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一种基于季节性和密度依赖性的入侵杂草管理模型。

A seasonal, density-dependent model for the management of an invasive weed.

机构信息

Biology Department MS-34, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA.

Department of Biology, Washington University in St. Louis, St. Louis, Missouri 63130, USA.

出版信息

Ecol Appl. 2013 Dec;23(8):1893-905. doi: 10.1890/12-1712.1.

Abstract

The population effects of harvest depend on complex interactions between density dependence, seasonality, stage structure, and management timing. Here we present a periodic nonlinear matrix population model that incorporates seasonal density dependence with stage-selective and seasonally selective harvest. To this model, we apply newly developed perturbation analyses to determine how population densities respond to changes in harvest and demographic parameters. We use the model to examine the effects of popular control strategies and demographic perturbations on the invasive weed garlic mustard (Alliaria petiolata). We find that seasonality is a major factor in harvest outcomes, because population dynamics may depend significantly on both the season of management and the season of observation. Strategies that reduce densities in one season can drive increases in another, with strategies giving positive sensitivities of density in the target seasons leading to compensatory effects that invasive species managers should avoid. Conversely, demographic parameters to which density is very elastic (e.g., seeding survival, second-year rosette spring survival, and the flowering to fruiting adult transition for maximum summer densities) may indicate promising management targets.

摘要

收获对种群的影响取决于密度制约、季节性、阶段结构和管理时机之间的复杂相互作用。在这里,我们提出了一个周期性的非线性矩阵种群模型,该模型将季节性密度制约与阶段选择性和季节性选择性收获结合在一起。我们将新开发的摄动分析应用于该模型,以确定种群密度如何响应收获和人口参数的变化。我们使用该模型来研究流行的控制策略和人口统计摄动对入侵杂草大蒜芥(Alliaria petiolata)的影响。我们发现季节性是收获结果的一个主要因素,因为种群动态可能在很大程度上取决于管理季节和观察季节。在一个季节减少密度的策略可能会导致另一个季节的增加,而在目标季节对密度有积极敏感性的策略会导致补偿效应,入侵物种管理者应该避免这种效应。相反,密度非常有弹性的人口统计参数(例如,播种存活率、第二年春季存活的莲座叶、夏季最高密度的开花到结实成体过渡)可能表明有希望的管理目标。

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