Interactions between species attributes explain population dynamics in stream fishes under changing climate

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.

The combined effects of climate change and river fragmentation on the distribution of Andean Amazon fishes

“…In the United States, species with wide niches (e.g., omnivores) have been related to stronger upstream predicted movements than specialized species (e.g., invertivores; Whitney, Whittier, Paukert, Olden, & Strecker, 2017). Considering complementary life‐history traits linked to reproduction, trophic regime and growth have shown better explanatory power between species traits and responses to climate change (Chevalier, Comte, Laffaille, & Grenouillet, 2018).…”

Reliability analysis of fish traits reveals discrepancies among databases

“…increasing lateral connectivity and minimum flow) generally favoured traits of low fecundity, intermediate growth rates, late maturation age, intermediate length at maturity, large size, and high mobility. Other studies used fish traits to compare features of successful versus failed alien fish introduction and to compare features of successful alien versus native fish species (Erös, ; Grabowska & Przybylski, ; Olden, Poff, & Bestgen, ; Ribeiro, Elvira, Collares‐Pereira, & Moyle, ; Vila‐Gispert, Alcaraz, & García‐Berthou, ), to assess species responses to climate change (Chevalier, Comte, Laffaille, & Grenouillet, ; Daufresne, Roger, Capra, & Lamouroux, ), and to predict and understand local species extinctions (Angermeier, ; Johnston, ; Olden, Poff, & Bestgen, ; Parent & Schriml, ; Reynolds, Webb, & Hawkins, ). Fish traits also constitute a central component of biotic indices that have been widely used to evaluate the integrity of freshwater ecosystems (García‐Berthou et al, ; Logez & Pont, ; Oberdorff, Pont, Hugueny, & Porcher, ).…”

Combining species distribution models and population genomics underlines the determinants of range limitation in an emerging parasite

“…Water temperature was not available across all sampling sites and was thus modeled based on air temperature variables. More specifically, we predicted the mean annual water temperature based on a combination of the mean annual air temperature, and the mean monthly air temperatures for January and July extracted for each site from the SAFRAN database (LeMoine 2002) using a random forest model (see Chevalier et al 2018 for more details). Hydrographic variables were extracted from the ‘Réseau Hydrographique Théorique' database (RHT, Pella et al 2012) at each site: altitude (m), river slope (%), riverine distance of each site from its river source (km), area of the upstream watershed (km 2 ), Strahler index (i.e.…”