A climate of uncertainty: accounting for error in climate variables for species distribution models

Stoklosa, Jakub; Daly, Christopher; Foster, Scott D.; Ashcroft, Michael B.; Warton, David I.

doi:10.1111/2041-210x.12217

Cited by 75 publications

(72 citation statements)

References 51 publications

(91 reference statements)

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“…Much has been said about model uncertainty regarding the use of species distribution models (SDMs) to assess climate change effects on the geography of biodiversity [12,15,68,69]. Addressing the impacts of yet-to-come threats to biodiversity is a task inherently fraught with uncertainty.…”

Section: Discussionmentioning

confidence: 99%

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Model uncertainties do not affect observed patterns of species richness in the Amazon

et al. 2017

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BackgroundClimate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution.MethodsWe modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors.ResultsThe greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses.ConclusionsFrom a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.

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Section: Discussionmentioning

confidence: 99%

“…Uncertainties on SDMs emerge as mathematical modeling methods can provide dramatically different responses [12]. Also, the climate forecasts, or coupled Atmosphere-Ocean General Circulation Models–AOGCMs, may lead to distinct results, as well as should distinct greenhouse gas emission scenarios [1,15]. …”

Section: Introductionmentioning

confidence: 99%

Model uncertainties do not affect observed patterns of species richness in the Amazon

et al. 2017

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“…Of note is that the upper confidence interval also indicates a drastically reduced range in 2075, information that might be used to prioritize conservation efforts (Burgman et al., ; Guisan et al., ). Such applications would require predictions from multiple climate models and emissions scenarios (Keith et al., ) as well as careful thought about the impact of uncertainty in the modelled climate variable projections (Stoklosa, Daly, Foster, Ashcroft, & Warton, ). For example, a range of uncertainties in the modelled climate projections, ecological lags and the coarser spatial resolution of projected data could account for any differences in projected and observed distributions in the future.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous vegetation classification and mapping at large spatial scales

Lyons

Foster

Keith

2017

Journal of Biogeography

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Aim Multivariate mixture models offer the ability to streamline the typically multi‐stage process of vegetation classification and mapping into a single, simultaneous analytical step. Our aim is to demonstrate the model's utility over large and diverse areas, with comparison to existing classification and mapping conventions. Location New South Wales, Australia. Methods We demonstrate a statistical model that uses both multivariate species data and environmental covariate data to simultaneously classify observations into groups and predict those groups out into environmental and geographical space. We used two large data sets to demonstrate the method: an ~810,000 km2 area with 4,715 sites and 488 species and a ~220,000 km2 area with 5,183 sites and 446 species. A range of topographic, terrain, climate and soil predictors were used as environmental variables, including future projected climate variables. Models can be fit with the R package “RCPmod.” Results The method results in probabilistic memberships to vegetation assemblages, for both the classification and map. There was general agreement between our approach and existing vegetation classification conventions, but we explore some notable differences. We were able to examine the environmental gradients that define the predicted vegetation distributions, as well as make predictions about how the distribution of species assemblages might shift as a result of climate change. Main conclusions Our approach tightens the link between description of biodiversity patterns and their depiction in space, by considering both biotic and spatially explicit abiotic information simultaneously. The method allows uncertainty to be quantified objectively across a consistent set of groups for both vegetation classification and mapping, which is rarely the case in traditional multi‐stage approaches. A simultaneous modelling approach increases capacity to make predictions into varying spatial, temporal and environmental dimensions, providing new ecological insights and increasing capacity for evidence‐based decision making.

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“…PRISM data are calculated from a local climate-elevation regression function for each grid cell on a digital elevation model [34]. Stations are assigned weights based on the physiographic similarity of the station to the grid cell.…”

Section: Spatial Analysismentioning

confidence: 99%

Tracers Reveal Recharge Elevations, Groundwater Flow Paths and Travel Times on Mount Shasta, California

Peters

Visser

Esser

et al. 2018

Water

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Mount Shasta (4322 m) is famous for its spring water. Water for municipal, domestic and industrial use is obtained from local springs and wells, fed by annual snow melt and sustained perennially by the groundwater flow system. We examined geochemical and isotopic tracers in samples from wells and springs on Mount Shasta, at the headwaters of the Sacramento River, in order to better understand the hydrologic system. The topographic relief in the study area imparts robust signatures of recharge elevation to both stable isotopes of the water molecule (δ 18 O and δD) and to dissolved noble gases, offering tools to identify recharge areas and delineate groundwater flow paths. Recharge elevations determined using stable isotopes and noble gas recharge temperatures are in close agreement and indicate that most snowmelt infiltrates at elevations between 2000 m and 2900 m, which coincides with areas of thin soils and barren land cover. Large springs in Mt Shasta City discharge at an elevation more than 1600 m lower. High elevation springs (>2000 m) yield very young water (<2 years) while lower elevation wells (1000-1500 m) produce water with a residence time ranging from 6 years to over 60 years, based on observed tritium activities. Upslope movement of the tree line in the identified recharge elevation range due to a warming climate is likely to decrease infiltration and recharge, which will decrease spring discharge and production at wells, albeit with a time lag dependent upon the length of groundwater flow paths.

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A climate of uncertainty: accounting for error in climate variables for species distribution models

Cited by 75 publications

References 51 publications

Model uncertainties do not affect observed patterns of species richness in the Amazon

Model uncertainties do not affect observed patterns of species richness in the Amazon

Simultaneous vegetation classification and mapping at large spatial scales

Tracers Reveal Recharge Elevations, Groundwater Flow Paths and Travel Times on Mount Shasta, California

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