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Week 2: Some more Theory, then Rudimentary Species Distribution Modeling: Habitat Suitability

Models and Logistic Regression

GIS 5306 GIS in Environmental Systems Last Time

• Intro and Logistics• Main Questions

– How are organisms distributed on the surface of the Earth, and why?– As Earth’s 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 interactionsInterspecies interactionsDispersal, 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 week’s 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.

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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: – http://biodiversityinformatics.amnh.org/index.php?section_id=105&content_id=345

• DMAP – distribution mapping software – http://www.dmap.co.uk/

• MAXENT (Tutorial Available)– http://sourceforge.net/projects/maxent/, http://www.cs.princeton.edu/~schapire/maxent/

• GARP – http://www.nhm.ku.edu/desktopgarp/

• Openmodeller (multiple methods)– 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– 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

Modeling Framework

Map of predicted occurrence

Species occurrence data

Environmental Data

Validation

Candidate variablesScale

Sample Design

Redrawn from Franklin, J. 2009. Mapping Species Distributions. Cambridge.

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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.

Species-by-species

Longleaf Pine

Bald Cypress

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.

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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, soil-moisture 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

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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

BiodiversityBiodiversity low

Areas suitable for ski areas

Site Suitability Analysis, or

Multi-Criteria Evaluation

Areas Suitable for Ski Resort Development

http://gis.ncsu.edu/research/tourism/GIS_Tourism_Projects.htm

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.

McHargian analysis: Woodlands

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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.htmData available at FGDL data repository www.fgdl.org

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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_Updated_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

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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

• Distribution and Commercial Importance

• Life History Overview

– HABITAT REQUIREMENTS• food

• Cover

– HABITAT SUITABILITY INDEX (HSI) MODEL• 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.

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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

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

Standard Classification:

An area either is or is

not a member of the set

of xxxxx land cover.

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).

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 Elanoidesforficatus

• 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 long-term goal of STK conservancy

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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 Society’s Watchlist as a “species of critical concern”

• Designated by the U. S. Geological Survey’s 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 Elanoidesforficatus

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).

From Gruber, 2009

Swallow-Tailed Kite Elanoidesforficatus: Natural History

• Predator

– Insects (airborne)

– Herps

– Small Mammals

– Small birds (nests!)

• Neo-tropical Migrant

• Breeds in SE US, winters

in South AmericaGruber 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.

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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

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LiteratureCely, 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 (Elanoidesforficatus) 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: 321–336.

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 ALLIANCE—A PARTNERSHIP TO ADVANCE CONSERVATION OF A VULNERABLE SPECIES.