Uncertainty in ensembles of global biodiversity scenarios

Thuiller, Wilfried; Guéguen, Maya; Renaud, Julien; Karger, Dirk Nikolaus; Zimmermann, Niklaus E.

doi:10.1038/s41467-019-09519-w

Cited by 314 publications

(338 citation statements)

References 29 publications

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“…There are a number of modelling approaches available for SDM studies including classification, regression, and machine learning methods. To reduce the model-based uncertainty and produce reliable predictions, the ensemble modelling technique has been proposed and frequently used, which combines prediction results of multiple modelling algorithms thus can reduce the uncertainties in model predictions (Araújo & New, 2007;Guisan et al, 2017;Thuiller et al, 2014Thuiller et al, , 2019. Previous studies have revealed that different single modelling algorithms have different predictive ability and can produce largely variable results (Elith & Graham, 2009;Pearson et al 2006;Qiao et al, 2015).…”

Section: Potential Distributions Under Current and Future Climate Smentioning

confidence: 99%

“…It has been revealed that high uncertainties in future projections of species distributions would arise from differences among GCMs (Goberville, Beaugrand, Hautekèete, Piquot, & Luczak, 2015;Tang et al, 2018;Thuiller et al, 2019). Therefore, it is recommended that used as future climates which is believed to reduce uncertainties associated with different GCMs (Yan et al, 2017).…”

Section: Potential Distributions Under Current and Future Climate Smentioning

confidence: 99%

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Impacts of climate change on the global potential distribution of two notorious invasive crayfishes

et al. 2019

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1. Invasive alien species and climate change are two of the most serious global environmental threats. In particular, it is of great interest to understand how changing climates could impact the distribution of invaders that pose serious threats to ecosystems and human activities.2. In this study, we developed ensemble species distribution models for predicting the current and future global distribution of the signal crayfish Pacifastacus leniusculus and the red swamp crayfish Procambarus clarkii, two of the most highly problematic invaders of freshwater ecosystems worldwide. We collected occurrence records of the species, from native and alien established ranges worldwide.These records in combination with averaged observations of current climatic conditions were used to calibrate a set of 10 distinct correlative models for estimating the climatic niche of each species. We next projected the estimated niches into the geographical space for the current climate conditions and for the 2050s and 2070s under representative concentration pathway 2.6 and 8.5 scenarios.3. Our species distribution models had high predictive abilities and suggest that annual mean temperature is the main driver of the distribution of both species.Model predictions indicated that the two crayfish species have not fully occupied their suitable climates and will respond differently to future climate scenarios in different geographic regions. Suitable climate for P. leniusculus was predicted to shift poleward and to increase in extent in North America and Europe but decrease in Asia. Regions with suitable climate for P. clarkii are predicted to widen in Europe but contract in North America and Asia.4. This study highlights that invasive species with different thermal preference are likely to respond differently to future climate changes. Our results provide important information for policy makers to design and implement anticipated measures for the prevention and control of these two problematic species. K E Y W O R D Sclimate change, habitat suitability, Pacifastacus leniusculus, Procambarus clarkii, species distribution modelling

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Section: Potential Distributions Under Current and Future Climate Smentioning

confidence: 99%

Section: Potential Distributions Under Current and Future Climate Smentioning

confidence: 99%

Impacts of climate change on the global potential distribution of two notorious invasive crayfishes

et al. 2019

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“…see Barbosa, Estrada, Márquez, Purvis, & Orme, 2012;Fourcade, 2016;Herkt et al, 2017;Meyer et al, 2015), coming to mixed conclusions; however, EOO range maps are still widely used in macroecological research (Belmaker & Jetz, 2015;Hof et al, 2018;Slavenko & Meiri, 2015;Thuiller et al, 2019;Torres-Romero & Olalla-Tárraga, 2015;Zurell et al, 2018). see Barbosa, Estrada, Márquez, Purvis, & Orme, 2012;Fourcade, 2016;Herkt et al, 2017;Meyer et al, 2015), coming to mixed conclusions; however, EOO range maps are still widely used in macroecological research (Belmaker & Jetz, 2015;Hof et al, 2018;Slavenko & Meiri, 2015;Thuiller et al, 2019;Torres-Romero & Olalla-Tárraga, 2015;Zurell et al, 2018).…”

Section: Future Pat Tern S Of S Pecie S Richne Ssmentioning

confidence: 99%

A comparison of macroecological and stacked species distribution models to predict future global terrestrial vertebrate richness

