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GIS in Wildlife Modeling Applications RESM 493q Wed Nov 11

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Title: GIS in Wildlife Modeling Applications RESM 493q Wed Nov 11


1
GIS in Wildlife Modeling Applications RESM
493qWed Nov 11
2
Overview
  • GIS and wildlife modeling in general
  • Inventory and monitoring
  • Predictive modeling
  • Wildlife management
  • Aquatic wildlife modeling with GIS
  • Terrestrial wildlife modeling with GIS

3
Wildlife management
  • Purposes of wildlife management
  • Protect and enhance wildlife populations and
    their habitats
  • Promote opportunities for recreation, research,
    and education related to wildlife

4
GIS in wildlife management
  • GIS provides a tool for analyzing wildlife
    distributions and assess habitat
  • Assess the physical and environmental conditions
    to determine where an organism or group of
    organisms can live most comfortably
  • The factors in determining suitable habitat are
    inherently spatial, they may include climate,
    vegetation, land use, surface features, etc

5
1. Inventory and monitoring
  • Uses of location records
  • Location records indicate where different
    wildlife species have been observed. These
    records can be used simply to determine species
    present (inventory) or to help determine key land
    cover and habitat elements used by various
    species (modeling).

The nature conservancy inventory of plants in
Canaan Valley
6
Recording observations with GPS
Counties with Species X
7
Inventory and monitoring
  • Data from Wildlife Surveys and Inventories Ways
    to collect and store the information
  • -X/Y point locations from GPS observations
  • Quads, quarter quads, hexagons, etc.
  • Does your sampling allow you to record just
    presence/absence or the of individuals
  • Further calculations of species richness or
    diversity

Canada Warbler Presence/absence
All birds Species richness
8
Inventory and monitoring
  • Uses of location records
  • Example fish collection info (fishinfo.dbf and
    collections.dbf)

Inventory and monitoring
9
Inventory and monitoring
  • Species richness total number of different
    species (carp, bluegill, bass 3)
  • Species diversity total number of individuals
    across the species (1 carp, 10 bluegill, 1 bass
    low diversity)
  • Calculations for diversity
  • Shannon diversity index
  • Simpsons

Inventory and monitoring
10
Using join with fish collection data
  • Join the fishinfo.dbf to streams based on common
    dnrcode field

Inventory and monitoring
11
Example Observations along transects
Monterey Bay, CA Monitoring Rockfish along
transects Using Remotely Operated Vehicle (ROV)
Transects
12
Example Thermal imaging
  • Thermal imaging is a form of remote sensing
  • Infrared detection of heat sources
  • Helps in visual census of wildlife

Video Red Wolf in soybean field Alligator River
NWR, NC
Source (Video) Field Trip Earth media
gallery http//www.fieldtripearth.org
13
Thermal imaging (2)
No deer
N5 deer
  • Advantages
  • Fixed wing aircraft
  • Does not disturb animals (1000-1500 ft above
    ground)
  • Lower cost, covers more area than human observers
  • Geo-referenced images provide permanent record
  • Used for deer, moose, sage grouse, bighorn sheep,
    cranes, and others

Source Vision Air Research http//www.visionairre
search.com
14
Species movements
  • Remote monitoring of species movements
  • Mapping home ranges
  • Radio telemetry/transmitters
  • Transmitter on animal
  • Radio receiver/data logger
  • GPS tracking
  • Collars on animals
  • Monitor animal continuously

15
Example Monitoring trends
  • North American Breeding Bird Surveys
  • Volunteers record birds seen along transects
  • Combine results by year, species

North American Breeding Bird Survey Trend Map
1966-2003 Cerulean Warbler
Source USGS Patuxent Wildlife Research
Center http//www.mbr-pwrc.usgs.gov/bbs/
16
Example Tracking migratory birds
Tundra swans Monitored with satellite
transmitters Sir Syd 2 ½ years. 16,000 km
Source http//www.bsc-eoc.org/lpbo/swans/eachswa
n.html
17
Telemetry
  • Mapping and Tracking Animal Movements
  • Telemetry mapping telemetry results
  • Temporal comparisons
  • Daily movements
  • Seasonal migrations
  • Year to year movements/changes

