When does seed limitation matter for scaling up reforestation from patches to landscapes?
When does seed limitation matter for scaling up reforestation from patches to landscapes?
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DOI：
10.1002/eap.1410
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发表时间：
2016-12-01
影响因子：
5
通讯作者：
Lichstein, Jeremy W.
中科院分区：
文献类型：
--
作者：
Caughlin, T. Trevor;Elliott, Stephen;Lichstein, Jeremy W.
在数亿公顷退化土地上恢复森林已成为固碳和保护生物多样性国际计划的核心内容。森林景观恢复需要从小规模实地研究中扩大二次演替的生态知识，以预测异质景观中的森林恢复率。然而，生态实地研究揭示了森林恢复的时间差异很大，部分原因是景观特征难以在实证研究中复制。种子雨可以决定重新造林率，并取决于超出大多数实地研究规模的景观特征。我们开发数学模型来量化景观配置如何影响退化斑块中的种子雨和森林再生。这些模型显示了景观特征如何改变其他相同斑块的演替轨迹，从而深入了解为什么一些实证研究揭示了种子雨对次生演替的强烈影响，而另一些则不然。我们表明，当斑块接近演替停止阈值时，当树冠覆盖和种子雨之间在演替早期发生正反馈时，以及当定向扩散导致斑块间相互作用时，种子雨将强烈限制重新造林率。相比之下，种子雨在多种条件下对重新造林率的影响微弱，包括当景观规模的种子可用性非常高或非常低时。我们的建模框架包含了重新造林实地研究中通常估计的生长和生存参数。我们展示了数学模型如何通过允许土地管理者预测自然更新足以恢复树木覆盖的地方来为森林景观恢复提供信息。将定量预测转化为森林景观恢复的空间针对性干预措施可以支持恢复数百万公顷退化土地的目标，并有助于缓解全球气候变化。
Restoring forest to hundreds of millions of hectares of degraded land has become a centerpiece of international plans to sequester carbon and conserve biodiversity. Forest landscape restoration will require scaling up ecological knowledge of secondary succession from small-scale field studies to predict forest recovery rates in heterogeneous landscapes. However, ecological field studies reveal widely divergent times to forest recovery, in part due to landscape features that are difficult to replicate in empirical studies. Seed rain can determine reforestation rate and depends on landscape features that are beyond the scale of most field studies. We develop mathematical models to quantify how landscape configuration affects seed rain and forest regrowth in degraded patches. The models show how landscape features can alter the successional trajectories of otherwise identical patches, thus providing insight into why some empirical studies reveal a strong effect of seed rain on secondary succession, while others do not. We show that seed rain will strongly limit reforestation rate when patches are near a threshold for arrested succession, when positive feedbacks between tree canopy cover and seed rain occur during early succession, and when directed dispersal leads to between-patch interactions. In contrast, seed rain has weak effects on reforestation rate over a wide range of conditions, including when landscape-scale seed availability is either very high or very low. Our modeling framework incorporates growth and survival parameters that are commonly estimated in field studies of reforestation. We demonstrate how mathematical models can inform forest landscape restoration by allowing land managers to predict where natural regeneration will be sufficient to restore tree cover. Translating quantitative forecasts into spatially targeted interventions for forest landscape restoration could support target goals of restoring millions of hectares of degraded land and help mitigate global climate change.