Similarity in seasonal flow regimes, not regional environmental classifications explain synchrony in brown trout population dynamics in France
Authors: Bergerot Benjamin , Bret Victor , Cattanéo Franck 
Published: March 2019

Investigating the variables driving synchrony in populations and the spatial scales at which they operate is worthwhile for both a basic understanding of population dynamics and for management. Various environmental classifications have been developed to group sites according to predefined similarities in abiotic variables (with spatial and/or temporal components) and to explain population dynamics in running waters. These classifications may be useful to managers and decision makers, as studies have shown relationships between time‐dependent population variability and environmental classifications on different spatial scales.

We investigated whether synchrony among brown trout (Salmo trutta) populations could be explained by three regional (hydro‐ecoregions, hydrological units, and natural flow regime classification) and one local environmental classification and their associated variables. We first analysed whether densities of two size classes (0+ and >0+ fish) varied over a large spatial scale (i.e. 112 sites spread throughout France sampled for >11 years). We then determined whether the degree of synchrony matched the spatial patterns defined by the environmental classifications. Finally, we investigated whether variables such as river distance between sites, hydrology, and temperature were associated with patterns of synchrony.

Overall, relationships observed between synchronies in environmental variables and trout population densities were weak. Synchrony was best explained by the classification based on hydrological units, defined as discrete physical units that provided boundaries with real biological significance for river assemblages (12% for 0+ density and 9% for >0+ density and total densities). However, when considering environmental variables alone, synchrony in trout populations was mainly associated (from 11% to 23%) with seasonal flow variables such as high flows during reproductive and emergence periods. Temperature and river distance did not explain synchrony between trout populations.

High flows during emergence appear to be a key driver of brown trout population dynamics. Floods during the reproductive period could explain synchrony among brown trout populations, although the effect was weaker than that of high flows during the emergence period.

We concluded that existing physical, morphological, and biogeographical classifications at regional spatial scales were not sufficiently integrative to account for environmental drivers (such as flow variables) that were most associated with the spatiotemporal patterns of synchrony in trout populations. Flow explained synchrony better than classifications did. Further investigations should focus on the spatial patterns of flow variations during the emergence and reproductive periods to derive geographical areas that best capture synchrony patterns in trout dynamics. This approach could be extended to other freshwater fish species for which climatic drivers are important, and may help to explain the distributional changes of exotic species, the propensity of populations to fluctuate synchronously and the scale of synchrony patterns under different scenarios of climate change, or the threat of extinction of native species if the Moran effect is high.