GIS 5306 GIS in Environmental Systems - CLAS Users

GIS 5306 GIS in Environmental Systems - CLAS Users

GIS 5306 GIS in Environmental Systems Week 2: Some more Theory, then Rudimentary Species Distribution Modeling: Habitat Suitability Models and Logistic Regression Last Time Intro and Logistics Main Questions How are organisms distributed on the surface of the Earth, and why? As Earths environment changes (climate, land use, geomorphologic, geologic, biologic), how will the distribution of organisms change?

Species Distribution Maps: Basic unit of Biogeography Dot maps Outline maps Contour maps Hybrids Physical Factors (Abiotic Environment) of species distributions Ecological Foundations of species distributions Population growth intraspecies interactions Interspecies interactions Dispersal, extinction not dealt with

First: Project Options 1. Either a) the class or b) each student (or student team) will develop a research question (or questions). 2. Each student will argue for a taxon or group of taxa to study, including the study area (Florida gets highest priority?). 3. When 1&2 are decided, Each student will then learn, evaluate, and teach one or more methods for studying the taxa to answer the research question to the class.

Option b When the methods have been examined, the class will decide the best methods and data required to answer the original research question. Then the whole class will conduct various aspects of the analysis (comparison of multiple methods?). Then we will write the paper, and submit it to an appropriate journal.

Pre-project exploration Each student (or team of 2 depending on the final size of the class) will become familiar with one or two of the methods for species distribution modeling, then present the theory of the method, the software for the method, and an example laboratory exercise to the class, including providing data for the exercise. This can be the tutorial produced by the developer of the software, or an exercise developed by the student(s). Work Assignments

Instructor will conduct next weeks class: Theory of predictive vegetation mapping (subset of Species Distribution Modeling) Selection and description of one method for predictive vegetation mapping (Habitat Suitability) Tutorial for working through a Habitat Suitability example. Provision of data for working up several examples. Read the assigned papers linked on the syllabus. Methods from which to Choose (See Class References)

Habitat Suitability Modeling (Binford) Gradient Analysis (Ordination) methods (surprisingly not a major part of the literature) Logistic Regression Fuzzy Set Approaches (also more seen as part of habitat suitability modeling) Biodiversity Informatics Facility compilation of multiple software sites:

DMAP distribution mapping software http://openmodeller.sourceforge.net/index.php?option=com_frontpage&Itemid=1 http://www.springerlink.com/content/n805714x26265573/ http://mac.downloadatoz.com/openmodeller-desktop/

R-forge contributions for Species Distribution Modeling http://www.nhm.ku.edu/desktopgarp/ Openmodeller (multiple methods)

http://sourceforge.net/projects/maxent/, http://www.cs.princeton.edu/~schapire/maxent/ GARP http://www.dmap.co.uk/

MAXENT (Tutorial Available) http://biodiversityinformatics.amnh.org/index.php?section_id=105&content_id=345 https://r-forge.r-project.org/R/?group_id=697 Land-change Modeler for IDRISI and ArcGIS http://clarklabs.netkeepers.com/products/Land-Change-Modeling-IDRISI.cfm

Free GIS: DIVA-GIS: http://www.diva-gis.org/ Distribution of Species Main Points 1. Individuals of each species have ecological requirements and limits that, along with historical factors, determine distribution. 2. Physical environmental factors create gradients of tolerance and optimality for organisms. 1. E.g. water, light, pH, temperature, salinity, etc. 3. Biotic environment (other organisms) also create gradients of tolerance and optimality.

1. Population growth (intraspecific interactions) 2. External (predation, competition, mutualism) 3. Internal (pathogens, parasites) New Topic: Distribution of Species Secondary Points 1. Interactions of physical and biotic factors always occur. 2. Very difficult to demonstrate mechanisms of distribution limitation, but straightforward to show correlations among physical and biotic factors and distributions.

The Niche Also implies that there can be multiple, or combinations of limiting factors All Models Follow the Same Principle Mapping from species and environmental factor distribution (Geographic Space), modeled in Environmental Space, and re-mapped into Geographic Space

Figures from Elith and Leathwick 2009 Another Way to Look at it. Theory Sample Design Candidate variables Scale Species occurrence data

Environmental Data Validation Modeling Framework Map of predicted occurrence Redrawn from Franklin, J. 2009. Mapping Species Distributions. Cambridge. This Time A little more general theory Binford: Rudimentary SDM.

