes, such as excluding competitors, enhancing species coexistence, or facilitating survival. Consequently, they play a crucial role in modulating the local species pool alongside other ecological factors, including environmental and other biological components (Ferreira et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; V\u0026aacute;zquez et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Coker et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Understanding these interaction networks is essential for predicting how reef fish communities respond to environmental changes, such as habitat degradation and climate change, which can alter interaction dynamics and, ultimately, ecosystem functioning. In this study, we investigated the role of reef fish species abundances in structuring agonistic and feeding interaction networks in the coral reefs of Cura\u0026ccedil;ao Island, in the Caribbean. Additionally, we explored the influence of biological traits ( trophic group, body size, and mobility), and environmental traits (proportion of benthic coverage) on shaping these interaction networks. These traits are fundamental to understanding the functional roles that species play within the ecosystem and how they navigate the trade-offs associated with resource use and competition (Quimbayo et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Waechter et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Accordingly, we expected species abundance to influence interactions differently depending on the type of interaction. Abundance would be positively related to feeding interactions, meaning that more abundant species would engage in more feeding interactions. In contrast, we expected an opposite pattern for agonistic interactions, which could be linked to the agonistic behavior and ecological strategy of certain species, such as territorial damselfish from the Pomacentridae family. These species are known to be central in agonistic interaction networks. Due to their territorial behavior, they tend to live individually, occurring at low local densities, yet they frequently engage in agonistic interactions when their territory is invaded. Regarding biological traits, we expected trophic group to be the most important predictor of both agonistic and feeding interactions. A similar pattern was expected for environmental traits, such as the coverage of benthic organisms. By elucidating the dynamics of these interactions and their underlying determinants, our study contributes to a more comprehensive understanding of local ecological processes within coral reefs. Furthermore, it provides insights into the broader patterns of how biological traits shape interactions in marine ecosystems, which is crucial for conservation strategies and predicting ecosystem responses to environmental change.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy area\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFeeding and agonistic interactions among fish were documented at seven reef sites at Cura\u0026ccedil;ao Island in the Caribbean (12\u0026deg;10\u0026apos;10.4520\u0026apos;\u0026apos; N, 68\u0026deg;59\u0026apos; 24.0756\u0026apos;\u0026apos;W). These sites were Westpunt, Porto Marie, Oostpunt, Playa Largo, Marie Pampoen, Snake Bay, and Water Factory (Fig. 1). We used the remote underwater video methodology (RUVs), which consists of video-plots, to sample fish interaction in each site. This RUV method is recognized for its ability to minimize the direct influence of divers on fish behavior, which is essential when studying organisms\u0026rsquo; behavior and interactions. In addition, this approach allows us to collect data and rewatch it at any time, proving to be a valuable tool (Harvey et al. 2004; Longo and Floeter 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed 109 video-plots along seven sites, the sampling area was limited to 2 m\u003csup\u003e2\u003c/sup\u003e and to the 10 central minutes of each video to avoid any potential influence from the diver\u0026rsquo;s presence over the fish behavior. During this time, we identified all observed fish species and recorded all feeding and agonistic interactions. A feeding interaction was considered when a fish took a bite on the reef substratum (Longo et al. 2014). We considered agonistic interactions whenever a species was chasing another individual, one swimming toward the other with no intentions of preying on it (Canterle et al. 2020; Fontoura et al. 2020). Each fish species recorded in the videos was categorized into three biological traits according to Quimbayo et al. (2021): 1) Trophic group (herbivore-detritivores, macroalgae feeders, mobile invertebrate feeders, sessile invertebrate feeders, omnivores, piscivores and planktivores); 2) mobility (sedentary species and species mobile within a reef); and 3) body size classes (0\u0026ndash;7 cm, 7.1\u0026ndash;15 cm, 15.1\u0026ndash;30 cm, 30.1\u0026ndash;50 cm, 50.1\u0026ndash;80 cm and \u0026gt;80 cm).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFish Abundance and Benthic Cover\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe estimated fish abundance in each video-plot using the MaxN (Maximum Abundance) method (Harvey et al. 2012). This approach calculates the maximum number of individuals of each species observed together within the entire video-plot (2 m\u0026sup2;) during the central 10 minutes of recording (Unsworth et al. 2014). In addition to fish abundance, we estimated the abundance of benthic organisms (benthic cover). To calculate benthic cover, we used photographs taken at the sites where the videos were recorded. These photos were analyzed using the Photoquad software. For each photo, a transect area of 30 cm \u0026times; 30 cm was delimited, and 50 random points were marked. To minimize potential bias due to the low resolution of some photographs, we categorized