2019
DOI: 10.1002/ecy.2750
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The Australian National Rabbit Database: 50 yr of population monitoring of an invasive species

Abstract: With ongoing introductions into Australia since the 1700s, the European rabbit (Oryctolagus cuniculus) has become one of the most widely distributed and abundant vertebrate pests, adversely impacting Australia's biodiversity and agroeconomy. To understand the population and range dynamics of the species and its impacts better, occurrence and abundance data have been collected by researchers and citizens from sites covering a broad spectrum of climatic and environmental conditions in Australia. The lack of a co… Show more

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Cited by 12 publications
(18 citation statements)
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“…We used a two-phased analytical approach to select the best model (e.g., Wadley, Austin, & Fordham, 2014). We did this preliminary analysis only with the expert occurrence data, which is more precise and reliable (Roy-Dufresne, Lurgi, et al, 2019;Roy-Dufresne, Saltré, et al, 2019) and, therefore, provides a better reflection of the pattern of occurrence for the focal species. We ranked the models using the Akaike's information criterion corrected for small sample size (AIC c ) and assessed their probability relatively to the entire set of candidate models using the AIC c weights (wAIC c ) and their corresponding percentage of TA B L E 1 Name, description, and range of value of selected covariates to describe the distribution of the rabbits in Australia deviance explained (Burnham & Anderson, 2010).…”
Section: Model Construction and Evaluationmentioning
confidence: 99%
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“…We used a two-phased analytical approach to select the best model (e.g., Wadley, Austin, & Fordham, 2014). We did this preliminary analysis only with the expert occurrence data, which is more precise and reliable (Roy-Dufresne, Lurgi, et al, 2019;Roy-Dufresne, Saltré, et al, 2019) and, therefore, provides a better reflection of the pattern of occurrence for the focal species. We ranked the models using the Akaike's information criterion corrected for small sample size (AIC c ) and assessed their probability relatively to the entire set of candidate models using the AIC c weights (wAIC c ) and their corresponding percentage of TA B L E 1 Name, description, and range of value of selected covariates to describe the distribution of the rabbits in Australia deviance explained (Burnham & Anderson, 2010).…”
Section: Model Construction and Evaluationmentioning
confidence: 99%
“…In step 2, we generated a separate candidate model set with all potential combinations of covariates from the best-ranked models (wAICc = 1) in step 1. We did this preliminary analysis only with the expert occurrence data, which is more precise and reliable (Roy-Dufresne, Lurgi, et al, 2019;Roy-Dufresne, Saltré, et al, 2019) and, therefore, provides a better reflection of the pattern of occurrence for the focal species.…”
Section: Model Construction and Evaluationmentioning
confidence: 99%
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