Spatiotemporal exploratory models for broad-scale survey data
Spatiotemporal exploratory models for broad-scale survey data
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DOI：
10.1890/09-1340.1
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发表时间：
2010-12-01
影响因子：
5
通讯作者：
Kelling, Steve
中科院分区：
文献类型：
--
作者：
Fink, Daniel;Hochachka, Wesley M.;Kelling, Steve
动物种群的分布随着时间的推移而变化和进化。迁徙物种在一年中的不同时间利用不同的栖息地。决定一个物种生活地点的生物和非生物特征因自然和人为因素而异。这种时空变化需要在任何物种分布的建模中加以考虑。在本文中，我们介绍了一个半参数模型，提供了一个灵活的框架，分析动态模式的物种发生和丰富的大规模调查数据。时空探索模型（STEM）增加了必要的时空结构，现有的技术开发物种分布模型，通过一个简单的参数结构，而不需要详细了解的基本动态过程。STEM使用多尺度策略来区分本地和全球尺度的时空结构。一个用户指定的物种分布模型占空间和时间模式在地方一级。然后，这些局部模式被允许通过整体平均到更大的尺度来"按比例放大"。这使得STEM特别适合于探索各种过程产生的分布动态。使用eBird，一个在线公民科学鸟类监测项目的数据，我们证明了每月的变化分布的迁徙物种，树燕子（Tachycineta bicolor），可以更准确地描述与STEM比传统的袋装决策树模型中，时空结构还没有强加。我们还表明，有没有损失的模型预测能力时，一个干被用来描述一个时空分布的时空变化非常小，一个非迁徙物种的分布，北方红衣主教（红雀）。
The distributions of animal populations change and evolve through time. Migratory species exploit different habitats at different times of the year. Biotic and abiotic features that determine where a species lives vary due to natural and anthropogenic factors. This spatiotemporal variation needs to be accounted for in any modeling of species' distributions. In this paper we introduce a semiparametric model that provides a flexible framework for analyzing dynamic patterns of species occurrence and abundance from broad-scale survey data. The spatiotemporal exploratory model (STEM) adds essential spatiotemporal structure to existing techniques for developing species distribution models through a simple parametric structure without requiring a detailed understanding of the underlying dynamic processes. STEMs use a multi-scale strategy to differentiate between local and global-scale spatiotemporal structure. A user-specified species distribution model accounts for spatial and temporal patterning at the local level. These local patterns are then allowed to "scale up'' via ensemble averaging to larger scales. This makes STEMs especially well suited for exploring distributional dynamics arising from a variety of processes. Using data from eBird, an online citizen science bird-monitoring project, we demonstrate that monthly changes in distribution of a migratory species, the Tree Swallow (Tachycineta bicolor), can be more accurately described with a STEM than a conventional bagged decision tree model in which spatiotemporal structure has not been imposed. We also demonstrate that there is no loss of model predictive power when a STEM is used to describe a spatiotemporal distribution with very little spatiotemporal variation; the distribution of a nonmigratory species, the Northern Cardinal (Cardinalis cardinalis).