the benthic organisms observed at each point into broad functional groups: coral, turf, algae, rubble, sand, or others (e.g., sponges, worms, and ascidians). This process resulted in a percentage of cover for each category in every photo. Out of the 60 videos where feeding interactions were recorded, only 29 had associated photographs for benthic analysis. To address this limitation, we calculated an overall mean benthic cover for each category using data from the available photos. For the videos without photos, these mean values were used as a proxy for benthic cover, ensuring that all observations could be included in the analysis. To ensure the robustness of our approach, we initially fitted two separate models: one using only the benthic cover data derived from photos (29 videos) and another incorporating both photo-derived data and the overall mean benthic cover (60 videos). The results of these models were consistent, showing no significant differences in the key patterns observed. Based on this comparison, we chose to use the second model (with both photo-derived and mean values) to maximize the sample size and include all observations in the analysis. We fitted a nested linear model to investigate the factors influencing the number of feeding interactions. The predictor variables included fish abundance, trophic group, benthic cover proportions, and coverage origin (a categorical variable indicating whether benthic cover data were derived from photos or mean values). Interaction terms between the trophic group and benthic cover proportions were also included. The nested model was constructed incrementally as follows:\u003c/p\u003e\n\u003cp\u003eModel 1: Fish abundance as the sole predictor.\u003c/p\u003e\n\u003cp\u003eModel 2: Fish abundance and trophic group.\u003c/p\u003e\n\u003cp\u003eModel 3: Fish abundance, trophic group, and benthic cover proportions.\u003c/p\u003e\n\u003cp\u003eModel 4: Fish abundance, trophic group, benthic cover proportions, and coverage origin.\u003c/p\u003e\n\u003cp\u003eTo evaluate the fit of the models and identify the best-fitting model, we conducted likelihood ratio tests comparing successive models in the nested sequence. Additionally, the Akaike Information Criterion (AIC) was calculated for each model to assess model parsimony and balance between fit and complexity. The most complete model was the one with the lowest AIC, which also showed the best results in the likelihood ratio tests (LRT). Hence this model was selected as the final model, as it provided the best trade-off between explanatory power and simplicity while incorporating all relevant predictors and interactions. All analyses were performed using the R statistical software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork structure and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe described the feeding interaction using two network metrics i.e., nestedness and modularity. A feeding network was defined as a matrix M, in which the element mij corresponds to the number of bites that a fish species i invested in the reef substratum j (i.e., type of substrate bitten). Nestedness describes an asymmetric, hierarchical distribution of interactions among species (Bascompte et al. 2003), whereas modularity describes a compartmentalized distribution of interactions among species (Olesen et al. 2007). We calculated nestedness using the NODF (Nestedness metric based on Overlap and Decreasing Fill) metric as described by Almeida-Neto and Ulrich (2011). This metric quantifies nestedness for the entire matrix and also determines the contribution of individual species and sites (i.e., rows and columns). The NODF nestedness score ranges from 0 (non-nested) to 100 (perfectly nested). For our analysis, we utilized the \u0026lsquo;nestednodf\u0026rsquo; function from the Bipartite package in R (Dormann and Strauss 2014). For network modularity, we applied the Q metric that measures the difference between the observed fraction of links connecting species within the same module and the fraction expected by chance (Newman 2006) using an algorithm modified for two‐mode networks (Dormann and Strauss 2014). For this, we used the \u0026lsquo;computeModules\u0026rsquo; function from the Bipartite package with the \u0026lsquo;DormannStrauss\u0026rsquo; method. We assessed nestedness and modularity significance through a null model where nestedness and modularity values were contrasted with those obtained from null models. This null model was built using the \u0026lsquo;oecosimu\u0026rsquo; function from the Vegan package and 1,000 iterations (Jonsson 2001). The observed nested or modular structure of the feeding network was considered significant when its observed NODF and Q‐value, respectively, lay outside of the 95% confidence intervals of their corresponding null distributions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe inclination of each species to engage in agonistic interactions was assessed by computing the network\u0026rsquo;s out-degree centrality. This metric reflects the total number of other species that each fish species was observed interacting with. Species with a greater number of outward links demonstrate higher out-degree centrality (Fontoura et al. 2020). We tested the influence of fish abundance on the number of feeding and agonistic interactions using a null model implemented through the \u0026lsquo;permatswap\u0026rsquo; function under the \u0026lsquo;quasiswap\u0026rsquo; method, from the Vegan R package (Oksanen et al. 2007). For the agonistic network analysis, we explored the relationship between interaction frequency and fish trophic groups. To achieve this, we employed a linear model to investigate how all species traits influenced the contribution to out-degree centrality within the agonistic network. We used the \u0026apos;lm\u0026apos; function from \u0026lsquo;the lme4\u0026rsquo; package, treating the seven sampled sites in Cura\u0026ccedil;ao as random effects while considering traits as fixed effects. All three networks were built using the \u0026lsquo;Igraph\u0026rsquo; package in R Software (Csardi and Nepusz 2006).