Climate Change and Biodiversity: Implications for Bay Area Conservation by David Ackerly

Climate change and biodiversity: implications for Bay Area conservation

• Bay Area climate: historical patterns and future changes • Climate impacts on Bay Area vegetation • Climate heterogeneity and biodiversity • Management in the face of change

Bay Area Climate Change and Protected Areas Workshop ‘The Pepperwood Meeting’

July 19-21, 2010

Left to right: Miguel Fernandez, Jim Thorne, Mary Lee Hannah, Alicia Torregrosa, Stu Weiss, Mike Hamilton, Meg Krawchuk, Will Cornwell, Nicole Heller, Al Flint, David Ackerly, Lorrie Flint, Ryan Branciforte, Scott Loarie, Dave Conklin, Jason Kreitler, Sam Veloz, Lisa Micheli, Healy Hamilton, Max Moritz, Morgan Kennedy, Beth Sabo, Jim Johnstone

Missing: Kirk Klausmeyer, Lee Hannah, Diana Stalberg, Phil Duffy, Karen Gaffney, Adina Merenlender

, 3,000 Native Plant Species

Biodiversity hotspots in the United States

from Precious Heritage, 2000, Nature Conservancy and NatureServe

Global CO2 emissions – IPCC 4th assessment

Raupach et al. 2007 PNAS

A2

B1: stabilizing population, rapid technology conversion

growing population, high carbon energy sources

Figure 10.4

IPCC 2007, Fig. 10.4

Projections of future temperature – IPCC 4th assessment

A2

B1

Bay Area climate

summer max temperature

precipitation

water deficit

winter min temperature

PRISM climate layers downscaled to 270 m by Al and Lorrie Flint, USS

Temperature increase, averaged over Bay Area 1.6 – 4°C = 3 – 7 °F

Winter minimum temperatures Summer maximum temperatures

Year (end of 30 year periods)

+1.7 °C

+3.9 °C

+1.6 °C

+3.8 °C

preliminary analyses – please do not distribute without permission

Climatic Water Deficit: excess evaporative demand relative to available water

PET depends on temperature and

insolation Water availability

depends on precipitation, soil storage and runoff

CWD 2001

<775775 - 800800 - 825825 - 850850 - 875875 - 900900 - 925925 - 950950 - 975975 - 1000

!mm/yr"

Climatic Water Deficit

Annual evaporative demand that exceedsavailable water

Potential ‒ Actual Evapotranspiration

courtesy: Al and Lorrie Flint, USGS see Stephenson 1998 J. Biogeog.

Year (end of 30 year periods)

Clim

atic

wat

er d

efic

it (m

m)

Water deficits are projected to increase due to evaporative demand (whether precip goes up or down)

historical


The future is expected to be warmer and drier – the uncertainty is about how fast these changes occur

Summer maximum temperatures (°C)

Clim

atic

wat

er d

efic

it (m

m)

historical


Distance from the ocean is the primary influence on regional temperature patterns

Summer max temperatures

Distance to coast or bay

4°C per 10 km

1°C per 10 km

Winter minima: 0.35°C colder per 10 km

Summer maxima

prel

imin

ary

anal

yses

– p

leas

e do

not

dis

trib

ute

with

out p

erm

issi

on

B

A

Summer and winter temperatures are negatively correlated across the Bay Area

Historical 1971-2000 C

A

B

C

A

Due to the coastal-inland pattern, rising temperatures create novel climates throughout the Bay Area

Historical 1971-2000

GFDL A2 2041-2070

A

B

C

B

A

C

prel

imin

ary

anal

yses

– p

leas

e do

not

dis

trib

ute

with

out p

erm

issi

on

1900 1950 2000 2050 2100

1718

1920

2122

23

!

!

!

!

!

!!!!

!

!

!

!!

!

!

!

!

!

!

!

!!!!

!

!

!

!

!!

!

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!

GFDL SRES A2 GFDL SRES B1

1970-2000 vs 2011-2040



1970-2000 vs 2011-2040

1970-2000 vs 2041-2070 1970-2000 vs 2041-2070

1970-2000 vs 2071-2100 1970-2000 vs 2071-2100

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14


1970-2000 vs 2011-2040



1970-2000 vs 2011-2040

1970-2000 vs 2041-2070 1970-2000 vs 2041-2070

1970-2000 vs 2071-2100 1970-2000 vs 2071-2100

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

B1 A2

Mid-century

End-century


1970-2000 vs 2011-2040



1970-2000 vs 2011-2040

1970-2000 vs 2041-2070 1970-2000 vs 2041-2070

1970-2000 vs 2071-2100 1970-2000 vs 2071-2100

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14


1970-2000 vs 2011-2040



1970-2000 vs 2011-2040

1970-2000 vs 2041-2070 1970-2000 vs 2041-2070

1970-2000 vs 2071-2100 1970-2000 vs 2071-2100

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

Future climates will rapidly exceed the range of recent historical variability

analysis and figure: Sam Veloz, PRBO


1970-2000 vs 2011-2040



1970-2000 vs 2011-2040

1970-2000 vs 2041-2070 1970-2000 vs 2041-2070

1970-2000 vs 2071-2100 1970-2000 vs 2071-2100

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

SED0.2 - 2

2.1 - 3.9

4 - 5

5.1 - 9.9

10 - 14

Year

Aver

age

Tmax

(°C

)

1971 – 2000

±1 s.d. interannual variability

Berkeley CA small symbols: annual

large symbols: 30 yr means


figure: Johnstone and Dawson 2010 PNAS

Year

map: Loarie and Johnstone unpubl. please do not distribute without permission

Fog frequency, 2000-2010 Modis satellite imagery

Impacts on biodiversity: observation, experiments, models

Several, independent approaches to vegetation modeling agree: future climates favor shrub and grassland at the expense of forest

