Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change
Brian W. Miller 1,2 , , ,
Tony Chang 4 ,
Nathan Piekielek 5 ,
Andrew J. Hansen 4 ,
Jeffrey T. Morisette 6
Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, 80523-1499, USA;
Department of the Interior North Central Climate Science Center, Fort Collins, CO, 80523-1499, USA;
Apex Resource Management Solutions Ltd., Bowen Island, BC V0N 1G1, Canada;
Montana State University, Bozeman, MT, 59717, USA;
The Pennsylvania State University, University Park, PA, 16802, USA;
U.S. Geological Survey, Department of the Interior North Central Climate Science Center, Fort Collins, CO, 80523-1499, USA
Received: 30 January 2015 Accepted: 13 April 2015 Published: 12 May 2015
State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine (Pinus albicaulis). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle, Dendroctonus ponderosae), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.
climate change,
Greater Yellowstone Ecosystem,
mountain pine beetle,
spatially explicit simulation,
ST-Sim,
VisTrails:SAHM,
Citation: Brian W. Miller, Leonardo Frid, Tony Chang, Nathan Piekielek, Andrew J. Hansen, Jeffrey T. Morisette. Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change[J]. AIMS Environmental Science, 2015, 2(2): 400-426. doi: 10.3934/environsci.2015.2.400
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Brian W. Miller
Tony Chang
Nathan Piekielek
Andrew J. Hansen
Jeffrey T. Morisette
Brian W. Miller, Leonardo Frid, Tony Chang, Nathan Piekielek, Andrew J. Hansen, Jeffrey T. Morisette. Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change[J]. AIMS Environmental Science, 2015, 2(2): 400-426. doi: 10.3934/environsci.2015.2.400
Figure 1. Study area, highlighting the approximate extent of the Greater Yellowstone Ecosystem
Figure 2. Modeled suitable bioclimatic envelope for adult WBP (938 presence, 1631 absence; accuracy rate of about 93%) (left panel), and for seedlings (497 presence, 2072 absence; accuracy rate of about 84%) (right panel)
Figure 3. Binary classification of modeled presence for WBP adults (blue) and seedlings (red) display similar environmental gradient distributions
Figure 4. Simplified pathway diagram of the integration of species distribution model output into the state-and-transition simulation model, including dispersal (blue arrow), depletion of the seedbank or mortality of juvenile trees (black arrow), successful establishment (whose probability is characterized by the SDM output, red arrow), and aging (green arrow). Other transitions (e.g., MPB infection, blister rust infection, fire) were omitted from the figure due to space considerations and because each forest stratum had a unique pathway diagram
Figure 5. Results of model calibration using four different maxima for the probability of mountain pine beetle (MPB) infection (bars) as compared to observations (points). Columns represent the means of 40 Monte Carlo simulations and error bars the standard deviations
Figure 6. Comparison of mountain pine beetle mortality for four different model specifications (three different probabilities of the shift from dead forest to open shrub-herb, and a no climate change scenario)
Figure 7. Mean areas of three forest types in 2100 under five different climate scenarios. Error bars represent the 95% percentile range for 40 Monte Carlo simulations
Figure 8. Projected area of WBP forest under five different climate scenarios. Lines represent the means of 40 Monte Carlo simulations and shaded regions represent the 95% percentile range
Figure 9. Projected area of WBP mortality from mountain pine beetle under five different climate scenarios. Lines represent the means of 40 Monte Carlo simulations and shaded regions represent the 95% percentile range