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eFeeding interactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 34 reef fish species were observed in the video-plots of Cura\u0026ccedil;ao. Among these, 25 fish species interacted with the benthos through feeding interactions, and 33 species interacted agonistically. The fish-benthos interaction network structure was not influenced by fish abundance, \u003cem\u003ei.e.\u003c/em\u003e, the species that interact more frequently with benthic organisms are not the most abundant ones in video-plots (Fig. 2). The majority of feeding interactions were performed by \u003cem\u003eScarus iseri\u0026nbsp;\u003c/em\u003e(25%)\u003cem\u003e, Acanthurus tractus\u0026nbsp;\u003c/em\u003e(17%),\u003cem\u003e\u0026nbsp;Sparisoma viride\u0026nbsp;\u003c/em\u003e(10.5%)\u003cem\u003e, S. aurofrenatum\u0026nbsp;\u003c/em\u003e(7.5%)\u003cem\u003e, A. coeruleus\u0026nbsp;\u003c/em\u003e(7%) and \u003cem\u003eScarus taeniopterus\u0026nbsp;\u003c/em\u003e(4%), respectively. The food items with the highest bite rates were turf algae and coral. The reef fish-benthos feeding interaction network exhibited a high NODF value (NODF = 69.17), suggesting a nested pattern. However, when compared to the null model, the observed nestedness was not statistically significant (p = 0.402). Further details are provided in the supporting information (Table S2). We identified five modules where species interact more frequently (Q = 0.196). However, modularity was not significant and not influenced by fish traits, as species belonging to different trophic groups appear together in three out of five modules.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe nested model analysis provided insights into the drivers of feeding interactions of reef fishes on Cura\u0026ccedil;ao Island. The model\u0026apos;s likelihood and AIC improved progressively as additional predictors were incorporated (table S3), ultimately leading to the final model with the best fit (Chisq = 18.238, p = 0.001, AIC = 4077.866). The maximum abundance had a light positive effect on the feeding interactions, meaning that the greater abundance leads to higher feeding interactions (estimate = 0.702, p \u0026lt; 0.005, Fig. 3a). Additionally, within the trophic groups, herbivores (estimate = 9.197, p \u0026lt; 0.05), sessile (estimate = 6.268, p \u0026lt; 0.05), and mobile invertivores (estimate = 6.006, \u0026nbsp;p \u0026lt; 0.05) positively influenced the feeding interactions (Fig. 3b). Regarding the benthos coverage, turf (estimate = -0.146, p \u0026lt; 0.05) and rubble (estimate =-0.151, p \u0026lt; 0.05) were the only benthos categories to affect the interactions (Fig. 3c). Both categories had an inverse relationship with interactions, i.e. the higher the coverage proportion, the lower the frequency of feeding interactions. Lastly, the coverage origin (whether derived from photos or mean values) was not significant (p = 0.07), this indicates that there was no statistically significant difference between using proportions derived solely from the photos and the proportions imputed with the mean values. Overall, the findings highlight that the trophic group mediates the effect of fish abundance on feeding interactions, and that coral cover has a negative influence. The consistency of results across models supports the robustness of these conclusions (table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Linear model results with the effect of biological traits (MaxN, and trophic group) and environmental traits (benthic coverage proportion) on species feeding interactions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eBiological and environmental traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMaximum abundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eTrophic groups\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eHerbivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e5.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMobile invertivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.006 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSessile invertivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eOmnivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e7.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eBenthic coverage proportion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eTurf proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-2.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eCoral proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eRubble proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-2.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eAlgae proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-1.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eCoverage origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAgonistic interactions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 33 reef fish species were observed interacting agonistically, either chasing other fish species or being chased. The most relevant families to agonistic interaction networks were Pomacentridae, Labridae, and Acanthuridae. The agonistic network structure revealed that fish species that engage in agonistic interactions are not necessarily the most abundant, this result was corroborated by the nested model (Fig. 4a). The fish species that interact more frequently belong to the \u003cem\u003eStegastes\u003c/em\u003e genus: \u003cem\u003eS. partitus\u003c/em\u003e (68% each), followed by\u003cem\u003e\u0026nbsp;S. adustus\u0026nbsp;\u003c/em\u003e(45%), and\u003cem\u003e\u0026nbsp;S. planifrons\u0026nbsp;\u003c/em\u003e(24%)\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Fig. 5b). While these are central species in Cura\u0026ccedil;ao agonistic networks, \u003cem\u003eBodianus rufus, Caranx bartholomei, Cephalopholis fulva\u003c/em\u003e and \u003cem\u003eChaetodon striatus\u003c/em\u003e were peripheral species, for which only one agonistic interaction was registered. When trophic groups were shown in network graphs, these revealed that the central species are herbivorous species from the \u003cem\u003eStegastes\u003c/em\u003e genus, which were chasing more frequently other herbivorous species (Fig. 4b).