PREDICTING MOOSE POPULATION PARAMETERS FROM HUNTING STATISTICS

Abstract

We developed six regression models predicting density, harvest rate, % calves in autumn, annual rate of increase, and number of males and calves per 100 females in winter, based on 52 aerial surveys related to 10 hunting statistics. The 6 models were statistically significant by predictions for densities and harvest rates has higher R² (0.74-0.68) than the other 4 predicted variables (0.23-0.52). We are confident that predicted population parameters are adequate for managing moose populations. They are straightforward and easier to interpret than the traditional hunting statistics used up until now for an annual monitoring of moose populations. Regression models made it possible to reconstruct population parameters for the 1971-1992 period even if routine aerial censuses only began in the mid-eighties. predictions appeared less precise for some hunting zones, particularly for southern Québec where few aerial surveys were available to construct regression models.

Five homogeneous groups of hunting zones (and moose populations) were delineated by cluster analysis applied to hunting statistics. Southern populations were characterized by high productivity and low densities, due to intensive harvest rates. Southwestern populations were the least productive but, due to high densities, had relatively high yields per surface area. Northern populations were characterized by very low densities, low productivity and low harvest rates. Central and north-central populations exhibited intermediate characteristics. These results indicate that it will be necessary to develop independent models for each group of hunting zones to improve the accuracy of the predictions when sufficient data becomes available.