Study Designs and Tests for Comparing Resource Use and Availability II

Abstract

We review 87 articles published in the Journal of Wildlife Management from 2000 to 2004 to assess the current state of practice in the design and analysis of resource selection studies. Articles were classified into 4 study designs. In design 1, data are collected at the population level because individual animals are not identified. Individual animal selection may be assessed in designs 2 and 3. In design 2, use by each animal is recorded, but availability (or nonuse) is measured only at the population level. Use and availability (or unused) are measured for each animal in design 3. In design 4, resource use is measured multiple times for each animal, and availability (or nonuse) is measured for each use location. Thus, use and availability measures are paired for each use in design 4. The 4 study designs were used about equally in the articles reviewed. The most commonly used statistical analyses were logistic regression (40%) and compositional analysis (25%). We illustrate 4 problem areas in resource selection analyses: pooling of relocation data across animals with differing numbers of relocations, analyzing paired data as though they were independent, tests that do not control experiment wise error rates, and modeling observations as if they were independent when temporal or spatial correlations occurs in the data. Statistical models that allow for variation in individual animal selection rather than pooling are recommended to improve error estimation in population‐level selection. Some researchers did not select appropriate statistical analyses for paired data, or their analyses were not well described. Researchers using one‐resource‐at‐a‐time procedures often did not control the experiment wise error rate, so simultaneous inference procedures and multivariate assessments of selection are suggested. The time interval between animal relocations was often relatively short, but existing analyses for temporally or spatially correlated data were not used. For studies that used logistic regression, we identified the data type employed: single sample, case control (used‐unused), use‐availability, or paired use‐availability. It was not always clear whether studies intended to compare use to nonuse or use to availability. Despite the popularity of compositional analysis, we do not recommend it for multiple relocation data when use of one or more resources is low. We illustrate that resource selection models are part of a broader collection of statistical models called weighted distributions and recommend some promising areas for future development.

Ring‐necked pheasant hens select managed Conservation Reserve Program grasslands for nesting and brood‐rearing

“…Biologists saw responses from spring rooster crowing counts (crows/ stop doubled), roadside surveys of broods in August (young/mile increased by 400%), and spring and summer rural mail carrier surveys (pheasants/100 km doubled) during the period of time in which land under CRP contract in our study area was disturbed through mid-contract management (S. Taylor, unpublished data). Recently, biologists have debated the use of compositional analysis for analyzing habitat preference (Thomas andTaylor 2006, Bingham et al 2007). Our results, using both discrete choice and compositional analysis to compare brood-site preference, did not differ in habitat preference rankings.…”

Improving broad scale forage mapping and habitat selection analyses with airborne laser scanning: the case of moose

“…Candidate models were selected a priori to assess whether the ALS variables improved the predictive ability of the RSFs, either directly by quantifying cover, or through better forage estimates. Coefficients of the exponential RSFs were estimated from use-availability data in a mixed-effects logistic regression (design III data; Thomas and Taylor 2006) with moose ID as a random intercept (Gillies et al 2006). Mixedeffect logistic regressions were fitted using the library 'lme4' (Bates et al 2012) implemented in R (R Development Core Team 2011).…”

Effects of winter recreation on northern ungulates with focus on moose (Alces alces) and snowmobiles

“…Animals providing more data had more weight in the estimates of coefficients and standard errors for the final model (Thomas and Taylor 2006). To estimate standard errors (SEs) and 90 % confidence intervals (CIs) for model coefficients, we bootstrapped data from the individual animals 500 times (Manly 2007).…”

“…To estimate standard errors (SEs) and 90 % confidence intervals (CIs) for model coefficients, we bootstrapped data from the individual animals 500 times (Manly 2007). Bootstrapping individuals treated the animal as the experimental unit and ensured that we were estimating the correct error for population level selection (Thomas and Taylor 2006). Percentile-based CIs were reported, and coefficients with 90 % CIs that did not encompass 0.0 were considered statistically significant (α=0.10).…”