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Forecasting spatially structured populations: the role of dispersal and scale

机译：预测空间结构化人口：分散和规模的作用

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We forecasted spatially structured population models with complex dynamics. focusing on the effect of dispersal and spatial scale on the predictive capability of nonlinear forecasting (NLF). Dispersal influences NLF ability by its influence on population dynamics. For simple 2-cell models, when dispersal is small, our ability to predict abundance in subpopulations decreased and then increased with increasing dispersal. Spatial heterogeneity, dispersal manner. and environmental noise did not qualitatively change this result. But results are not clear for complex spatial configurations because of complicated dispersal interactions across subpopulations. Populations undergoing periodic fluctuations could be forecasted perfectly for all deterministic cases that we studied, but less reliably when environmental noise was incorporated. More importantly, for all models that we have examined, NLF was much worse at larger spatial scales as a consequence of the asynchronous dynamics of subpopulations when the dispersal rate was below some critical value. The only difference among models was the critical value of dispersal rate. which varied with growth rate, carrying capacity, mode of dispersal, and spatial configuration. These results were robust even when environmental noise was incorporated. Intermittency, common in the dynamics of spatially structured populations, lowered the predictive capability of NLF. Forecasting population behaviour is of obvious value in resource exploitation and conservation. We suggest: that forecasting at local scales holds promise, whereas forecasting abundance at regional scales may yield poor results. Improved understanding of dispersal can enhance the management and conservation of natural resources. and may help is to understand resource-exploitation strategies employed by local indigenous humans. (C) 2004 Elsevier Ltd. All rights reserved.
机译：我们预测了具有复杂动力学的空间结构人口模型。着重研究分散和空间尺度对非线性预测（NLF）预测能力的影响。分散性通过影响人口动态来影响NLF能力。对于简单的2单元模型，当分散较小时，我们预测亚种群丰度的能力会随着分散的增加而降低，然后增加。空间异质性，分散性。而且环境噪声并未从本质上改变这一结果。但是对于复杂的空间配置，结果还不清楚，这是因为子种群之间存在复杂的分散相互作用。在我们研究的所有确定性案例中，都可以完美预测经历周期性波动的人口，但是当引入环境噪声时，预测的可靠性较低。更重要的是，对于我们研究的所有模型，当分散率低于某个临界值时，由于亚群的异步动力学，在更大的空间尺度上，NLF更为严重。模型之间的唯一区别是分散速率的临界值。它们随增长率，承载能力，分散方式和空间配置而变化。即使纳入环境噪声，这些结果也很可靠。在空间结构人口动态中常见的间歇性降低了NLF的预测能力。预测人口行为在资源开发和保护中具有明显的价值。我们建议：在地方范围内进行预测具有希望，而在区域范围内进行预测可能会产生较差的结果。更好地了解扩散可以加强自然资源的管理和保护。并可能有助于了解当地土著人民所采用的资源开发策略。 （C）2004 Elsevier Ltd.保留所有权利。

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