Can managers inform models? Integrating local knowledge into models of red deer habitat use
Can managers inform models? Integrating local knowledge into models of red deer habitat use
复制标题
管理者可以告知模型吗？
DOI：
10.1111/j.1365-2664.2009.01626.x
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发表时间：
2009
影响因子：
5.7
通讯作者：
Irvine R
中科院分区：
文献类型：
--
作者：
Irvine R
1许多基于生态的野生动物栖息地模型对观察到的数据只能提供有限的解释，因为它们没有考虑到驱动分布的关键因素与当地管理相互作用的方式。如果模型要成为开发野生动物管理解决方案的可靠工具，它们需要将科学知识与管理这些资源的人持有的丰富知识相结合。2在这项研究中，我们开发了一种参与式方法，将鹿管理者的当地知识与正式的科学理解和生态空间数据整合在一个简单的地理信息系统(GIS)中，以预测马鹿。分布于苏格兰高地。我们评估了通过这一过程改进预测的程度。3最初的地理信息系统预测在大约50%的情况下与管理者对鹿位置的经验和独立获得的鹿点计数数据相匹配。4对管理者的访谈分析表明，对于马鹿来说，栖息地特征提供的庇护所比地形庇护所或栖息地的饲料价值更重要。干扰、坡度和海拔也很重要。对管理者定义的鹿群偏好区域的潜在空间特征的分析表明，这些因素在驱赶鹿的分布中具有类似的相对重要性。5模型经过修改，纳入了管理者的知识，并根据现有的鹿分布数据对新的预测进行了评估。点数与模型预测的鹿栖息地高度适宜性面积的匹配率从50%左右增加到80%左右。6综合和应用。我们的评估证明了利用当地知识的有效性，它可以显著改善从简单的鹿栖息地适宜性空间模型预测的结果。我们的方法使来自不同来源和不同空间尺度的知识能够结合在一起，从而在适当的尺度上对鹿的分布做出现实的预测。这种参与性的野生动物栖息地模型开发方法有可能改善存在不同管理目标的所有权边界之间的沟通和共识。
1Many ecologically based wildlife‐habitat models provide only limited explanations of the observed data because they do not take account of the way in which key factors driving distribution interact with local management. If models are to be credible tools for developing solutions for wildlife management, they need to integrate scientific knowledge with the wealth of knowledge held by those who manage these resources.2In this study, we develop a participatory approach to integrate local knowledge from deer managers with formal scientific understanding and ecological spatial data in a simple Geographic Information System (GIS) to predict red deerCervus elaphusL. distribution in the uplands of Scotland. We evaluate the extent to which the predictions are improved by this process.3The initial GIS prediction matched both managers’ experience of deer locations and the independently derived deer point count data in around 50% of all cases.4An analysis of interviews with managers indicated that for red deer, shelter provided by habitat characteristics was more important than topographic shelter or the forage value of the habitat. Disturbance, slope and elevation were also important. Analysis of the underlying spatial characteristics of those areas preferred by deer, as defined by managers, indicated similar relative importance of these factors in driving deer distribution.5The model was modified to incorporate the managers’ knowledge and new predictions were evaluated against existing deer distribution data. The match between point counts and areas predicted by the model as being highly suitable for deer increased from around 50% to around 80%.6Synthesis and applications.Our evaluations demonstrate the validity of using local knowledge which can substantially improve the predictions from simple spatial models of deer habitat suitability. Our approach enables knowledge from different sources and at different spatial scales to be combined to give realistic predictions of deer distribution at an appropriate scale. Such participatory approaches to wildlife‐habitat model development have the potential to improve communication and consensus across ownership boundaries where different management objectives exist.
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