Predicting breeding success of Atlantic population Canada geese from meteorological variables

Management strategies for sustained harvest and long-term viability of Atlantic Population Canada geese (Branta canadensis) require evaluations of annual breeding success before establishing fall harvest regulations. The only quantitative measure of the annual breeding success of this population is the proportion of young geese in the fall harvest that is not available when harvest regulations are set in late July. Because the majority of Atlantic Population Canada geese breed in the sub-arctic regions of the Ungava Peninsula in northern Quebec, spring climatic conditions are potential predictors of annual production for this population. We used tail-fan data from the Maryland harvest to calculate an index of the proportion of young geese (Y-i) in the fall population, 1963-94. We used 1963-87 weather data to develop multiple linear regression models to predict Y-i and validated these models by predicting Y-i for 1988-94. Models with the greatest predictive ability included the average daily mean temperature and the number of days of snowfall in May and June. The final model included 6 parameters and accounted for 78.7% of the total variability in Y-i (P = 0.001). This analysis demonstrates the potential use of climatic data to predict an index of annual production derived from harvest age ratios. The usefulness of this technique will depend on periodic assessment of predictive models as more data is gathered, and evaluation of harvest tail-fan surveys as indices to breeding success.