\u003c/p\u003e\n\u003cp\u003eThe nested model with the best fit and AIC value was the third and most complete model (Table S4). Our results revealed that the maximum abundance negatively influenced the out-degree centrality of agonistic interactions (estimate = -0.087, p \u0026lt; 0.05). This result represents that the species responsible for initiating the agonistic interactions are the least abundant in the network (table 2). Considering other biological traits, the diet was positively correlated to the out-degree centrality throughout all the different trophic groups (p \u0026lt; 0.05). Although different trophic groups had higher effects, herbivores (estimate = 16.854) and planktivores (estimate = 16.983) had the highest effect, followed by omnivores (estimate = 12.220), mobile (estimate = 12.219), and sessile invertivores (estimate = 6.127). Additionally, mobility (estimate = -7.18836, p \u0026lt; 0.05) and body size (estimate = 4.342, p \u0026lt; 0.05) were the other biological traits to influence the out-degree centrality. Sedentary (territorial) species were the ones to engage in more agonistic interactions compared to species that move within the reef or between reefs. Regarding body size, larger fishes represented a higher out-degree centrality than small ones. Lastly, the proportion of benthos coverage, three of the four benthic categories lightly influenced species centrality. The higher coverage proportion of turf (estimate = 0.082, p \u0026lt; 0.05), macroalgae (estimate= 0.085, p \u0026lt; 0.05), and corals (estimate = 0.060, p \u0026lt; 0.05) indicated a higher out-degree centrality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Linear model results with the effect of biological traits (MaxN, trophic group, mobility, and body size) and environmental traits (benthic coverage proportion) on species out-degree centrality\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eBiological and environmental traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cem\u003eMaximum abundance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-2.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cem\u003eTrophic groups\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eHerbivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e16.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e5.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMobile invertivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSessile invertivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0453*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ePlanktivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e16.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e5.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eOmnivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-14.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-3.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0003***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cem\u003eMobility\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-7.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-8.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cem\u003eBody size\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cem\u003eBenthic coverage proportion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eTurf proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eCoral proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0005***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eRubble proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eAlgae proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0002***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study investigated the factors shaping feeding and agonistic interactions within a coral reef fish community on Cura\u0026ccedil;ao Island. We found that these two interaction types are driven by distinct sets of biological attributes of the fish species. The feeding interaction network, unlike the agonistic network, did not exhibit a clear structure. Null model analyses revealed no significant deviations from random expectations in terms of nestedness or modularity. This suggests that feeding interactions among these reef fishes are not strongly influenced by factors like competitive hierarchies or specialized feeding guilds, i.g. specialization, but probably for opportunistic reasons. This may reflect a high behavioral plasticity of reef fish species, allowing them to adapt flexibly to available resources, especially in dynamic environments or those subject to anthropogenic and natural disturbances (Thompson et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For example, less structured networks are often observed in systems with lower biodiversity or in locations where environmental conditions are unstable, leading species to feed on a wide range of resources to ensure survival (Bascompte et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Olesen et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, despite the lack of a discernible overall structure, certain species traits significantly influenced feeding interactions. Notably, we observed a positive correlation between the local maximum abundance