Detection, Analysis and Prediction of Change in Ecology – Problems of Living Systems Travis E....

Detection, Analysis and Prediction of Change in Ecology – Problems of Living Systems

Travis E. Huxman

Ecology and Evolutionary Biology

University of Arizona

UNESCO - Tucson, Arizona - March 26-28, 2007

Outline

• Living things & earth system processes

• Ecological theory• Water as a nutrient• Time in biological systems• Understanding change• Directions

Berner 1997, Science; Berner et al., 1998

C-sedimentation rates, [CO2] in the atmosphere, plant evolution, and ecohydrology

Coming to the ideas in this presentation

• Summarize the challenges you are facing detecting and analyzing change and feedbacks and assigning causality to them– In ecology we have three problems that limit our ability to think about change (1) homeostasis, (2) density-

dependence, and (3) life-environment interactions• How do you define change (e.g. time scale, magnitude, direction, significance of change, what signal to noise ratios are you

used to working with)? – Defining change is most difficult in ecosystem science – pick your process (ANPP and Tilman; Community

composition and Collins; Energetics and Enquist; PCA’s and Potts)– Placing change in context is additionally difficult (multiple stable states – ball-and-cup models; alternative stable

states, ecosystem decline (desertification example))– Time-scales are attempted to be understood within the context of the demography of the dominant organisms –

creating difficulty in making measurements and detecting significant change or direction of change• What are the most important changes being investigated in your field and why – what is the motivation?• Does your science deal with changes in extremes or changes in average behavior?

– Both – tolerances affect species presence / absence, changes in average behavior are likely important for structuring ecological interactions.

• What are the most important positive and negative feedback loops and what methods do you use to identify feedbacks?• When does the delta change approach (or incremental change approach) fail?

– In ecology this approach consistently fails to predict significant system wide shifts of interest to Earth Systems• Amazon example from PIRE

• How do you treat non-linearities?– We are forced to use models to interpret data relating to non-linearities.

• In approaching the problem – do you typically collect more data, pool data, use simulation approaches, or some combination thereof?

– Data-model interactions are typically the only way forward, from simplistic interpretations of demography, to complex ecosystem models.

• Classical vs non-classical statistical methods – are there advantages to one or the other in your field?– Bayesian forms are expanding in their use.

• When you get a final estimate of change, how do you describe it? With what confidence? – Ecology is still grappling with becoming a predictive science

• How do you establish design criteria (e.g. high-tides with a 100 year return period, wind loads on structures, etc.) in the midst of change, if applicable?

– Active area of research• How do you deal with model outputs from other fields, if they are used (i.e. GCMs)?

– We assume hydrologist get it right! (Soil moisture example)

Where and what kind of biology might matter (maybe)

Plot-scale, vegetation-soil coupling

Disturbance, extreme events

Conceptual approaches / challenges

Physicochemical SystemsEcology

Mechanistic processmodels as tools for

understanding

Statistical approaches tounderstanding

Approaches for prediction / understanding change

Resource Theory / Stoichiometry

Pastor and Bridgham 1999

•Changes in mean and variance of resource density can predict ecosystem behavior (or be used to understand change)

•Known characteristics of biology can be incorporated (high efficiency species, low efficiency species)

Bridgham et al., 1995

Terrestrial Water Limitation

Number of Months where Precipitation < Potential EvapotranspirationBlue = NeverDarker Orange = Increasing number of months (1-12)

Data from Ahn and Tateishi 1994; Cramer on going

Rain Use Efficiency and PPT

Global ANPP Data

Precipitation (mm y-1)

0 500 1000 1500 2000 2500 3000

Abo

vegr

ound

prim

ary

prod

uctio

n (g

m-2

y-1

)

0

200

400

600

800

1000

1200

0 500 1000 1500 2000 2500 3000

Rai

n U

se E

ffic

ienc

y(A

NP

P /

PP

T)

0.0

0.2

0.4

0.6

0.8

1.0

Precipitation acts like any limiting resource?

Predictable use-efficiency across gradients of availability

Global data set highlights the increase in RUE with decreasing PPT

How does variation in biome specific RUE functions behave across PPT gradients?

