Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection
Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection
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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：
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
L. Stone
中科院分区：
文献类型：
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作者：
V. Koshkina;Yang Wang;A. Gordon;R. Dorazio;M. White;L. Stone
物种分布模型（SDMs）的两个主要数据来源是来自计划调查的地点-占用（SO）数据，以及来自机会性调查和其他来源的存在-背景（PB）数据。SO调查提供了关于特定地区物种存在和缺失的高质量数据。然而，由于它们的高成本，相对于PB数据，它们通常覆盖的面积较小，并且通常不能代表物种的地理范围。相比之下，PB数据丰富，覆盖面积更大，但由于缺乏物种缺失的信息，可靠性较差，并且通常具有偏差抽样的特点。在这里，我们提出了一种整合这两种数据类型的物种分布建模的新方法。我们使用非齐次泊松点过程作为构建同时适合PB和SO数据的集成SDM的基础。这是首次使用重复调查占用数据并结合检测概率的综合SO-PB模型的实现。使用模拟数据对集成模型的性能进行了评估，并与单独使用PB或SO数据的方法进行了比较。结果表明，该方法在物种空间分布预测方面具有优势，即使在有限区域内收集的SO数据较少的情况下也能提高物种空间分布的预测精度。当环境协变量显著相关时，综合模型也被发现是有效的。我们的方法用澳大利亚东南部黄腹滑翔机（Petaurus australis）的真实SO和PB数据进行了验证，综合模型的预测性能再次被证明是优越的。已知PB模型会对物种占用或丰度产生有偏差的估计。SO数据集的小样本量往往导致较差的样本外预测。综合模型结合了这两个来源的数据，与单独使用任何一个数据源相比，提供了更好的物种丰度预测。与传统sdm在其预测中具有限制性的尺度依赖性不同，我们的集成模型基于点过程模型，并且没有这种尺度依赖性。它可以用于任何空间尺度的丰度预测，同时仍然保持丰度与面积之间的基本关系。
Two main sources of data for species distribution models (SDMs) are site‐occupancy (SO) data from planned surveys, and presence‐background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types. We have used an inhomogeneous Poisson point process as the basis for constructing an integrated SDM that fits both PB and SO data simultaneously. It is the first implementation of an Integrated SO–PB Model which uses repeated survey occupancy data and also incorporates detection probability. The Integrated Model's performance was evaluated, using simulated data and compared to approaches using PB or SO data alone. It was found to be superior, improving the predictions of species spatial distributions, even when SO data is sparse and collected in a limited area. The Integrated Model was also found effective when environmental covariates were significantly correlated. Our method was demonstrated with real SO and PB data for the Yellow‐bellied glider (Petaurus australis) in south‐eastern Australia, with the predictive performance of the Integrated Model again found to be superior. PB models are known to produce biased estimates of species occupancy or abundance. The small sample size of SO datasets often results in poor out‐of‐sample predictions. Integrated models combine data from these two sources, providing superior predictions of species abundance compared to using either data source alone. Unlike conventional SDMs which have restrictive scale‐dependence in their predictions, our Integrated Model is based on a point process model and has no such scale‐dependency. It may be used for predictions of abundance at any spatial‐scale while still maintaining the underlying relationship between abundance and area.
影响因子：
5.9
作者：
Hastie T;Fithian W
通讯作者：
Fithian W
在统计模型中，有限样本的等效性仅在于仅存在的数据。
DOI：
10.1214/13-aoas667
发表时间：
2013-12-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Fithian W;Hastie T
通讯作者：
Hastie T