Correlations between spatial sampling biases and environmental niches affect species distribution models
Correlations between spatial sampling biases and environmental niches affect species distribution models
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DOI：
10.1111/geb.13491
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发表时间：
2022-03-23
影响因子：
6.4
通讯作者：
Gaston, Kevin J.
中科院分区：
文献类型：
--
作者：
Baker, David J.;Maclean, Ilya M. D.;Gaston, Kevin J.
生物多样性数据中的空间抽样偏差是由于地理、物种特征和人类行为之间复杂的相互作用而产生的，包括对特定物种或生境的偏好;因此，偏差不一定独立于物种的环境生境。我们评估的空间采样偏差和环境生态位之间的相关性可能会影响物种分布模型（SDM）的开发和不尝试纠正这些偏见。创新一个虚拟物种和虚拟生态学家框架被用来模拟生物多样性数据，没有空间采样偏差或偏差相关（正或负）的环境变量之一，用于定义环境生态位的物种。用于定义物种生态位的环境变量进行了模拟与空间自相关操作在多个空间尺度。然后，虚拟样本被用来模拟物种分布，模型评估的基础上，他们的能力，正确地排名网站的适合性。主要结论空间抽样偏差和环境生态位之间的相关性经常降低模型预测的秩相关性，但这些影响的相对重要性随物种类型（更大的秩相关性下降的环境生态位扩大）和数据类型（模型建立使用检测/非检测数据的影响较小，比那些使用仅检测数据）。偏差校正的有效性取决于空间偏差的结构，但也是高度可变的方法和数据类型的依赖。这些结果的含义是，空间采样偏差是空间数据模型的一个更大的问题，其中：（1）努力的分布相对于被认为与物种分布相关的环境梯度是非随机的;（2）被建模的物种具有广泛的环境生态位;（3）建模数据仅包含关于检测的信息（即，只存在）。
Aim Spatial sampling biases in biodiversity data arise because of complex interactions between geography, species characteristics and human behaviour, including preferences for or against particular species or habitats; biases are therefore not necessarily independent of the environmental niches of species. We evaluate when correlations between spatial sampling biases and environmental niches are likely to affect species distribution models (SDMs) developed both with and without attempts to correct these biases. Innovation A virtual species and virtual ecologist framework was used to simulate biodiversity data with either no spatial sampling bias or biases that were correlated (positively or negatively) with one of the environmental variables used to define the environmental niches of the species. The environmental variables used to define the species niche were simulated with spatial autocorrelation operating at multiple spatial scales. Virtual samples were then used to model species distributions, with models evaluated based on their ability to rank the suitability of sites correctly. Main conclusions Correlations between spatial sampling bias and environmental niches frequently reduced the rank correlation of model predictions, but the relative importance of these effects varied with species type (greater decline in rank correlation as the environmental niche broadens) and data type (models built using detection/non-detection data were less affected than those using detection-only data). Bias-correction effectiveness varied depending on the structure of the spatial bias but was also highly variable across methods and dependent on data type. The implications of these results are that spatial sampling bias is a greater concern for SDMs where: (1) the distribution of effort is non-random with respect to an environmental gradient thought to be correlated with a species' distribution; (2) the species being modelled has a broad environmental niche; and (3) the data for modelling contain only information on detections (i.e., presence only).