2017
DOI: 10.1002/ece3.3131
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Discrepancies in occupancy and abundance approaches to identifying and protecting habitat for an at‐risk species

Abstract: Predicting how environmental factors affect the distribution of species is a fundamental goal of conservation biology. Conservation biologists rely on species distribution and abundance models to identify key habitat characteristics for species. Occupancy modeling is frequently promoted as a practical alternative to use of abundance in identifying habitat quality. While occupancy and abundance are potentially governed by different limiting factors operating at different scales, few studies have directly compar… Show more

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Cited by 29 publications
(23 citation statements)
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“…These replicates, along with occupancy and detection covariates, were used for estimating detection and occupancy probability of the species. We pooled data across seasons (winter, summer, and monsoon) to increase the number of sampling occasions for improving occupancy estimates (i.e., accuracy and precision), as in previous studies [37][38][39][40]. We computed the correlation and variance inflation factor (VIF) for all covariates.…”
Section: Sampling Designmentioning
confidence: 99%
“…These replicates, along with occupancy and detection covariates, were used for estimating detection and occupancy probability of the species. We pooled data across seasons (winter, summer, and monsoon) to increase the number of sampling occasions for improving occupancy estimates (i.e., accuracy and precision), as in previous studies [37][38][39][40]. We computed the correlation and variance inflation factor (VIF) for all covariates.…”
Section: Sampling Designmentioning
confidence: 99%
“…All these methods, however, assume that there are correlations between abundance and habitat characteristics, but the support for such correlations is often mixed [62]. Growth data in contrast are infrequently used in restoration ecology.…”
Section: Discussionmentioning
confidence: 99%
“…A set of best performed models contained all models located within 0.9 AIC cw (AIC cumulative weight) and had a ΔAIC c /QAIC c score less than "4", and from this set we performed model-averaging to obtain overall estimates of occupancy and detection probability (Burnham and Anderson 2002;MacKenzie et al 2002;Sewell et al 2012). We assessed the relative importance of each variable by summing the AIC weights of all the models containing a given variable (Burnham and Anderson 2002), and variables with relative importance >0.2 was considered the most significant variable (Dibner et al 2017;Oberosler et al 2017).…”
Section: Occupancy Modelmentioning
confidence: 99%