To test whether more evolutionarily distinct bird and plant species were associated with larger body mass and seed diameter, respectively, we used nonparametric Spearman correlation tests (48). The extremely large sample size caused an overfit of any modeling attempt to assess the form of these relationships, so we resorted to use nonparametric Spearman correlation tests to highlight any nonrandom associations between traits and ED.

Asymmetries in EDi were calculated as the difference between the ED values of both interacting partners. Bird-skewed asymmetries correspond to interactions in which the bird species has higher ED than the partner plant species, and plant-skewed asymmetries correspond to interactions in which the plant species brings more ED than the partner bird species. We apply a binomial test to estimate which type of asymmetry (bird skewed or plant skewed) is significantly more predominant among the 2668 unique combinations of bird and plant species interactions found in our communities. Then, we used a generalized linear mixed-effects model in the lme4 package (49) to test which type of EDi asymmetry is more persistent in the fragmented landscape. In this model, we fit the number of fragments in which the interaction occurs as a response variable against the asymmetry value of EDi as the independent variable. Bird species and plant species were used as random factors to control for pseudoreplication, given that a single species can perform more than one interaction. We fit the model with a Poisson distribution.

To understand the amount of the cumulative EDi of bird–seed dispersal interactions lost in the gradient of defaunation across tropical forest fragments, we fitted three different linear models. We started by quantifying the cumulative EDi in each fragment by summing up the EDi values of all bird–seed dispersal interactions recorded per fragment. Defaunation was estimated as the difference between the sum of all bird body masses at the regional landscape scale [considering all frugivore bird species present in the Atlantic-Frugivory database (21) as a proxy for the regional pool of species] and the summed body masses of the birds that occur in each fragment. For the first model, we simply used the cumulative EDi as the response variable fitted against the defaunation index to test for the absolute loss of EDi as defaunation increases. For the second model, we used the same approach while controlling for fragment area. We tested for the correlation between fragment area and defaunation using a Pearson correlation test. Then, we used the residuals of a linear regression of defaunation index on fragment area as the independent variable fitted against the cumulative EDi. Last, we used a null model to test whether the cumulative EDi per fragment was significantly different from that expected by the number of bird–seed dispersal interactions recorded in each community. We created an amn matrix in which m and n correspond to the study site and the bird–seed dispersal interactions, respectively. The amn element of the matrix is filled according to the presence of an interaction n in site m quantified by the corresponding EDi. Therefore, the marginal total of m corresponds to its observed cumulative EDi. We applied the “shuffle.web” method within the “nullmodel” function in the bipartite package (50), which relocates the amn element within the matrix while maintaining its dimensionality, i.e., constraining the number of sites and interactions while shuffling the EDi among them. We obtained the mean and SD of the EDi per site over 100 simulations and calculated the corresponding z score based on the observed cumulative EDi. The z scores were fitted against the defaunation index in a linear model. Positive z scores indicate that the site holds more cumulative EDi than expected by the number of bird-plant interactions recorded in that area. On the other hand, negative z scores indicate that the site holds less cumulative EDi than expected by the number of interactions that it encompasses. Statistical analyses were performed in R version 3.4.3 (51).

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