Abondance index variation and North Atlantic Oscillation Index

• Main Authors: Côté, Caroline, Castonguay, Martin, Gagnaire, Pierre-Alexandre, Bourret, Vincent, Verreault, Guy, Bernatchez, Louis
• Format: Dataset
• Language: unknown
• Published: Dryad Digital Repository 2012
• Subjects: Conservation Biology; Conservation Genetics; Ecological Genetics; Fisheries Management; Population Genetics - Empirical; Wildlife Management; Florida 30.02N -81.33W Guana River Dam; Georgia 31.31N -81.47W Mornings-AR; South Carolina 32.93N -80.01W Cooper River; North Carolina 34.77N -76.81W Black Creek; Virginia 37.22N -76.49W Wormley Creek York; Delaware 38.59N -75.29W Millsboro Pond Spillway; New Jersey 39.56N -74.58W Patcong Creak Linwood; Pennsylvania 40.05N -74.98W Crum Creek; Connecticut 41.30N -72.40W Tyler River; Massachusetts 41.68N -70.92W Parker River; New Hampshire 42.93N -70.86W Tayler River; Nova Scotia 44.59N -64.17W Mira River; Newfoundland 47.84N -59.26W Codroy River; Québec 48.78N -67.69W Grande Rivière Blanche; Prince Edward Island 46.43N -63.24W Rustico Bay; Ontario 45.01N -74.79W Moses-Saunders Dam; Québec 45.31N -73.90W Beauharnois Dam; Québec 45.44N -73.26W Chambly Dam; Québec 48.28N -68.95W Rivière du Sud-Ouest; Québec 49.52N -67.28W Rivière de la Petite Trinité; Québec 48.82N -64.83W Rivière Saint-Jean; Maine 43.84N -69.65W West Harbor Pond; New Brunswick 47.52N -64.91W Tracadie River; New Brunswick 47.09N -65.22W Miramichi Estuary; New Brunswick 45.87N -66.15W Grand-Lake; Nova Scotia 44.36N -64.46W La Have River; Nova Scotia 45.84N -60.80W Bras d’Or Lake; Nova Scotia 46.43N -61.10W Margaree Harbour; Newfoundland 47.60N -53.26W Roberts Bay; Mesozoic; Anguilla rostrata; Blanche; Creak; Mira; Moses; Rivière du Sud; Roberts Bay; Saunders; Newfoundland; North Atlantic; North Atlantic oscillation; Prince Edward Island
• Online Access: https://dx.doi.org/10.5061/dryad.39jb0/2; http://datadryad.org/resource/doi: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.