Modeling Approach Applied for Plant Species
This project models present and future vegetation at the plant species level within the western half of the continental United States. Also, studies of historic and paleoecological vegetation change have typically concentrated at this species level, providing our project with background data on how past climate changes have affected our species. Some projects, above this sub-continental scale, are required to model vegetation at more complex levels of biome, functional-type, or community. Alternately, finer-resolution projects model ecotypic or genetic variation within single species or variety.
All plant species are limited in some way by climate extremes, hence the total absence of plants in mainland Antarctica. Yet the direct correlation between specific climate variables and plant species distributions are more direct and quantifiable for some species than others. For example, columnar cacti can be shown to be directly killed by freezing temperatures lasting more than a certain number of hours. But other species, such as riparian species, are affected more by complex changes in the amount or duration of stream flow or underground hydrology than specific local climate extremes. Because our science of modeling the effects of future climate is a new field of unproven reliability, we have attempted to model those south-western plant species which seem to be the most directly influenced by the climate variables of monthly temperature and precipitation. We leave it to future efforts to model more complex interactions involving variables outside the scope of our project such as alluvial flow, carbon dioxide fertilization, and changes in soil microorganisms.
Our project considers the relationship between climate and plant distribution across the entire range of each species within the United States. This gives the project a somewhat flexible study area, which expands or contracts depending upon the individual species being modeled, within the extent of the PRISM data. We believe that this approach is required because species are affected by different climate variables in different parts of their range. For example, many species are limited by extreme cold temperatures at their most northerly latitudes, but limited by drought in the south. If one were to focus upon a high correlation between one, or just a couple, climate variables that are important in only a part of the species range, the resulting model would be vulnerable to the shifting of climate variables relative to each other that occurred in the past, and will certainly occur again in the future. Although certain climate variables may be highly correlated with each other today, it is unlikely that these specific correlations will continue in the future. In order to protect ourselves from this error, we use the entire range of the species as a test of its tolerances to many combinations of climate variables. This approach (ClimLim) maximizes our ability to detect specific favorable combinations of climate variables, which may be uncommon today, yet destined to become common in the future. Next, we use the most important variables identified during our ClimLim analysis to develop a bioclimatic model using a multivariate response surface specifically designed for each species.
Our approach to the relationship between the matrix of climate variables and a species’ distribution is based upon ecological niche theory. Niche theory predicts that each species has an area where it grows (the realized niche) which is surrounded by areas where it could grow, yet it is absent (the potential niche). Species are prevented from occupying all of the area where is climatically suitable by other factors such as the rate at which they disperse into new areas, incompatible soil types, past history of disturbance events, and biotic interactions such a competition for light with other species.. As a result, once we have selected those most critical limiting climate variables from the matrix of monthly temperature extremes over a specified period of time and monthly precipitation means, our model projects a region of potential climate niche encircling the areas of the realized niche.
Finally, we attempt not only to model the future potential climatic range of each species, but to also to incorporate estimates of how far each species could naturally migrate into these new potential areas between now and our final target period of 60 to 90 years in the future (2070 to 2099). We know from observations of plant movements and recovery from disturbances that plant species have been migrating naturally over the Twentieth Century. But other than widely-dispersed early successional species, or exotic species dispersed partly through things like cattle relocation, natural dispersal distances for native species have been less than 100 km over the last century, and typically much less than 10 km. We incorporate observed rates of historic and ongoing migration and succession, along with autecologic data on species dispersal mechanisms, with paleoecological data on responses to past climate changes to generate a migration rate estimate for each species. Alternatively, because species migration has been observed to often be periodic, perhaps concentrated in one or two favorable decades per century, we could select a plausible alternative of no discernable migration over the next 60 to 90 years. Either of these alternatives seems preferable to the assumption of an infinite rate of migration, expanding instantly into all areas of future suitable climate.
The GCM data we are using (IPCC (Intergovernmental Panel on Climate Change) AR4 (Fourth Assessment Report) GCM (general circulation model) simulation results) was obtained the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset (Meehl et al., 2007)
Citations
Garfin, G.M., J.K. Eischeid, M. Lenart, K.L. Cole, K. Ironside, and N.S. Cobb. 2010. Downscaling climate projections to model ecological change on topographically diverse landscapes of the arid Southwestern United States. In: The Colorado Plateau IV: Shaping Conservation Through Science and Management (van Riper, C., III, B. F. Wakeling, and T. D. Sisk, Eds). University of Arizona Press, Tucson, AZ. 368 pp..
Meehl, G.A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.F.B., Stouffer, R.J. & Taylor, K.E. (2007) The WCRP CMIP3 multi-model dataset: A new era in climate change research. Bulletin of the American Meteorological Society, 88, 1383-1394.
More info on the models can be found at:
http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php
