Prediction of potential areas of species distributions based on presence-only data
Prediction of potential areas of species distributions based on presence-only data
复制标题
DOI：
10.1007/s10651-005-6816-2
复制
发表时间：
2005-03-01
影响因子：
3.8
通讯作者：
Soberón, J
中科院分区：
文献类型：
--
作者：
Argáez, JA;Christen, JA;Soberón, J
我们介绍了一种方法来推断一个物种的栖息地的高潜力区，有用的生物多样性，保护，地理，生态，或可持续利用的管理。推断是基于一组已报告该物种存在的地点。每个位点与协变量值相关，在离散尺度上测量。我们计算的预测概率，该物种是目前在一个规则的网格的每个节点。可能的空间偏见的网站的存在占。由于所得后验分布不具有封闭形式，因此实施马尔可夫链蒙特卡罗（MCMC）算法。然而，我们还描述了一个近似的后验分布，这避免了MCMC。这种方法的有关特点是，考虑到了诸如取样强度和可探测性等数据采集的具体概念，并明确纳入了关于物种分布区的现有先验信息。这些概念，出现在仅在场的上下文中，没有在替代方法中解决。我们还考虑了一个不确定性地图，它测量网格上每个节点的预测概率的变化。进行了模拟研究，以测试和比较我们的方法与其他标准方法。还介绍了两个案例研究。(c)2005 Springer Science + Business Media，Inc.
We introduce a methodology to infer zones of high potential for the habitat of a species, useful for management of biodiversity, conservation, biogeography, ecology, or sustainable use. Inference is based on a set of sites where the presence of the species has been reported. Each site is associated with covariate values, measured on discrete scales. We compute the predictive probability that the species is present at each node of a regular grid. Possible spatial bias for sites of presence is accounted for. Since the resulting posterior distribution does not have a closed form, a Markov chain Monte Carlo (MCMC) algorithm is implemented. However, we also describe an approximation to the posterior distribution, which avoids MCMC. Relevant features of the approach are that specific notions of data acquisition such as sampling intensity and detectability are accounted for, and that available a priori information regarding areas of distribution of the species is incorporated in a clear-cut way. These concepts, arising in the presence-only context, are not addressed in alternative methods. We also consider an uncertainty map, which measures the variability for the predictive probability at each node on the grid. A simulation study is carried out to test and compare our approach with other standard methods. Two case studies are also presented. (c) 2005 Springer Science + Business Media, Inc.