A quantitative approach to conservation planning: using resource selection functions to map the distribution of mountain caribou at multiple spatial scales
A quantitative approach to conservation planning: using resource selection functions to map the distribution of mountain caribou at multiple spatial scales
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DOI：
10.1111/j.0021-8901.2004.00899.x
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发表时间：
2004-04-01
影响因子：
5.7
通讯作者：
Boyce, MS
中科院分区：
文献类型：
--
作者：
Johnson, CJ;Seip, DR;Boyce, MS
1.可视化稀有或受威胁物种的分布对于有效实施保护措施是必要的。广义线性模型和地理信息系统（GIS）现在是保护规划的有力工具，但数据可用性、比例尺和模型外推等问题使一些应用复杂化。山地驯鹿是一种濒临灭绝的林地驯鹿生态类型，分布在加拿大不列颠哥伦比亚省的中部和南部。目前，保护专业人员使用重要栖息地的粗糙小比例尺地图来管理驯鹿山脉北方范围内的森林采伐和人类进入。这些地图是在现有数字空间信息出现之前制作的，并以专家意见和有限的经验数据为基础。为了完善现有的地图，我们使用的调查结果，无线电遥测位置和GIS数据构建资源选择函数（RSF），量化的栖息地亲和力和预测的相对概率发生的山驯鹿在两个空间尺度。在补丁的规模，最简约的RSF模型包括植被的协变量，并恰当地预测驯鹿的发生在低至中海拔的栖息地，但在陡峭的高山地形表现不佳。在景观尺度上，包含海拔和坡度的高斯项的模型在预测更广泛的加勒比海分布方面是有效的。我们制作了一张地图，由补丁和景观RSF的相对概率的产品。最后的地图代表了驯鹿在植被斑块中发生的相对概率，并由整个较大研究区域发生的相对概率加权。我们发现，根据专家意见制定的重要驯鹿栖息地的当前定义与根据经验数据生成的基于RSF的地图之间存在很强的一致性。合成与应用。专家意见和基于RSF的方法都为保护测绘提供了独特的优势。在评价一项技术时，应考虑结果的可解释性、方法和数据的记录和可重复性、精确度和成本的估计。我们认为，对于某些物种和地理位置，RSF是一种上级技术，但专家意见应在模型开发和解释中发挥作用。
1. Visualizing the distribution of rare or threatened species is necessary for effective implementation of conservation initiatives. Generalized linear models and geographical information systems (GIS) are now powerful tools for conservation planning, but issues of data availability, scale and model extrapolation complicate some applications.2. Mountain caribou are an endangered ecotype of woodland caribou Rangifer tarandus caribou that occurs across central and southern British Columbia, Canada. Currently, conservation professionals use coarse small-scale maps of important habitats to manage forest harvesting and human access across the northern extent of mountain caribou range. These maps were produced before the advent of readily available digital spatial information and are based on expert opinion and limited empirical data.3. With the purpose of refining existing maps, we used survey results, radio-telemetry locations and GIS data to construct resource selection functions (RSF) that quantified the habitat affinities and predicted the relative probability of occurrence of mountain caribou at two spatial scales. At the scale of the patch, the most parsimonious RSF model consisted of covariates for vegetation and aptly predicted the occurrence of caribou across low- to mid-elevation habitats, but performed poorly across steep alpine terrain. At the landscape scale, a model containing Gaussian terms for elevation and slope was effective at predicting the broader distribution of caribou.4. We produced a map consisting of the product of the relative probabilities of the patch and landscape RSF. The final map represented the relative probability of occurrence of caribou in vegetative patches weighted by the relative probability of occurrence across the larger study area. We found strong agreement between current definitions of important caribou habitats developed from expert opinion and RSF-based maps generated from empirical data.5. Synthesis and applications. Both expert opinion and RSF-based approaches offer unique advantages for conservation mapping. Interpretability of results, documentation and repeatability of methods and data, estimates of precision and costs should all be considered when evaluating a technique. We argue that for some species and geographical locations, RSF is a superior technique, but expert opinion should play a role in model development and interpretation.