Mapping and modeling a landscape and predicting species dispersal through it Presented at: NASA Biod

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Mapping and modeling a landscape and predicting
species dispersal through itPresented at NASA
Biodiversity Ecological Forecasting Team
MeetingWashington DC, August 29-31 2005
Fred Watson1 Co-PIs, Co-Authors,
Acknowledgements Bob Garrott2, PJ White3 , Susan
Alexander1Wendi Newman1 , Thor Anderson1 , Simon
Cornish1 , Jon Detka1Ryan Lockwood1, Rick
Wallen3, Hank Heasler3, Marc Kramer4, and
numerous other grad students, trackers,
volunteers 1California State University Monterey
Bay 2Montana State University Bozeman3National
Park Service 4University of California Santa Cruz
/ NASA Ames
Funding NASA NCC2-1186 NCC13-03009, NSF DEB
0074444 DEB-0413570, and NPS
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MovementSpecies cant achieve their
distributions without dispersing through their
habitat to get there
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Part IMaking maps and models
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Part IMaking maps and models

• Geothermals
• Vegetation
• Wind
• Snowpack

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Intensity of Yellowstones geothermal
areas(paper submitted)
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Geothermal perimeter survey
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Geothermal map validation
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Meadow phenology survey
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Forage phenology map validation
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Wind (and snow) data collection
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Snow redistributed by wind.Prediction from CFD
model running on Columbia (Kramer and colleagues)
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Wind model predicts snow
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Snowpack model validation
(Two papers in review)
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Snowpack model validation
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Snowpack model validation
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Part IISome early progress in forecasting
species dispersal
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Part IISome early progress in forecasting
species dispersali.e. an aspect of our work that
is most relevant to NASAs Ecological Forecasting
Team
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Step 1Take the Kernel Density Estimationmethod
of estimating distribution
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Step 1Take the Kernel Density Estimationmethod
of estimating distribution
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Step 2Take a Niche Modeling method of
estimating distribution(e.g. a Resource
Selection Function)
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Step 2Take a Niche Modeling method of
estimating distribution(e.g. a Resource
Selection Function)
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Step 3Combine the two methodsyielding an
environmentally sensitiveempirical density
estimatorthat explicitly models movementin
order to estimate distribution
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Step 3Combine the two methodsyielding an
environmental sensitiveempirical density
estimatorthat explicitly models movementin
order to estimate distribution
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Step 4Look at maximum probable movements to
predict tracks
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Fitting a GPS collar
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Step 5Compare predicted versus observed
tracks(red vs yellow)
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Step 5Compare predicted versus observed
tracks(red vs yellow)
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Utility

• Use of project products by NPS (present, and
  future)
• Raw data model output
• e.g. geothermal map, SWE predicting during bison
  corralling
• Knowledge as basis for decisions
• Journal papers
• Visualization
• Visitor Education Center
• auditorium film
• interactive kiosk
• Web
• for public, and managers
• Use of species dispersal theory
• when distribution change is constrained by
  movement
• WNV, tamarisk, polar bears
• when habitat connectivity / migration corridors
  are critical
• when the actual movement is of interest
• e.g. preventing species movement across a
  boundary by controlling the forces that cause it,
  or at least knowing when you cant
• when you want to understand the biological
  processes that constrain movement (or
  distribution that is constrained by movement)

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Snowpack model sequence(a small part of a long
video, reproduced here as a sequence of slides)
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Plant phenology sequence(MODIS EVI-based)(a
small part of a long video, reproduced here as a
sequence of slides)
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