A food web/landscape interaction model for microtine rodent density cycles
A food web/landscape interaction model for microtine rodent density cycles
复制标题
微型啮齿动物密度循环的食物网/景观相互作用模型
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
Jr. William Z. Lidicker
中科院分区：
文献类型：
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作者：
Jr. William Z. Lidicker
我在这里提出了一个概念模型，它结合了几个因素，单独已知是重要的microtine人口动态，但单独作为因果代理是不够的。具体来说，我汇集营养网理论的见解，特别是通才和专业捕食者的作用，景观理论，特别是栖息地斑块对人口过程的影响。由此产生的营养/ROMPA相互作用模型（TRIM）是足够复杂和现实的基板，用于建立更复杂和/或本地特定的模型。然而，它仍然足够简单，可以普遍适用。该模型表明，捕食者是一个重要的，但不是足够的因素，造成多年周期大于两年的长度。在一个斑块系统中，通才捕食者有一个稍微更严重的影响比专家，溢出捕食起着重要的作用。最佳的边缘斑块面积（ROMPA）的比例影响的趋势，多年周期发生这样的中等比例产生最强的倾向，走向循环。然而，周期可以发生与高ROMPA和专业捕食者。具有三个以上营养级和更大物种多样性的社区将不太可能显示周期。最后，应该指出的是，该模型结合了固有的因素microtines，外在的食物网的相互作用，和上下文景观过程到一个综合框架。由此产生了大量的零假设，可以将人口数据与之进行比较。
I present here a conceptual model that combines several factors known individually to be important in microtine population dynamics, but are insufficient separately as causal agents. Specifically, I bring together insights from trophic web theory, particularly the role of generalist and specialist predators, with landscape theory, particularly the influence of habitat patchiness on demographic processes. The resulting trophic/ROMPA interaction model (TRIM) is sufficiently complex and realistic to be used as a substrate for building even more complex and/or locally specific models. Yet, it remains simple enough to be general and widely applicable. The model suggests that predators are an important but not sufficient factor in causing multi-annual cycles greater than two years in length. In a patchy system, generalist predators have a slightly more severe impact than do specialists, and spillover predation plays a significant role. The ratio of optimal to marginal patch areas (ROMPA) influences the tendency for multi-annual cycles to occur such that medium ratios generate the strongest tendency toward cycling. However, cycles can occur with high ROMPA and specialist predators. Communities with more than three trophic levels and greater species diversity will be less likely to show cycles. Finally, it should be noted that the model combines factors intrinsic to microtines, extrinsic food-web interactions, and contextual landscape processes into an integrated framework. A plethora of null hypotheses are thereby generated against which population data can be compared.