of a fish species and the frequency of its feeding interactions. This finding supports the idea of ​​density-dependent interactions, where a greater number of individuals increases the probability of encounters and, consequently, interactions (Dingle and Caldwell, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Quimbayo et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, while greater abundance can promote feeding interactions, it can also intensify competition for resources, especially in communities where benthic resources are limited (Freeman and Byers, 2006). For example, high population densities can lead to changes in feeding behavior, such as an increase in opportunistic foraging, corroborating the argument that the lack of structure in the network also leads to opportunistic foraging (Hixon and Beets, 1993). This dynamic highlights the role of abundance not only as a promoter of interactions but also as a regulatory factor that influences the quality and nature of these interactions. While abundance plays a critical role in shaping feeding interactions, the feeding interactions of reef fishes in Cura\u0026ccedil;ao Island are further influenced by other species' attributes.\u003c/p\u003e \u003cp\u003eThe trophic groups of reef fish species significantly shaped feeding interactions, with herbivores, sessile, and mobile invertivores being the most prominent contributors. Among these, herbivores emerged as the primary drivers of feeding interactions, a pattern commonly observed in Caribbean reef systems (Longo et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This finding aligns with the observed interaction network, where herbivore species occupy the highest position, representing a high frequency of interactions. Within the herbivore species from the Cura\u0026ccedil;ao community, some families distinguish themselves by the frequency of feeding interactions, such as parrotfishes (Labridae), surgeonfishes (Acanthuridae), and damselfishes (Pomacentridae). Species such as \u003cem\u003eScarus iseri\u003c/em\u003e, \u003cem\u003eAcanthurus tractus\u003c/em\u003e, and \u003cem\u003eStegastes adustus\u003c/em\u003e demonstrated intense foraging activity on algae, detritus, and other organisms within the epilithic algal matrix (EAM), corals, and rubble. The coexistence of Labridae, Pomacentridae, and Acanthuridae at the apex of the coral reef food web is probably explained by resource partitioning. For example, while scrapers such as members of the family Labridae, including \u003cem\u003eScarus iseri\u003c/em\u003e, remove encrusting algae attached to hard surfaces, grazers such as \u003cem\u003eAcanthurus tractus\u003c/em\u003e and \u003cem\u003eStegastes adustus\u003c/em\u003e consume filamentous algae and macroalgae available in larger quantities. These differences in feeding habits not only diversify the types of resources consumed but also reduce overlapping trophic niches, which minimize direct competition between these families and allow coexistence. While partitioning the resource, these herbivore species continue to play a critical role in shaping benthic community dynamics. By removing encrusting algae and macroalgae, these species prevent these competing plants from outcompeting corals for space and light, allowing corals to maintain their ecological dominance. Furthermore, by feeding on organisms associated with algae and the epilithic mat (EAM), herbivores increase nutrient cycling in the ecosystem, promoting a more balanced and diverse environment. For these reasons, these species can be considered keystone species, whose activity shapes the composition of the benthic community and regulates ecological processes crucial to the health of the reef ecosystem. However, the relationship between trophic groups and feeding interactions can be context-dependent, being influenced by resource disponibility, revealing an even more complex dynamic.\u003c/p\u003e \u003cp\u003eBenthic cover significantly influenced feeding and agonistic interactions. While turf algae and rubble had a negative effect on feeding interactions, a higher coverage proportion of turf, macroalgae, and coral led to a higher out-degree centrality. For feeding interactions, this inverse pattern suggests that substrates such as turf and rubble provide fewer nutritional resources or make food access more challenging. Previous studies have shown that herbivores dominate feeding interactions in Caribbean reef environments, including Cura\u0026ccedil;ao (Longo et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This dominance is often associated with the high availability and nutritional value of benthic food items in tropical regions, as well as the increased metabolic and feeding rates driven by higher temperatures (Demko et al. 2019; Duran et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, our results indicate that habitats dominated by substrates like rubble and turf may significantly reduce these interactions, possibly due to the low nutritional quality or lower structural complexity of these benthic components (Barott et al. 2012). Although herbivores frequently consume turf algae, the dominance of these substrates in the habitat may limit the efficiency of feeding interactions, restricting the availability of optimal resources and negatively impacting the reef trophic network (Adam et al. 2015). These findings align with the observed positive impact of benthic cover on agonistic interactions. While turf and rubble cover reduced feeding interactions, greater coverage of these same substrates had a slightly positive effect on agonistic interactions, suggesting that habitat characteristics shape different behavioral dynamics. This result contrasts with our initial hypothesis that lower resource availability (lower coverage proportion) would increase territorial and agonistic interactions due to intensified competition. However, it is possible that even with a higher proportion of turf, its low nutritional quality may still promote agonistic behaviors as individuals compete over suboptimal but spatially concentrated resources. Alternatively, the dominance of turf may alter the spatial distribution of fish by forcing them to forage in areas where resources are scarce or patchily distributed. This can lead to uneven spatial distribution, with individuals competing for limited areas where resources are more abundant or accessible, as a consequence, the frequency of agonistic interactions would increase, as fish compete for these restricted resources.