Evaluation of ultrastructure and random effects band recovery models for estimating relationships between survival and harvest rates in exploited populations

Evaluation of ultrastructure and random effects band recovery models for estimating relationships between survival and harvest rates in exploited populations.— Increased population survival rate after an episode of seasonal exploitation is considered a type of compensatory population response. Lack of an increase is interpreted as evidence that exploitation results in added annual mortality in the population. Despite its importance to management of exploited species, there are limited statistical techniques for comparing relative support for these two alternative models. For exploited bird species, the most common technique is to use a fixed effect, deterministic ultrastructure model incorporated into band recovery models to estimate the relationship between harvest and survival rate. We present a new likelihood–based technique within a framework that assumes that survival and harvest are random effects that covary through time. We conducted a Monte Carlo simulation study under this framework to evaluate the performance of these two techniques. The ultrastructure models performed poorly in all simulated scenarios, due mainly to pathological distributional properties. The random effects estimators and their associated estimators of precision had relatively small negative bias under most scenarios, and profile likelihood intervals achieved nominal coverage. We suggest that the random effects estimation method approach has many advantages compared to the ultrastructure models, and that evaluation of robustness and generalization to more complex population structures are topics for additional research.

[1]  J. Lebreton,et al.  Testing the additive versus the compensatory hypothesis of mortality from ring recovery data using a random effects model , 2004, Animal Biodiversity and Conservation.

[2]  D. Otis Survival models for harvest management of mourning dove populations , 2002 .

[3]  David R. Anderson,et al.  Estimation of long-term trends and variation in avian survival probabilities using random effects models , 2002 .

[4]  Richard J. Barker,et al.  Measuring density dependence in survival from mark-recapture data , 2002 .

[5]  J. Andrew Royle,et al.  Random effects and shrinkage estimation in capture-recapture models , 2002 .

[6]  Kenneth P. Burnham,et al.  Evaluation of some random effects methodology applicable to bird ringing data , 2002 .

[7]  James E. Hines,et al.  Identification and synthetic modeling of factors affecting American black duck populations , 2002 .

[8]  Gary C. White,et al.  Seasonal Compensation of Predation and Harvesting , 1999 .

[9]  David R. Anderson,et al.  Model Selection and Multimodel Inference , 2003 .

[10]  E. Rexstad Effect of hunting on annual survival of Canada geese in Utah , 1992 .

[11]  Graham W. Smith,et al.  Hunting and mallard survival, 1979-88 , 1992 .

[12]  J. Nichols,et al.  Effect of hunting on annual survival of grey ducks in New Zealand , 1991 .

[13]  David R. Anderson,et al.  Estimating the effect of hunting on annual survival rates of adult mallards , 1984 .

[14]  David R. Anderson,et al.  Compensatory mortality in waterfowl populations: A review of the evidence and implications for research and management , 1984 .

[15]  Gary C. White,et al.  Numerical estimation of survival rates from band-recovery and biotelemetry data , 1983 .

[16]  David R. Anderson,et al.  Statistical Inference from Band Recovery Data: A Handbook , 1978 .

[17]  David R. Anderson,et al.  Population ecology of the mallard: VI. The effect of exploitation on survival , 1976 .

[18]  Paul L. Errington,et al.  Predation and Vertebrate Populations , 1946, The Quarterly Review of Biology.