SCGIS Hands-on Workshop: Introduction to GIS for Habitat Analysis and Home Range Estimation

1. SCGIS Hands-on Workshop: Introduction to GIS for Habitat Analysis and Home Range Estimation Sadie Ryan, UC Berkeley

2. 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

3. 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.

4. How do we collect spatial data? • Radio collars

5. How do we collect spatial data? • Direct observation and paper maps • Museum records of collection locations

6. 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.

7. How do we use spatial data? • Home ranges • Habitat selection • Biogeography questions

8. 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

9. Home range methods • 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 Adaptive Kernel Harmonic mean

10. 1 Buffalo Herd, 4085 locations in 2000

11. 1 Buffalo Herd, 4085 locations in 2000 MCP Minimum Convex Polygon

12. 1 Buffalo Herd, 4085 locations in 2000 Kernel Method 95% 50% Default Smoothing H

13. 1 Buffalo Herd, 4085 locations in 2000 k-NNCH Nearest Neighbor Convex Hull (Getz & Wilmers, 2004) k=5 neighbors shown

14. 1 Buffalo Herd, 4085 locations in 2000 k-NNCH Nearest Neighbor Convex Hull (Getz & Wilmers, 2004) k=20 neighbors shown

15. 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

16. Study Site

17. Study Site Kruger National Park Klaserie Private Nature Reserve 1993-2000 3 herds

18. Selected type 2 and 5, and not 3 and 8 • A. nigrescens and Grewia sp.: open woodland • Mixed Acacia sp.: shrubveld • Mixed woodland • C. apiculatum, S. birrea: open woodland; • C. apiculatum, S. caffra, Grewia sp.: short woodland • C. apiculatum, C. mollis, Grewia sp.: closed short woodland • C. apiculatum, C. mopane: woodland • C. mopane: woodland and shrubveld Habitat Selection: Vegetation Type

22. Hawth’s tools is a free extension: www.spatialecology.com