stephen gray, usgs tucson with: julio betancourt, lisa graumlich, steve jackson, mark lyford, jodi...

Stephen Gray, USGS Tucson

With: Julio Betancourt, Lisa Graumlich, Steve Jackson,

Mark Lyford, Jodi Norris, and Greg Pederson

Nonlinear Interactions Between Climate, Landscape Structure, and

Plant Migration

Nonlinear Interactions Between Climate, Landscape Structure, and

Plant Migration

Global ChangeImpacts?

TNC Invasives Project

Plant Migration and Invasion

• Expect significant shifts in the distribution of plant species

• Will contribute to major vegetation/ ecosystem change across the West

• Driven by:– Changing climate– Land use– Exotic introductions– Human vectors, etc.

Forecasting Environmental Change

• Sustainable land management requires realistic predictions for future vegetation change – Provide viable scenarios for planning

and policy – Tool for policy makers and

stakeholders to explore potential ecological outcomes and the costs/consequences of management and mitigation efforts

Nonlinear Behavior and Environmental

Forecasting• Nonlinearity is a major obstacle to

environmental forecasting• Examples of nonlinear behavior-

– Threshold responses– Feedbacks– Cascading responses– Cross-scale interactions

The Classic Example: Nonlinear Behavior in theThe Classic Example: Nonlinear Behavior in theSpread of Large FiresSpread of Large Fires

Peters et al (2004)Peters et al (2004) PNAS PNAS

Ignition- single treeIgnition- single tree

Spread within patchSpread within patch

Spread among patchesSpread among patches

Large Area: feedbacks and Large Area: feedbacks and nonlinear interactionsnonlinear interactions

Predictability?Predictability?

Nonlinearity in Western Ecosystems

• Focus on inherent complexity in biological processes or cross-scale interactions

Nonlinearity in Western Ecosystems

• Focus on inherent complexity in biological processes or cross-scale interactions

• But, non-stationary (i.e. regime-like) behavior in the climate system may also produce nonlinear dynamics in natural systems

Nonlinearity in Western Ecosystems

• Focus on inherent nonlinearity in biological processes or cross-scale interactions

• But, non-stationary (i.e. regime-like) behavior in the climate system may also produce nonlinear dynamics in natural systems

• Examples: Woody plant migration and invasion in western North America

Traditional View: Climate as stochastic

variations around STATIONARY mean

0 10050

The Ecologist’s Concept of Climate

7525

North American Tree-ring Network

Spring 2005

NOAA-NCDC

Year AD1250 1350 1450 1550 1650 1750 1850 1950

An

nu

al P

reci

p. (

cm)

20

30

40

50

60

70

•High variance explained (r2 = 0.58)•Well replicated (n = 133)•Long segments (Avg. Length = 385 yr)•Conservative detrending

Test Case: Greater Yellowstone Precipitation

Gray, Graumlich and Betancourt (in review) Quat. Res.

Test Case: Greater Yellowstone Precipitation

21-yr Spline

60-yr Spline

Test Case: Greater Yellowstone Precipitation

Rocky Mountain Climate-Reconstruction Network

Gray et al. GRL (2003)

Gridded PDSI reconstructions from Cook et al. 2004, Science

D2M variability and associated wet/dry regimes can become synchronized across

large portions of the West

Non-stationary (regime-like) behavior

Sta

nd

ard

de

via

tio

ns

Example: Upper Colorado Basin Annual Precipitation

Year AD

- The mean, SD, probability of extreme single year events, etc.

changes over D2M timescales Hidalgo 2004; Gray et al. 2003, 2004

Is this D2M Variability Real?• Not an artifact of tree-ring methodology• Signals are coherent at regional to sub-

continental scales• Feature of winter and growing season

temp/precip• Recent modeling studies reproduce D2M

variability – Schubert et al. (2004) Science– Seager et al. (2005) J. Climate– Sutton and Hodson (2005) Science

• But, will D2M variability continue in the future?

