A practical guide for combining data to model species distributions
A practical guide for combining data to model species distributions
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DOI：
10.1002/ecy.2710
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发表时间：
2019-06-01
期刊：
影响因子：
4.8
通讯作者：
Dorazio, Robert M.
中科院分区：
文献类型：
--
作者：
Fletcher, Robert J., Jr.;Hefley, Trevor J.;Dorazio, Robert M.
理解和准确地模拟物种分布是生态学、进化和保护中许多问题的核心。越来越多的数据来源可用于模拟物种分布，例如来自公民科学计划，地图集，博物馆和计划调查的数据。然而，可靠地组合数据源可能具有挑战性，因为数据源在设计、覆盖的梯度和潜在的采样偏差方面可能差异很大。我们回顾，综合，并说明在结合多种来源的数据物种分布建模的最新进展。我们确定了五种方法，其中多个来源的数据通常结合建模物种分布。这些方法在适应采样设计、偏差和不确定性的能力上各不相同，在物种分布模型中量化环境关系。通过谨慎使用综合物种分布模型，可以解决组合数据的许多挑战：该模型同时联合收割机组合有关物种位置的不同数据源，以量化环境关系来解释物种分布。我们说明这些方法使用计划的调查数据，再加上机会主义收集的eBird数据在美国东南部的24种鸟类。这个例子说明了数据集成的一些好处，例如提高环境关系的精度，更高的预测准确性，以及考虑样本偏差。然而，它也说明了将具有截然不同的采样方法和数据量的数据源相结合的挑战。我们提供了一个解决方案，通过使用加权联合似然这一挑战。加权联合似然提供了一种基于不同标准强调数据源的手段（例如，样本量），我们发现加权改善了所有考虑的物种的预测。最后，我们提供了实用的指导，结合多种来源的数据建模物种分布。
Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.