\u003c/p\u003e \u003cp\u003eThe out-degree centrality of the agonistic interactions network was negatively influenced by the species abundance, meaning that the most central species were the least abundant in the agonistic interactions network. These species at the center of the network were mainly territorial damselfishes from the \u003cem\u003eStegastes\u003c/em\u003e genus (\u003cem\u003eS. adustus\u003c/em\u003e, \u003cem\u003eS. partitus\u003c/em\u003e, and \u003cem\u003eS. planifrons\u003c/em\u003e), this result aligns with their well-known territorial behavior (Barneche et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Fontoura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Damselfishes can vary at different levels of territoriality (aggressiveness), more territorial species (i.e. more aggressive) tolerate less other individuals of the same species or other species in their territory. Thus, the abundance of these territorial species is limited, corroborating the inverse effect of abundance on centrality in interactions. Along with abundance, diet positively affected the out-degree centrality, between the different trophic groups, herbivores and planktivores were the ones to have a higher influence in the agonistic interactions. This goes along with damselfishes as the central species in the network since these species are territorial herbivores that cultivate they\u0026rsquo;re own algae \u0026lsquo;farm\u0026rsquo;. Through algae farming, these fishes shape the composition of algae and micro-invertebrates on reefs, influencing primary production as well as the flow of energy and nutrients (Klumpp et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Ferreira et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), an activity that helps maintain reef structure and functioning (Randazzo Eisemann et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Notably, the interactions mediated by herbivore-detritivores were disproportionately high when considering their local abundances, categorizing them as keystone species in interaction networks (Paine \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1969\u003c/span\u003e; Quimbayo et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This crucial role of territorial herbivores on agonistic interaction networks has been demonstrated in reef ecosystems worldwide (Fontoura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Due to their active territorial defense and agonistic behavior, these fish commonly occupy central positions in these networks, indicating a higher frequency of interactions relative to other species (Fontoura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, other biological traits of the reef fish species had a significant effect on the out-degree centrality of the agonistic network. Mobility had a negative effect on the out-degree centrality, sedentary species engaged in more agonistic interactions when compared to species that are mobile within the reef and between reefs. This could be attributed to the fact that territorial species, such as damselfishes, tend to have a limited home range and typically occupy only a few meters of habitat throughout their lives. In a nutshell, mobility denotes species behavior, which plays a fundamental role in network structuring. Fontoura et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) similarly identified that herbivorous species with small habitat ranges often occupied central positions in agonistic networks. Besides mobility, body size was the other biological trait to influence the out-degree centrality, with larger fish displaying higher centrality values than smaller ones. This result contrasts with the expectation that small body size species (e.g. \u003cem\u003eStegastes\u003c/em\u003e spp.) would contribute more to the out-degree centrality. On the other hand, species such as \u003cem\u003eScarus iseri, Acanthurus coeruleus\u003c/em\u003e and \u003cem\u003eCephalopholis cruentata\u003c/em\u003e are usually at least three times larger than damselfishes (Welsh and Bellwood \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and performed a peripheral role in the agonistic interactions network. The presence of larger species likely influenced out-degree centrality indirectly, while traits like territorial behavior, abundance, and diet positioned herbivorous territorial species at the center of the agonistic interactions network. This highlights that biological traits play a pivotal role in shaping the structure of agonistic interaction networks, with sedentary, territorial, and herbivorous species driving the networks\u0026rsquo; structure. However, the unexpected influence of larger body sizes on the network centrality highlights the complexity of ecological interactions, suggesting that both interspecific dynamics and behavioral traits can outweigh traditional size-based expectations.