Neural hierarchical models of ecological populations

• Published in: Ecology letters Vol. 23; no. 4; pp. 734 - 747
• Main Authors: Joseph, Maxwell B., Boettiger, Carl
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.04.2020
• Subjects: Bird populations; Colonization; Deep learning; Ecological models; Ecology; hierarchical model; Machine learning; Mathematical models; neural network; Neural networks; Neural Networks, Computer; Occupancy; Populations; Regression analysis; Species extinction; Deep learning; hierarchical model; occupancy; neural network
• ISSN: 1461-023X; 1461-0248
• DOI: 10.1111/ele.13462

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. Neural hierarchical models combine the flexibility of deep learning with the inferential machinery of hierarchical models. For example, a deep neural network might be used to parameterize a hidden Markov model that represents ecological state transitions. This new class of models paves the way for science‐based deep learning in ecology.