Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size
Abstract Repeat measurement surveys of tree size are used in forests to estimate growth behaviour, biomass and population dynamics. Although size is measured with error and individuals vary in their growth trajectories, current size‐based growth modelling approaches do not usually or fully account f...
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Wiley
2025-01-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.14463 |
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| author | Tess O'Brien David Warton Daniel Falster |
| author_facet | Tess O'Brien David Warton Daniel Falster |
| author_sort | Tess O'Brien |
| collection | DOAJ |
| description | Abstract Repeat measurement surveys of tree size are used in forests to estimate growth behaviour, biomass and population dynamics. Although size is measured with error and individuals vary in their growth trajectories, current size‐based growth modelling approaches do not usually or fully account for both of these features, and therefore under‐utilise available data. We present a new method that leverages the auto‐correlation structure of repeat surveys into a hierarchical Bayesian longitudinal growth model. This new structure allows users to correct for measurement error and capture individual‐level variation in growth trajectories and parameters. To demonstrate the new method we applied it to a sample of tropical tree survey data from long‐term monitoring sites at Barro Colorado Island. We were able to reduce estimated error in size and growth, and extract individual‐ and population‐level growth parameter estimates. We used simulation to evaluate the ability of the new method to improve estimates of growth rate and size, and estimate individual and species‐level parameters. Our method substantially improved the root mean squared error (RMSE) for growth by an average of 61% compared to existing approaches using pairwise differences and reduced RMSE in estimated size RMSE compared to ‘observed’ values in simulated data. Better numerical integration methods (Runge–Kutta fourth order in comparison to Euler and midpoint) provided better estimates of parameters, but did not improve the estimation of size and growth. The choice of a positive growth function eliminated all negative increments without data exclusion. Overall, this study shows how we can gain new and improved insights on growth, using repeat forest surveys. Our new method offers improved biomass dynamics estimation through reduced error in sizes over time, coupled with novel information about within‐species variation in growth behaviour that is inaccessible with species average models, such as individual parameters for the growth function which allows for relationships between parameters to be considered for the first time. |
| format | Article |
| id | doaj-art-efcc3c22cf3b451f997c6a599735fe47 |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-efcc3c22cf3b451f997c6a599735fe472025-01-08T05:44:10ZengWileyMethods in Ecology and Evolution2041-210X2025-01-0116118319610.1111/2041-210X.14463Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and sizeTess O'Brien0David Warton1Daniel Falster2Evolution and Ecology Research Centre University of New South Wales Sydney Kensington New South Wales AustraliaEvolution and Ecology Research Centre University of New South Wales Sydney Kensington New South Wales AustraliaEvolution and Ecology Research Centre University of New South Wales Sydney Kensington New South Wales AustraliaAbstract Repeat measurement surveys of tree size are used in forests to estimate growth behaviour, biomass and population dynamics. Although size is measured with error and individuals vary in their growth trajectories, current size‐based growth modelling approaches do not usually or fully account for both of these features, and therefore under‐utilise available data. We present a new method that leverages the auto‐correlation structure of repeat surveys into a hierarchical Bayesian longitudinal growth model. This new structure allows users to correct for measurement error and capture individual‐level variation in growth trajectories and parameters. To demonstrate the new method we applied it to a sample of tropical tree survey data from long‐term monitoring sites at Barro Colorado Island. We were able to reduce estimated error in size and growth, and extract individual‐ and population‐level growth parameter estimates. We used simulation to evaluate the ability of the new method to improve estimates of growth rate and size, and estimate individual and species‐level parameters. Our method substantially improved the root mean squared error (RMSE) for growth by an average of 61% compared to existing approaches using pairwise differences and reduced RMSE in estimated size RMSE compared to ‘observed’ values in simulated data. Better numerical integration methods (Runge–Kutta fourth order in comparison to Euler and midpoint) provided better estimates of parameters, but did not improve the estimation of size and growth. The choice of a positive growth function eliminated all negative increments without data exclusion. Overall, this study shows how we can gain new and improved insights on growth, using repeat forest surveys. Our new method offers improved biomass dynamics estimation through reduced error in sizes over time, coupled with novel information about within‐species variation in growth behaviour that is inaccessible with species average models, such as individual parameters for the growth function which allows for relationships between parameters to be considered for the first time.https://doi.org/10.1111/2041-210X.14463Bayesian modelsforest ecologygrowth modellingrepeat measurement surveys |
| spellingShingle | Tess O'Brien David Warton Daniel Falster Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size Methods in Ecology and Evolution Bayesian models forest ecology growth modelling repeat measurement surveys |
| title | Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size |
| title_full | Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size |
| title_fullStr | Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size |
| title_full_unstemmed | Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size |
| title_short | Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size |
| title_sort | yes they re all individuals hierarchical models for repeat survey data improve estimates of tree growth and size |
| topic | Bayesian models forest ecology growth modelling repeat measurement surveys |
| url | https://doi.org/10.1111/2041-210X.14463 |
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