Curtis J M R, Naujokaitis-Lewis I
Centre for Applied Conservation Research, University of British Columbia, Forest Sciences Building, 2424 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada.
Ecol Appl. 2008 Jun;18(4):1002-13. doi: 10.1890/07-1306.1.
Metapopulation dynamics are influenced by spatial parameters including the amount and arrangement of suitable habitat, yet these parameters may be uncertain when deciding how to manage species or their habitats. Sensitivity analyses of population viability analysis (PVA) models can help measure relative parameter influences on predictions, identify research priorities for reducing uncertainty, and evaluate management strategies. Few spatial PVAs, however, include sensitivity analyses of both spatial and nonspatial parameters, perhaps because computationally efficient tools for such analyses are lacking or inaccessible. We developed GRIP, a program to facilitate sensitivity analysis of spatial and nonspatial input parameters for PVAs created in RAMAS Metapop, a widely applied software program. GRIP creates random sets of input files by varying parameters specified in the PVA model including vital rates and their correlations among populations, the number and configuration of populations, dispersal rates, dispersal survival, initial population abundances, carrying capacities, and the probability, intensity, and spatial extent of catastrophes, while drawing on specified parameter distributions. We evaluated GRIP's performance as a tool for sensitivity analysis of spatial PVAs and explored the consequences of varying spatial input parameters for predictions of a published PVA model of the sand lizard (Lacerta agilis). We used GRIP output to generate standardized regression coefficients (SRCs) and nonparametric correlation coefficients as indices of the relative sensitivity of predicted conservation status to input parameters. GRIP performed well; with a single analysis we were able to rank the relative influence of input parameters identified as influential by the PVA's original author, S. A. Berglind, who used three separate forms of sensitivity analysis. Our analysis, however, also underscored the value of exploring the relative influence of spatial parameters on PVA predictions; both SRCs and correlation coefficients indicated that the most influential parameters in the sand lizard model were spatial in nature. We provide annotated code so that GRIP may be modified to reflect particular species biology, customized for more complex spatial PVA models, upgraded to incorporate features added in newer versions of RAMAS Metapop, used as a template to develop similar programs, or used as it is for computationally efficient sensitivity analyses in support of conservation planning.
集合种群动态受到空间参数的影响，这些参数包括适宜栖息地的数量和布局，但在决定如何管理物种或其栖息地时，这些参数可能并不确定。种群生存力分析（PVA）模型的敏感性分析有助于衡量参数对预测的相对影响，确定减少不确定性的研究重点，并评估管理策略。然而，很少有空间PVA包括对空间和非空间参数的敏感性分析，这可能是因为缺乏或无法获得用于此类分析的计算效率高的工具。我们开发了GRIP程序，以促进对在广泛应用的软件程序RAMAS Metapop中创建的PVA的空间和非空间输入参数进行敏感性分析。GRIP通过改变PVA模型中指定的参数来创建随机的输入文件集，这些参数包括生命率及其在种群间的相关性、种群的数量和配置、扩散率、扩散存活率、初始种群丰度、承载能力以及灾难的概率、强度和空间范围，同时利用指定的参数分布。我们评估了GRIP作为空间PVA敏感性分析工具的性能，并探讨了改变空间输入参数对已发表的沙蜥（Lacerta agilis）PVA模型预测结果的影响。我们使用GRIP输出生成标准化回归系数（SRC）和非参数相关系数，作为预测保护状态对输入参数相对敏感性的指标。GRIP表现良好；通过一次分析，我们就能对被PVA的原作者S. A. Berglind确定为有影响的输入参数的相对影响进行排名，他使用了三种不同形式的敏感性分析。然而，我们的分析也强调了探索空间参数对PVA预测相对影响的价值；SRC和相关系数都表明，沙蜥模型中最有影响的参数本质上是空间参数。我们提供了带注释的代码，以便可以修改GRIP以反映特定物种的生物学特性，为更复杂的空间PVA模型进行定制，升级以纳入RAMAS Metapop新版本中添加的功能，用作开发类似程序的模板，或者直接用于支持保护规划的计算效率高的敏感性分析。
