Department of Ecology and Evolutionary Biology, Long Marine Laboratory, University of California, Santa Cruz, California 95064, USA.
Ecology. 2011 Apr;92(4):836-46. doi: 10.1890/10-1354.1.
How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (-25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities.
如何最好地预测生态群落干扰的影响一直是应用和基础生态学的长期目标。最近，新的经验数据、新的分析方法和更快的计算速度使这一探索得以复兴，人们希望从对群落相互作用的有限了解中获得生态上重要的见解。我们使用基于经验的和模拟的网络，其大小和连接度不同，以评估预测多物种群落中扰动响应的两个限制：（1）物种相互作用强度的经验量化的不准确性；（2）由于网络大小和结构的间接效应而导致的物种响应的不确定性。我们发现，即使是适度的物种丰富度和连接度（-25 个成对相互作用）也对相互作用强度的估计提出了很高的要求，因为系统的不确定性迅速超过了预测的洞察力。然而，即使是估计不佳的相互作用强度也比仅使用相互作用的符号的方法提供了更大的平均预测确定性。我们的模拟为在最大化网络方法在预测多物种群落动态方面的效用方面的权衡提供了指导。
