The relative contribution of terrain, land cover, and vegetation structure indices to species distribution models
The relative contribution of terrain, land cover, and vegetation structure indices to species distribution models
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DOI：
10.1016/j.biocon.2013.04.021
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发表时间：
2013-08
影响因子：
5.9
通讯作者：
John W. Wilson;J. Sexton;R. Jobe;N. Haddad
中科院分区：
文献类型：
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作者：
John W. Wilson;J. Sexton;R. Jobe;N. Haddad
生物多样性保护生境评估往往因缺乏对研究物种分布的详细了解而变得复杂。作为获取此类信息的资源密集型实地方法的一种替代方法，遥感产品可用于物种分布模型，以推断物种的分布和生态需求。在这里，我们演示了如何仲裁的各种遥感预测变量，以估计主要发生在交通不便的地区的濒危蝴蝶物种的分布和生态需求。我们将19个连续的环境预测变量分为三个概念上独立的预测类，地形，土地覆盖和垂直植被结构，并比较了竞争Maxent栖息地模型组成的每个类的不同组合的准确性。每个班级都对我们考虑了所有19个变量的最可靠模型做出了贡献，尽管不成比例。我们证实，从遥感器获得的变量可以有效地估计一个相对未知的濒危物种发生在人迹罕至的地方的分布和生态需求。重要的是，通过多传感器融合增加预测类别的种类比增加预测变量的绝对数量导致更高的模型准确性。
Habitat assessments for biodiversity conservation are often complicated by the lack of detailed knowledge of a study species’ distribution. As an alternative to resource-intensive field-based methods to obtain such information, remotely sensed products can be utilized in species distribution models to infer a species’ distribution and ecological needs. Here we demonstrate how to arbitrate among a variety of remotely sensed predictor variables to estimate the distribution and ecological needs of an endangered butterfly species occurring mainly in inaccessible areas. We classified 19 continuous environmental predictor variables into three conceptually independent predictor classes, terrain, land cover, and vertical vegetation structure, and compared the accuracy of competing Maxent habitat models consisting of different combinations of each class. Each class contributed, though disproportionately, to our most reliable model that considered all 19 variables. We confirm that variables obtained from remote sensors can effectively estimate the distribution and ecological needs of a relatively unknown imperiled species occurring in inaccessible locations. Importantly, increasing the variety of predictor classes through multi-sensor fusion resulted in greater model accuracy than increasing the absolute number of predictor variables.