Abstract

AbstractAimForest fragmentation is among the principal causes of global biodiversity loss, yet how it affects mutualistic interactions between plants and animals at large spatial scale is poorly understood. In particular, tropical forest regeneration depends on animal‐mediated seed dispersal, but the seed‐dispersing animals face rapid decline due to forest fragmentation and defaunation. Here, we assess how fragmentation influences the pairwise interactions between 407 seed disperser and 1,424 tree species in a highly fragmented biodiversity hotspot.LocationAtlantic Forest, South America.MethodsWe predicted interaction networks in 912 sites covering the entire biome by combining verified interaction data with co‐occurrence probabilities obtained from a spatially explicit joint species distribution model. We identified keystone seed dispersers by computing a species‐specific keystone index and by selecting those species belonging to the top 5% quantile.ResultsWe show that forest fragmentation affects seed dispersal interactions negatively, and the decreased area of functionally connected forest, rather than increased edge effects, is the main driver behind the loss of interactions. Both the seed disperser availability for the local tree communities and in particular the proportion of interactions provided by keystone seed dispersers decline with increasing degree of fragmentation. Importantly, just 21 keystone species provided >40% of all interactions. The numbers of interactions provided by keystone and non‐keystone species, however, were equally negatively affected by fragmentation, suggesting that seed dispersal interactions may not be rewired under strong fragmentation effects.ConclusionsWe highlight the importance of understanding the fragmentation‐induced compositional shifts in seed disperser communities as they may lead to lagged and multiplicative effects on tree communities. Our results illustrate the utility of model‐based prediction of interaction networks as well as model‐based identification of keystone species as a tool for prioritizing conservation efforts. Similar modelling approaches could be applied to other threatened ecosystems and interaction types globally.

Highlights

  • Deforestation is among the principal causes of global biodiversity loss (Haddad et al, 2015)

  • We examined how connectance (CON), nestedness (NODF), seed dispersal availability (SA) and proportion of interactions provided by keystone seed dispersers (KEY), co-varied with these three fragmentation covariates

  • We found that link connectance (CON), nestedness (NODF), seed dispersal availability (SA) and proportion of interactions provided by keystone seed dispersers (KEY) were influenced by the area of functionally connected forest, but not by core-to-edge ratio or distance to nearest road (Figure 2)

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Summary

| INTRODUCTION

Deforestation is among the principal causes of global biodiversity loss (Haddad et al, 2015). The effects of forest loss and fragmentation on biodiversity are often difficult to disentangle without appropriate sampling design as they can occur in synergy (Fahrig, 2003) Together these processes may cause frugivore population declines or extinctions (Cordeiro & Howe, 2003), and changes in the frugivore community composition (Santos & Tellería, 1994) or in the capacity of functional complementarity among frugivores (Lehouck, Spanhove, Demeter, Groot, & Lens, 2009). As our focus was on seed dispersal networks, we included only animals identified to species-level and reported as frugivores (≥10% of the diet consists of fruits) in the EltonTraits database (Wilman et al, 2014) We included those tree species that were identified as zoochoric (Almeida-Neto, Campassi, Galetti, Jordano, & Oliveira-Filho, 2008; Bello et al, 2017) and had ≥5 occurrences in the original data. We included these overlapping species in the joint species distribution models of both data sets with the aim of testing the robustness of the predicted occurrence probabilities for data collected on the same species by different methods

| METHODS
Findings
| DISCUSSION

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