Presentation on theme: "SCGIS Hands-on Workshop: Introduction to GIS for Habitat Analysis and Home Range Estimation Sadie Ryan, UC Berkeley."— Presentation transcript:
SCGIS Hands-on Workshop: Introduction to GIS for Habitat Analysis and Home Range Estimation Sadie Ryan, UC Berkeley
What is GIS? Geographic Information System – object – often uses software Geographic Information Science – discipline –Blueprint of a house – simplest GIS Important qualities: –Overlay operations Map equations in layers –Spatial relationships point to point –Ancillary data data associated with locations Points vs. Grids
Jargon Geospatial/Georeferenced – spatial data that has been located in reference to a standard coordinate system for the Earth (e.g. longitude, latitude) Projection – system for transforming a known location on the non-flat earth to a flat plane – this is extremely important for manipulation of areas – a circle drawn on a lat/long earth is not a circle, unless you make it infinitely small at the equator.
How do we collect spatial data? Radio collars
Direct observation and paper maps Museum records of collection locations How do we collect spatial data?
GPS –Collars/patches that upload –Handheld records of indicators – scat, tracks Remotely sensed data –Satellite imagery Vegetation, landcover, climate –Aerial photgraphy –Radar etc. How do we collect spatial data?
How do we use spatial data? Home ranges Habitat selection Biogeography questions
Home range methods Traditional: Minimum Convex Polygon (MCP) –Join the outermost points together –Useful for delineating overall area used – useful for conservation and reserve design with sparse data –You can use just 95% of points to define error, but no clear selection method for it –Assumes animals are using the whole area equally
Kernel methods –Smoothing of points, predicts likelihood of occurrence, even beyond points –Shows areas of higher and lower densities – useful to define key areas like feeding grounds –Similar assumption of whole area use; no holes –Alarming property of increasing as you add data –Harmonic Mean –Accents areas of higher density –Similar to Kernel methods Local convex hull method – LoCoH –More data needs newer methods (GPS data is huge) –Good for ID of non-use areas –Allows for physical barriers to movement Home range methods Harmonic mean Adaptive Kernel
1 Buffalo Herd, 4085 locations in 2000
MCP Minimum Convex Polygon
1 Buffalo Herd, 4085 locations in 2000 Kernel Method 95% 50% Default Smoothing H
Habitat Selection methods Points on a map –Points are then associated with location-specific data e.g. vegetation type, distance from water, slope, elevation, aspect, soil type etc. –Many different statistical analyses of results –Demo of simple proportional occurrence Buffalo and vegetation type, distance to water
Klaserie Private Nature Reserve Kruger National Park 1993-2000 3 herds Study Site
1. A. nigrescens and Grewia sp.: open woodland 2. Mixed Acacia sp.: shrubveld 3. Mixed woodland 4. C. apiculatum, S. birrea: open woodland; 5. C. apiculatum, S. caffra, Grewia sp.: short woodland 6. C. apiculatum, C. mollis, Grewia sp.: closed short woodland 7. C. apiculatum, C. mopane: woodland 8.C. mopane: woodland and shrubveld Selected type 2 and 5, and not 3 and 8 Habitat Selection: Vegetation Type
Hawth’s tools is a free extension:www.spatialecology.com