D2M Variability and Internal Ocean Processes

Ocean ‘thermostat’ mechanism (Clement et al. 1996)Ocean ‘thermostat’ mechanism (Clement et al. 1996)

Uniform heatingUniform heating

Larger Larger temperaturetemperatureresponse in response in the Westthe West

Cooling by upwelling Cooling by upwelling opposes forcing in the opposes forcing in the East, reducing East, reducing temperature responsetemperature response

Coupled interactions Coupled interactions ((i.e. the Bjerknes i.e. the Bjerknes feedbackfeedback) amplify the ) amplify the East/west East/west temperature temperature difference difference

Warm, mixedSurface layer

Deep, coldocean waters

~20ºC

~20ºC

0 m0 m

10050

150

The Big Question…

• How does D2M variability and associated climatic regimes impact plant invasion and migration processes?

Tree rings:Tree rings:Climate/DemographyClimate/Demography

Climatic Regimes Pace Migration/Invasion Events

Dutch John Mtn., Utah-Northernmost P. edulis-Study encompasses 25 km2 watershed-Reconstructed pinyon dynamics from woodrat middens and dated wood

Jackson et al. (2005), J. Biogeography 32:1085-1106. Gray et al. (in press), Ecology

Migration Dynamics at the Landscape/Watershed Scale

% A

rea

Occ

up

ied

no sites

all sites

Step-like change in the distribution and abundanceof pinyon pine at the watershed/landscape scale

no pinyon

pinyondominates

Medieval Dry Period

Little or nosuccessful

establishment

Migration Dynamics at the Landscape/Watershed Scale

Mo

dif

ied

dro

ug

ht

ind

ex%

Are

a O

ccu

pie

d

no sites

all sites

Migration Dynamics at the Landscape/Watershed Scale

Sm

all

Po

pu

lati

on

“Great Drought”M

od

ifie

d d

rou

gh

t in

dex

% A

rea

Occ

up

ied

no sites

all sites

Migration Dynamics at the Landscape/Watershed Scale

“Great Wet”

Step-like changein pinyon

abundance &distribution

Mo

dif

ied

dro

ug

ht

ind

ex%

Are

a O

ccu

pie

d

no sites

all sites

Switching between dry/wet regimes drives Switching between dry/wet regimes drives nonlinear invasion dynamicsnonlinear invasion dynamics

““D2M” WetD2M” WetRegimeRegime

Step-likeStep-likeChangeChange

Rapid RecruitmentLow Mortality

Switching between dry/wet regimes drives Switching between dry/wet regimes drives non-linear invasion dynamicsnon-linear invasion dynamics

““D2M” WetD2M” WetRegimeRegime

BroadscaleMortality

Abundance of Open Niches

Step-likeStep-likeChangeChange

Rapid RecruitmentLow Mortality

““D2M” DryD2M” DryRegimeRegime

Modern (shaded)Glacial (>13 kyr BP)

Rocky Mts

presentabsent

Distribution of Utah Juniper:

Holocene Migration Dynamics: Utah Juniper

- Reconstructed from 205 woodrat middens at 14 sites

-Lyford et al. (2003) Ecol. Monog. 73:567-583

cal yr B.P.0123456

Sit

es

Oc

cu

pie

d

0

2

4

6

8

10

12

Lyford et al. (2003) Ecol. Monog. 73:567-583

CLIMATIC REGIMES AND UTAH JUNIPER MIGRATION

10,000 yr BP

- Reconstructed from 205 woodrat middens at 14 sites

-Climate inferred from lake sediments and dune records

MT

WY

CurrentDist.

cal yr B.P.0123456

Sit

es

Oc

cu

pie

d

0

2

4

6

8

10

12

Mig

ratio

n S

talls

Du

ring

Co

ld P

erio

ds

Lyford et al. (2003) Ecol. Monog. 73:567-583

CLIMATIC REGIMES AND UTAH JUNIPER MIGRATION

10,000 yr BP

- Reconstructed from 205 woodrat middens at 14 sites

-Climate inferred from lake sediments and dune records

MT

WY

CurrentDist.

