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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

Simulator estimates of mean canopy top height change at 1 ha resolution (2005–1998, y-axis) compared to the corresponding observed mean canopy top height change (x-axis).Solid-line 1:1 line. Green symbols: simulator predicted height change due to growth. Red symbols: simulator predicted height change due to mortality. Blue symbols: simulator predicted net height change. Black symbols: lidar- initialized ED prediction vs. ΔCTH. Replacing the complex pattern of spatial disturbances with a uniform pattern in the simulator results in fine-resolution errors in predicted mortality that propagate to errors in net predicted height change similar to that predicted by ED.
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pone.0152883.g006: Simulator estimates of mean canopy top height change at 1 ha resolution (2005–1998, y-axis) compared to the corresponding observed mean canopy top height change (x-axis).Solid-line 1:1 line. Green symbols: simulator predicted height change due to growth. Red symbols: simulator predicted height change due to mortality. Blue symbols: simulator predicted net height change. Black symbols: lidar- initialized ED prediction vs. ΔCTH. Replacing the complex pattern of spatial disturbances with a uniform pattern in the simulator results in fine-resolution errors in predicted mortality that propagate to errors in net predicted height change similar to that predicted by ED.

Mentions: Analyses using the second simpler model (simulator) indicate these errors are likely due to the fine-scale spatial pattern of disturbance per se. Model error contours generated using the simulator (Fig 5) illustrate that a simulation with no error in growth or mortality rates (0% contour), but which failed to capture the complex spatial pattern of disturbance, produced results similar to those found using ED. Systematic bias in either of the two other potential factors of growth or mortality rates (>0% contours) did not produce results such as this, but instead increase the expected RMSE at all spatial scales. Deconstructing the net height change at 1 ha resolution into predicted growth and mortality components (Fig 6) illustrates that application of uniform disturbance rates at this scale results in error in the mortality component which propagated to error in the predicted net height change similar to that predicted by ED. These errors in predicted net height change occurred despite accurate predictions of growth.


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)

Simulator estimates of mean canopy top height change at 1 ha resolution (2005–1998, y-axis) compared to the corresponding observed mean canopy top height change (x-axis).Solid-line 1:1 line. Green symbols: simulator predicted height change due to growth. Red symbols: simulator predicted height change due to mortality. Blue symbols: simulator predicted net height change. Black symbols: lidar- initialized ED prediction vs. ΔCTH. Replacing the complex pattern of spatial disturbances with a uniform pattern in the simulator results in fine-resolution errors in predicted mortality that propagate to errors in net predicted height change similar to that predicted by ED.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0152883.g006: Simulator estimates of mean canopy top height change at 1 ha resolution (2005–1998, y-axis) compared to the corresponding observed mean canopy top height change (x-axis).Solid-line 1:1 line. Green symbols: simulator predicted height change due to growth. Red symbols: simulator predicted height change due to mortality. Blue symbols: simulator predicted net height change. Black symbols: lidar- initialized ED prediction vs. ΔCTH. Replacing the complex pattern of spatial disturbances with a uniform pattern in the simulator results in fine-resolution errors in predicted mortality that propagate to errors in net predicted height change similar to that predicted by ED.
Mentions: Analyses using the second simpler model (simulator) indicate these errors are likely due to the fine-scale spatial pattern of disturbance per se. Model error contours generated using the simulator (Fig 5) illustrate that a simulation with no error in growth or mortality rates (0% contour), but which failed to capture the complex spatial pattern of disturbance, produced results similar to those found using ED. Systematic bias in either of the two other potential factors of growth or mortality rates (>0% contours) did not produce results such as this, but instead increase the expected RMSE at all spatial scales. Deconstructing the net height change at 1 ha resolution into predicted growth and mortality components (Fig 6) illustrates that application of uniform disturbance rates at this scale results in error in the mortality component which propagated to error in the predicted net height change similar to that predicted by ED. These errors in predicted net height change occurred despite accurate predictions of growth.

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