An Entity Relationship Model of Wildlife Habitat Associations

Southwest Regional GAPArizona, Colorado, Nevada, New

Mexico, Utah

US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

Robert A. DeitnerKenneth G. Boykin

New Mexico Cooperative Fish & Wildlife Research UnitNew Mexico State University

Outline•Introduction /Why?

•Description of Entity Relationship (ER) models

•Description of Wildlife Habitat Associations (WHR)

•ER model of WHR(s)

Scalability

• More pixels

• More animals

• More attribute maps

• More suitability measures

• More people involved

• More speed

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

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

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.

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.

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

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.

Clause TaxonomyFour 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.

Statements Combine Clauses Using a Decision Matrix

Not Suitable Suitable Breeding

Not Suitable Not Suitable

(Not Suitable)

Not Suitable

(Suitable)

Not Suitable

Suitable Not Suitable

(Suitable)

Suitable

(Suitable)

Suitable

(Breeding)

Breeding Not Suitable

(Breeding)

Suitable

(Breeding)

Suitable

(Breeding)

“and” decision matrix / (“or” decision matrix)

Example as algebraic expression

SmartOverlay($[Or matrix],

Smartoverlay($[and matrix],

$[clause a],

$[clause b]),

Smartoverlay($[and matrix],

$[clause a],

$[clause c],

$[clause d])

)

USGS HUCS

Ecol. Systems

Restrictive Overlay

Liberal Overlay

Alliance

Result

Wildlife Habitat Relationship

Statement Clause

Crosswalkdetails

Classificationdetails

Score details

NumericalClassification

Citation Literature

Taxon Attribute Metadata

Results

The End