Modelling the effects of management on population dynamics: some lessons from annual weeds
Modelling the effects of management on population dynamics: some lessons from annual weeds
复制标题
模拟管理对种群动态的影响：一年生杂草的一些教训
DOI：
10.1111/j.1365-2664.2008.01469.x
复制
发表时间：
2008
影响因子：
5.7
通讯作者：
Freckleton R
中科院分区：
文献类型：
--
作者：
Freckleton R
1农业杂草和入侵杂草是管理和自然生态系统的主要威胁，每年造成数十亿美元的损失。可耕地和入侵杂草种群动态模型可以通过生成种群密度预测以及这些预测可能如何应对不断变化的管理来促进问题物种的管理。然而，通常很少有人注意量化模型参数估计的误差以及这些误差如何影响模型预测的稳健性。大多数杂草模型使用现场估计和文献推导的参数组合进行参数化。只有很少的估计误差可用于所有参数，尽管事实上，大多数参数将受到相当大的误差。3接近灭绝的边界，人口密度的预测可能是高度敏感的模型参数中的小误差。然而，由于它们通常具有高繁殖能力，许多杂草可能以高密度和经济上显著的密度出现，同时接近灭绝。因此，模型可能是数值上不稳定的生态现实的密度。4我们审查的方法处理参数的不确定性杂草建模。我们强调，重要的是要认识到，许多模型可能在结构上是不正确的，所有的稳定机制可能没有被确定。此外，替代模型的形式很少被探索，虽然各种替代传统的差分方程存在。5合成和应用。只有当不同的管理干预措施有很大的对比效果的人口规模是杂草种群的大多数模型可能提供定性正确的预测其影响。人口模型的定量预测通常会有很大的误差。考虑到空间异质性和其他稳定效应的建模方法可能会产生更准确的预测;然而，其参数化往往需要与目前使用的方法截然不同的数据收集方法。
1Agricultural and invasive weeds are major threats to managed and natural ecosystems, costing billions of dollars annually. Models for arable and invasive weed population dynamics can contribute to the management of problem species through generating predictions of population densities, and how those are likely to respond to changing management. Frequently, however, little attention is paid to quantifying the errors in estimates of model parameters and how these may affect the robustness of model predictions.2Most weed models are parameterized using a combination of field‐estimated and literature‐derived parameters. Only rarely are estimates of error available for all parameters, despite the fact that most parameters will be subject to considerable error.3Close to extinction boundaries, predictions of population densities may be highly sensitive to small errors in model parameters. However, because of their generally high reproductive capacities, many weeds may occur at high and economically significant densities while close to extinction. Consequently, models may be numerically unstable at ecologically realistic densities.4We review methods of dealing with parameter uncertainty in weed modelling. We stress that it is important to recognize that many models may be structurally incorrect and all stabilizing mechanisms may not have been identified. Also, alternative model forms have seldom been explored, although a variety of alternatives to conventional difference equations exist.5Synthesis and applications.Only when different management interventions have greatly contrasting effects on population sizes are most models of weed populations likely to provide qualitatively correct predictions of their effects. Quantitative predictions from demographic models will usually be subject to large errors. Modelling methods that account for spatial heterogeneity and other stabilizing effects may yield more accurate predictions; however, their parameterization will often require approaches for data collection very different from those currently used.