Modeling Subalpine and Upper Montane Forest-Climate Interactions in Colorado: A Comparative Study Using GIS

Modeling Subalpine and Upper Montane Forest-Climate Interactions in Colorado: A Comparative Study Using GIS

Steven Jennings (Department of Geography and Environmental Studies, University of Colorado, Colorado Springs, CO, USA) and Eric Billmeyer (Department of Geography and Environmental Studies, University of Colorado, Colorado Springs, CO, USA)
Copyright: © 2014 |Pages: 14
DOI: 10.4018/ijagr.2014100102
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The correlation of the distribution of five subalpine and montane tree species with precipitation and temperature were modeled using GIS. The results were compared with data presented by Thompson et al. (2000). Distributions of subalpine fir (Abies concolor), Engelmann spruce (Picea engelmannii), lodgepole pine (Pinus contorta), limber pine (Pinus flexilis) and bristlecone pine (Pinus aristata) were compared to estimated precipitation and temperature fields that had been constructed from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), National Climatic Data Center (NCDC) station data, and Snowpack Telemetry (SNOTEL) system data. Plant distribution maps from Little (1971) and CoGAP (2001) were used to determine the temperature and precipitation associated with the selected tree species. The estimates from this study were compared to those of Thompson, Anderson & Bartlein (2000). In many cases precipitation and temperatures values were higher than those of Thompson, Anderson & Bartlein (2000). Suggestions are made to improve the predictive power of GIS analysis for mapping climate and plant variability.
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1. Introduction

Inputs of energy and moisture to the biological system dictate the distribution of organisms across the face of the earth. While other factors are important in determining the distribution of organisms (Aide & Van Auken, 1985, Bailey, 1996; Seastedt, Bowman, Caine, McKnight, Townsend & Williams, 2004; Whittaker, 1960), on regional and local scales temperature and precipitation are of paramount importance in determining the distribution of plants (Major, 1951; Woodward, 1987). The relationship between climate and plant distributions has been of long standing interest to ecologists and biogeographers (Bailey, 1996). A limitation to accurately quantifying this relationship is the low spatial density of climate stations globally. Measuring the continuously distributed components of the global climate system at discrete points only portrays the general characteristics of the climate for any given region. This adds uncertainty in understanding causal links between inputs of energy and moisture and the global distribution of organisms. Since collected climate data are limited temporally and spatially, researchers have modeled the distribution of temperature and precipitation to fill in places where climate data are lacking (Arundel, 2005; Daly, Halbleib, Smith, Gibson, Doggett, Taylor, Curtis & Pateris, 2008; Kearney & Porter, 2004; Thompson, Anderson & Bartlein, 2000). Generally these models are based on large-scale distributions of temperature and precipitation related to variability of atmospheric conditions as modified by large continental and oceanic features (Daley, 2006). These models have been used to describe past climates and predict future climates and generally have limited resolution. The application of models to understand the distribution of climate and the relationship between other natural systems like plant communities shows promise in greatly reducing the limitations of spatially and temporally disparate climatic data sets. However, the use of a climate envelope to describe the relationship between climate and plant distributions is at best a correlation of variables (Box, Crumpacker & Hardin, 1993). Conceptual constructs like plant zones build on these correlations and are a convenient way to organize the distribution of co-occurring species that are controlled by similar environmental constraints. In reality, each species has an individualistic response to environmental variability (Knapp & Smith, 1981). The dimensions of the constraint envelope (Gosz, 1992; O’Neill, Johnson & King, 1989; Noble & Alexander, 1977; Segurado & Araújo, 2004; Veblen, 1986; Villalba, Veblen & Ogden, 1994; Wardle, 1968) that defines the environmental factors that limit the distribution of a particular species are generally not completely known. It is also important to recognize that the distribution of any species is probably not representative of that species’ fundamental niche (Kearney & Porter, 2004). With these limitations accounted for, new models are being developed to understand the relationship between the environment and plant distributions. Examples include modeling the spread of invasive plants (Barni, Bacaro, Falzoi, Spanna, & Siniscalo, 2012; Gasso, Thuiller, Pino & Montserrat, 2012), plant community response to climate change (Beaumont, Pitman, Poulsen & Hughes, 2007; Iverson & Prasad, 1998; Rehfeldt, Crookston, Warwell & Evans, 2006; Shafer, Bartlein & Thompson, 2001; Trivedi, Berry & Morecroft, 2008), plant interactions (Meineri Skarpaas & Vandvik, 2012) and the distribution of ecosystem services (Rolf, Lenz & Peters, 2012).

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