High resolution run of the MC1 vegetation model for the Bay Area

Analysis and figures: Dave Conklin, Conservation Biology Institute

present climate MIROC A2, end-century

conifer

hardwood forest

hardwood woodland

grassland

shrubland

please do not distribute without permission

Diana Stalberg et al. 2010 PLoS ONE (PRBO) Will Cornwell et al. in prep. (UC Berkeley)

Several, independent approaches to vegetation modeling agree: future climates favor shrub and grassland at the expense of forest

‘Random forest’ model of CalVeg types 800 m resolution, UCSC regional climate model

Predictive vegetation modeling of Bay Area vegetation 270 m downscaled climate, GFDL mid-century future

forest remaining

forest woodland

forest shrubland


source: Bay Area Open Space Council, Ryan Branciforte & Stu Weiss

Bay Area Vegetation Map Upland Habitat Goals Project

60 cover types 51 natural/semi-natural

30 m mapping units

Remote imagery + vegetation plots + expert opinion

Altamont Pass, Livermore CA

Vegetation 30m

Spatial downscaling to model vegetation-climate relationships

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PRISM climate 800m

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30 Arc Second GridOver 1/9 Arc Second DEM

1/9 sec DEM Mask

Meters

High : 691

Low : 94

Elevation 3m

800 m

Topoclimate influence on vegetation

Modeling Bay Area Vegetation Desired features: 1) small grain model with a realistic representation of

topography (30 m) 2) simultaneous model of all vegetation types 3) comparison with documented vegetation transitions

Predictive layers 1) Seasonal water deficit (270 m) Al and Lorrie Flint (USGS) 2) Potential annual insolation

(annual, 30 m) 3) Min Temp (270 m downscaled

from PRISM) 4) Max Temp (270 m downscaled

from PRISM) 5) Wind (100 m) 6) Soil Depth (STATSGO)

Modeling Bay Area Vegetation

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DED

DEF

DEG

DEH

DEI

JED

Vector of probabilities for each veg type in each

pixel

redwood

Doug fir

Montane hardwood Baccharis California bay live oak

Other

multinomial logistic regression

W. Cornwell et al. in prep. please do not distribute without permission

Relative probability of vegetation transition (GFDL A2, mid-century vs.

present)

The vulnerability of vegetation types is very patchy: high probabilities of change occur where vegetation patches are

near the edge of their climate envelope

W. Cornwell et al. in prep. please do not distribute without permission

Regional and topographic shifts in vegetation types Blue oak example

0 20 40 60 80

0.00

0.05

0.10

0.15

0.20

Distance from coast or bay (km)

Fre

quency o

f B

lue O

ak

05000

10000

15000

Insolation north-facing south-facing

Distance from coast or bay (km)

present

A2 mid-century

present

A2 mid-century


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GFDL A2 2041-2070 versus present

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Native vegetation transitions vs. alien invasions

vegetation transitions depend on: 1) mortality of existing mature plants 2) propagule sources for new species

source: Larry Workman QIN, Panoramio.com

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Agents of mortality: Fire

Historical probability of fire 1950-2003

(climate-driven model) 2010-2039 (A2) 2070-2099 (A2)

16 GCM ensemble (A2 scenario): change relative to historical period

Figures: courtesy Meg Krawchuck and Max Mortiz, UC Berkeley Historical: Parisien and Moritz 2009 Ecol. Monogr.

Futures: Moritz et al. in review please do not distribute without permission

Agents of mortality: Disease

source: UC Davis; http://www.sciencedaily.com/releases/2007/08/070815145316.htm

Sudden oak death

source: Center for Invasive Species Research UC Riverside

Agents of mortality: Drought and pests

piñon pine mortality credit: Craig Allen, USGS

Documented vegetation transitions (coastal CA)

Cornwell, Sandel and Ackerly, unpublished please do not distribute without permission

Documented vegetation transitions vs. projected transitions

Cornwell, Sandel and Ackerly, in prep. please do not distribute without permission

Local diversity provides seed sources for vegetation shifts

Heterogeneous landscapes support a greater diversity of vegetation types

analysis and figures: Jason Kreitler, USGS and Nicole Heller, Climate Central

UHG Vegetation Layer # of veg classes/12km cell Climatic water deficit

Legend12km

<VALUE>0 - 150

151 - 400

401 - 500

501 - 600

601 - 700

701 - 800

801 - 900

901 - 1,000

1,001 - 1,100

1,101 - 1,257

LegendVarietyValue

High : 26

Low : 1

N 0 25 5012.5 Kilometers

CWD # veg types per 12km cell Veg map

100 300 500 700

05

1015

2025

RANGE of CWDN

umbe

r of V

eg T

ypes


Loarie et al. 2009 Nature

Velocity of climate change: how fast will populations need to move to offset rising temperature?

rate of climate change (°C/yr) ÷ spatial climate gradient (°C/km) = velocity (km/yr)

Eradication of invasives is more important than ever in

the face of changing climates!

sources: nps.gov, cal-ipc.org

thermal refugia

Implications for conservation and management

Large, climatically heterogeneous reserves are critical to maintain diverse local species pools as propagule sources for potential vegetation transitions

In restoration and revegetation projects, a diverse pool of species and genotypes may enhance success in the face of uncertain future climate

Implications for strategic acquisition priorities.... Stu and Ryan – next talk!

Climate Change and Biodiversity: Implications for Bay Area Conservation by David Ackerly

Environment

c preliminary analyses

bay area historical

gfdl a2

winter temperatures

water deficits

historical patterns

future changes climate

available water pet