Correction to ‘Critical variables and their thresholds for the precise density estimation of wild felids with camera traps and spatial capture-recapture (SCR) methods’

IF 4.3 2区 生物学 Q1 ECOLOGY Mammal Review Pub Date : 2024-06-06 DOI:10.1111/mam.12368
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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’.

We apologise for this error.

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对 "利用相机陷阱和空间捕获-再捕获(SCR)方法精确估算野生猫科动物密度的关键变量及其阈值 "的更正
Palmero S, Premier J, Kramer-Schadt S, Monterroso P, Heurich M (2023) 用相机陷阱和空间捕获-再捕获方法精确估计野生猫科动物种群密度的采样变量及其阈值。Mammal Review, 53, 223-237。https://doi.org/10.1111/mam.12320In "讨论 "部分第 6 段,"我们的结果表明贝叶斯方法比 MLE 效果更好。这一结果与 Royle 等人(2009 年)的研究结果一致,他们认为贝叶斯方法更适合小样本量。考虑到大样本量往往难以实现,贝叶斯方法通常更可取,而且许多 R 软件包都支持这种方法,例如 Royle 等人(2014 年)提供了几个编码示例 "是不正确的,因为我们对两种方法之间的比较得出了错误的结论。这一结论与 Royle 等人(2009 年)的观点一致,他们认为贝叶斯方法更适合小样本量。不过,需要考虑的是,这两种方法对观察到的个体数量的建模方式不同,即 MLE 和贝叶斯方法分别使用泊松分布和二项分布。此外,贝叶斯方法在模型中使用先验。因此,无法对这两种方法的性能得出公正的结论"。我们对这一错误表示歉意。
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来源期刊
Mammal Review
Mammal Review 生物-动物学
CiteScore
12.20
自引率
4.10%
发文量
29
审稿时长
>12 weeks
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.
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