Across biomes – compensatory behavior in the sensitivity of ANPP &

runoff to PPT

Mean Annual Precipitation (mm)

0 500 1000 1500 2000 2500 3000

Sen

sitiv

ity o

f AN

PP

-0.2

0.0

0.2

0.4

0.6

0.8

Sen

sitiv

ity o

f Run

off

-0.2

0.0

0.2

0.4

0.6

0.8

Runoff

ANPP

Production at Barro Colorado Island

Precipitation or Evapotranspiration

0 500 1000 1500 2000 2500 3000 3500

AN

PP

(g

m-2

)

0

200

400

600

800

1000

1200

1400

Runoff adjusted

EnergyLimitationGradient

Water doesn’t work well as a nutrient because its function in environment-life couplings is so scale dependent

Lack of predictable stoichiometry

Outline

• Living things & earth system processes

• Ecological theory• Water as a nutrient• Time in biological systems• Understanding change• Directions

Characteristics (problems) of LifeCharacteristics of Life1. Homeostasis2. Organization3. Metabolism4. Growth5. Reproduce6. Adapt7. Interacts with, &

modifies its environment

Interactions are emergent properties of trade-offs in 1-7

Conceptual approaches / challenges

Biological “Interactions”DominatedSystems



understanding


Approaches for prediction /Understanding change

Sensitivity of leaf area is temporal sequence ofsoil moisture and temp

Gutschick and Bassirad 1999

PP

TT

min

SW

CR

WC

LA

Sensitivity of leaf area to drought by functional type

Gutschick and Bassirad 1999

The importance of buffering temporal variation – bet hedging

Productivity and diversity relationships in terrestrial ecosystems

Tilman et al., (2001)

Relatively simplistic view of vegetation-soil

coupling

Scheffer et al., (2005)

Schemes that do not consider the complexity of species behavior over-emphasizes the importance of ‘optimization’ in biological systems as they are coupled to physical processes

Does that mean that all schemes should include species dynamics? (no-but they should not always imply optimization)

Outline

• Living things & earth system processes• Ecological theory• Water as a nutrient• Time in biological systems• Understanding change

– Benchmarks– Tools for understanding

• Directions

Benchmarks to consider

Plot-scale, vegetation-soil coupling, management dynamics


Shrub encroachment – San Pedro River

Scott et al., 2006

A. L. Westerling et al., 2006 Science

Benchmark change to consider

•Increase in the frequency of large fires

•Increase in the length of the fire season


•Can be spatially expressed to provide assessments of risk for vegetation transformation

The benchmark - > 3 million acres of forest affected by tree mortality

http://www.fs.fed.us/r3/resources/health/beetle/index.shtml

Southwest Climate

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Ave

rag

e A

nn

ua

l T

em

pe

ratu

re (

Fa

rein

he

it)

53

54

55

56

57

58

Ave

rage

Pre

cip

(In

)

8

9

10

11

12

13

TemperaturePrecipitation

Breshears et al. 2005 PNAS, 102:15144-15148; graphic from Neil Cobb

Predicting plant response to drought

1950s drought

2000s drought

1900s drought

Could we have predicted this?S

oil

Mo

istu

re(%

)

15

20

25

30

35

Date

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03

ND

VI

0.1

0.2

0.3

0.4

0.0

0.5

Pre

cip

itat

ion

(mm

)

020406080

100120140

Mo

rtal

ity

(%)

0

50

100

B)

C)

D)

E)

Breshears et al. 2005 PNAS, 102:15144-15148; graphic from Neil Cobb

Complex responses that

derive from vegetation-soil

coupling

Scheffer et al., (2005)

If we haven’t seen it before, we have a difficult time relating specific mechanisms to phenomena (assigning causality)

direct effects of drought?

indirect effects of drought? (e.g., pathogens)

Climate induced mortality vs climate related disturbance

http://www.fs.fed.us/r3/resources/health/beetle/index.shtml


High-frequency, small scale events - vs - wide-spread, synchronous events at low temporal frequencies

We’re data limited, despite a greater fundamental understanding of the problem (e.g., plant growth vs community assembly)

Where and what kind of biology might matter (maybe)

Plot-scale, vegetation-soil coupling


Highly non-linear, poorly scaled processes

Equilibrium, systems

dynamics approaches

Change in the context of these contrasting processes

• Predictable perturbation relating to equilibrium biology (may or may not be operating over large scales)

• Problems of biology as they often relate to time: (1) homeostasis, (2) density-dependence, (3) life-environment couplings

Outline

• Living things and earth system processes• Ecological theory• Water as a nutrient• Time in biological systems• Understanding change


