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Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models

Harshinie Karunarathna Orcid Logo, Konstans Wells Orcid Logo, Nicholas J. Clark Orcid Logo

Ecological Modelling, Volume: 490

Swansea University Authors: Harshinie Karunarathna Orcid Logo, Konstans Wells Orcid Logo

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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...

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Published in: Ecological Modelling
ISSN: 0304-3800
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65678
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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. 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spelling v2 65678 2024-02-24 Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models 0d3d327a240d49b53c78e02b7c00e625 0000-0002-9087-3811 Harshinie Karunarathna Harshinie Karunarathna true false d18166c31e89833c55ef0f2cbb551243 0000-0003-0377-2463 Konstans Wells Konstans Wells true false 2024-02-24 CIVL 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. Journal Article Ecological Modelling 490 Elsevier BV 0304-3800 Ecological time series forecasting; species abundance; generalized additive models; Bayesian approach;distributed lagged predictors 1 4 2024 2024-04-01 10.1016/j.ecolmodel.2024.110648 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University Another institution paid the OA fee 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. 2024-03-26T11:27:36.8659099 2024-02-24T08:50:00.3226528 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Harshinie Karunarathna 0000-0002-9087-3811 1 Konstans Wells 0000-0003-0377-2463 2 Nicholas J. Clark 0000-0001-7131-3301 3 65678__29850__eef794fcb6274290a06ff75c19d2ce51.pdf 65678.VOR.pdf 2024-03-26T11:25:05.9479957 Output 3869612 application/pdf Version of Record true © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models
spellingShingle Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models
Harshinie Karunarathna
Konstans Wells
title_short Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models
title_full Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models
title_fullStr Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models
title_full_unstemmed Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models
title_sort Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models
author_id_str_mv 0d3d327a240d49b53c78e02b7c00e625
d18166c31e89833c55ef0f2cbb551243
author_id_fullname_str_mv 0d3d327a240d49b53c78e02b7c00e625_***_Harshinie Karunarathna
d18166c31e89833c55ef0f2cbb551243_***_Konstans Wells
author Harshinie Karunarathna
Konstans Wells
author2 Harshinie Karunarathna
Konstans Wells
Nicholas J. Clark
format Journal article
container_title Ecological Modelling
container_volume 490
publishDate 2024
institution Swansea University
issn 0304-3800
doi_str_mv 10.1016/j.ecolmodel.2024.110648
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
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description 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.
published_date 2024-04-01T11:27:33Z
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