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Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection

Moudrý Vítězslav, Keil Petr, Gábor Lukáš, Lecours Vincent, Zarzo-Arias Alejandra, Barták Vojtěch, Malavasi Marco, Rocchini Duccio, Torresani Michele, Gdulová Kateřina, Grattarola Florencia, Leroy François, Marchetto Elisa, Thouverai Elisa, Prošek Jiří, Wild Jan et Šímová Petra. (2023). Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection. Progress in Physical Geography: Earth and Environment, 47, (3), p. 467-482.

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URL officielle: https://doi.org/10.1177/03091333231156362

Résumé

There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km 2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response variables.

Type de document:Article publié dans une revue avec comité d'évaluation
ISSN:0309-1333
Volume:47
Numéro:3
Pages:p. 467-482
Version évaluée par les pairs:Oui
Date:21 Juin 2023
Nombre de pages:16
Identifiant unique:10.1177/03091333231156362
Sujets:Sciences naturelles et génie > Sciences mathématiques > Informatique
Sciences naturelles et génie > Sciences naturelles > Biologie et autres sciences connexes
Sciences naturelles et génie > Sciences naturelles > Sciences de la terre (géologie, géographie)
Département, module, service et unité de recherche:Unités de recherche > Centre de recherche sur la Boréalie (CREB)
Départements et modules > Département des sciences humaines
Mots-clés:Environmental niche modelling, grain, land cover, predictor, resolution, scale, species distribution models, SDM, variable
Déposé le:14 mai 2024 20:34
Dernière modification:31 juill. 2024 15:44
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