Abondance index variation and North Atlantic Oscillation Index

• Main Authors: Côté, Caroline, Castonguay, Martin, Gagnaire, Pierre-Alexandre, Bourret, Vincent, Verreault, Guy, Bernatchez, Louis
• Format: Report
• Language: unknown
• Published: 2012
• Subjects: Conservation Biology; Conservation Genetics; Ecological Genetics; Fisheries Management; Population Genetics - Empirical; Wildlife Management; Blanche; Creak; Mira; Moses; Rivière du Sud; Roberts Bay; Saunders; Newfoundland; North Atlantic; North Atlantic oscillation; Prince Edward Island
• Online Access: http://hdl.handle.net/10255/dryad.42543; https://doi.org/10.5061/dryad.39jb0/2

We tested for statistical associations among several combinations of the NAO time series and temporal fluctuations of three variables used as proxies of relative abundance. Variation in Nb was used as a proxy for relative variation in the number of breeders. Allelic richness (Ar), measured for each cohort, was used as a proxy for the relative abundance of recruits since it has previously been proposed to correlate with offspring recruitment (McCusker & Bentzen 2010). YCSI was used as a second proxy of recruit abundance. We first tested for pairwise correlations between Nb, Ar, and YCSI, and time series of these three parameters were then compared with the monthly normalized NAO (http://www.cgd.ucar.edu). The “corresponding year” between time series represented the year when glass eels reached the continent for the Ar, Nb, and YCSI time series. To test environmental influence on previous life stages, +2 to -2 year lags were also tested. To assess the statistical significance of climate influence on eel abundance, multivariate models were run where the explanatory variables considered were the NAO time series. Stepwise regressions of the three relative abundance variables (Nb, Ar, and YCSI) were fitted to the explanatory variables to determine which ones were significant. The Akaike Information Criterion (AIC) was used to select models. Cross-validation R2 was computed to determine the prediction strength of the selected model and semi-partial R2 were computed to assess the relative importance of each selected variable. Analyses were performed using SAS 9.2 software.