Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

• Published in: PloS one 2012-12, Vol.7 (12), p.e49410-e49410
• Main Authors: Graves, Tabitha A, Royle, J Andrew, Kendall, Katherine C, Beier, Paul, Stetz, Jeffrey B, Macleod, Amy C
• Format: Article
• Language: English
• Subjects: Abundance; Animal Distribution; Animal populations; Animals; Binomial distribution; Biology; Ecology; Ecosystems; Estimates; Female; Grizzly bear; Grizzly bears; Heterogeneity; Hypotheses; Identification methods; Mathematical models; Mathematics; Methods; Models, Theoretical; National parks; Sampling methods; Spatial distribution; Studies; Ursidae; Ursus arctos; Variables; Wildlife management
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0049410

Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.