Habitat Suitability/Cartographic Overlay National Fish and Wildlife Service/Agency Wildlife-habitat relationship (WHR) models GAP Analysis Project Workshop: Swallow-tailed Kite in North-central and northwest Florida. Predictive vegetation mapping: Franklin 1995 Fundamental paper reviewing practice to 1994; 30 papers! Principles of Predictive Species Distribution

Modeling (PSDM) Predictive Vegetation Mapping Habitat Modeling Dependence of predictive vegetation mapping on ecological niche theory and gradient analysis Cited by 363 more recent papers as of 30 August 2010 http://www.nrs.fs.fed.us/atlas/

Definition: Predictive vegetation mapping by Climate Envelopes Predicting the vegetation composition across a landscape from mapped environmental variables. Bald Cypress Longleaf Pine Species-by-species Live Oak

Longleaf Pine Niche Graph and Importance Value Map http://www.nrs.fs.fed.us/atlas/tree/niche_121.html Gradient Analysis and Continuum Concept (Whittaker 1951) Whittaker 1973 continuum concept more explicitly puts forth hypotheses about species response functions

(curves) to environmental gradients, e.g., that they are Gaussian. Predictive vegetation mapping: Modeling Always starts with the development of some type of model, followed by the application of that model to a geographic database to produce the predictive map, a realization of the model. Franklin 1995

Foundations and Premises Predictive vegetation mapping is founded in ecological niche theory and vegetation gradient analysis. Premise: vegetation distribution can be predicted from the spatial distribution of environmental variables that correlate with or control plant distributions. Franklin 1995 Pragmatism

Further, maps of the environmental variables or their surrogates must be available, or easier to map than the vegetation itself, in order for predictive vegetation mapping to be a practical or informative exercise. Franklin 1995 Model Foundations In order to extrapolate over space (predictive vegetation mapping) or time (vegetation change modeling), direct gradients or their

surrogates must be mapped (temperature, potential solar radiation, precipitation, soilmoisture availability, geology or soil chemistry). Spatial Focus Focus on the prediction of plant species distributions or vegetation patterns at the 'regional' scale, e.g., where the mapped extent of the predictions are generally at or within the biogeographic range of the dominant plant species.

Franklin 1995 Models from Franklin 1995 Boolean Statistical Methods Regression GLM GAM Logistic (Logit) Baysian

MONOMAX Maximum Likelihood Classification Rule-based Methods Multivariate Methods Discriminant analysis Canonical correlation

Classification Trees Neural Networks Decision-tree Classification Genetic Algorithms Potential Vegetation vs. Actual Vegetation

Figure 1 Conceptual model showing the relationship between direct gradients (nutrients, moisture, temperature), their environmental determinants (climate, geology, topography) and potential natural vegetation, and the processes that mediate between the potential and actual vegetation cover (the latter is sensed by a remote sensing device).

Franklin 1995 page 479 Austin, M.P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 157:101-118. Cited by 495 Rudimentary SDM: Habitat

Suitability/Cartographic Overlay Goes back to McHarg 1969: Design with Nature Suitability for Ski Areas Not drinking water Slope between 5% and 75%; Aspect N, W, E Soils not erodible, not high runoff Vegetation

Vegetation: grass, degraded forest Biodiversity low Biodiversity Areas suitable for ski areas Site Suitability Analysis, or Multi-Criteria Evaluation This project aimed at identifying the best area

suitable for development of a ski resort in Mitchell and Yancey Counties in NC. The following factors were included in the suitability analysis: Land cover, access, snow precipitation, land ownership, elevation, aspect and slope. Areas Suitable for Ski Resort Development http://gis.ncsu.edu/research/tourism/GIS_Tourism_Projects.htm

McHargian analysis: Woodlands Works for SDM/Habitat Modeling, too What are habitat requirements for Single species Multiple species Vegetation communities, assemblages Biodiversity SCALE ISSUE Dot maps vs. area maps. Fine vs. coarse scale Depends on question!

Animal Species Distribution Modeling Habitat Suitability/Wildlife-Habitat Relationship (WHR) Models GAP Analysis Project: Keeping Common Species Common Where are the gaps in biodiversity protection? http://gapanalysis.nbii.gov/portal/server.pt Research/Management Question How well are we protecting common plants and animals? Corollary: if we are not protecting them well,

what can we do about it? Land-use planning Conservation purchases and easements Originally not common species but biodiversity. GAP Procedure 1. Map LAND COVER of the dominant ecological systems 2. Map and model SPECIES ranges and distributions

3. Map land STEWARDSHIP 4. Conduct the ANALYSIS All from http://gap.greeninfo.org/ GAP Procedures Note WHRM is an associational database: what animals would be expected to be found in vegetation alliance (habitat). Qualitative! From Complete GAP Handbook available from ftp://ftp.gap.uidaho.edu/products/handbookpdf/ CompleteHandbook.pdf

Florida GAP http://www.wec.ufl.edu/coop/gap/lcmapping.htm Data available at FGDL data repository www.fgdl.org Definition: Habitat Modeling Habitat where and animal lives the living and non-living characteristics of a landscape that an animal uses what animals need to survive and reproduce

