Abstract
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.
Highlights
Uncertainty is an important consideration for all climate change assessments
We initially focused on the logistic outputs from the maximum entropy (MaxEnt) model, which are typically interpreted as the probability of species presence [55, 59]
For all but three of the 21 bamboo species, the MaxEnt model ranked the bioclimatic variables differently when calibrated using the two climate datasets, and interpretation of the climatic determinants of the distribution of the bamboo species varied by dataset
Summary
Uncertainty is an important consideration for all climate change assessments. Ignoring or minimizing the importance of uncertainty can negatively affect the usefulness of assessment outcomes for decision making and planning [1]. Uncertainty is a particular concern for climate change assessments of future species distributions, as sensitivity analyses have identified multiple sources of uncertainty that can substantially impact assessment findings. These include the availability and quality of species information (e.g., [2,3]), methodologies used to develop species distribution models (e.g., [4,5,6,7,8]), selection of predictor variables that capture environmental influences on species distributions (e.g., [9,10,11]), thresholds used to convert likelihood of occurrence to binary predictions of species presence (e.g., [12,13]), parameterizations and tuning of model settings (e.g., [14]), and choice of future climate simulations (e.g., [15,16,17]). A challenge is that the spatial resolution of climate observing networks, ranging from tens of kilometers in developed regions to hundreds of kilometers in remote areas or at high elevations [20,21], may be insufficient to capture the local and regional climatic gradients that influence the distribution of a particular species [22]
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