Biber

Voskamp

Niamir

et al. 2019

Journal of Biogeography

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Aim Predicting future changes in species richness in response to climate change is one of the key challenges in biogeography and conservation ecology. Stacked species distribution models (S‐SDMs) are a commonly used tool to predict current and future species richness. Macroecological models (MEMs), regression models with species richness as response variable, are a less computationally intensive alternative to S‐SDMs. Here, we aim to compare the results of two model types (S‐SDMS and MEMs), for the first time for more than 14,000 species across multiple taxa globally, and to trace the uncertainty in future predictions back to the input data and modelling approach used. Location Global land, excluding Antarctica. Taxon Amphibians, birds and mammals. Methods We fitted S‐SDMs and MEMs using a consistent set of bioclimatic variables and model algorithms and conducted species richness predictions under current and future conditions. For the latter, we used four general circulation models (GCMs) under two representative concentration pathways (RCP2.6 and RCP6.0). Predicted species richness was compared between S‐SDMs and MEMs and for current conditions also to extent‐of‐occurrence (EOO) species richness patterns. For future predictions, we quantified the variance in predicted species richness patterns explained by the choice of model type, model algorithm and GCM using hierarchical cluster analysis and variance partitioning. Results Under current conditions, species richness predictions from MEMs and S‐SDMs were strongly correlated with EOO‐based species richness. However, both model types over‐predicted areas with low and under‐predicted areas with high species richness. Outputs from MEMs and S‐SDMs were also highly correlated among each other under current and future conditions. The variance between future predictions was mostly explained by model type. Main conclusions Both model types were able to reproduce EOO‐based patterns in global terrestrial vertebrate richness, but produce less collinear predictions of future species richness. Model type by far contributes to most of the variation in the different future species richness predictions, indicating that the two model types should not be used interchangeably. Nevertheless, both model types have their justification, as MEMs can also include species with a restricted range, whereas S‐SDMs are useful for looking at potential species‐specific responses.

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“…Altitudinal midpoint correlated F I G U R E 3 Relationship between raw mean difference (D, %) and species midpoint of altitude (a), IUCN categories of extinction risk (b) and clutch size (c) for atmospheric-ocean global circulation models projections of UKMO. Our estimates of variation in range shifts are likely to be conservative because a recent study by Thuiller et al (2019) showed that ENMs differ greatly in predicting future distributions even when only ENMs with high predictive accuracies are considered in the analyses. Fitted lines represent partial effects of each moderator variable on D positively with differences in range of occurrence.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Diniz-Filho et al (2009) showed that the estimated shifts in species' geographic ranges varied broadly, mainly due to the use of different Ecological Niche Models (ENMs hereafter). Furthermore, the characteristics of the modeled organisms (e.g., life history and dispersal ability) can also influence model outcomes (Buisson, Thuiller, Casajus, Lek, & Grenouillet, 2010;Lawler et al, 2010;Thuiller, Guéguen, Renaud, Karger, & Zimmermann, 2019). In other words, different model projections are obtained when different combinations of ENMs, GES, and AOGCMs are used.…”

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confidence: 99%

Meta‐analyzing the likely cross‐species responses to climate change

Ortega

Machado

Diniz‐Filho

et al. 2019

Ecology and Evolution

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Ecological Niche Models (ENMs) have different performances in predicting potential geographic distributions. Here we meta‐analyzed the likely effects of climate change on the potential geographic distribution of 1,205 bird species from the Neotropical region, modeled using eight ENMs and three Atmosphere‐Ocean General Circulation Models (AOGCM). We considered the variability in ENMs performance to estimate a weighted mean difference between potential geographic distributions for baseline and future climates. On average, potential future ranges were projected to be from 25.7% to 44.5% smaller than current potential ranges across species. However, we found that 0.2% to 18.3% of the total variance in range shifts occurred “within species” (i.e., owing to the use of different modeling techniques and climate models) and 81.7% to 99.8% remained between species (i.e., it could be explained by ecological correlates). Using meta‐analytical techniques akin to regression, we also showed that potential range shifts are barely predicted by bird biological traits. We demonstrated that one can combine and reduce species‐specific effects with high uncertainty in ENMs and also explore potential causes of climate change effect on species using meta‐analytical tools. We also highlight that the search for powerful correlates of climate change‐induced range shifts can be a promising line of investigation.

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Uncertainty in ensembles of global biodiversity scenarios

Cited by 314 publications

References 29 publications

Impacts of climate change on the global potential distribution of two notorious invasive crayfishes

Impacts of climate change on the global potential distribution of two notorious invasive crayfishes

A comparison of macroecological and stacked species distribution models to predict future global terrestrial vertebrate richness

Meta‐analyzing the likely cross‐species responses to climate change

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