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首页> 外文期刊>Ecological Modelling >Spatial prediction of species' distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging
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Spatial prediction of species' distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging

机译：仅根据发生的记录进行物种分布的空间预测：结合点模式分析，ENFA和回归克里格法

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摘要

A computational framework to map species' distributions (realized density) using occurrence-only data and environmental predictors is presented and illustrated using a textbook example and two case studies: distribution of root vole (Microtes oeconomus) in the Netherlands, and distribution of white-tailed eagle nests (Haliaeetus albicilla) in Croatia. The framework combines strengths of point pattern analysis (kernel smoothing), Ecological Niche Factor Analysis (ENFA) and geostatistics (logistic regression-kriging), as implemented in the spatstat, adehabitat and gstat packages of the R environment for statistical computing. A procedure to generate pseudo-absences is proposed. It uses Habitat Suitability Index (HSI, derived through ENFA) and distance from observations as weight maps to allocate pseudo-absence points. This design ensures that the simulated pseudo-absences fall further away from the occurrence points in both feature and geographical spaces. The simulated pseudo-absences can then be combined with occurrence locations and used to build regression-kriging prediction models. The output of prediction are either probabilitiesy of species' occurrence or density measures. Addition of the pseudo-absence locations has proven effective - the adjusted R-square increased from 0.71 to 0.80 for root vole (562 records), and from 0.69 to 0.83 for white-tailed eagle (135 records) respectively; pseudo-absences improve spreading of the points in feature space and ensure consistent mapping over the whole area of interest. Results of cross validation (leave-one-out method) for these two species showed that the model explains 98% of the total variability in the density values for the root vole, and 94% of the total variability for the white-tailed eagle. The framework could be further extended to Generalized multivariate Linear Geostatistical Models and spatial prediction of multiple species. A copy of the R script and step-by-step instructions to run such analysis are available via contact author's website.
机译：通过教科书示例和两个案例研究，提出并举例说明了使用仅发生的数据和环境预测因子绘制物种分布（实际密度）的地图计算框架：荷兰的根田鼠（Microtes oeconomus）的分布以及白鲸的分布克罗地亚的燕尾鹰巢（Haliaeetus albicilla）。该框架结合了R模式环境的spatstat，adehabitat和gstat软件包中实现的点模式分析（内核平滑），生态位因子分析（ENFA）和地统计学（逻辑回归克里格法）的优势，用于统计计算。提出了一种生成伪缺席的过程。它使用栖息地适宜性指数（HSI，通过ENFA得出）和距观测值的距离作为权重图来分配伪缺点。这种设计可确保模拟的伪缺失在特征和地理空间中都远离出现点。然后可以将模拟的伪缺席与出现位置组合，并用于建立回归克里金预测模型。预测的输出要么是物种发生的概率，要么是密度度量。事实证明，增加假缺位位置是有效的-调整后的R平方根田鼠的R平方从0.71增加到0.80（562条记录），白尾鹰的R平方从0.69增加到0.83（135条记录）;伪缺失改善了点在特征空间中的分布，并确保在整个感兴趣区域上的映射一致。这两个物种的交叉验证（留一法）的结果表明，该模型解释了根田鼠密度值的98％的总变异性，以及白尾鹰的94％的总变异性。该框架可以进一步扩展到广义多元线性地统计模型和多种物种的空间预测。可通过联系作者的网站获得R脚本的副本和进行此类分析的分步说明。

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