Different kinds of habitat: Food, water, hiding cover (prey) or ambush cover (predators), thermal cover (against heat or cold or both), and nest sites (or other special needs for reproduction), the minimum amounts and spatial arrangement of the first 5 components Habitat Modeling Expert opinion Literature Compilation into database

What should occur where Absences difficult to model Database query What collection of vertebrates should be in what vegetation alliances? Calculate biodiversity hotspots Stewardship determination What is not already protected? Considers management objectives of public agencies

Gaps Identified! Now what? http://gapanalysis.nbii.gov/portal/community/GAP_Analysis_Program/Communities/Maps,_Data,_&_Reports/Find_Upd ated_GAP_Regional_Data/ Animal Species Distribution Models Habitat Suitability Indices US Fish and Wildlife Service http://www.nwrc.usgs.gov/wdb/pub/hsi/hsiintro.htm

Habitat Suitability Modeling; Wildlife-Habitat Relationship Models: Theory Habitat suitability index (HSI) model is intended for use with the habitat evaluation procedures (HEP) developed by the U.S. Fish and Wildlife Service (1980) for impact assessment and habitat management. The model was developed from a review and synthesis of existing information, and includes unpublished information that reflects the opinions of persons familiar with black-shouldered kite ecology. It is scaled to produce an index of habitat suitability between 0 (unsuitable habitat) and 1.0 (optimally suitable habitat). This model is a hypothesis of species-habitat relations, not a

statement of proven cause and effect. The model has not been field tested. HIS: Theory All from literature or experts Distribution and commercial importance Life history overview Habitat requirements Food for both adults and immatures (and associated habitat) Cover for both adults and immatures Breeding habitat (e.g. nesting) if different

Geographic area and season Minimum habitat area Example Habitat Suitability Index: American Alligator (Alligator mississippiensis) The American alligator is characteristically a resident of river swamps, lakes, bayous, and marshes of the Gulf and Lower Atlantic Coastal Plains from Texas to North Carolina.

HSI publications have standard format: INTRODUCTION HABITAT REQUIREMENTS

food Cover HABITAT SUITABILITY INDEX (HSI) MODEL

Distribution and Commercial Importance Life History Overview Model Applicability Model Description Suitability Index (SI) Graphs for Model Variables Component Index (CI) Equations and HSI Determination Field Use of Model

Interpreting Model Outputs REFERENCES http://www.nwrc.usgs.gov/wdb/pub/hsi/hsiindex_byauthor.htm American Alligator HSI Model American Alligator HSI Model: Data American Alligator HSI Model: Data

American Alligator HSI Model: Data American Alligator HSI Model If all of these components can be mapped in a GIS, then the map of habitat suitable for alligators can be produced. American Alligator HSI Model

American Alligator HSI Model If all of these components can be mapped in a GIS, then the map of habitat suitable for alligators can be produced. Fuzzy? Several Classification Approaches Probabilistic and Fuzzy Classification: An area

has a XX% probability of belonging to the xxxxx set of land cover, or a membership value of XX (fuzzy). Standard Classification: An area either is or is not a member of the set of xxxxx land cover. Source: Hill, K.E. 1997. The Representation of Categorical Ambiguity: A Comparison of Fuzzy, Probabilistic, Boolean, and Index Approaches in Suitability Analysis. Dissertation, Harvard University

Workshop Model the distribution of an interesting animal using a habitat-suitability/WHR approach. Swallow-Tailed Kite Elanoides forficatus Seen occasionally in Florida Data and ArcGIS project available at S:\geog\ geoglab\GEO5306_GIS_Applications_Environment\ Swallow-Tailed_Kite_distribution_modeling Download the folder to your computer. It is big!

Swallow-Tailed Kite Elanoides forficatus Diminishing population Habitat loss, fragmentation Formerly in 21 States Possibly fewer than 5000 remain, with 60-65% of the population breeding in Florida during the summer months.

Habitat Modeling is said to be a major longterm goal of STK conservancy Swallow-Tailed Kite Conservation Status Listed as imperiled by The Nature Conservancy. The predominant identified threat is the loss of suitable nesting habitat. National Audubon Societys Watchlist as a species of critical concern Designated by the U. S. Geological Surveys Biological Resources Division as a Species at Risk IUCN Species of Least Concern FL Freshwater Fish and Wildlife Conservation Commission: one

of Florida's most vulnerable and poorly understood species Habitat Modeling is said to be a major long-term goal of STK conservancy Swallow-Tailed Kite Conservation Status The current challenges to kite conservation include wetland loss and drainage, extensive clear-cutting, short rotation timber harvesting, and significant land use changes along migration routes and wintering habitats in South America (Gruber 2009).

