Correction to ‘Critical variables and their thresholds for the precise density estimation of wild felids with camera traps and spatial capture-recapture (SCR) methods’
{"title":"Correction to ‘Critical variables and their thresholds for the precise density estimation of wild felids with camera traps and spatial capture-recapture (SCR) methods’","authors":"","doi":"10.1111/mam.12368","DOIUrl":null,"url":null,"abstract":"<p>Palmero S, Premier J, Kramer-Schadt S, Monterroso P, Heurich M (2023) Sampling variables and their thresholds for the precise estimation of wild felid population density with camera traps and spatial capture–recapture methods. <i>Mammal Review</i>, 53, 223–237. https://doi.org/10.1111/mam.12320</p><p>In paragraph 6 of the ‘Discussion’ session, the text ‘Our results indicated that Bayesian methods performed better than MLE. This finding is consistent with Royle et al. (2009), who demonstrated that Bayesian methods cope better with small sample sizes. Considering that large sample sizes are often hard to achieve, Bayesian methods are generally preferable, and many R packages are available to support the methods, for example Royle et al. (2014), which provides several coding examples’ was incorrect because we drew wrong conclusions on the comparison between the two methods.</p><p>This should read ’Our results indicated that Bayesian methods performed better than MLE. This finding is consistent with Royle et al. (2009), who demonstrated that Bayesian methods cope better with small sample sizes. However, it needs to be taken into account that the two approaches model the number of individuals observed differently, that is a Poisson and binomial distribution is used for the MLE and Bayesian methods, respectively. Additionally, Bayesian methods use priors in the model. Therefore, conclusions on the performance of the two methods cannot be fairly drawn’.</p><p>We apologise for this error.</p>","PeriodicalId":49893,"journal":{"name":"Mammal Review","volume":"54 4","pages":"441"},"PeriodicalIF":4.3000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mam.12368","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mammal Review","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/mam.12368","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Palmero S, Premier J, Kramer-Schadt S, Monterroso P, Heurich M (2023) Sampling variables and their thresholds for the precise estimation of wild felid population density with camera traps and spatial capture–recapture methods. Mammal Review, 53, 223–237. https://doi.org/10.1111/mam.12320
In paragraph 6 of the ‘Discussion’ session, the text ‘Our results indicated that Bayesian methods performed better than MLE. This finding is consistent with Royle et al. (2009), who demonstrated that Bayesian methods cope better with small sample sizes. Considering that large sample sizes are often hard to achieve, Bayesian methods are generally preferable, and many R packages are available to support the methods, for example Royle et al. (2014), which provides several coding examples’ was incorrect because we drew wrong conclusions on the comparison between the two methods.
This should read ’Our results indicated that Bayesian methods performed better than MLE. This finding is consistent with Royle et al. (2009), who demonstrated that Bayesian methods cope better with small sample sizes. However, it needs to be taken into account that the two approaches model the number of individuals observed differently, that is a Poisson and binomial distribution is used for the MLE and Bayesian methods, respectively. Additionally, Bayesian methods use priors in the model. Therefore, conclusions on the performance of the two methods cannot be fairly drawn’.
期刊介绍:
Mammal Review is the official scientific periodical of the Mammal Society, and covers all aspects of mammalian biology and ecology, including behavioural ecology, biogeography, conservation, ecology, ethology, evolution, genetics, human ecology, management, morphology, and taxonomy. We publish Reviews drawing together information from various sources in the public domain for a new synthesis or analysis of mammalian biology; Predictive Reviews using quantitative models to provide insights into mammalian biology; Perspectives presenting original views on any aspect of mammalian biology; Comments in response to papers published in Mammal Review; and Short Communications describing new findings or methods in mammalian biology.