Abstract
Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the “Moran effect”). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.
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Acknowledgments
We thank the CALMIP group, in particular Nicolas Renon, as part of this work was performed using HPC resources from CALMIP. We are indebted to the French National Agency for Water and the Aquatic Environment (Onema) for providing fish data, and we would like to thank the many fieldworkers who contributed to the fish records. We are grateful to Monika Ghosh for correcting the English text. This study is part of the projects PRIOFISH (financially supported by the Fondation de Recherche pour la Biodiversité, Région Nord-Pas-de-Calais and Agence de l’Eau Artois Picardie) and Adapt’eau (projet ANR-11-CEPL-008). EDB is part of the Laboratoire d’Excellence (LABEX) entitled TULIP (ANR-10-LABX-41).
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Communicated by Joel Trexler.
Highlighted student research: This paper represents an outstanding contribution to the field of spatial population synchrony. Using empirical and simulated data sets, we highlighted the influence of time series transformation (TSTs) on several measures classically used in synchrony studies to identify the determinants of spatial population synchrony (i.e., large-scale climatic factors such as climate or local factors such as dispersion of individuals between localities). Our results highlight how TSTs influence both synchrony measurements and the conclusions regarding the determinants of population synchrony. Based on these results, we provide guidelines about how time series should be handled in synchrony studies. These guidelines are expected to improve our general understanding of the drivers influencing spatial population synchrony.
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Chevalier, M., Laffaille, P., Ferdy, JB. et al. Measurements of spatial population synchrony: influence of time series transformations. Oecologia 179, 15–28 (2015). https://doi.org/10.1007/s00442-015-3331-5
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DOI: https://doi.org/10.1007/s00442-015-3331-5