Scott Saleska Paul Moorcroft David Fitzjarrald (SUNY-Albany) Geoff Parker (SERC) Plinio Camargo (CENA-USP) Steven Wofsy Natural disturbance regimes and.

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Scott Saleska Paul Moorcroft David Fitzjarrald (SUNY-Albany) Geoff Parker (SERC) Plinio Camargo (CENA-USP) Steven Wofsy Natural disturbance regimes and tropical forest carbon balance: integrating canopy structure, flux measurements, and modeling across the landscape (Harvard)

Prior results I: eddy flux measurement show net loss of C in Tapajos National forest of Amazônia, attributable to recent disturbance event(s) uptake loss to atmosphere Accumulated Mg(C) ha -1 Source: Saleska et al. (2003) Science

Source: Moorcroft, Hurtt & Pacala (2001) Modeled flux following disturbance in gap of a “balanced-biosphere” Amazon rainforest Gap Age (time since disturbance in years) Note ecological sign convention! npp = net primary productivity (uptake) rh = heterotrophic respiration (loss) nep = net ecosystem productivity (change in carbon balance) Positive productivity (=uptake from atm) Negative productivity (=loss to atm) Mg (C) ha -2 yr -1

Source: Moorcroft, Hurtt & Pacala (2001) Modeled flux following disturbance in gap of a “balanced-biosphere” Amazon rainforest Gap Age (time since disturbance in years) Note ecological sign convention! npp = net primary productivity (uptake) rh = heterotrophic respiration (loss) nep = net ecosystem productivity (change in carbon balance) Positive productivity (=uptake from atm) Negative productivity (=loss to atm) Mg (C) ha -2 yr -1 Tapajos Km 67 site? (loss)

Source: Moorcroft, Hurtt & Pacala (2001) Modeled flux following disturbance in gap of a “balanced-biosphere” Amazon rainforest Gap Age (time since disturbance in years) Tapajos Km 67 site? (loss) (1)where measurement sites fall on this graph (2)frequency distribution of gap ages across the landscape Extrapolating measurements to landscape or region requires knowing: Mg (C) ha -2 yr -1 Note ecological sign convention!

Source: Moorcroft, Hurtt & Pacala (2001) Modeled flux following disturbance in gap of a “balanced-biosphere” Amazon rainforest Gap Age (time since disturbance in years) Tapajos Km 67 site? (loss) (1)where measurement sites fall on this graph (2)frequency distribution of gap ages across the landscape Extrapolating measurements to landscape or region requires knowing: Mg (C) ha -2 yr -1 selection bias towards pristine-looking sites? (uptake) Note ecological sign convention!

Poses hard question: how to reliably estimate C-balance at large spatial scales Requirements: A.Model that links disturbance state and forest carbon balance B.Measurements of forest disturbance state to constrain model at large scales C.Ability to test forest structure-constrained model predictions at points distributed across landscapes/regions

A. Modeling: the Ecosystem Demography (ED) Model Plant community dynamics carbon and nitrogen biogeochemistry Explicit representation of size- and age- structure of ecosystem heterogeneity (Moorcroft, Hurtt and Pacala, 2001; Medvigy et al., 2004)

ED simulations relating forest size and age structure to carbon balance: Max canopy height and … Tapajos National Forest

ED simulations relating forest size and age structure to carbon balance: Max canopy height and … Net Ecosystem Production (NEP)

Aircraft-based Lidar gives canopy structure (at landscape scale) Source: Hurtt et al. (2004) B. Measurements of forest disturbance state (as embodied in canopy structure)

distance along transect (m) Source: Fitzjarrald & Parker, personal communication height (m) km 67 tower site, July 2003 B. Measurements of forest disturbance state (as embodied in canopy structure) Ground-based Lidar gives canopy structure (local scale)

distance along transect (m)height (m) Ground-based Lidar km 67 tower site, July 2003 Source: Fitzjarrald & Parker, personal communication

distance along transect (m) Source: Fitzjarrald & Parker, personal communication height (m) fraction height (m) Future Work (2). Observations over larger spatial scales Ground-based Lidar km 67 tower site, July 2003 Gap fraction (canopy < 10 m)  25%

10 km C. Testing model predictions across landscape Question: What is the range of disturbance across the landscape scale in the Tapajos National Forest?

