A new statistical approach for identifying rare species under imperfect detection
A new statistical approach for identifying rare species under imperfect detection
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一种在不完善检测下识别稀有物种的新统计方法
DOI：
10.1111/ddi.13495
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发表时间：
2022
影响因子：
4.6
通讯作者：
Belmont J
中科院分区：
文献类型：
--
作者：
Belmont J
物种稀有度常被用来衡量物种灭绝的风险，因此，已经开发了不同的方法来描述生物群落中稀有物种的组成。这些方法通常依赖于物种的属性，并不总是可用的，往往忽略不完善的物种检测。在这项工作中，我们开发了一种新方法来表征物种检测不完全时群落中物种的稀有性。我们的建模框架是基于贝叶斯占有模型，使用存在-非检测数据估计不完美检测下的物种分布。创新我们提出了一个有限混合物占有模型，根据它们的占有和类成员概率来识别稀有物种。在这里，我们探索了一个两类有限混合模型，以区分稀有和常见的物种类别，并提出了一个具有两个以上类别的问题的一般建模框架。通过使用模拟，我们能够比较不同场景下的模型结果，在所有场景中获得高分类性能。此外，我们应用我们的模型的蜻蜓发生记录的数据集，部分观察到由于不完善的检测和量化的比例在全国范围内的水体在英国。主要conclusionsNowadays，生物多样性保护涉及到监测计划，针对多个物种在一个社区内，个别物种的反应可能会有很大的不同。这种高变异性使得识别驱动稀有物种分布的生态过程的任务变得困难。因此，我们的方法代表了一种新的方法来表征社区的组成，在物种稀有性，同时纠正可检测性偏差。我们的建模框架还建议了研究路线和未来的发展，以了解如何在广泛的场景中测量物种的稀有性。
AimSpecies rarity is often used as a measure to assess the risk of extinction of species, and thus, different methods have been developed to describe the composition of rare species in biological communities. These methods usually depend on species attributes that are not always available and very often ignore imperfect species detection. In this work, we developed a new method to characterize species rarity in a community when species are detected imperfectly. Our modelling framework is based on Bayesian occupancy models to estimate species distributions under imperfect detection using presence‐nondetection data.InnovationWe propose a finite mixture occupancy model to identify rare species based on their occupancy and class‐membership probabilities. Here, we explored a two‐class finite mixture model to distinguish between rare and common species classes and presented the general modelling framework for a problem with more than two classes. By using simulations, we were able to compare our model results under different scenarios obtaining a high‐classification performance across all of them. Additionally, we applied our model to a data set of Odonata occurrence records that were partially observed due to imperfect detection and quantified the proportion of rare species on a national scale across waterbodies in the United Kingdom.Main conclusionsNowadays, biodiversity conservation involves monitoring programmes that target multiple species within a community where individual species responses may vary widely. This high variability makes the task of identifying the ecological processes that drive distributions of rare species difficult. Thus, our method represents a new approach to characterize the composition of a community in terms of species rarity while correcting for detectability bias. Our modelling framework also suggests lines of research and future developments for the understanding of how species rarity can be measured in a wide range of scenarios.
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哺乳动物的七种稀有形式
DOI：
--
发表时间：
2000
期刊：
影响因子：
--
作者：
Jinping Yu;F. Dobson
通讯作者：
F. Dobson
影响因子：
1.3
作者：
G. Powney;S. Brooks;Louise J Barwell;Philip Bowles;Robert N. L. Fitt;Alyson T Pavitt;Rebecca A. Spriggs;N. Isaac
通讯作者：
N. Isaac
影响因子：
5
作者：
Tyre, AJ;Tenhumberg, B;Possingham, HP
通讯作者：
Possingham, HP
影响因子：
1.9
作者：
Pledger, S
通讯作者：
Pledger, S
影响因子：
6.9
作者：
Golfieri, Bruno;Hardersen, Soenke;Surian, Nicola
通讯作者：
Surian, Nicola