\u003c/p\u003e \u003cp\u003eIn conclusion, our study reveals that agonistic and feeding interactions in reef environments are shaped by a shared set of biological and environmental traits, though in distinct ways. For instance, local abundance positively influenced feeding interactions, highlighting how resource availability can promote trophic activity. Conversely, species with lower local abundances played central roles in agonistic networks, indicating that these interactions may serve functions beyond resource competition, such as territorial defense (Ceccarelli et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fontoura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, the trophic group of fish species emerged as a fundamental trait influencing both interaction types. Herbivores, in particular, stood out as keystone species, significantly contributing to interaction frequency. This underscores their critical role in controlling algal growth, maintaining ecosystem balance, and reinforcing the urgent need for their conservation. On the other hand, less complex substrates, such as rubble and turf, negatively impacted feeding interactions while fostering agonistic behaviors. This pattern suggests that degraded benthic habitats not only disrupt trophic dynamics but also intensify territorial disputes, likely due to reduced resource availability and consequent competition. Environments dominated by rubble or turf are often associated with disturbances, such as reef degradation, which compromise feeding interactions and overall ecosystem health. This highlights the importance of conservation of reef habitats in a climate change moment Furthermore, the agonistic interactions performed by territorial species, such as \u003cem\u003eStegastes\u003c/em\u003e, are crucial for the maintenance of territorial areas with greater structural complexity, which, in turn, support more diverse and functional communities. Despite their ecological significance, agonistic interactions are still underrepresented in studies when compared to trophic interactions. Given the variability in territorial behavior among species (Pratchett et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), further research is warranted to understand how these behaviors influence ecosystem structure and function. Lastly, our study shows how connected reef species are through interactions, emphasizing the importance of identifying the environmental drivers or biological traits that shape ecological interactions and processes. This knowledge enhances our understanding of reef ecosystem functionality and provides insights into the roles of species within one of the most diverse and vital habitats on Earth.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was observational, and the ethical approval was not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the discussion of the data from the manuscript, the methodology used, including the statistical analyses, the writing, and the presentation of the figures shown in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study will be available within the paper and its Supplementary Information, as well as the datasets generated during the current study, which will be available in the GitHub repository.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdler PB, Lauenroth WK (2003). 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Trends Ecol Evolution 20(7): 402-409. https://doi.org/10.1016/j.tree.2005.04.005\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Abundance. benthic community. ecological networks. fish interactions","lastPublishedDoi":"10.21203/rs.3.rs-6330935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6330935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFish are the most diverse and abundant vertebrate group on Earth and special reef fishes take part in numerous interactions, such as cleaning, feeding, and agonistic interactions. Despite the growing literature on the patterns of fish interactions in reef environments, few studies have assessed the role of abundance in modulating reef fish interactions. This study examines how the local abundance, other biological traits of reef fish and benthic coverage influence the feeding and agonistic interactions networks in Cura\u0026ccedil;ao an island located in the Caribbean. We used 109 video-plots at seven reef sites around the island in October of 2013 to estimate fish species abundance (MaxN), feeding and agonistic interactions rates, and benthic coverage. Considering these assemblages metrics we calculated network metrics, including centrality, nestedness, and modularity.\u003c/p\u003e \u003cp\u003eResults indicate that fish abundance had a weak positive effect on feeding interactions, with herbivores, sessile, and mobile invertivores engaging in most interactions. Turf and rubble cover negatively influenced feeding interactions. For agonistic interactions, less abundant species initiated more aggressive encounters, with diet, mobility, and body size influencing interactions patterns. Herbivores showed the highest out-degree centrality, while sedentary and larger species engaged more frequently in agonistic interactions. Additionally, higher turf, macroalgae, and coral cover were associated with increased agonistic interactions. These findings highlight the role of herbivorous reef fish in structuring interactions networks and suggest that factors beyond local abundance, such as species traits and habitat characteristics, shape these interactions in Cura\u0026ccedil;ao\u0026rsquo;s reefs.\u003c/p\u003e","manuscriptTitle":"Ecosystem Architects: how herbivory and interaction networks shape reef communities in the Caribbean","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 15:16:39","doi":"10.21203/rs.3.rs-6330935/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3416be3-4137-4ef3-b8c3-821d33eea929","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:08:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 15:16:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6330935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6330935","identity":"rs-6330935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}