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

1 Buffalo Herd, 4085 locations in 2000

MCPMinimumConvexPolygon

1 Buffalo Herd, 4085 locations in 2000

Kernel Method95%50%Default Smoothing H

1 Buffalo Herd, 4085 locations in 2000k-NNCHNearest NeighborConvex Hull(Getz & Wilmers, 2004)k=5 neighbors shown

1 Buffalo Herd, 4085 locations in 2000k-NNCHNearest NeighborConvex Hull(Getz & Wilmers, 2004)k=20 neighbors shown

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

Study Site

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 woodland4. C. apiculatum, S. birrea: open woodland; 5. C. apiculatum, S. caffra, Grewia sp.: short woodland6. 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

0.00

0.10

0.20

0.30

0.40

0.50

1 2 3 4 5 6 7 8Vegetation Type

Proportion of Area

Proportion ofObservations

Habitat Selection: Vegetation Type

0

0.1

0.2

0.3

0.4

0.5

1 2 3 4+

km from water

Proportion of Area

Proportion of Observations

Hawth’s tools is a free extension:

www.spatialecology.comwww.spatialecology.com