a dynamic species modeling approach to assessing impacts from climate change on vegetation lydia p...
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![Page 1: A dynamic species modeling approach to assessing impacts from climate change on vegetation Lydia P Ries Biogeography Lab Bren School of Environmental Sciences](https://reader035.vdocuments.site/reader035/viewer/2022070411/56649f3a5503460f94c57865/html5/thumbnails/1.jpg)
A dynamic species modeling approach to assessing impacts
from climate change on vegetation
Lydia P RiesBiogeography Lab
Bren School of Environmental Sciences & ManagementUniversity of California, Santa Barbara
ICESS SeminarApril 29 2008
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Post-doctoral Collaborators:Frank Davis, David Stoms Biogeography Lab, Bren
Lee Hannah Conservation International, Bren
International Collaborators:Guy Midgley Kirstenbosch Research Institute,
South African National Biodiversity Institute
Ian Davies Australian National UniversityResearch School of Biological Sciences
Wilfried Thuiller Université J. Fourier,
Laboratoire d'Ecologie Alpine
Changwan Seo Seoul National University
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Climate change…
…is affecting ecosystems worldwide.
species distribution
phenology
community associations
Now what?
Euphydryas editha
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-assess climate change impacts on CA biodiversity,
-develop a tool to estimate changes in potential and realized niches for individual species under climate change scenarios.
The PIER Ecosystem Modeling Project
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Outline
Species distribution modeling
Dynamic species model development: BioMove
BioMove applications
Case study examples
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Species distribution modeling
Lessons from the past: climate oscillates pollen records-species shift latitude and elevation shifts maintain biodiversity
Using same principles, model future response to climate change
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Species Distribution Modeling (Thuiller et al. 2003)
• Building and validating individual species models
• Reducing uncertainty of models
• Predicting potential niches
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Building & validating modelsBuilding & validating models
Selecting species & environmental dataSelecting species & environmental data
GLM/GAM/GBM/CART/ANN
Species Distribution Model Methods
Bayesian Approach
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Climate data Soil data
Max Temp of Warmest Month
Min Temp of Coldest Month
Annual Temp Range
Mean Temp of Wettest Quarter
Mean Temp of Driest Quarter
Precipitation of Wettest Quarter
Precipitation of Driest Quarter
Available Water Capacity
Soil depth
Soil pH
Salinity
Depth of water table
+
Species Distribution Model Methods
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Building & validating modelsBuilding & validating models
Selecting species & environmental dataSelecting species & environmental data
Projecting models to the future environmentProjecting models to the future environment
Estimating the change of species range Estimating the change of species range
GLM/GAM/GBM/CART/ANN
Optimizing model outputsOptimizing model outputsConsensus Model
Bayesian Approach
Species Distribution Model Methods
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Quercus agrifoliaHadley A2 Scenario
Present
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Quercus agrifoliaHadley A2 Scenario
20802080
loss
gain
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Species Distribution Model Output To Date:
314 Species
13 Pinus spp.15 Quercus spp.13 Ceanothus spp.9 Arctostaphylous spp.
89 California Endemic
Mojave
Mixed conifer
Coastal sage scrub
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Endemic RichnessCurrent
2050
high
low
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Species distribution modeling
Lessons from the past:
climate oscillates
pollen records-species shift
On what time scale?
Rapid events
Reid’s paradox (observed > calculated)
Other mechanisms of movement?
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What about…Dispersal?
Short and long distance events
Competition?
Plant functional type-age and size classes, fecundity, mortality, germination
Disturbance?
