Transcript A few of the big questions in global change science

Big questions in global change
science
What controls biodiversity?
How will it be affected by climate
change?
Includes students, postdocs, other faculty on campus: Pankaj Agarwal,
Dave Bell, Mike Dietze, Alan Gelfand, Michelle Hersh, Ines Ibanez,
Shannon LaDeau, Scott Loarie, Sean McMahon, Jessica Metcalf, Jackie
Mohan, Emily Moran, Carl Salk, Rob Schick, Mike Wolosin, Hai Yu
Nature supports huge diversity
It is threatened with extinction
•Nature, 2004: 15-37% 'committed to extinction.'
•IPCC: 20-30% risk extinction if temperatures rise 2°C.
•Araújo: from 92% range reduction to 322% expansion.
60%
Predicted
bird losses
10%
Conservation and Policy
A Framework for Debate of Assisted Migration in an
Era of Climate Change
JASON S. MCLACHLAN,＊†‡ JESSICA J. HELLMANN,† AND MARK W. SCHWARTZ＊
Conservation Biology 21, No. 2, 297–302
?
Hannah, L., Midgley, G. F., Lovejoy, T., Bond, W. J.,
Bush, M., Lovett, J. C., Scott, D. & Woodward, F. I.
Conservation of Biodiversity in a Changing
Climate. Conservation Biology 16, 264-268.
Guidance from science:
We can’t get coexistence in models
Diversity in nature, but not in models
of it
Stochasticity can help, but not
much
What’s missing?
Brief history of ecological theory
1920’s to today: Systems of nonlinear differential equations
-
Experiments to mimic these
models
1970’s to today: Forward simulation
-
Large models produce a mishmash of output
Parameterization by guesswork
Simple models with careful designs
extend analytical results
2000’s:Inferential modeling
-
Assimilate information
Understand more of the processes
• Insights:
– Need N limiting factors to
explain N species
• Insights:
– Variation can increase
diversity, but not by much
– Still cannot predict diverse
ecosystems
• Hypothesis:
– Many processes required to
maintain diversity
– Species-specific
A role for
modeling/computation
• Simple deterministic models cannot
predict diverse ecosystems
• Adding stochastic elements to an
otherwise simple model is not enough
• Need to better understand complexity
Challenges
• Many indirect and sparse sources of
information
• Complex interactions, poorly
understood
Many types of data
Experimental hurricanes
CO2 fumigation of forests
Effects of high CO2 on demogr
Remote sensing
Inference on light capture by canopies
Telemetry of
animal movement
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Inferring pronghorn responses
Wireless sensor networks at Duke Forest
Slow variables
Predictable variables
Events
Less predictable
Where could a
model stand in
for data?
Molecular evidence for infection
another site
another site
site j
Dispersal
among sites
Transmission
within sites
Pathogen
detection
environment at j
Pathogen taxa
Host species
Host survival
An application
• Hypothesis: tradeoffs among
traits needed for coexistence
• Challenge: cannot estimate
the traits
– They interact in unknown ways
– Many types of data, all indirect
• Approach:
– hierarchical Bayes inference
on all traits simultaneously
Spatio-temporal
demographic data
Acer trees and seeds
Experimental gaps
Demographic monitoring
Pretreatment /intervention
Spatio temporal covariates
Individual responses with interactions
Demography of an
individual tree
Fecundity/dispersal
Seed
bank
germination
dormancy
Immature
tree
Seedling
maturation
growth
mortality
Mature
tree
Individual responses with interactions
A forest
Resources, environ
Information
Remote sensing:
Canopy light
Covariate data:
Temp, soil moisture, elevation, CO2, N
Seed
Traps
Demographic census:
Size, survival, maturation status, canopy status
Covariates
Fecundity/dispersal
Seed
bank
germination
Seedling
growth
Immature
tree
maturation
dormancy
mortality
Priors
Mature
tree
a) Liriodend
ronseed rain estimates
Example data model
1500
Seed rain conditionally depends on all
trees
500

ZIP s jk,t A jk g jk,t , 
500

nj
g jk,t   f ij,t K rik ;u

i1
10 m contours
b) Sum with implied seed shadows
gjk,t 
fij,tK(r)
Individual
seed shadows
0
100
Distance (m)
fij,t - fecundity of tree i
t - year
j - plot
k - seed trap sample
sjk,t - seed count
Ajk - seed trap area
gjk,t - dispersal from trees on j
pd X,y,D,  
 pd D,  , p X,yd
Growth
Fecundity
Latent states vs
predictive intervals
Green dots are
posterior means
Mortality
Joint life history prediction
•
•
•
•
Parameters for process and
observation errors
Fixed year effects
Random effects (growth and
fecundity)
Latent states (canopy area,
diameter, fecundity, maturation
status, mortality risk)
Evaluation
Dashed line: 95% predictive interval
Green lines: tree ring data
-200 yr ahead prediction has good coverage of
tree-ring data
- note: no age data enter model
Predictive means
Tradeoffs among species?
•
•
Not classical tradeoffs
Within species variance large-consistent with multiple limitations
Individual variation
What’s ahead
• A shift to inferential modeling (including prediction)
– Getting the data in
– Determining how things work
– Finding what’s important
• Revisit analysis with new insight on how to simplify
and where to retain complexity
Seed
bank
Seedling
Immature
tree
Mature
tree