Predicting Ecosystem Pattern across Landscapes

Basquill, Sean; Leroux, Shawn J.

doi:10.22541/au.166854256.67412903/v1

Cited by 1 publication

(1 citation statement)

References 61 publications

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“…EDMs indirectly give larger weight to sparsely sampled species and can be generated faster and interpreted more readily than, for instance, assemblages of individual species distribution models, potentially allowing for increased efficiency in decision making (Ferrier and Guisan 2006, Lindenmayer et al 2007). EDMs can also help inform decision makers by answering where ecosystems are, and why they are there (Basquill and Leroux 2023). Ferrier and Guisan (2006) recognise three community‐level modelling strategies that can be extended to EDM.…”

Section: Introductionmentioning

confidence: 99%

Choice of predictors and complexity for ecosystem distribution models: effects on performance and transferability

Naas,

Keetz,

Halvorsen

et al. 2024

Ecography

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There is an increasing need for ecosystem‐level distribution models (EDMs) and a better understanding of which factors affect their quality. We investigated how the performance and transferability of EDMs are influenced by 1) the choice of predictors and 2) model complexity. We modelled the distribution of 15 pre‐classified ecosystem types in Norway using 252 predictors gridded to 100 × 100 m resolution. The ecosystem types are major types in the ‘Nature in Norway' system mainly defined by rule‐based criteria such as whether soil or specific functional groups (e.g. trees) are present. The predictors were categorised into four groups, of which three represented proxies for natural, anthropogenic, or terrain processes (‘ecological predictors') and one represented spectral and structural characteristics of the surface observable from above (‘surface predictors'). Models were generated for five levels of model complexity. Model performance and transferability were evaluated with data collected independently of the training data. We found that 1) models trained with surface predictors only performed considerably better and were more transferable than models trained with ecological predictors, and 2) model performance increased with model complexity, levelling off from approximately 10 parameters and reaching a peak at approximately 20 parameters, while model transferability decreased with model complexity. Our findings suggest that surface predictors enhance EDM performance and transferability, most likely because they represent discernible surface characteristics of the ecosystem types. A poor match between the rule‐based criteria that define the ecosystem types and the ecological predictors, which represent ecological processes, is a plausible explanation for why surface predictors better predict the distribution of ecosystem types. Our results indicate that, in most cases, the same models are not well suited for contrasting purposes, such as predicting where ecosystems are and explaining why they are there.

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