Hurtt G C, Thomas R Q, Fisk J P, Dubayah R O, Sheldon S L
Department of Geographical Sciences, University of Maryland, College Park, MD, United States of America.
Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA, United States of America.
PLoS One. 2016 Apr 19;11(4):e0152883. doi: 10.1371/journal.pone.0152883. eCollection 2016.
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.
森林生态系统模型的预测在一定程度上受到陆地表面当前状态的巨大不确定性的限制，因为先前的干扰对生态系统结构和通量有着重要且持久的影响，而这些影响可能难以察觉。同样，未来的干扰也给预测带来了挑战，因为它们的动态是偶发性的且复杂的，并且发生在一系列空间和时间尺度上。虽然诸如热带气旋、火灾或虫害爆发等大型极端事件可能会产生巨大后果，但小规模的细尺度干扰事件通常更为常见，而且可能同样重要甚至更重要。本研究聚焦于这些较小干扰事件对植被动态和碳通量可预测性的影响。利用在两个不同时间为同一区域收集的植被结构数据，即“重复激光雷达数据”，我们在哥斯达黎加拉塞尔瓦生物站的一个重要热带森林站点，测试了一系列空间尺度上植被动态和碳通量的高分辨率模型预测。我们发现，一个高度结构化的生态系统模型预测的高度变化与在区域尺度（约150公顷）上激光雷达测量的高度变化相当吻合，但随着评估的空间尺度降至20公顷以下，模型与数据的不匹配呈指数级增加。我们证明，这种尺度依赖性误差可归因于预测细尺度森林干扰模式时的误差。本研究结果表明，细尺度森林干扰对森林动态有强烈影响，最终限制了准确模型预测的空间分辨率。