Swallow-Tailed Kite Elanoides forficatus From Gruber, 2009 The disappearance of this species from three-fourths of its breeding range between 1880 and 1910 (Figure 1) was one of the most dramatic range contractions of any bird species before the highly publicized post-WWII Peregrine falcon crash (Cely 2005). Swallow-Tailed Kite Elanoides forficatus: Natural History Predator Insects (airborne)

Herps Small Mammals Small birds (nests!) Neo-tropical Migrant Breeds in SE US, winters in South America Gruber 2009 Habitat Requirements (Gruber 2009): Different Scale than Area Map Riparian and bottomland forest

Mixed pine Strong preference for nesting in dominant or co-dominant loblolly pine (Pinus taeda) stands growing near or within wetlands (Cely 2005). The physical structure of the forest stand is perhaps more important than the specific vegetation communities. Tall, easily accessible nest trees near open areas that provide sufficient prey (Meyer 1995). Nests are typically built at the top of the tallest trees in the stand; the preferred surrounding stand is usually low density and has an uneven height/age structure (Meyer 1995). (Continued)

Habitat Requirements (Gruber 2009) (continued) Kites have large home ranges encompassing thousands of acres, and will often commute long distances, up to 24 km, from the nest site to forage. The main food source of kites is large insects caught on the wing; a variety of other prey such as snakes, anoles, frogs, nestling birds, and wasps nests, are gleaned from vegetation. Large communal roosts near nesting areas are common. Social nesting behavior: clustered distribution. May restrict dispersal into unused habitat.

Data http://www.natureserve.org/ Ridgely, R. S., T. F. Allnutt, T. Brooks, D. K. McNicol, D. W. Mehlman, B. E. Young, and J. R. Zook. 2007. Digital Distribution Maps of the Birds of the Western Hemisphere, version 3.0. NatureServe, Arlington, Virginia, USA. Data Data: Local Data

www.fgdl.gov and others Habitat (vegetation, land-cover) Topography (elevation, slope, aspect) Soils (SSURGO) Hydrography (all water bodies including wetlands) Species data: difficult to come by. Found sightings in SPECIES_OBS_APR10, data available from FGDL Habitat Model The first component of the habitat model was

to identify land cover types most likely to be used as nesting sites (Beyeler 2008). Nest points were buffered by 1000 m, and all land use/land cover types found within those 3.14 km2 buffers were identified as potentially suitable for nesting (Table 1) (Gruber 2009) Water Management District Classification: FLUCCS Code Suitable Habitat Literature

Cely, J.E. 2005. Swallow-tailed Kite (Elanoides forficatus) Fact Sheet. South Carolina Department of Natural Resources. http://www.dnr.sc.gov/cwcs/pdf/Swallowtailedkite.pdf. Gruber, J. 2009. Targeting Potential Conservation Sites for Swallow-tailed Kites (Elanoides forficatus) in Levy County, Florida. Unpublished M.E.M. thesls. Duke University Meyer, K. D, and M. W Collopy. 1990. Status, distribution, and habitat requirements of the American Swallow-tailed Kite (Elanoides forficatus) in Florida. Final report. Florida Nongame Wildlife Program, Florida Game and Fresh Water Fish Commission, Tallahassee. Meyer, K.D. 1995. Swallow-tailed Kite (Elanoides forficatus). In A. Poole and F. Gill, editors. The Birds of North America, Number 138. Academy of Natural Science, Philadelphia, PA, and American Ornithologists Union, Washington, D.C., USA. Ridgely, R. S., T. F. Allnutt, T. Brooks, D. K. McNicol, D. W. Mehlman, B. E. Young, and J. R. Zook. 2007. Digital Distribution Maps of the Birds of the Western Hemisphere, version 3.0.

NatureServe, Arlington, Virginia, USA. Sykes Jr, P. W, C. B Kepler, K. L Litzenberger, H. R Sansing, E. T.R Lewis, and J. S Hatfield. 1999. Density and Habitat of Breeding Swallow-Tailed Kites in the Lower Suwannee Ecosystem, Florida (Densidad y Habitat Reproductivo de Elanoides forficatus en la Parte Inferior del Ecosistema Suwanee, Florida). Journal of Field Ornithology 70, no. 3: 321336. Wright, M. H, R. O Green, and N. D Reed. 1970. A collection of observations and field notes on the nesting activities of the swallow-tailed kite (Elanoides forficatus) in the Everglades National Park. ZIMMERMAN, G. PRIORITIES FOR RESEARCH AND MONITORING, MANAGEMENT, AND OUTREACH AS DETERMINED BY THE SWALLOW-TAILED KITE CONSERVATION ALLIANCEA PARTNERSHIP

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