Km km tower Question: What is the range of disturbance across the landscape scale in the Tapajos National Forest? C. Testing model predictions across landscape 20 ha of biometry transects in tower footprint estab- lished 1999 Tapajos National Forest region, central eastern Amazônia

Km 117 Km 72 Km 67 T3 T2 T1 T4 10 km 25m 15m 500m 1000m2000m2500m0m1500m tower Question: What is the range of disturbance across the landscape scale in the Tapajos National Forest? C. Testing model predictions across landscape 40 ha of new Transects at Km72 (T1 & T2) and Km117 (T3 & T4) estab- lished summer ha of biometry transects in tower footprint estab- lished 1999 Tapajos National Forest region, central eastern Amazônia

Km 117 Km 72 Km 67 T3 T2 T1 T4 10 km Question: What is the range of disturbance across the landscape scale in the Tapajos National Forest? C. Testing model predictions across landscape 40 ha of new Transects at Km72 (T1 & T2) and Km117 (T3 & T4) estab- lished summer ha of biometry transects in tower footprint estab- lished 1999 Tapajos National Forest region, central eastern Amazônia Large-scale Observations: 1. Live aboveground biomass 2. Vines 3. Coarse Woody Debris 4. Soil characteristics 5. Canopy structure (via ground-based Lidar) 6. Remote-sensing Lidar campaign (airborne LVIS, airborne Lidar or IceSat data) At spatially- distrib- uted transects Spatially continuous in 20 x 60 km box

Canopy Height Gap Age Age-Height Relation Gap Age (time since disturbance in years) Mg (C) ha -2 yr -1 Flux-Age relation (Moorcroft, et al., 2001) (A) ED model (includes canopy structure)

Canopy Height Gap Age Age-Height Relation Gap Age (time since disturbance in years) Mg (C) ha -2 yr -1 Flux-Age relation (Moorcroft, et al., 2001) (A) ED model (includes canopy structure) (B) Observation: canopy height distribution Height Tree Density

Model prediction: carbon balance across landscape Canopy Height Gap Age Age-Height Relation Gap Age (time since disturbance in years) Mg (C) ha -2 yr -1 Flux-Age relation (Moorcroft, et al., 2001) (A) ED model (includes canopy structure) (B) Observation: canopy height distribution Height Tree Density

Model prediction: carbon balance across landscape Eddy fluxes (over time) Landscape-scale plots (over space) (C) Carbon Flux Observations Test Canopy Height Gap Age Age-Height Relation Gap Age (time since disturbance in years) Mg (C) ha -2 yr -1 Flux-Age relation (Moorcroft, et al., 2001) (A) ED model (includes canopy structure) (B) Observation: canopy height distribution Height Tree Density

Initial Results (and one big caveat) Canopy Height Distribution ED Km 67 fraction Height (m)

Canopy Height Distribution Large-scale sites (km 72 & km 117) ED Km 67 fraction Height (m) Initial Results (and one big caveat)

Canopy Height Distribution Large-scale sites (km 72 & km 117) Km67 Km72 km117 site ED Km 67 Corresponding ED-predicted Fluxes (95% confidence intervals from bootstrapping height data) fraction Height (m) Carbon Uptake (MgC/ha/yr Loss | gain Initial Results (and one big caveat)

Canopy Height Distribution Large-scale sites (km 72 & km 117) ED Km 67 Corresponding ED-predicted Fluxes (95% confidence intervals from bootstrapping height data) eddy flux C-balance range biometry C- balance range fraction Height (m) Carbon Uptake (MgC/ha/yr Loss | gain Initial Results (and one big caveat) Km67 Km72 km117 site

