Arctostaphylos hookeri habitat suitability model

Transcription

1 Arctostaphylos hookeri habitat suitability model ANTHONY N. MACHARIA G TH DEC 2004 The objective of this project was to evaluate the habitat characteristics defining the distribution of Arctostaphylos hookeri (G. Don) as well as evaluate the directional trend in distribution of Arctostaphylos species in Marin County. I also wanted to evaluate the directional pattern of some shared morphological characters (namely, the type of stem and bracts) among the species in Marin County. Generally, any habitat quality varies on a continuous scale and designation of areas as habitat and non-habitat may be somewhat arbitrary. The underlying premise is that the suitability of habitat for a species depends on more than one factor, and some of these factors are not easily observable, the habitat patchiness we observe may differ from the patchiness from a species' point of view. I used various categorical variables as my proxies to define the habitat qualities of various species of Arctostaphylos in Marin County based on their distribution pattern. Since the point features only represent the sample locations where the specimens were obtained, they layers that the points intersect were extracted and used in generating Arctostaphylos hooheri species habitat model. The frequencies of occurrence for each layer intersecting the species sample locations were used to create a weighted overlay scheme integrating all the all layers intersected. The assumption in this case is that the higher frequency of a given layer, the higher suitability for the species. In reality, this may be true since the habitat distributions are not random and that s why some species are considered endemic or patchy in distribution depending habitat availability and quality. I used the soil qualities based on the USDA-NRCS soil maps for Marin County. The soil map was obtained from the USDA-NRCS SURGOwebsite: ftp://ftpfc.sc.egov.usda.gov/soildatamart/export/e_38919/soil_ca041.zip Upon downloading the data and uncompressing it, I also imported the MS Access SSURGO template database (soildb_us_2002.mdb) from the same website. The soil maps are in ArcView shapefile file format and UTM Zone 10, Northern Hemisphere (NAD 83) projection. The vegetation layer was obtained from California Vegetation (Calveg) found in the following website: yitem&layer=1&code=06041&view=nil&identify.x=0&identify.y=0. Additional layers were part of the Feature Attributes Table of Marin_04_soils shapefile. The model integrates soil characteristics layers from USDA NRCS, vegetation data from CNPS and geocoded data from herbarium collections. I used the following python script to clip the projected DEM to the extent of the marin_soils04 shapefile.

2 # geoprocessing boilerplate import win32com.client gp = win32com.client.dispatch("esrigeoprocessing.gpdispatch.1") gp.overwriteoutput = 1 gp.workspace = "I:/macharia/macharia/project/California_Data" # describe woodside elevation raster, the use the extent to clip the Climardem raster mask = gp.describe("marin_soil04.shp") gp.clip_management("i:/macharia/macharia/project/california_data/marindem(m)", mask.extent,"newmarindem") I developed the following data processing models corresponding to each the variable; Habitat Projections & Transformation model: This model converts data from native form into a common map units and projections. This standardization helps in overlying layers during subsequent analysis. The datasets are projected from their native projections into NAD 83 projection (the native projection of Marin_soils shapefile). Bract type directional model:

3 This model indicated a strong directional pattern parallel to the coast-range. Stem Type Directional Model

4 This model indicates a similar trend to the Bracts type model. These patterns seem to follow the distribution pattern of the sample points. Habitat Selection Model: This model is used to select the layers intersected by the point features of sample locations.

5 The habitat model

6 The following layers were selected to be used in generate the species habitat suitability model. 1. Annual grasses and redwood habitat types from the habitat type layer 2. Argixerols and Xerochrepts soil great groups layers from the great group layer 3. Dystric Lithic Xeropcrepts from the soil subgroup layer 4. The selected soil series from soil series layer were; Clay-skeletal, serpentinite, thermic, lithic, Argixerolls Fine, Mixed, thermic, typic, Argixerolls Loamy, Mixed, Dystric, lithic, xerochrepts Habitat Suitability Model: This model overlays layers of soil series, great groups, ph, with vegetation layers both at species and community level to determine the levels of correlation between the layers. The output grid is then added to an insolation model as well as the aspect model to determine the trend in the species distribution within Marin County. Califonia_Vegetation layers of habitat types, dominant species types, and land cover types into Marin specific layers using the Marin Soils as a template. Then the clipped shapefiles are converted into grids of habitat types, land cover types and species types. Similarly, I evaluated directional distribution pattern using the bract codes (1 for leafy bracts and 2 for scaly bracts). Both the leafy and the scaly-bracted species exhibited a strong directionality with the leafy bracts exhibiting the strongest. I then used map algebra operators to created a species distribution raster for each species. used the intersect tool to The areas that inter