Volatility in coral cover erodes niche structure, but not diversity, in reef fish assemblages

Abstract

The world’s coral reefs are experiencing increasing volatility in coral cover, largely because of anthropogenic environmental change, highlighting the need to understand how such volatility will influence the structure and dynamics of reef assemblages. These changes may influence not only richness or evenness but also the temporal stability of species’ relative abundances (temporal beta-diversity). Here, we analyzed reef fish assemblage time series from the Great Barrier Reef to show that, overall, 75% of the variance in abundance among species was attributable to persistent differences in species’ long-term mean abundances. However, the relative importance of stochastic fluctuations in abundance was higher on reefs that experienced greater volatility in coral cover, whereas it did not vary with drivers of alpha-diversity. These findings imply that increased coral cover volatility decreases temporal stability in relative abundances of fishes, a transformation that is not detectable from static measures of biodiversity.

Broad‐scale analysis of fish community data suggests critical need to support regional connectivity of coral reefs

“…We would expect that more precise and detailed measures of disturbance history and habitat quality, for example using coral species composition and structure (and reliably separating algae from coral), would explain a considerable proportion of the remaining 48% of community‐level variance (Messmer et al, 2011, but see also Wismer et al, 2019). Indeed, Tsai et al (2022) used fine‐grained data on coral community composition to explain 40% of variance in fishes' long‐term mean abundance as a consequence of volatility in coral cover. It is not possible to reconstruct the ACA for 2016; but, looking ahead and assuming that AIMS continues collecting fish community data, future iterations of the ACA will be able to use the current version of the ACA data set as a baseline and consider whether the balance of regional and local influences is shifting as the relative composition of reef habitats and the structure of the reef network change.…”

“…To quantify local habitat, we used the recently completed Allen Coral Atlas (ACA, see https://allencoralatlas.org; Lyons et al, 2020). Although more detailed coral cover data are available for the fish data collection sites from long‐term AIMS coral surveys (Sweatman et al, 2011; Tsai et al, 2022), we deliberately used the ACA because it provides a single, highly standardized data layer that includes broader elements of coarse‐grained structure and geomorphology for each individual reef.…”

“…Others have shown that the more detailed AIMS coral cover data set can explain around 40% of the local, fine‐grained variance in fish abundance (Tsai et al, 2022). However, the selective focus of the AIMS data on a relatively small and ecologically similar area of each reef means that their description of coral community composition describes broader patch characteristics poorly.…”

High response diversity and conspecific density‐dependence, not species interactions, drive dynamics of coral reef fish communities

“…Abundance dynamics are assumed to follow the multivariate Gompertz model (Ives et al., 2003). This model has been used previously to model the dynamics of reef fishes (Thibaut et al., 2012; Tsai et al., 2022), and it characterizes the density‐dependent dynamics of reef fishes better than models of logistic form, (Thibaut et al., 2012), as it does for many other taxa (Sibly et al., 2005; Thibaut & Connolly, 2020). The model follows:$$$$ \log \left({\mu}_{i,t}\right)={a}_i+\sum \limits_{j=1}^S{b}_{i,j}\log \left({\mu}_{j,t-1}\right)+{e}_{i,t} $$$$or, in the matrix form:$$$$ \log \left({\boldsymbol{\mu}}_{\boldsymbol{t}}\right)=\boldsymbol{a}+\boldsymbol{B}\ \log \left({\boldsymbol{\mu}}_{\boldsymbol{t}-\mathbf{1}}\right)+{\boldsymbol{e}}_{\boldsymbol{t}} $$$$where log( μ t ) is a vector containing species’ estimated log‐abundances at time t , μ t = ( μ 1, t , μ 2, t , μ 3, t ,…, μ i , t ).…”

An intuitive approach for measuring the resilience of biological communities to environmental change

“…Moreover, quantifying these metrics across different systems could help identify general patterns in the processes affecting community resilience. So far, empirical studies have identified the key role of species heterogeneity (Saether et al, 2023a;Solbu et al, 2022;Tsai et al, 2022), highlighting the prevalence of inherent differences among species in their carrying capacities and of species that are persistently rare.…”