Dataset Information

A tool for simulating and communicating uncertainty when modelling species distributions under future climates.

ABSTRACT: Tools for exploring and communicating the impact of uncertainty on spatial prediction are urgently needed, particularly when projecting species distributions to future conditions.We provide a tool for simulating uncertainty, focusing on uncertainty due to data quality. We illustrate the use of the tool using a Tasmanian endemic species as a case study. Our simulations provide probabilistic, spatially explicit illustrations of the impact of uncertainty on model projections. We also illustrate differences in model projections using six different global climate models and two contrasting emissions scenarios.Our case study results illustrate how different sources of uncertainty have different impacts on model output and how the geographic distribution of uncertainty can vary.Synthesis and applications: We provide a conceptual framework for understanding sources of uncertainty based on a review of potential sources of uncertainty in species distribution modelling; a tool for simulating uncertainty in species distribution models; and protocols for dealing with uncertainty due to climate models and emissions scenarios. Our tool provides a step forward in understanding and communicating the impacts of uncertainty on species distribution models under future climates which will be particularly helpful for informing discussions between researchers, policy makers, and conservation practitioners.

SUBMITTER: Gould SF

PROVIDER: S-EPMC4278828 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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A tool for simulating and communicating uncertainty when modelling species distributions under future climates.

Gould Susan F SF Beeton Nicholas J NJ Harris Rebecca M B RM Hutchinson Michael F MF Lechner Alex M AM Porfirio Luciana L LL Mackey Brendan G BG

Ecology and evolution 20141203 24

Tools for exploring and communicating the impact of uncertainty on spatial prediction are urgently needed, particularly when projecting species distributions to future conditions.We provide a tool for simulating uncertainty, focusing on uncertainty due to data quality. We illustrate the use of the tool using a Tasmanian endemic species as a case study. Our simulations provide probabilistic, spatially explicit illustrations of the impact of uncertainty on model projections. We also illustrate dif ...[more]

PMID: 25558370

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Project description:Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

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Dataset Information

A tool for simulating and communicating uncertainty when modelling species distributions under future climates.

Publications

A tool for simulating and communicating uncertainty when modelling species distributions under future climates.

Similar Datasets

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