Improving species distribution models for invasive non-native species with biologically informed pseudo-absence selection
Improving species distribution models for invasive non-native species with biologically informed pseudo-absence selection
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DOI：
10.1111/jbi.13555
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发表时间：
2019-05-01
影响因子：
3.9
通讯作者：
Tanner, Rob
中科院分区：
文献类型：
--
作者：
Chapman, Daniel;Pescot, Oliver L.;Tanner, Rob
目的：提出一种新的物种分布模型(SDMS)策略，旨在预测范围扩大的外来入侵物种(INN)的潜在分布。该战略结合了两个确定背景区域的既定观点，以便对迄今仅单独应用的“假缺席”进行抽样。这些区域是考虑到扩散限制的可接近区域，以及物种环境范围之外的区域，因此被认为不适合该物种。我们测试了一种通过使用两种背景的背景样本(伪缺失)来拟合SDMS来结合这些方法的方法。位置：全球分类：入侵的非本地植物：Humulus scensens、Lygodium Japan onicum、胡枝子、Triadica sebifera、Cinnamomum Camphor.方法：针对亚洲原产、其他地方入侵的5种植物的潜在全球分布，开发了存在-背景(或仅存在)SDMS，并优先作为欧洲新兴Inn进行风险评估。我们比较了从可获得的背景、不适合的背景(使用物种关键限制因素的生物学知识定义)或从两种类型的背景中选择伪缺席的模型。结果：结合不适合和可访问的背景扩大了可用于模型拟合的环境范围，并导致关于生态不适宜性的生物学知识影响拟合的物种-环境关系。这提高了全球分布预测的真实性和准确性，通常是在物种的范围内。主要结论：相关的SDMS对于Inns的风险测绘和管理仍然很有价值，但经常因为缺乏生物学基础而受到批评。我们的方法部分解决了这一问题，使用物种要求或耐受性的先验知识来定义不适合建模的背景，同时也通过考虑可获得性来适应扩散限制。它可以用当前的SDM软件来实施，并产生更准确和真实的分布预测。因此，更广泛的采用有可能改善支持客栈风险评估的SDMS。
Aim: We present a novel strategy for species distribution models (SDMs) aimed at predicting the potential distributions of range-expanding invasive non-native species (INNS). The strategy combines two established perspectives on defining the background region for sampling "pseudo-absences" that have hitherto only been applied separately. These are the accessible area, which accounts for dispersal constraints, and the area outside the environmental range of the species and therefore assumed to be unsuitable for the species. We tested an approach to combine these by fitting SDMs using background samples (pseudo-absences) from both types of background.Location: Global.Taxon: Invasive non-native plants: Humulus scandens, Lygodium japonicum, Lespedeza cuneata, Triadica sebifera, Cinnamomum camphora.Methods: Presence-background (or presence-only) SDMs were developed for the potential global distributions of five plant species native to Asia, invasive elsewhere and prioritised for risk assessment as emerging INNS in Europe. We compared models where the pseudo-absences were selected from the accessible background, the unsuitable background (defined using biological knowledge of the species' key limiting factors) or from both types of background.Results: Combining the unsuitable and accessible backgrounds expanded the range of environments available for model fitting and caused biological knowledge about ecological unsuitability to influence the fitted species-environment relationships. This improved the realism and accuracy of distribution projections globally and, generally, within the species' ranges.Main conclusions: Correlative SDMs remain valuable for INNS risk mapping and management, but are often criticised for a lack of biological underpinning. Our approach partly addresses this concern by using prior knowledge of species' requirements or tolerances to define the unsuitable background for modelling, while also accommodating dispersal constraints through considerations of accessibility. It can be implemented with current SDM software and results in more accurate and realistic distribution projections. As such, wider adoption has potential to improve SDMs that support INNS risk assessment.