Allegheny woodrat Fitted with collar
Inventory and monitoring
18
Example Tracking large mammals with GPS
  • Individual animals with collars
  • Collars transmit signals to satellite
  • Satellite transmits location of individuals back
    to headquarters
  • Complete record of animal movement
  • Locations plotted on maps, overlaid with other
    data, even included on live Internet mapping sites

Colorado Division of Wildlife Grand Mesa Moose
Tracking Project Students track moose over
Internet
moose
Source Colorado Division of Wildlife http//wild
life.state.co.us/Education/StudentActivities/Grand
MesaMooseTracking/
19
Example Mapping home ranges
  • Animal telemetry locations used to map home range
    polygons
  • Many methods

Minimum Convex Polygon Contains all
points (Simple method)
White-tailed Deer Home Ranges using MCP
method Westvaco Research Forest, Randolph Co., WV
Source Shawn Crimmins, WVU Wildlife Graduate
Student, RESM 593 Student Project http//www.nrac.
wvu.edu/RESM493/Fall05/students/Crimmins/index.htm
20
Example Mapping home ranges
Wood Thrush Pre/Post Fledging Home
Ranges Westvaco Research Forest, WV
  • Animal Movement extension tools
  • Map daily movements
  • Map 95 minimum convex polygons

Fitting bird with radio-tag harness
Source Tim Dellinger, RESM 593 Student Project
Fall 2003
21
2. Predictive Modeling
  • Statistical modeling with GIS
  • Use GIS to characterize sites with known
    occurrence
  • Extrapolate to broader regions
  • Kentucky Warbler model
  • Key landscape variables
  • Proportion of forested land cover
  • Brown-headed cowbird abundance
  • Mean wetness

Source USGS Upper Midwest Science Center Dr.
Wayne Thogmartin http//www.umesc.usgs.gov/terrest
rial/migratory_birds/bird_conservation/bcr28_kentu
cky_warbler.html
22
Predictive modeling
  • Scales of modeling
  • Landscape scale - (1,000 1,000,000 ha)
  • Site-specific scale or microhabitat evaluating
    habitat characteristics at these scales is
    generally finer than data allows
  • Assessing results of predictive modeling
  • Chi-square analysis (compare results to random)
  • Other statistical methods

Predictive modeling
23
Example Aquatic habitat mapping
Crean Lake, Canada Data collected for lake trout
study
Source ESRI 2006 Map Book Saskatchewan
Environment Hydro-acoustics program http//www.se.
gov.sk.ca/
24
Predictive modeling tool - Spatial correlation
analysis (SCA)
  • Primary objective is to reveal relationships
    between different types of spatial features
  • It determines whether the distribution of one
    type of feature, organized in a particular data
    layer is related to the distribution of features
    organized in another data layer
  • In SCA you are investigating whether a frequency
    is higher in certain areas (Does location
    matter?)
  • If two layers are correlated, then the
    information on these layers may be redundant
  • You can save money or time in not collecting
    redundant GIS information
  • If you have nominal data then use contingency
    table and chi squared goodness of fit tests
  • If you have interval or ratio scale data, then
    use correlation coefficient and regression models

Predictive modeling
25
Example A
  • Is there a relationship between observed species
    locations and land cover type?

Predictive modeling
26
Example A
  • We are testing the hypothesis There is NO
    significant relationship between observed species
    locations and land cover type
  • Use tables in a stat book to get prob(chi squared
    lt .99) for 4 degrees of freedom

If computed statistic gt 95 confidence interval
for chi squared Then reject hypothesis
Our case computed statistic 95 confidence
interval 279.01 gt 13.28 So, we
reject the hypothesis and conclude that there IS
a significant Relationship between observed
species locations and land cover type
Predictive modeling
27
Example B
  • Is there a relationship between the number of
    observed species locations and elevation?

