Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics
Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics
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DOI：
10.1111/geb.12426
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发表时间：
2016-04-01
影响因子：
6.4
通讯作者：
Couvreur, T. L. P.
中科院分区：
文献类型：
--
作者：
Deblauwe, V.;Droissart, V.;Couvreur, T. L. P.
目标物种分布模型通常完全或部分依赖于气候变量作为预测因子，忽略了这些本身就是具有相关不确定性的预测。这是特别重要的，当这样的预测值之间插入稀疏的站数据，如在热带地区。这项研究的目标是提供一套新的基于卫星的气候预测数据，并评估其潜力，以改善模拟的物种气候协会和可转移到新的地理区域。印度和南美洲的西高止山脉。方法我们比较了广泛使用的WorldClim站校准的模型-用来自CRU、MODIS、TRMM和CHIRPS的数据替代WorldClim的温度或降水数据的模型内插气候数据。每个预测器集用于对451种植物物种分布进行建模。为了测试的机会协会，我们设计了一个空模型，以比较每个species. ResultsWhere不到一半的研究雨林物种分布匹配的气候模式比随机分布的准确度度量。包括来自遥感的MODIS温度和CHIRPS降水估计，每个允许一个更好的比随机适合分别为40%和22%以上的物种比模型在WorldClim上校准。此外，他们的列入是正相关的一个更好的可转移性模型到新regions.Main conclusionsWe提供了一个新组装的数据集的生态有意义的变量来自MODIS和CHIRPS下载，并提供了一个基础，供选择的过多的可用的气候数据集。我们强调，在对气象站数据稀少的区域开展工作时，需要考虑制作气候数据所使用的方法。在这方面，遥感数据应是首选，特别是在涉及到新气候的模型可移植性或因果关系推断时。
AimSpecies distribution modelling typically relies completely or partially on climatic variables as predictors, overlooking the fact that these are themselves predictions with associated uncertainties. This is particularly critical when such predictors are interpolated between sparse station data, such as in the tropics. The goal of this study is to provide a new set of satellite-based climatic predictor data and to evaluate its potential to improve modelled species-climate associations and transferability to novel geographical regions.LocationRain forests areas of Central Africa, the Western Ghats of India and South America.MethodsWe compared models calibrated on the widely used WorldClim station-interpolated climatic data with models where either temperature or precipitation data from WorldClim were replaced by data from CRU, MODIS, TRMM and CHIRPS. Each predictor set was used to model 451 plant species distributions. To test for chance associations, we devised a null model with which to compare the accuracy metric obtained for every species.ResultsFewer than half of the studied rain forest species distributions matched the climatic pattern better than did random distributions. The inclusion of MODIS temperature and CHIRPS precipitation estimates derived from remote sensing each allowed for a better than random fit for respectively 40% and 22% more species than models calibrated on WorldClim. Furthermore, their inclusion was positively related to a better transferability of models to novel regions.Main conclusionsWe provide a newly assembled dataset of ecologically meaningful variables derived from MODIS and CHIRPS for download, and provide a basis for choosing among the plethora of available climate datasets. We emphasize the need to consider the method used in the production of climate data when working on a region with sparse meteorological station data. In this context, remote sensing data should be the preferred choice, particularly when model transferability to novel climates or inferences on causality are invoked.