Prediction in ecology: a first-principles framework
Prediction in ecology: a first-principles framework
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DOI：
10.1002/eap.1589
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发表时间：
2017-10-01
影响因子：
5
通讯作者：
Dietze, Michael C.
中科院分区：
文献类型：
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作者：
Dietze, Michael C.
定量预测在生态学中是普遍存在的，但在这一领域的预测的性质有有限的讨论。在这里，我得出一个一般的定量框架，分析和分区的不确定性控制可预测性的来源。这个框架的影响进行了概念性的评估，并与生态学中的经典问题，如内源性（密度依赖）与外源性因素，稳定性与漂移，以及过程的空间尺度的相对重要性。该框架被用来进行一些新的预测和重构方法的实验设计，模型选择和假设检验。接下来，定量应用的框架划分的不确定性，说明使用短期预测的净生态系统交换。最后，我提倡一种新的比较方法来研究不同生态系统和过程的可预测性，并提出了一些关于限制可预测性的假设，以及这些限制如何在空间和时间上扩展。
Quantitative predictions are ubiquitous in ecology, yet there is limited discussion on the nature of prediction in this field. Herein I derive a general quantitative framework for analyzing and partitioning the sources of uncertainty that control predictability. The implications of this framework are assessed conceptually and linked to classic questions in ecology, such as the relative importance of endogenous (density-dependent) vs. exogenous factors, stability vs. drift, and the spatial scaling of processes. The framework is used to make a number of novel predictions and reframe approaches to experimental design, model selection, and hypothesis testing. Next, the quantitative application of the framework to partitioning uncertainties is illustrated using a short-term forecast of net ecosystem exchange. Finally, I advocate for a new comparative approach to studying predictability across different ecological systems and processes and lay out a number of hypotheses about what limits predictability and how these limits should scale in space and time.