Model‐based uncertainty in species range prediction

Abstract

International audienceAim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main Conclusions We highlight an important source of uncertainty in assessments of the impacts of climate change on biodiversity and emphasize that model predictions should be interpreted in policy-guiding applications along with a full appreciation of uncertainty

Predicting the future of forests in the Mediterranean under climate change, with niche- and process-based models: CO2 matters!

“…This adds to the mounting evidence that environmental conditions strongly influence species distribution patterns locally and regionally, as they do world-wide (Hawkins et al, 2003). Indeed, most of the selected variables were related to water and energy, which is consistent with the widely documented trend of plant species to be climatically driven by An important issue regarding niche modeling is the variability of results when using different modeling techniques Araú jo et al, 2005a, b;Pearson et al, 2006). The identification of five distinct patterns of range prediction from nine models highlights the differences between modeling approaches, while providing a foundation for further investigation as to which technique, or group of techniques, may be most appropriate for predicting future ranges but inevitably calls for an ensemble forecasting to determine species distributions (Araú jo & New, 2007).…”

“…This may make their extrapolation to future scenarios questionable for some species and drivers (e.g. terrestrial vegetation and CO 2 fertilization) (Guisan & Thuiller, 2005;Pearson et al, 2006;Rickebusch et al, 2008). One technique to reduce prediction uncertainty is to fit ensembles of forecasts by simulating across more than one set of initial conditions, model classes, model parameters, and boundary conditions (see Araú jo & New, 2007, for a review) and analyze the resulting range of uncertainties with probabilistic methodologies rather than using a single modeling outcome (Thuiller et al, 2006a, b;Araú jo & New, 2007).…”

Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal

“…all the binary maps. This conjugation allowed us to reduce the uncertainty that would have resulted from to the output of a single method (Pearson et al, 2006), which is similar what has been proposed by Araújo and New (2006) concerning uncertainty reduction for projecting species distributions in future climate scenarios. Figure 2 shows the resulting model amounting to the agreement between the three binary models.…”

“…The use of several predictive models is suggested as a method for reducing the uncertainty on species distribution modelling (Pearson et al, 2006).…”

VisTrails SAHM: visualization and workflow management for species habitat modeling

“…In much of the modeling literature, emphasis is placed on the correlative modeling routines with little attention given to the decisions and steps taken both before and after the actual modeling routine is run or proper documentation of those steps. However, there is evidence that some of these pre-and post-modeling steps can signifi cantly aff ect results (Heikkinen et al 2006, Pearson et al 2006, Diniz et al 2009, Nenz é n and Ara ú jo 2011, Rodda et al 2011, Synes andOsborne 2011). At the very least, it is essential to document all of these steps if the results are to be reproduced.…”