The caveat: scale used for LIDAR data aggregation Meters of transect Height (m) Bin-size, 10 m Gap width = 40m

The caveat: scale used for LIDAR data aggregation Meters of transect Height (m) Bin-size, 20 m Gap width = 20m

The caveat: scale used for LIDAR data aggregation fraction Height (m) Baseline km67 data (horiz bin=2m) Km 67 LIDAR data only

fraction Height (m) Baseline km67 data (horiz bin=2m) Km 67 LIDAR data only 4m bin The caveat: scale used for LIDAR data aggregation

fraction Height (m) Baseline km67 data (horiz bin=2m) Km 67 LIDAR data only 10m bin 4m bin The caveat: scale used for LIDAR data aggregation

fraction Height (m) Baseline km67 data (horiz bin=2m) Km 67 LIDAR data only 20m bin 10m bin 4m bin The caveat: scale used for LIDAR data aggregation

fraction Height (m) Baseline km67 data (horiz bin=2m) Km 67 LIDAR data only 20m bin 10m bin 4m bin LIDAR horiz bin width LIDAR-constrained ED prediction Carbon Uptake (MgC/ha/yr Loss | gain 4m bin 10m bin 20m bin The caveat: scale used for LIDAR data aggregation

fraction Height (m) Baseline km67 data (horiz bin=2m) Km 67 LIDAR data only 20m bin 10m bin 4m bin LIDAR horiz bin width LIDAR-constrained ED prediction Carbon Uptake (MgC/ha/yr Loss | gain Scale of ED model 4m bin 10m bin 20m bin The caveat: scale used for LIDAR data aggregation

Future work Incorporate CWD explicitly into ED model: Loss | gain Patch Age (yrs) Carbon Uptake (MgC/ha/yr Current ED

Loss | gain Patch Age (yrs) Carbon Uptake (MgC/ha/yr Current ED Expected effect of CWD module: smooth out decomp losses Future work Incorporate CWD explicitly into ED model:

Loss | gain Patch Age (yrs) Carbon Uptake (MgC/ha/yr Current ED Expected effect of CWD module: smooth out decomp losses Less loss early More loss late Future work Incorporate CWD explicitly into ED model:

Conclusions 1.LIDAR detects variation in canopy structure across the landscape (km 67 different from km’s 72 and 117). 2. ED model can map LIDAR-detected canopy structure to distribution of patch age, and thence to carbon balance; and it predicts significantly different balances across the landscape 3. ED model-predicted fluxes are highly sensitive to spatial scale of LIDAR-data aggregation: key to match spatial scale of LIDAR data to scale of model 4. When the scales of observation and model are matched, the modeled carbon balance does not agree with observed balance at km67 5. incorporation of CWD in ED model will likely improve the ability of ED to predict observed carbon balance.

LAI (cm2/m2 in each 2m ht bins) Height (m)

Km67 SAI(=Surface Area Index) is lower than LAI in ED ED mean LAI height Km67 mean SAI height

Effect of Aggregation on patchwise mean LAI height 2m bins 20 m bins Aggregating SAI bins has no effect on mean, but narrows the distribution As distrib. narrows, lose lowest heights associated with big negative (loss) fluxes As distrib. narrows, lose high heights but these have similar positive fluxes to those just below Mean height

o Max ht approach  Mean SAI-ht approach LAI approach is less sensitive than max-ht. approach, but it is still scale-dependent.

Km km tower (B)

Km km tower (B)

Km 67 Km km tower (B)

Km 117 Km 72 Km 67 Km 83 T3 T2 T1 T4 10 km tower (A) (B)

Km 117 Km 72 Km 67 Km 83 T3 T2 T1 T4 10 km Center Path 25m 15m CWD Line DBH> 30 cm, dead or alive DBH> 10 cm, dead or alive, including lianas 500m1000m2000m2500m0m 10m Line Intercept CWD measure CWD > 7.5 cm DBH For CWD >30cm DBH, measure orientation 1500m tower (A) (B) (C)