High uncertainty in the effects of data characteristics on the performance of species distribution models

Species distribution models (SDM) are widely used as indicators of different aspects of geographical ranges for many purposes, from conservation to biogeographical and evolutionary analyses. However, these techniques are susceptible to various sources of uncertainty. Data coverage, species’ ecology,...

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Autores principales:	Geiziane Tessarolo, Jorge M. Lobo, Thiago Fernando Rangel, Joaquín Hortal
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Ecological traits Marginality ROA Scarabaeoidea dung beetles Species distribution modelling Uncertainty Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/fb4f164ffcfe4adcbaff7c9b80a28863
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spelling	oai:doaj.org-article:fb4f164ffcfe4adcbaff7c9b80a288632021-12-01T04:36:48ZHigh uncertainty in the effects of data characteristics on the performance of species distribution models1470-160X10.1016/j.ecolind.2020.107147https://doaj.org/article/fb4f164ffcfe4adcbaff7c9b80a288632021-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20310864https://doaj.org/toc/1470-160XSpecies distribution models (SDM) are widely used as indicators of different aspects of geographical ranges for many purposes, from conservation to biogeographical and evolutionary analyses. However, these techniques are susceptible to various sources of uncertainty. Data coverage, species’ ecology, and the characteristics of their geographic distributions can affect SDM results, often generating critical errors in predicted distribution maps. We assess the influence of data quality, the characteristics of species distributions, and ecological traits on SDM performance. We predict the distributions of dung beetle species in Madrid region (central Spain) using six SDM techniques and validate them on an independent dataset. We relate variations in model performance with environmental completeness, data characteristics, and species traits through a partial least squares analysis. In this analysis, body size, nesting behaviour, marginality, rarity, data prevalence, Relative Occurrence Area (ROA), range size, niche breadth, and completeness are used as predictors of six assessment metrics (sensitivity, specificity, kappa, TSS, CCR, and AUC). Marginality and data prevalence were the variables that most influenced SDM performance, followed by range size, ROA, and niche breadth: species presenting higher marginality and data prevalence, and smaller ROA and niche breadth were associated with better models. Nesting behaviour, rarity, niche completeness, and body size had minor importance for SDM performance. Our results highlight the importance of taking species’ and data characteristics into account when modelling and comparing large groups of species using SDM. This implies that estimates of species richness and composition based on stacked SDMs can show high levels of error if they are constructed for groups of species with diverse ecological traits and types of geographic distributions. We suggest that the species holding characteristics that lead to poor SDM performance should not be included when constructing composite biodiversity variables. Further effort is needed to develop SDM methodologies and protocols that account for such source of uncertainty.Geiziane TessaroloJorge M. LoboThiago Fernando RangelJoaquín HortalElsevierarticleEcological traitsMarginalityROAScarabaeoidea dung beetlesSpecies distribution modellingUncertaintyEcologyQH540-549.5ENEcological Indicators, Vol 121, Iss , Pp 107147- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ecological traits Marginality ROA Scarabaeoidea dung beetles Species distribution modelling Uncertainty Ecology QH540-549.5
spellingShingle	Ecological traits Marginality ROA Scarabaeoidea dung beetles Species distribution modelling Uncertainty Ecology QH540-549.5 Geiziane Tessarolo Jorge M. Lobo Thiago Fernando Rangel Joaquín Hortal High uncertainty in the effects of data characteristics on the performance of species distribution models
description	Species distribution models (SDM) are widely used as indicators of different aspects of geographical ranges for many purposes, from conservation to biogeographical and evolutionary analyses. However, these techniques are susceptible to various sources of uncertainty. Data coverage, species’ ecology, and the characteristics of their geographic distributions can affect SDM results, often generating critical errors in predicted distribution maps. We assess the influence of data quality, the characteristics of species distributions, and ecological traits on SDM performance. We predict the distributions of dung beetle species in Madrid region (central Spain) using six SDM techniques and validate them on an independent dataset. We relate variations in model performance with environmental completeness, data characteristics, and species traits through a partial least squares analysis. In this analysis, body size, nesting behaviour, marginality, rarity, data prevalence, Relative Occurrence Area (ROA), range size, niche breadth, and completeness are used as predictors of six assessment metrics (sensitivity, specificity, kappa, TSS, CCR, and AUC). Marginality and data prevalence were the variables that most influenced SDM performance, followed by range size, ROA, and niche breadth: species presenting higher marginality and data prevalence, and smaller ROA and niche breadth were associated with better models. Nesting behaviour, rarity, niche completeness, and body size had minor importance for SDM performance. Our results highlight the importance of taking species’ and data characteristics into account when modelling and comparing large groups of species using SDM. This implies that estimates of species richness and composition based on stacked SDMs can show high levels of error if they are constructed for groups of species with diverse ecological traits and types of geographic distributions. We suggest that the species holding characteristics that lead to poor SDM performance should not be included when constructing composite biodiversity variables. Further effort is needed to develop SDM methodologies and protocols that account for such source of uncertainty.
format	article
author	Geiziane Tessarolo Jorge M. Lobo Thiago Fernando Rangel Joaquín Hortal
author_facet	Geiziane Tessarolo Jorge M. Lobo Thiago Fernando Rangel Joaquín Hortal
author_sort	Geiziane Tessarolo
title	High uncertainty in the effects of data characteristics on the performance of species distribution models
title_short	High uncertainty in the effects of data characteristics on the performance of species distribution models
title_full	High uncertainty in the effects of data characteristics on the performance of species distribution models
title_fullStr	High uncertainty in the effects of data characteristics on the performance of species distribution models
title_full_unstemmed	High uncertainty in the effects of data characteristics on the performance of species distribution models
title_sort	high uncertainty in the effects of data characteristics on the performance of species distribution models
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/fb4f164ffcfe4adcbaff7c9b80a28863
work_keys_str_mv	AT geizianetessarolo highuncertaintyintheeffectsofdatacharacteristicsontheperformanceofspeciesdistributionmodels AT jorgemlobo highuncertaintyintheeffectsofdatacharacteristicsontheperformanceofspeciesdistributionmodels AT thiagofernandorangel highuncertaintyintheeffectsofdatacharacteristicsontheperformanceofspeciesdistributionmodels AT joaquinhortal highuncertaintyintheeffectsofdatacharacteristicsontheperformanceofspeciesdistributionmodels
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High uncertainty in the effects of data characteristics on the performance of species distribution models

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