Interactions between species attributes explain population dynamics in stream fishes under changing climate. Issue 1 (17th January 2018)

• Record Type: Journal Article
• Authors: Chevalier, Mathieu; Comte, Lise; Laffaille, Pascal; Grenouillet, Gaël
• Is Part Of: Ecosphere. Volume 9:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-01-17
• Subjects: Bayesian inference -- climate mean -- climate variability -- density dependence -- growth rate -- N‐mixture models -- trait‐based approach -- water temperature; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.2061
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Species responses to climate change have been shown to vary in both direction and magnitude. Understanding these idiosyncratic responses is crucial if we are to predict extinction risk and set up efficient conservation strategies. The variations observed across species have been related to several species attributes including intrinsic traits such as physiological tolerances or life‐history strategies but also to niche characteristics (e.g., niche breadth [NB], niche position [NP]). However, although previous studies have successfully linked species attributes to population dynamics or range shifts, few have considered synergistic effects to explain responses to climate variations. Here, we assessed whether five species attributes (fecundity, thermal safety margin, trophic position [TP], NB, and NP) explained interspecific differences in four parameters influencing population dynamics of 35 stream fish species at the French scale. We used Bayesian N‐mixture models to estimate posterior distributions of the growth rate, the strength of density dependence, and the influence of both mean temperature and temperature variability on populations for each species. We then used phylogenetic generalized least squares (PGLS) models to investigate the influence of species attributes and their interactions on interspecific differences in each of the four parameter driving population dynamics. The percentage of variance explained by the PGLS models was relatively high (around 40% on average), indicating that species attributes are good predictors of species population dynamics. Furthermore, we showed that the influence of these single attributes was mediated by other attributes, especially NP and TP. Importantly, we found that models including interaction terms had greater support over simple additive models in explaining interspecific differences in population dynamics. Taken together, these results point to the importance of considering the interplay between species attributes in unraveling the mechanisms involved in population dynamics and understanding the vulnerability of species to global change.