Lyford et al. (2003) Ecol. Monog. 73:567-583

CLIMATIC REGIMES AND UTAH JUNIPER MIGRATION

MT

WY

Oldest

Youngest

10 kyr BP

Lyford et al. (2003) Ecol. Monog. 73:567-583

CLIMATIC REGIMES AND UTAH JUNIPER MIGRATION

10 kyr BP

MT

WY 5.7 kyr BP

6.4 kyr BP

MT

WY

Youngest

Oldest

10 kyr BP

Modern Climate Cold Scenario

UTAH JUNIPER DISTRIBUTION IN RELATION TO CLIMATE AND SUBSTRATE (Lyford et al. 2003)

WY

~ 60 km

> 350 km

•Less suitable habitat in northern areas•Requires long-distance dispersal

•Abundant habitat in northern areas• Short distances between suit. hab.

Lyford et al. (2003) Ecol. Monog. 73:567-583

Higher probability of survival

Lower probability of survival

INTERACTION BETWEEN CLIMATIC REGIMES AND LANDSCAPE STRUCTURE

Favorable Climatic Regime Less-favorable Regime

++

Reduced connectivityHigh connectivity

INTERACTION BETWEEN CLIMATIC REGIMES AND LANDSCAPE STRUCTURE

Favorable Climatic Regime Less-favorable Regime

Climatic Regimes Nonlinear Dynamics

Regime-like behaviorin the climate systempromotes step-like changes that may persist for decades to millennia

Interactions betweenclimate and other factors may introduce marked spatial and temporal complexity to ecological processes

10 kyr BP

MTWY 5.7 kyr BP

6.4 kyr BP

How/why does climate drive nonlinear change?

How/why does climate drive nonlinear change?

• Climate affects large areas simultaneously

CLIMATIC REGIMES MAY BECOME SYNCHRONIZED OVER WIDE AREAS

After Fye et al. 2003

What Governs the Impact of Climatic Regimes?

Magnitude/Rate of Shift?

Past Present

Magnitude/Duration ofregimes?

Does the Frequency of Regime Shifts Alter the Ecological Impact of Climate?

Woo

dho

use

, G

ray

and

Mek

o (in

rev

iew

)

= sig. (p < 0.05) decadal to multidecadal power

Decadal to Multidecadal VariabilityDecadal to Multidecadal Variability

Lees FerryLees Ferry 2525 and 50 yr splines and 50 yr splines

How/why does climate drive nonlinear change?

• Climate affects large areas simultaneously

Impacts depend on:• Total area affected by regime• Magnitude and duration of

regimes• Speed/amplitude of switching

How/why does climate drive nonlinear change?

• Climate affects large areas simultaneously

Impacts depend on:• Total area affected by regime• Magnitude and duration of

regimes• Speed/amplitude of switching

• Possibility that the stressor and not the biological response behaves in a nonlinear manner?

Are current prediction methods adequate?

Statistical biogeographic models cannot account for the impacts of D2M variability,

land use/land cover, migration processes, etc.Thompson et al. 2003

Climate/Vegetation Change

Climate/Vegetation Change

What’s Next?• Dynamic Vegetation Models are a good

start (Neilson et al. 2005, Bioscience)• DVMs model changes in vegetation

based on knowledge of plant population and migration processes

• But, current DVMs capture spatial heterogeneity in the environment better than temporal variability

Thanks! Thanks!

Funding:U.S. Geological Survey-

National Research Council Associates Program

USGS Mapping DivisionNational Science Foundation

Thanks!

• Funding:– U.S. Geological Survey-National

Research Council Associates Program

– USGS Mapping Division– National Science Foundation