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MODELING BIGHORN SHEEP HABITAT IN NORTHWESTNEBRASKA

Kyle M. Forbes

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Abstract

In recent years geographic information system (GIS) and remote sensing technologies have been used extensively in habitat suitability modeling. By incorporating these technologies, scientists often gain the ability to formulate and implement a more detailed habitat suitability model with the addition of more detailed spatial information. A habitat suitability model was developed to quantify and describe the available suitable Rocky Mountain bighorn sheep (Ovis canadensis canadensis) habitat in the Pine Ridge of northwestern Nebraska and determine if a minimum viable population (MVP) of this species can be supported naturally in the region. The model included a strong spatial analysis component, extensively utilizing satellite imagery as an input to the strong spatial analysis capabilities of the GIS. A spatial juxtaposition algorithm was developed to analyze the spatial configuration of escape terrain around each pixel of potential habitat in the study area. Landscape metrics were computed and suitable habitat was quantified in five suitability intervals for 1) the entire study area, 2) three focus areas (subdivisions) within the study area, and 3) six population locales (possible herd locations). Quantity of habitat, mean nearest neighbor, and contagion landscape metrics were used to rank each of the six population locales from most to least likely to support a herd of bighorn sheep. Results from the model indicated that sufficient suitable habitat exists within the Pine Ridge to support a MVP, though suitable lambing habitat is severely limited. A large amount of land was deemed unsuitable by the model due to a lack of escape terrain, which provides the bighorn sheep the ability to flee from predators. The ranking of the six population locales indicated that the Ponderosa Wildlife Management Area is most capable of supporting a suitable population of bighorn sheep when compared to other locales in the study area. Advisor: James W. Merchant

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What this paper is about

In recent years geographic information system (GIS) and remote sensing technologies have been used extensively in habitat suitability modeling. By incorporating these technologies, scientists often gain the ability to formulate and implement a more detailed habitat suitability model with the addition of more detailed spatial information. A habitat suitability model was developed to quantify and describe the available suitable Rocky Mountain bighorn sheep (Ovis canadensis canadensis) habitat in the Pine Ridge of northwestern Nebraska and determine if a minimum viable population (MVP) of this species can be supported naturally in the region. The model included a strong spatial analysis component, extensively utilizing satellite imagery as an input to the strong spatial analysis capabilities of the GIS. A spatial juxtaposition algorithm was developed to analyze the spatial configuration of escape terrain around each pixel of potential habitat in the study area. Landscape metrics were computed and suitable habitat was quantified in five suitability intervals for 1) the entire study area, 2) three focus areas (subdivisions) within the study area, and 3) six population locales (possible herd locations). Quantity of habitat, mean nearest neighbor, and contagion landscape metrics were used to rank each of the six population locales from most to least likely to support a herd of bighorn sheep. Results from the model indicated that sufficient suitable habitat exists within the Pine Ridge to support a MVP, though suitable lambing habitat is severely limited. A large amount of land was deemed unsuitable by the model due to a lack of escape terrain, which provides the bighorn sheep the ability to flee from predators. The ranking of the six population locales indicated that the Ponderosa Wildlife Management Area is most capable of supporting a suitable population of bighorn sheep when compared to other locales in the study area. Advisor: James W. Merchant

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Available abstract

In recent years geographic information system (GIS) and remote sensing technologies have been used extensively in habitat suitability modeling. By incorporating these technologies, scientists often gain the ability to formulate and implement a more detailed habitat suitability model with the addition of more detailed spatial information. A habitat suitability model was developed to quantify and describe the available suitable Rocky Mountain bighorn sheep (Ovis canadensis canadensis) habitat in the Pine Ridge of northwestern Nebraska and determine if a minimum viable population (MVP) of this species can be supported naturally in the region. The model included a strong spatial analysis component, extensively utilizing satellite imagery as an input to the strong spatial analysis capabilities of the GIS. A spatial juxtaposition algorithm was developed to analyze the spatial configuration of escape terrain around each pixel of potential habitat in the study area. Landscape metrics were computed and suitable habitat was quantified in five suitability intervals for 1) the entire study area, 2) three focus areas (subdivisions) within the study area, and 3) six population locales (possible herd locations). Quantity of habitat, mean nearest neighbor, and contagion landscape metrics were used to rank each of the six population locales from most to least likely to support a herd of bighorn sheep. Results from the model indicated that sufficient suitable habitat exists within the Pine Ridge to support a MVP, though suitable lambing habitat is severely limited. A large amount of land was deemed unsuitable by the model due to a lack of escape terrain, which provides the bighorn sheep the ability to flee from predators. The ranking of the six population locales indicated that the Ponderosa Wildlife Management Area is most capable of supporting a suitable population of bighorn sheep when compared to other locales in the study area. Advisor: James W. Merchant

Key concepts: Ovis canadensis, Habitat, Geography, Ecology, Biology, Population, Sociology, Demography

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