| DOI: | 10.1111/ele.13462
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| 论文题名: | Neural hierarchical models of ecological populations |
| 作者: | Joseph M.B.
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| 刊名: | Ecology Letters
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| ISSN: | 1461023X
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| 出版年: | 2020
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| 卷: | 23, 期:4 | | 起始页码: | 734
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| 结束页码: | 747
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| 语种: | 英语
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| 中文关键词: | Deep learning
; hierarchical model
; neural network
; occupancy
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| 英文关键词: | artificial neural network
; bridge
; colonization
; ecological modeling
; extinction risk
; hierarchical system
; learning
; time series analysis
; Aves
; ecology
; Ecology
; Neural Networks, Computer
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| 英文摘要: | Neural networks are increasingly being used in science to infer hidden dynamics of natural systems from noisy observations, a task typically handled by hierarchical models in ecology. This article describes a class of hierarchical models parameterised by neural networks – neural hierarchical models. The derivation of such models analogises the relationship between regression and neural networks. A case study is developed for a neural dynamic occupancy model of North American bird populations, trained on millions of detection/non-detection time series for hundreds of species, providing insights into colonisation and extinction at a continental scale. Flexible models are increasingly needed that scale to large data and represent ecological processes. Neural hierarchical models satisfy this need, providing a bridge between deep learning and ecological modelling that combines the function representation power of neural networks with the inferential capacity of hierarchical models. © 2020 John Wiley & Sons Ltd/CNRS |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/166646
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| Appears in Collections: | 气候变化与战略
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作者单位: | Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, United States
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| Recommended Citation: | |
Joseph M.B.. Neural hierarchical models of ecological populations[J]. Ecology Letters,2020-01-01,23(4)
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