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ABSTRACT Primary productivity and trophic interactions are fundamentally linked. However, it remains largely unknown how food web structure varies along primary productivity gradients at continental scales or how the influence of primary productivity on food webs varies within regions. Furthermore, anthropogenic pressure threatens the integrity of food webs globally with potentially predictable food web disassembly. Here, we test how plant productivity and anthropogenic fragmentation predict the pairwise similarity of food web networks within and among regions for 127 protected areas spanning deserts to rainforests. We measured food web structural equivalence independent of species identities and accounted for inherent scaling of food web structure with richness and connectance. Food webs were significantly more similar at sites with similar plant productivity at the continental scale and within woodland savannas, and in tropical rainforests with similar anthropogenic fragmentation. These empirical results inform how food web structure mediates biodiversity and ecosystem function. 

Abstract Quantifying the structure and dynamics of species interactions in ecological communities is fundamental to studying ecology and evolution. While there are numerous approaches to analysing ecological networks, there is not yet an approach that can (1) quantify dissimilarity in the global structure of ecological networks that range from identical species and interaction composition to zero shared species or interactions and (2) map species between such networks while incorporating additional ecological information, such as species traits or abundances.To address these challenges, we introduce the use of optimal transport distances to quantify ecological network dissimilarity and functionally equivalent species between networks. Specifically, we describe the Gromov–Wasserstein (GW) and Fused Gromov–Wasserstein (FGW) distances. We apply these optimal transport methods to synthetic and empirical data, using mammal food webs throughout sub‐Saharan Africa for illustration. We showcase the application of GW and FGW distances to identify the most functionally similar species between food webs, incorporate additional trait information into network comparisons and quantify food web dissimilarity among geographic regions.Our results demonstrate that GW and FGW distances can effectively differentiate ecological networks based on their topological structure while identifying functionally equivalent species, even when networks have different species. The FGW distance further improves node mapping for basal species by incorporating node‐level traits. We show that these methods allow for a more nuanced understanding of the topological similarities in food web networks among geographic regions compared to an alternative measure of network dissimilarity based on species identities.Optimal transport distances offer a new approach for quantifying functional equivalence between networks and a measure of network dissimilarity suitable for a broader range of uses than existing approaches. OT methods can be harnessed to analyse ecological networks at large spatial scales and compare networks among ecosystems, realms or taxa. Optimal transport‐based distances, therefore, provide a powerful tool for analysing ecological networks with great potential to advance our understanding of ecological community structure and dynamics in a changing world. 

Abstract Changes in land use and climate change threaten global biodiversity and ecosystems, calling for the urgent development of effective conservation strategies. Recognizing landscape heterogeneity, which refers to the variation in natural features within an area, is crucial for these strategies. While remote sensing images quantify landscape heterogeneity, they might fail to detect ecological patterns in moderately disturbed areas, particularly at minor spatial scales. This is partly because satellite imagery may not effectively capture undergrowth conditions due to its resolution constraints. In contrast, soundscape analysis, which studies environmental acoustic signals, emerges as a novel tool for understanding ecological patterns, providing reliable information on habitat conditions and landscape heterogeneity in complex environments across diverse scales and serving as a complement to remote sensing methods.We propose an unsupervised approach using passive acoustic monitoring data and network inference methods to analyse acoustic heterogeneity patterns based on biophony composition. This method uses sonotypes, unique acoustic entities characterized by their specific time‐frequency spaces, to establish the acoustic structure of a site through sonotype occurrences, focusing on general biophony rather than specific species and providing information on the acoustic footprint of a site. From a sonotype composition matrix, we use the Graphical Lasso method, a sparse Gaussian graphical model, to identify acoustic similarities across sites, map ecological complexity relationships through the nodes (sites) and edges (similarities), and transform acoustic data into a graphical representation of ecological interactions and landscape acoustic diversity.We implemented the proposed method across 17 sites within an oil palm plantation in Santander, Colombia. The resulting inferred graphs visualize the acoustic similarities among sites, reflecting the biophony achieved by characterizing the landscape through its acoustic structures. Correlating our findings with ecological metrics like the Bray–Curtis dissimilarity index and satellite imagery indices reveals significant insights into landscape heterogeneity.This unsupervised approach offers a new perspective on understanding ecological and biological interactions and advances soundscape analysis. The soundscape decomposition into sonotypes underscores the method's advantage, offering the possibility to associate sonotypes with species and identify their contribution to the similarity proposed by the graph.