Predictive modeling
28
Example B
  • In this case since we have ratio data (elevation)
    with number of observations, we would use
    correlation coefficient and regression models
  • First perform overlay analysis to find the number
    of species at each elevation
  • Then plot the results







found













Elevation in meters
Predictive modeling
29
Predictive modeling of wildlife notes
  • Usefulness of Regional Modeling of Wildlife
  • Saves expense of ground sampling over wide area,
    remote regions
  • May help focus ground efforts in certain areas
    of interest
  • Regional perspective useful in broad management
    of species across jurisdictions
  • Interesting Problems Common to Predictive
    Modeling
  • Inaccuracy in associating wildlife sighting
    locations with a single land cover type (if land
    cover mapped as grid cells)
  • Bias in wildlife sighting locations (animals
    easier to see in open land cover types)

30
3. Wildlife management with GIS
  • Fishing
  • Hunting
  • Human dimensions
  • Conservation
  • Invasive species

31
Example GIS in fisheries
Deep sea corals
of fishing trips
Source NMFS http//www.nmfs.noaa.gov/gis/how/hab
itat.htm
32
Example Assessing hunting
  • Database of hunting results displayed on a map

WVDNR Bear Harvest Data by County, 2003 Total
bears 1713 (State record)
Source West Virginia Division of Natural
Resources http//www.wvdnr.gov/Hunting/BGB2003Bear
SResults.shtm
33
Example Human dimensions and hunting
  • Stedman et al. 2004 - Using GPS to assess hunter
    movements
  • Daily patterns of movement
  • Hunters stay close to roads (within 1/3 mi)
  • Hunters avoid steep slopes
  • Assess impressions of hunters
  • Actual distance traveled (measured with GPS) vs.
    perceived
  • Hunter density (lower than they think)

Sproul State Forest, PA Penn State human
dimensions study Researcher attaches GPS unit to
hunters arm
Source Stedman et al. 2004. Journal of
Wildlife Management 68(4)762-773.
34
Example Human-wildlife interaction
  • Model
  • Simple black bear habitat preferences
  • Buffered major road corridors

WVU Research - Garrett County, MD
Source Ed Arrow, WVU Division of Forestry and
Natural Resources graduate student
35
Management - Conservation
  • Introduction
  • Geospatial techniques
  • Applications
  • Conclusions
  • GIS can help prioritize or rank areas for
    conservation
  • Map different alternatives

New Hampshire State Wildlife Action Plan
Source New Hampshire Fish and Game
Department http//www.wildlife.state.nh.us/Wildlif
e/Wildlife_Plan/highest_ranking_habitat.htm
36
Management Invasive species
Source Zebra Mussel Distribution in North
America, USGS Dataset http//www.nationalatlas.gov
/mld/zmusslx.html
Zebra Mussel Distribution Spread from
1998-present Introduced in Lake St. Clair, MI
New sighting during the year
Previous locations
37
Presenting results
  • Maps - summarize information
  • Time series maps
  • Internet mapping tools

38
Presenting results
  • Google Earth
  • Internet mapping tool, no software required

USDA Oral Rabies Vaccine Program
2006 Distribution Data in Google Earth
Red Ground application of vaccines
Oral Rabies Vaccine Maps http//www.aphis.usda.g
ov/ws/rabies/orv/index.html
39
Resources
North American Breeding Bird Survey http//www.mp2
-pwrc.usgs.gov/bbs Wildlife Ecology Related
Software Links (not all GIS related) http//detrit
us.inhs.uiuc.edu/wes/ ESRIs Conservation GIS
program (grants and examples) http//www.conservat
iongis.org Strong 1991. Generation of Whitetailed
Deer (Odocoileus Virginianus) Forage/Browse and
Cover Estimates from Michigan Land Use/Land Cover
Data. Proceedings of the 21st Annual ESRI
International User Conference 2001, San Diego,
CA. http//gis.esri.com/library/userconf/proc01/p
rofessional/papers/pap772/p772.htm Church, R. L.,
D. M. Stoms, and F. W. Davis, 1996. Reserve
selection as a maximal covering location problem.
Biological Conservation 76 105-112 Tankersley,
R., Jr. 1996. Black Bear Habitat in the
Southeastern United States A Biometric Model of
Habitat Conditions in the Southern Appalachians.
M.S. Thesis, University of Tennessee, Knoxville,
TN. http//sunsite.utk.edu/samab/saa/saa_reports.h
tml.
40
References
  • Schamberger, M., Farmer, A. H., and Terrell, J.
    W. (1982). Habitat suitability index models
    Introduction, Report No. FWS/OBS-82/10, U.S.
    Fish and Wildlife Service, Washington, DC.
  • Steiner, F., Pieart, S., Cook, E., Rich, J., and
    Coltman, V. (1994). State wetlands and riparian
    area protection programs, Environmental
    Management 18(2), 183-201.
  • U.S. Fish and Wildlife Service. (1980). Habitat
    evaluation procedures (HEP), ESM 102, Division
    of Ecological Services, Washington, DC.
  • __________. (1981). Standards for the
    development of Habitat Suitability Index models,
    ESM 103, Division of Ecological Services,
    Washington, DC.
  • Wyman, R. L. (1990). What's happening to
    amphibians? Conservation Biology 4, 350-52.
  • OConnell, T.J., L.E. Jackson, R. P. Brooks,
    2000. Bird Guilds as Indicators of Ecological
    Condition in the Central Appalachians. Ecological
    Applications 10(6)1706-1721.
  •  
  • U.S. Environmental Protection Agency. MAIA
    Project Summary Birds Indicate Ecological
    Condition of the Mid-Atlantic Highlands,
    EPA/620/R-00/003, June 2000.
  • Currently being used in the EIS for Mountain Top
    Removal Mining in WV Coalfields

