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.

Predicted bird losses

10%

60%

Conservation and Policy

A Framework for Debate of Assisted Migration in anEra of Climate ChangeJASON 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 non-linear differential equations

- Experiments to mimic these models

1970’s to today: Forward simulation- Large models produce a mish-

mash 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 demography

Remote sensing

Inference on light capture by canopies

Telemetry of animal movement

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QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Inferring pronghorn responses

Wireless sensor networks at Duke Forest

Where could a model stand in for data?

Slow variables

Predictable variables

Events

Less predictable

Molecular evidence for infection

Pathogen detection

site j

another site

another site

Host survival

environment at j

Dispersal among

sites

Transmission within sites

Host species

Pathogen taxa

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

Acer trees and seeds

Experimental gaps

Demographic monitoring

Pretreatment /intervention

Spatio temporal covariates

Spatio-temporal demographic data

Individual responses with interactions

Seedbank

SeedlingImmature

treeMature

tree

maturationgermination

growth

mortality

Fecundity/dispersal

dormancy

Demography of an individual tree

Individual responses with interactions

Res

ourc

es, e

nvir

Resources, environ

A forest

Information

Seedbank

SeedlingImmature

treeMature

tree

maturationgermination growth

mortality

Fecundity/dispersal

dormancy

SeedTraps

Remote sensing:Canopy light

Covariate data:Temp, soil moisture, elevation, CO2, N

Covariates

Demographic census:Size, survival, maturation status, canopy status

Priors

Example data model Seed rain conditionally depends on all trees

500

500

1500

0 100

b) Sum with implied seed shadows

Individual seed shadows

Distance (m)

a) Liriodendron seed rain estimates

10 m contours

€

ZIP sjk,t Ajkgjk,t,β( )

€

gjk,t = fij,tK rik;u( )i=1

nj

∑

fij,t - fecundity of tree i t - yearj - plotk - seed trap samplesjk,t - seed countAjk - seed trap areagjk,t - dispersal from trees on j

fij,tK(r)

gjk,t

Latent states vs predictive intervals

€

p dX,y,D,λ( ) = p dD,λ,θ( )p θ X,y( )dθ∫

Mortality

Fecundity

Growth

Green dots are posterior means

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

-200 yr ahead prediction has good coverage of tree-ring data- note: no age data enter model

Dashed line: 95% predictive intervalGreen lines: tree ring data

Tradeoffs among species?• Not classical tradeoffs• Within species variance large--consistent with

multiple limitations

Predictive means Individual variation

What’s ahead

Seedbank

SeedlingImmature

treeMature

tree

• 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