Evaluation of statistical methods and sampling designs for the assessment of microhabitat selection based on point data

Abstract

1. Information on resource selection by a species is essential for understanding the species' ecology, distribution and requirements for survival. Research on habitat selection frequently relies on animal detection at point locations to determine which resource units are used. A variety of approaches and statistical tools can be employed for assessing selection based on habitat variables measured in those units. The aim of this work is to evaluate the reliability of common sampling designs and statistical methods in detecting habitat selection at fine scales based on point data 2. We reviewed literature on microhabitat selection to determine characteristics of typical studies and analysed simulated small mammal live-trapping data as a case study. We considered various scenarios differing in the number of sampled units and sampling duration. For each scenario, a set of simulated surveys was analysed through two univariate Accepted ArticleThis article is protected by copyright. All rights reserved.tests (Welch's t-and Mann-Whitney U-test), generalised linear models (GLMs), mixed-effect models (GLMMs) and occupancy models (OMs). 3.Analysis of simulated data revealed that overall performance of all statistical methods improved with increased trapping effort. Univariate tests were especially sensitive to the number of sampling units, while modelling methods took also advantage of longer trapping sessions. Univariate tests and GLMs provided partially correct information in most cases, whereas GLMMs and OMs offered higher probabilities of fully describing simulated habitat preferences. 4.With typical sampling efforts, appropriate statistical analysis of point data is able to provide a moderately accurate description of habitat selection at small scales, in spite of the violation of closure and independence assumptions of applied models. Modelling approaches are proliferating; we encourage using models that can deal with multiple sources of variability, such as GLMMs and OMs, when data are hierarchically structured.There is no a priori best survey design; it should be chosen according to the scope and goals of the study, environment heterogeneity, species characteristics and practical constraints.Researchers should realise that sampling design and statistical methods likely affect conclusions regarding habitat selection.

Are the Neglected Tipuloidea Crane Flies (Diptera) an Important Component for Subterranean Environments?

“…We then used a binomial generalized linear mixed model to assess which environmental factors influence the occupancy of crane flies in subterranean environments [57]. Their presence/absence was used as dependent variable, while the morphological and microclimatic features of cave sectors, along with depth, as independent variables; site and sector identity were used as random variables.…”

Advantages of combining generalized linear models and occupancy models to find indicators of habitat selection: Small mammals in agroecosystems as a case study

“…According to our design, the closure assumption was not met at this scale of analysis because animals likely moved from one trapping station to another during the sampling period. Although this leads to biased estimates of detectability and occupancy, Gorosito et al (2016) found that covariates affecting habitat selection can still be successfully identified. Therefore, abundance is not to be taken literally as such, but as a relative measure of the intensity of use.…”

Small rodent species on pig and dairy farms: habitat selection and distribution

“…Although this bias was expected, individuals were not captured independently of the microhabitat characteristics surrounding the trap stations. Habitat selection results from trapping studies should be compared with that of free‐roaming animals, that are more costly. A study on habitat selection of R. norvegicus using spool‐and‐line technique in two of the study farms yielded very similar results .…”