detection, analysis and prediction of change in ecology – problems of living systems travis e....
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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
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
Physicochemical SystemsEcology
Mechanistic processmodels as tools for
understanding
Statistical approaches tounderstanding
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
Disturbance, extreme events
Shrub encroachment – San Pedro River
Scott et al., 2006
Benchmarks to consider
Plot-scale, vegetation-soil coupling, management dynamics
Disturbance, extreme events
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
A. L. Westerling et al., 2006 Science
•Can be spatially expressed to provide assessments of risk for vegetation transformation
Benchmarks to consider
Plot-scale, vegetation-soil coupling, management dynamics
Disturbance, extreme events
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)
Benchmarks to consider
Plot-scale, vegetation-soil coupling, management dynamics
Disturbance, extreme events
Climate induced mortality vs climate related disturbance
http://www.fs.fed.us/r3/resources/health/beetle/index.shtml
A. L. Westerling et al., 2006 Science
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
Disturbance, extreme events
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
– Benchmarks– Tools for understanding
• 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
• Living things and earth system processes• Ecological theory• Water as a nutrient• Time in biological systems• Understanding change
– Benchmarks– Tools for understanding
• 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
• Living things and earth system processes• Ecological theory• Water as a nutrient• Time in biological systems• Understanding change
– Benchmarks– Tools for understanding
• Directions– Tools– Theory
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
HeteropogonEragrostis
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
HeteropogonEragrostis
Holocene
Time since pulse (d)
-1 1 2 3 4 7 11 15
-6
-4
-2
0
2
Holocene
Time since pulse (d)
-1 1 3 7 15
0
10
20
Pleistocene
Leaf
pho
tosy
nth
etic
rat
e (
mol
m-2
s-1
)
0
10
20
HeteropogonEragrostis
Holocene
Time since pulse (d)
-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
Physicochemical SystemsEcology
Mechanistic processmodels as tools for
understanding
Statistical approaches tounderstanding
?
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
– Benchmarks– Tools for understanding
• Directions– Tools– Theory
• Statement…
Jackson et al., 2006