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The Impact of Fine-Scale Disturbances on the Predictability of Vegetation Dynamics and Carbon Flux.

Hurtt GC, Thomas RQ, Fisk JP, Dubayah RO, Sheldon SL - PLoS ONE (2016)

Bottom Line: While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important.We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha.We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances.

View Article: PubMed Central - PubMed

Affiliation: Department of Geographical Sciences, University of Maryland, College Park, MD, United States of America.

ABSTRACT
Predictions from forest ecosystem models are limited in part by large uncertainties in the current state of the land surface, as previous disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect. Likewise, future disturbances also present a challenge to prediction as their dynamics are episodic and complex and occur across a range of spatial and temporal scales. While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important. This study focuses on the impacts of these smaller disturbance events on the predictability of vegetation dynamics and carbon flux. Using data on vegetation structure collected for the same domain at two different times, i.e. "repeat lidar data", we test high-resolution model predictions of vegetation dynamics and carbon flux across a range of spatial scales at an important tropical forest site at La Selva Biological Station, Costa Rica. We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha. We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances. The results of this study illustrate the strong impact fine-scale forest disturbances have on forest dynamics, ultimately limiting the spatial resolution of accurate model predictions.

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Related in: MedlinePlus

Lidar-initialized ED estimates of mean canopy top height change (2005–1998).The ED model was initialized with 1 ha lidar mean canopy top heights from 1998, and used to predict 1 ha mean canopy top height change in 2005.
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pone.0152883.g004: Lidar-initialized ED estimates of mean canopy top height change (2005–1998).The ED model was initialized with 1 ha lidar mean canopy top heights from 1998, and used to predict 1 ha mean canopy top height change in 2005.

Mentions: Following initialization, ED was used to predict gridded ecosystem dynamics including changes in mean canopy height and aboveground biomass at 1 ha resolution across the domain over the 7-year period between lidar data collections (Fig 4). For consistency, the same climatology used in the initialization was used drive the model forward in time. Hectares observed to be at or above modeled maximum canopy height were predicted to have no change in height, and the corresponding predicted aboveground biomass change was bracketed between zero (dynamic equilibrium) and the net flux estimated for stands when they first reach maximum canopy height.


The Impact of Fine-Scale Disturbances on the Predictability of Vegetation Dynamics and Carbon Flux.

Hurtt GC, Thomas RQ, Fisk JP, Dubayah RO, Sheldon SL - PLoS ONE (2016)

Lidar-initialized ED estimates of mean canopy top height change (2005–1998).The ED model was initialized with 1 ha lidar mean canopy top heights from 1998, and used to predict 1 ha mean canopy top height change in 2005.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836756&req=5

pone.0152883.g004: Lidar-initialized ED estimates of mean canopy top height change (2005–1998).The ED model was initialized with 1 ha lidar mean canopy top heights from 1998, and used to predict 1 ha mean canopy top height change in 2005.
Mentions: Following initialization, ED was used to predict gridded ecosystem dynamics including changes in mean canopy height and aboveground biomass at 1 ha resolution across the domain over the 7-year period between lidar data collections (Fig 4). For consistency, the same climatology used in the initialization was used drive the model forward in time. Hectares observed to be at or above modeled maximum canopy height were predicted to have no change in height, and the corresponding predicted aboveground biomass change was bracketed between zero (dynamic equilibrium) and the net flux estimated for stands when they first reach maximum canopy height.

Bottom Line: While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important.We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha.We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances.

View Article: PubMed Central - PubMed

Affiliation: Department of Geographical Sciences, University of Maryland, College Park, MD, United States of America.

ABSTRACT
Predictions from forest ecosystem models are limited in part by large uncertainties in the current state of the land surface, as previous disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect. Likewise, future disturbances also present a challenge to prediction as their dynamics are episodic and complex and occur across a range of spatial and temporal scales. While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important. This study focuses on the impacts of these smaller disturbance events on the predictability of vegetation dynamics and carbon flux. Using data on vegetation structure collected for the same domain at two different times, i.e. "repeat lidar data", we test high-resolution model predictions of vegetation dynamics and carbon flux across a range of spatial scales at an important tropical forest site at La Selva Biological Station, Costa Rica. We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha. We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances. The results of this study illustrate the strong impact fine-scale forest disturbances have on forest dynamics, ultimately limiting the spatial resolution of accurate model predictions.

Show MeSH
Related in: MedlinePlus