Tropical land-use change alters trait-based community assembly rules for dung beetles and birds

Abstract

Tropical rainforest disturbance and conversion are critical drivers of biodiversity loss. A key knowledge gap is understanding the impacts of habitat modification on mechanisms of community assembly, which are predicted to respond differently between taxa and across spatial scales. We use a null model approach to detect trait assembly of species at local- and landscape-scales, and then subdivide communities with different habitat associations and foraging guilds to investigate whether the detection of assembly mechanisms varies between groups. We focus on two indicator taxa, dung beetles and birds, across a disturbance gradient of primary rainforest, selectively logged rainforest, and oil palm plantations in Borneo, Southeast Asia. Random community assembly was predominant for dung beetles across habitats, whereas trait convergence, indicative of environmental filtering, occurred across the disturbance gradient for birds. Assembly patterns at the two spatial scales were similar. Subdividing for habitat association and foraging guild revealed patterns hidden when focusing on the overall community. Dung beetle forest specialists and habitat generalists showed opposing assembly mechanisms in primary forest, community assembly of habitat generalists for both taxa differed with disturbance intensity, and insectivorous birds strongly influenced overall community assembly relative to other guilds. Our study reveals the sensitivity of community assembly mechanisms to anthropogenic disturbance via a shift in the relative contribution of stochastic and deterministic processes. This highlights the need for greater understanding of how habitat modification alters species interactions and the importance of incorporating species’ traits within assessments.

Giant root-rat engineering and livestock grazing activities regulate plant functional trait diversity of an Afroalpine vegetation community in the Bale Mountains, Ethiopia

“…of the null data; positive and negative SES FDis values indicate higher or lower functional divergence than the null expectation values, respectively (Gotelli, 2000;Kembel et al 2010). In terms of assembly processes, positive SES FDis values are considered to be caused by competitive exclusion that limits functional similarity or by habitat heterogeneity that increases resources and thus reduces competition, negative SES FDis values by habitat ltering of functionally similar species, and values near zero indicates random distribution of traits or concurrent occurrence of competition or habitat heterogeneity and habitat ltering, off-setting their effects (Edwards et al 2021). Thus, we tested whether mean SES FDis was signi cantly different from zero using t-test.…”

Dung beetles maintain phylogenetic divergence but functional convergence across a highly fragmented tropical landscape

“…Conversely, environmental filtering could favour functional convergence within phylogenetically overdispersed assemblages (Cavender‐Bares et al, 2009). Besides, overall phylogenetic patterns may mask specific niche‐based processes emerging from ecologically similar subsets of species due to the interaction between different assembly processes (Edwards et al, 2021; Mayfield & Levine, 2010). To deeply understand the response of species assemblages to human‐modified landscapes, it is necessary to elucidate the influence of niche conservatism and functional convergence on phylogenetic diversity, integrating the functional traits of species and measuring their phylogenetic signal.…”

A roadmap towards predicting species interaction networks (across space and time)

“…Network approaches have been used for the prediction of risk and dynamics of dengue [18], Chagas disease [19], Rickettsiosis [20], Leishmaniasis [21] and a myriad of infectious diseases in livestock and wildlife [22]. Additionally, prediction of interaction networks is a growing imperative for next-generation biodiversity monitoring, requiring a conceptual framework and a flexible set of tools to predict interactions that is explicitly spatial and temporal in perspective [23][24][25]. Developing better models for prediction of these interactions will rely on integration of data from many sources, and the sources for this data may differ depending on the type of interaction we wish to predict [26].…”