A dynamic species modeling approach to assessing impacts from climate change on vegetation Lydia P Ries Biogeography Lab Bren School of Environmental Sciences & Management University of California, Santa Barbara ICESS Seminar April

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 University Research School of Biological Sciences Wilfried Thuiller Université J. Fourier, Laboratoire d'Ecologie Alpine Changwan Seo Seoul National University

Climate change… …is affecting ecosystems worldwide. species distribution phenology community associations Now what? Euphydryas editha

-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

Outline Species distribution modeling Dynamic species model development: BioMove BioMove applications Case study examples

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

Species Distribution Modeling ( Thuiller et al. 2003) Building and validating individual species models Reducing uncertainty of models Predicting potential niches

Building & validating models Selecting species & environmental data GLM/GAM/GBM/CART/ANN Species Distribution Model Methods Bayesian Approach

Climate dataSoil 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

Building & validating models Selecting species & environmental data Projecting models to the future environment Estimating the change of species range GLM/GAM/GBM/CART/ANN Optimizing model outputs Consensus Model Bayesian Approach Species Distribution Model Methods

Quercus agrifolia Hadley A2 Scenario Present

Quercus agrifolia Hadley A2 Scenario 2080 loss gain

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

Endemic Richness Current 2050 high low

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?

What about… Dispersal? Short and long distance events Competition? Plant functional type-age and size classes, fecundity, mortality, germination Disturbance? Fire, grazing

BioMove; a dynamic modeling approach A spatially explicit model used to predict the movement of individual species Simulates dispersal, competition and disturbance

Outline Species distribution modeling Dynamic species model development: BioMove BioMove applications Case study examples

Model Overview

Demographic model

Dispersal Sub-Model: Dispersal

Competition Sub-Model: Competition

Sub-Model: Climate Change

Disturbance Sub-Model: Disturbance

Outline Species distribution modeling Dynamic species model development: BioMove BioMove applications Case study examples

Model Applications 1. Assess threat to species from climate change 2 % listed at risk from CC based on bioclimatic envelopes extinction risk study assumptions

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

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 ) Need to incorporate spatial and temporal dynamics Bromus madritensis Calflora.net

Model Applications 4. Advanced climate change research The climate is changing fast…and so are the models! –inter-disciplinary model development –GHG stabilization

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 ppm CO 2 eq. Targets set to avoid ‘dangerous interference’ in the climate system, e.g. avoiding mass extinctions Current levels ppm CO 2 eq. (Pew Center on Global Climate Change, 2007)

BioMove: a tool for assessing GHG stabilization scenarios Time CO 2 Emissions (ppm) 550 ppm 450 ppm

Outline Species distribution modeling Dynamic species model development: BioMove BioMove applications Case study examples

BioMove; A Case Study Pinus 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)

Species distribution model results: Pinus lambertiana A2B2 C H

2010 BioMove; A Case Study Pinus lambertiana 2010

BioMove; A Case Study Pinus lambertiana 2050

BioMove; A Case Study Pinus lambertiana 2080

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 Study Pinus lambertiana Zaca Fire, 3 August 2007

Quercus douglasii (Blue Oak) Predicted current distributionPredicted change in distribution Mixed blue oak habitat

Blue oak under climate change

Bromus madritensis (red brome) Lower elevations distribution Fire effects from pine Moving upslope

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

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

thank you