1 / 37

NR 422- Habitat Suitability Models

NR 422- Habitat Suitability Models. Jim Graham Spring 2009. Habitat Suitability. Predict the potential distribution of a species based on finding suitable habitat Also known as: Niche modeling Predicting distributions. Terminology. Realized Niche – current distribution

erma
Download Presentation

NR 422- Habitat Suitability Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. NR 422- Habitat Suitability Models Jim Graham Spring 2009

  2. Habitat Suitability • Predict the potential distribution of a species based on finding suitable habitat • Also known as: • Niche modeling • Predicting distributions

  3. Terminology • Realized Niche – current distribution • Established species • Late succession (minimal disturbance) • Potential Niche – future distribution? • Invasive species • Theatened and endangered species

  4. Polar Bear

  5. Tamarisk

  6. RedSquirrel

  7. Arctic Tern

  8. BlueWhale

  9. Approaches • Mechanistic/Experimental • Based on understanding of a species requirements and experiments • Can miss the complexity of environmental conditions and genetic plasticity • Statistical • Based on the existing distribution of a species • Can miss the “realized niche” • Observational / Anecdotal • Hard to validate

  10. Basic Idea • Basic idea is to find a correlation between a species and a variable we can measure • Temperature • Precipitation • Surface type: Water, Rock, Soil Type • Distance to human activity • Other species!

  11. Process Occurrence Data Experiments And Observations Environmental Layers Statistical Model Parameters and Equations Results Processing Distribution Map Model Validation

  12. Correlations • Correlations between environmental variables and species requirements

  13. Tamarix – Invasive Species

  14. Tamarix and Precipitation

  15. Tamarix and Temperature

  16. Box Model 50 Precipitation (cm/year) 30 5.6 Temperature (degrees C)

  17. Tamarix Potential Habitat

  18. Vegetation Layers • Minimum temperatures at certain times of the year • Amount of sun • Precipitation • Soil type • Elevation • Slope • Aspect www.geography.hunter.cuny.edu

  19. Herbivore Layers • Vegetation layers • Proximity to cover • Distance to water www.ministryofpropaganda.co.uk media-2.web.britannica.com

  20. Carnivore Layers • Herbivore layers • Proximity to cover • Distance to water www.juneauempire.com

  21. Proxy Layers • Remotely sensed: • MODIS • LandSat • Aerial • Human disturbance • DEMs: Elevation, slope, aspect

  22. White Tailed Deer • Habitat Suitability Index (HSI) = Forage * Cover • Log(Deer Density) = a + b (HSI) Roseberry, J. L., Woolf, A. 1998. Habitat-Population Density Relationships for White-Tailed Deer in Illinois, Wildlife Society Bulletin, Vol. 26, No. 2 (Summer, 1998), pp. 252-258

  23. Black Bears in Rocky Baldwin, R.A., L. C. Bender. 2007. Den-Site Characteristics of Black Bears in Rocky Mountain National Park, Colorado, JOURNAL OF WILDLIFE MANAGEMENT 72(8):1717–1724

  24. Habitat Suitability Index • HIS = • 0 for least suitable • 1 for most suitable • HIS = V1 * V2 * V3 • Where each VX is a raster scaled from 0 to 1 • 0 = unsuitable factor • 1 = suitable factor • In between values for intermediate suitability

  25. Categories • Assign each category a value from 0 to 1 based on how suitable it is.

  26. Ranges • Create mask rasters for area below and above (0 for unsuitable, 1 for suitable) 1.0 0.0 Mask (0.0) 1.0 Mask (0.0)

  27. Gradients 1.0 0.0 Mask Gradient 1.0

  28. Envelopes 1.0 0.0 Mask Gradient 1.0 Gradient Mask

  29. Statistical Approaches • Linear Regression (continuous variables) • Logistic Regression (presence data) • Genetic Algorithm for Rule-set Production : GARP • Classification and Regression Trees: CART • MaxEnt (presence)

  30. Integrating Climate Change Japanese Honeysuckle

  31. Where to go from here • Spatial modeling • Robin’s class • OpenModeler

More Related