Tabor ReedyENVS420

10/8/14Suitability Change in the Pinyon Pine (Pinus edulis)

Introduction:

The primary focus of this project is to analyze the change in suitability of the Pinyon Pine through a projection of future climate change. Climate change will affect the areas that are suitable for every tree species because climate and weather will be changed. It is important to understand what the world and its fauna might look like given changes in climate. This understanding allows us to make economic decisions in forestry like logging and assisted forest migration. The Pinyon Pine is a culturally and economically important tree located in the South Western United States (Fig. 1). Along with being the state tree for New Mexico, the Pinyon Pine also yields pine nuts, an economically valuable crop.

Figure 1. Location of known Pinyon Pine (Pinus edulis) growth in the Southwestern United States.

Data:

The known distribution of the Pinyon Pine was originally compiled by United States Department of Agriculture and downloaded in vector format from the United States Geological Survey (USGS). Historical and projected climate data were originally prepared for the Climate Adaptation Conservation Planning Database for Western North America (CACPD) project, and were downloaded from the University of Alberta. I used the A2 climate projection which accounts for an increase in carbon dioxide emissions through 2080. Climate data downloaded is in raster format with four thousand meter grid cells. Vectors and rasters have been reprojected into Lambert Conformal Conic with a central meridian of -109, standard parallels of 31 and 41, a latitude of origin of 37, and D North American 1983 datum. Rasters use Bilinear Interpolation to resample. This projection makes all linear units meters. Dividing temperature rasters by 10 with raster calculator yields rasters in degrees Celsius.

Methods:

The first step in the analysis is to use historical data to determine the current climate envelope of the Pinyon Pine. I used twelve climate parameters that seemed to have the greatest effect on the species to determine the envelope. Mean annual temperature (MAT)(oC), potential evapotranspiration (PET), summer (june to August) climate moisture index (CMIJJA), climate moisture index (CMI), climate moisture index (CMD), degree Days below 0oC and above 5oC (DD0 and DD5)(Degree Days), difference between warmest and coldest month temperatures (TD)(oC), winter and summer mean temperatures (Tave_wt and Tave_sm)(oC), and mean summer precipitation (MSP)(oC). Zonal statistics as a table is used to calculate all the statistics of values in the known tree species area. Because the center of a species climate value is most representative of the whole group, the envelope is determined by the mean plus and minus one standard deviation (Table 1). I used raster calculator with the envelope values to reclassify the rasters into binary rasters where the value of 1 is the area inside the envelope, and 0 is outside the envelope. These binary rasters are then combined in raster calculator to create a final distribution map to show every cell that has a value of one in all the rasters (Fig. 2).

A similar process is used with the future climate data. Raster calculator is used on the same climate variables, but those that are the projected climate data. The same envelope is used because it represents the conditions preferred by the tree species. The new binary rasters are again combined in raster calculator to find the future areas that meet all the climatic requirements (Fig. 3). Change in species area is calculated with Raster Calculator by subtracting the historical suitability raster from the future suitability raster. This output gives you a -1 where area is lost, 0 where there is no change and 1 where area is gained (Fig. 4).

Results:

Zonal Statistics as a Table gives basic statistics of the area underlain by the tree species area. In this study, the valuable information is mean and standard deviation which are used to calculate the climatic area preferred by the tree species. Output units are the same as the parent rasters

value. With the expressions I used the raster calculator generates unitless binary rasters, which are combined into another binary total suitability raster. While these rasters are only zero or one, one represents areas that fall within 1.5 standard deviations of the mean original unit and zero falls outside 1.5 standard deviations from the mean.

The historic envelope generally does a reasonable job describing the distribution of the Pinyon Pine. There is significant area, especially North West of the current distribution that is climatically suitable in this model but does not contain Pinyon Pines. These inaccuracies in the historic distribution do affect the change in suitability. Generally, the suitable areas for the Pinyon Pine will move north to northwest, and will experience a loss in total suitable area of approximately 92,000 km2. To improve future analysis, more climate variables, as well as other distribution factors such as soils, geology, dispersal rates, and other competitive species would need to be taken into account. Producing rasters of these values with a similar method as in this study could produce a more accurate future projection.

Table 1. Climatic envelop of the Pinyon Pine (Pinus edulis), generated from Zonal Statistics as a table. The low end of the envelope is generated from subtracting 1.5 standard deviations from the mean value in the area of Pinyon Pine growth. The high value is generated by adding 1.5 standard deviations to the mean.

Envelope (mean +/- 1.5 std dev)

Low High

PET 800.308491 1398.794007

CMIJJA -54.236468 -26.80418

CMI -110.7030715 -35.0274685

CMD 470.712766 1080.674068

DD5 (oC) 1601.06328 3262.87278

DD0 (oC) -28.9148165 444.7381345

TD (oC) 18.9863571 26.7860967

MAT(oC) 6.26965035 13.33846605

Tave_wt (oC) -4.2740015 3.8080915

MAP (mm) 212.3634735 529.3953825

MSP (mm) 91.2658435 280.2273385

Tave_sm (oC) 16.5158205 23.9376015

Figure 2. Land that is currently suitable according to the climatic envelope calculated using Zonal Statistics. Current distributions are not perfectly reflected in the currently suitable climates. Areas not suitable for growth are not mapped.

Figure 3. Suitable growth areas for the Pinyon Pine in the year 2080 given the A2 climate projection change by the University of Alberta.

Figure 4. Change in suitable climatic growth area for the Pinyon Pine in the Southwestern United States given the A2 climate change projection by the University of Alberta. Decreases in area focused primarily in the south with grains in growth area toward the north.

Conclusion:

In this study I show that the suitable area for the Pinyon Pine (Pinus edulis) will decrease and move generally northward under the A2 climate change projection made by the University of Alberta. Loss of suitable climate area is centered in the southern states. Decreased populations of Pinyon Pine could lead to an increase in value of Pine Nuts, as well as increased preservation efforts in states like New Mexico, where the tree is culturally significant. Further analysis to improve the accuracy of the results in this project could include use of a scalar multi-criteria analysis rather than the binary approach I took in this study. Including more climatic and ecological variables such as geology, soils, and seed dispersion would also be valuable in the accuracy of this study.