Fire, grazing
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BioMove; a dynamic modeling approach
A spatially explicit model used to predict the movement of individual species
Simulates dispersal, competition and disturbance
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Outline
Species distribution modeling
Dynamic species model development: BioMove
BioMove applications
Case study examples
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Model Overview
Species demographic model
PFTssuccession model
Initial Conditions
Dispersal model
Disturbance model
Annual time-step
PFTs Species
BIOMOVE
Competition for light
Vegetation structure
Global Changemodel
Dispersal model
Disturbance model
Global Changemodel
Species demographic model
PFTssuccession model
Initial Conditions
Dispersal model
Disturbance model
Annual time-step
PFTs Species
BIOMOVE
Competition for light
Vegetation structure
Global Changemodel
Dispersal model
Disturbance model
Global Changemodel
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Demographic model
Death
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DispersalSub-Model: Dispersal
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CompetitionSub-Model: Competition
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Sub-Model:
Climate Change
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DisturbanceSub-Model: Disturbance
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Outline
Species distribution modeling
Dynamic species model development: BioMove
BioMove applications
Case study examples
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Model Applications
1. Assess threat to species from climate change
• 2 % listed at risk from CC• based on bioclimatic envelopes• extinction risk study assumptions
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Model Applications
2. Management coordination and decision support:
• Impact of fire on vegetation• Conservation area designation (leading, trailing)• Species of concern (threatened, small populations)• Land use planning
Zaca Fire, 3 August 2007
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Model Applications
3. Invasive species assessment
• Studies on conservation areas, agriculture, forestry– Global (Ficetola et al. 2007, Richardson & Thuiller 2007)– Regional (Parker-Allie et al. 2007)
• Need to incorporate spatial and
temporal dynamics
Bromus madritensisCalflora.net
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Model Applications
4. Advanced climate change research
• The climate is changing fast…and so are the models!– inter-disciplinary model development– GHG stabilization
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BioMove: a tool for assessing GHG stabilization scenarios
GHG Stabilization Targets:
EU: 2° C global mean temperature change (~450 ppm, IPCC 2007)
U.S.: USCAP 450-550 ppm CO2 eq.
Targets set to avoid ‘dangerous interference’ in the climate system, e.g. avoiding mass extinctions
Current levels 420-480 ppm CO2 eq.
(Pew Center on Global Climate Change, 2007)
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BioMove: a tool for assessing GHG stabilization scenarios
Time
CO
2 Em
issi
ons
(ppm
) 550 ppm
450 ppm
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Outline
Species distribution modeling
Dynamic species model development: BioMove
BioMove applications
Case study examples
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BioMove; A Case StudyPinus lambertiana
“This is the noblest pine yet discovered, surpassing all others not merely in size but also in kingly beauty and majesty. “ (John Muir, 1894)
http://www.lib.berkeley.edu/EART/tour/TahoeNationalForest.gif
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Species distribution model results: Pinus lambertiana
A2 B2
C
H
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2010
BioMove; A Case Study Pinus lambertiana 2010
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BioMove; A Case Study Pinus lambertiana 2050
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BioMove; A Case Study Pinus lambertiana 2080
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Disturbance: Fire• CA Dept. of Forestry and Fire Protection, Fire and
Resources Assessment Program (FRAP)• 54 Year Average Fire Frequency• 4 size classes
BioMove; A Case StudyPinus lambertiana
Zaca Fire, 3 August 2007
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Quercus douglasii (Blue Oak)
Habitat Prob.
High
Low
loss
no change
gain
outside range0 90 180 270 36045Kilometers
0 90 180 270 36045Kilometers
Predicted current distribution Predicted change in distribution
Mixed blue oak habitat
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Blue oak under climate change
B2 A2
C
H
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Bromus madritensis (red brome)Lower elevations distribution
Fire effects from pine
Moving upslope
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In summary
314 species library for distribution model outputs Coupled outputs with spatially explicit demographic
model Refined dynamic species model Predict climate driven shifts CA species Continue incorporating competition and disturbance
models Predict differences between GHG stabilization
projections Implications for management strategies, conservation
allocation
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Future work
Predict differences between GHG stabilization projections
Implications for management strategies, conservation allocation
Collaborations with:San Diego County 2050 Project
CA Air Resources Board
IUCN
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thank you
thank you