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Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models
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Konstans Wells ,
Nicholas J. Clark
Ecological Modelling, Volume: 490
Swansea University Authors:
Harshinie Karunarathna , Konstans Wells
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© 2024 The Authors. This is an open access article under the CC BY license.
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DOI (Published version): 10.1016/j.ecolmodel.2024.110648
Abstract
Modelling abundance fluctuations of species is a crucial first step for understanding and forecasting system dynamics under future conditions. But, especially in multivariate response data, this can be hampered by characteristics of the study system such as unknown complexity, differently formed spa...
| Published in: | Ecological Modelling |
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| ISSN: | 0304-3800 |
| Published: |
Elsevier BV
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65678 |
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| Abstract: |
Modelling abundance fluctuations of species is a crucial first step for understanding and forecasting system dynamics under future conditions. But, especially in multivariate response data, this can be hampered by characteristics of the study system such as unknown complexity, differently formed spatial and temporal dependency, non-linear relationships, and observation characteristics such as zero-inflation. This study aimed to explore how such challenges can be addressed by using hierarchical Dynamic Generalized Additive Models (DGAM) for multivariate count responses in a Bayesian framework while modelling multi-site monthly captures for the Desert Pocket Mouse (Chaetodipus penicillatus) over 23 years from a long-term study in Arizona, USA. By fitting models of increasing complexity and developing bespoke checking functions that captured targeted ecological aspects such as spatio-temporal dependence, we show how nonlinear dynamic models can be built to improve forecasts for multivariate count-valued time series.We found strong evidence that accounting for non-linear and time-lagged effects of as much as 12 months improved model fit and forecasting performance. Evaluation of models for other species in geographically different habits is essential for generalizing model strategies and insights into long-term abundance-environment relationships, while systematic comparisons will only be possible if multivariate modelling workflows account for the complexity of non-linear and lagged effects and potentially also other aspects such as biotic interactions. |
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| Keywords: |
Ecological time series forecasting; species abundance; generalized additive models; Bayesian approach;distributed lagged predictors |
| College: |
Faculty of Science and Engineering |
| Funders: |
We thank the many volunteers for their help during fieldwork to generate primary Portal data. This study was supported by an ARC DECRA Fellowship to N. J. Clark (DE210101439). The Portal Project has been funded nearly continuously since 1977 by the National Science Foundation, most recently by DEB-1929730 to S. K. M. Ernest and E.P. White. Development of portal software packages is supported by this NSF grant, NSF grant DEB-1622425 to S. K. M. Ernest, and the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through Grant GBMF4563 to E. P. White. |