1. An Entity Relationship Model of Wildlife Habitat Associations Southwest Regional GAP Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology Robert A. Deitner Kenneth G. Boykin New Mexico Cooperative Fish & Wildlife Research Unit New Mexico State University

2. Outline Introduction /Why? Description of Entity Relationship (ER) models Description of Wildlife Habitat Associations (WHR) ER model of WHR(s)

3. Scalability More pixels More animals More attribute maps More suitability measures More people involved More speed

4. Entity-Relationship Modeling Entity –Class of facts that are described by a consistent set of attributes. The basic building block Attributes –Specific quality of an entity (may in itself be an entity) Relations –Description of the association between entities –Cardinality, modality

5. Characteristics of ER modeling Abstract method of modeling data Graphical in nature Independent of analysis Beginning of a well designed database –Guarantee of “working” –Standards exist (SQL) –scalable solution

6. Wildlife Habitat Relations (WHR) Any rule based model that predicts habitat quality based on a set of landscape attributes. Wildlife Habitat Relations are used to create maps of habitat suitability. Usually by “overlay” of landscape attributes.

7. Example Suitable habitat for my favorite species occurs in the Rio Grande Basin at elevations over 1800 meters and less than 2400 meters. Breeding habitat occurs up to 2000 meters on South facing slopes.

8. A WHR is analogous to a paragraph Clause a: “Suitable habitat for my favorite species occurs in the Rio Grande basin” Clause b: “{Suitable habitat my favorite species occurs} between 1800 and 2400 meters” Clause c: “Breeding habitat {for my favorite species} occurs up to 2000 meters” Clause d: “{Breeding habitat for my favorite species} occurs on south facing slopes

9. A WHR Has Two Major Entities Clause: The relationship between a single attribute and its suitability to a particular taxon. Statement: An expression that contains the rules for combining multiple clauses into a single habitat prediction.

10. Clause Taxonomy Four types of clauses based on the nature of the inputs (attribute) and outputs (Suitability measure) Categorical Polygons (pixels) labeled a,b,f, and h are considered suitable habitat. Classification Polygons (pixels) between 100 and 200 are considered suitable habitat. Score Polygons (pixels) labeled a,b,f, and h are given a score of 50. Numerical Classification Polygons (pixels) between 100 and 200 are given a score of 50.

13. Statements Combine Clauses Using a Decision Matrix Not SuitableSuitableBreeding Not Suitable (Not Suitable) Not Suitable (Suitable) Not Suitable SuitableNot Suitable (Suitable) Suitable (Suitable) Suitable (Breeding) BreedingNot Suitable (Breeding) Suitable (Breeding) Suitable (Breeding) “and” decision matrix / (“or” decision matrix)

14. Example as algebraic expression SmartOverlay($[Or matrix], Smartoverlay($[and matrix], $[clause a], $[clause b]), Smartoverlay($[and matrix], $[clause a], $[clause c], $[clause d]) )

17. USGS HUCS Ecol. Systems Restrictive Overlay Liberal Overlay AllianceResult

19. Wildlife Habitat Relationship Statement Clause Crosswalk details Classification details Score details Numerical Classification Citation Literature Taxon Attribute Metadata Results The End