By Joshua Millspaugh, John M. Marzluff
Radio monitoring and Animal Populations is a succinct synthesis of rising applied sciences and their purposes to the empirical and theoretical difficulties of inhabitants review. The publication is split into sections designed to surround a few of the facets of animal ecology that could be evaluated utilizing radiotelemetry expertise - experimental layout, gear and expertise, animal circulate, source choice, and demographics. natural world biologists on the cutting edge of latest advancements within the know-how and its program have joined forces.
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Additional info for Radio Tracking and Animal Populations (IGN Outdoor Activities (Plein Air))
These assumptions can be verified using radio-marked animals. Survival Monitoring animals with radiotelemetry can dramatically improve the precision and quality of estimates of survival rates compared to the classic approaches based on band recovery (Brownie et al. 1985; see also Chapter 14). One of the most powerful and flexible approaches, based on numerical optimization, has been developed extensively by White (1983). His computer program SURVIV (see Appendix A) can be used to construct likehhood functions for a variety of survival models to derive maximum likelihood estimates of the parameters as well as to construct likelihood ratio tests between models.
Experimental Design for Radiotelemetry Studies 27 WHAT SAMPLING INTENSITY IS NEEDED? How many radio-marked animals should be monitored, and how many locations per animal are needed to accurately estimate the true parameters of animal movement, resource use, home range use, or survival? This question is fundamental to appropriate design of radiotelemetry studies but often is ignored. The classic approach to determining sample size requirements for experiments involves obtaining prehminary estimates of means and variances, or proportions, for the characteristics of interest, specifying the magnitude of the differences that are biologically meaningful and desirable to detect, choosing a desired power for the test, and calculating required sample sizes (Zar 1984).
1999) did not evaluate trade-offs between number of animals sampled vs. number of locations collected per animal, as done here. Moreover, Seaman et al. (1999) did not use empirical data for their simulations but generated independent random observations from underlying unimodal or multimodal bivariate normal distributions. Consequently, parameters associated with such simulations are likely to be different from the parameters inherent to our use of empirical data for simulation. These differences in methods of simulation between our work and that of Seaman et al.