41
Aquatic Wildlife Modeling
42
Reference book
NOTE graphics used in this lecture are from
chapters in this book
43
Why study this?
  • Fishery biologists ask questions that are related
    to location
  • Fish abundance, growth, survival catch rates vary
    across locations
  • How do landscape conditions influence these
    factors
  • Human barriers, land use impacts, etc

44
Fisheries data in GIS format
45
How GIS can be useful to a fisheries biologist
  • Overlay coverages to find emerging patterns
  • Analyze buffers
  • What are the attributes at a particular location
  • Which areas or features meet a set of criteria
  • What spatial patterns exist in the data
  • Ask what if questions in modeling efforts

46
Applications in rivers and streams
  • Linear habitat features
  • Streams drain watersheds
  • Spatial interconnectedness of the aquatic and
    terrestrial systems
  • Mapping and modeling fish habitats and
    distributions
  • Mapping watershed land uses and their impacts on
    habitats and fish populations

nested
47
Applications in rivers and streams
48
Applications in rivers and streams
49
Applications in reservoirs
  • Example questions GIS can help answer
  • What bottom topography is associated with
    spawning habitat?
  • Which areas have a slopelt 5 and gravel
    substrate?
  • Do fish use areas more during periods of higher
    inflows or water levels?
  • How much spawning habitat is lost if the water is
    drawn down 9 feet?

50
Applications in lakes
51
Marine applications
52
Applications in aquaculture
  • Worlds fastest food growing sector
  • Must address the sustainability for and
    consequences of aquaculture
  • Has spatial elements

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Objectives of the CSI
  • Analyze the status of native salmonids
  • Facilitate protection, restoration,
    reintroduction, and monitoring efforts
  • Using
  • Population integrity
  • Habitat integrity
  • Future security

Within historic range
55
Challenge
  • How to best conserve trout and salmon?
  • How to synthesize a myriad of population and
    habitat information collected at various temporal
    and spatial scales?
  • Complex mix of agency priorities, jurisdictions,
    and data availability
  • How to distribute results effectively?

56
Primary questions
  • What is the range-wide status of each species?
  • What are the primary existing threats to
    populations and habitats?
  • How secure are populations and habitats from
    likely future threats?
  • Where, from a broad-scale perspective, should we
    focus our limited conservation resources?
  • How do we measure the success of our conservation
    investments?
  • How does the status of multiple taxa compare and
    contrast across their respective ranges?

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Future of GIS in fisheries
65
Future of GIS in fisheries
  • More uniformity or cohesion of methods and
    standards
  • More user friendly software
  • 3d and 4d models
  • Data distribution through the internet
  • Integration with spatially explicit population
    dynamic models
  • More remote sensing instruments to get depth,
    temperature, channel units wood debris, etc.
  • Periodic classifications for change detection

66
And lastly.
  • Commitment by fisheries community to invest in
    data, technology, and training for students and
    professionals
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