• Directions

Huxman et al., (2004) Nature

Precipitation and productionContrasts, constrained within a domain (time or space) compared across domains (time or space)

Huxman et al., 2004

Le Houerou et al., (1988)

Rain-use efficiency (g m-2 mm-1)

0.0 0.2 0.4 0.6 0.8 1.0

Nu

mb

er

of

site

s (o

ut

of

75

)

0

2

4

6

8

10

12

Ehleringer 2000

Cummulative precipitation

Pro

du

ctiv

ity

Increasingdisturbance

Obvious drawbacks are related to resolving feedbacks and populating the analysis with sufficient data

Benefits relate to ability to falsify ecological theory

Process Modeling

• Statistical approaches incorporating both “systems ecology” and “traditional ecology”– e.g., Ecological Demography (Moorcroft et al.,

2001)

Clark (2007) Biotropica

Utilizes our understanding of systems organized around disturbance and body size (e.g., gap systems)

We use our understanding of an ecological principle to coordinate disparate data sets (e.g., ecosystem stocks, species behavior, environmental variability)

Outline



• Directions– Tools– Theory

Process Modeling• Hierarchical Bayesian

Modeling– Evaluate feedback

structure for well constrained processes operating at different scales

– Example, A leaf photosynthesis – stomatal conductance model embedded in a water uptake model (Ogle et al., 2004)

Outline




Pleistocene

-4

-3

-2

-1

0

HeteropogonEragrostis

Holocene

Day since pulse (d)

-1 1 3 7 15

Pre

-da

wn

Wa

ter

Po

ten

tial (

MP

a)

-4

-3

-2

-1

Time since pulse (d)

Eva

potr

ansp

iratio

n (m

mol

m-2

s-1

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

-1 1 2 3 4 7 11 150.0

0.5

1.0

1.5

2.0

2.5

3.0


Holocene

Pleistocene

Pleistocene

Ne

t Eco

syst

em

CO

2 E

xch

an

ge

( m

ol m

-2 s

-1)

-6

-4

-2

0

2


Holocene


-1 1 2 3 4 7 11 15

-6

-4

-2

0

2

Holocene


-1 1 3 7 15

0

10

20

Pleistocene

Leaf

pho

tosy

nth

etic

rat

e (

mol

m-2

s-1

)

0

10

20


Holocene


-1 1 3 7 15

Nig

htti

me

eco

syst

em

CO

2 e

fflu

x

(m

ol m

-2 s

-1)

0

1

2

3

4

Pleistocene

0

1

2

3

4

5

EragrostisHeteropogon

PleistoceneD

aily

Net

Eco

syst

em C

O2

Exc

hang

e (m

mol

m-2

d-1

)

-150

-100

-50

0

50

100

150

200

250

HeterapogonEragrostis

Holocene

Day since pulse

-1 1 3 7 15

-200

-150

-100

-50

0

50

100

150

200

Ecosystem response to rain

Points –

•Rapid change in state-space

•Slower recovery following a alternative steady-state?

•Strong resilience?

Potts et al., 2006

Approaches for prediction

Interception of approaches / challenges

Biological “Interactions”DominatedSystems



understanding


?

Ecology – density, body size and metabolism

Maximum density (# m-2)

Ave

rag

e m

ass

(kg

)

Bi b0e E i / kT Mi

3/ 4

QTot Be e E i / kT b0 MeTot m mm

1 / 4

m1

n

ln QTot ln Be E

1000k

1000

T

ln b0 C

Enquist, et al., (2003) Nature

Metabolic scaling theory

Ecosystem metabolic response to temperature

Many ecosystem have similar ‘functional responses’ – (slopes)

Magnitude of ecosystem flux differs across biomes (including NA and Euro)

Standardized ecosystem metabolic rates increase with latitude (cooler mean annual temperature – homeostatic adjustments at large-scales)

Enquist et al., (2003) Nature

Terrestrial Water Limitation

Number of Months where Precipitation < Potential EvapotranspirationBlue = NeverDarker Orange = Increasing number of months (1-12)

Data from Ahn and Tateishi 1994; Cramer on going

Outline

• Living things and earth system processes• Ecological theory applied to water• Water as a nutrient• Time in biological systems• Understanding change



• Statement…

Jackson et al., 2006

Detection, Analysis and Prediction of Change in Ecology – Problems of Living Systems Travis E....

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