Integrated species distribution models: A comparison of approaches under different data quality scenarios
Integrated species distribution models: A comparison of approaches under different data quality scenarios
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综合物种分布模型：不同数据质量场景下方法的比较
DOI：
10.1111/ddi.13255
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发表时间：
2021
影响因子：
4.6
通讯作者：
Ahmad Suhaimi S
中科院分区：
文献类型：
--
作者：
Ahmad Suhaimi S
综合物种分布模型已成为生态学家利用物种发生地的一系列信息的有用工具。特别是，将大量的临时或仅存在（PO）记录与更结构化的存在-不存在（PA）数据相结合的能力可以让生态学家解释PO数据中的偏差，这些偏差通常会混淆建模工作。已经提出了一系列建模技术来实现综合物种分布模型（IDM），包括联合似然模型，包括一个数据集作为协变量或信息先验，并拟合数据集之间的相关结构。我们的目标是研究不同类型的集成模型在现实生态数据场景下的性能。创新我们使用虚拟生态学家的方法来研究在不同水平的PO数据的空间偏差，PA数据的样本大小和数据集之间的空间重叠下，哪种集成模型是最有利的。主要结论当PO数据的空间偏差较低时，联合似然模型是性能最好的模型，或者可以建模，但当数据中存在未知偏差时，估计值很差。相关模型提供了良好的模型估计，即使有未知的偏见，当良好的质量PA数据空间有限。包括PO数据，通过提供信息的事先提供的改进不大，单独建模PA数据，是劣于使用联合可能性或相关性的方法。我们的研究结果表明，相关模型提供了一个强大的替代联合似然模型时，相关的努力或检测PO数据的协变量不可用。生态学家应该意识到每种方法的局限性，并在决定使用哪种类型的IDM时考虑数据中的偏差如何建模。
AimIntegrated species distribution modelling has emerged as a useful tool for ecologists to exploit the range of information available on where species occur. In particular, the ability to combine large numbers of ad hoc or presence‐only (PO) records with more structured presence–absence (PA) data can allow ecologists to account for biases in PO data which often confound modelling efforts. A range of modelling techniques have been suggested to implement integrated species distribution models (IDMs) including joint likelihood models, including one dataset as a covariate or informative prior, and fitting a correlation structure between datasets. We aim to investigate the performance of different types of integrated models under realistic ecological data scenarios.InnovationWe use a virtual ecologist approach to investigate which integrated model is most advantageous under varying levels of spatial bias in PO data, sample size of PA data and spatial overlap between datasets.Main conclusionsJoint likelihood models were the best performing models when spatial bias in PO data was low, or could be modelled, but gave poor estimates when there were unknown biases in the data. Correlation models provided good model estimates even when there were unknown biases and when good quality PA data were spatially limited. Including PO data via an informative prior provided little improvement over modelling PA data alone and was inferior to using either the joint likelihood or correlation approach. Our results suggest that correlation models provide a robust alternative to joint likelihood models when covariates related to effort or detection in PO data are not available. Ecologists should be aware of the limitations of each approach and consider how well biases in the data can be modelled when deciding which type of IDM to use.
综合物种分布模型：将存在背景数据和场地占用数据与不完善的检测相结合
DOI：
--
发表时间：
2017
期刊：
影响因子：
--
作者：
V. Koshkina;Yang Wang;A. Gordon;R. Dorazio;M. White;L. Stone
通讯作者：
L. Stone
整合不同调查类型的数据以监测濒危物种的种群：坡鹿的案例
影响因子：
4.6
作者：
D. Bowler;Erlend B. Nilsen;R. Bischof;R. O’Hara;T. T. Yu;Tun Oo;M. Aung;J. Linnell
通讯作者：
J. Linnell
影响因子：
3.1
作者：
Johnston, Alison;Moran, Nick;Baillie, Stephen R.
通讯作者：
Baillie, Stephen R.