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首页> 外文期刊>Ecological Applications >Predicting tree diversity across the United States as a function of modeled gross primary production
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Predicting tree diversity across the United States as a function of modeled gross primary production

机译：根据模型化的初级生产总值预测全美国的树木多样性

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At the regional and continental scale, ecologists have theorized that spatial variation in biodiversity can be interpreted as a response to differences in climate. To test this theory we assumed that ecological constraints associated with current climatic conditions (2000-2004) might best be correlated with tree richness if expressed through satellite-derived measures of gross primary production (GPP), rather than the more commonly used, but less consistently derived, net primary production. To evaluate current patterns in tree diversity across the contiguous United States we acquired information on tree composition from the USDA Forest Service's Forest Inventory and Analysis program that represented more than 174000 survey plots. We selected 2693 cells of 1000 km 2 within which a sufficient number of plots were available to estimate tree richness per hectare. Our estimates of forest productivity varied from simple vegetation indices indicative of the fraction of light intercepted by canopies at 16-d intervals, a product from the MODIS (Moderate Resolution Imaging Spectro-radiometer), to 8- and 10-d GPP products derived with minimal climatic data (MODIS) and SPOT-Vegetation (Systeme Pour l'Observation de la Terre), to 3-PGS (Physiological Principles Predicting Growth with Satellites), which requires both climate and soil data. Across the contiguous United States, modeled predictions of gross productivity accounted for between 51% and 77% of the recorded spatial variation in tree diversity, which ranged from 2 to 67 species per hectare. When the analyses were concentrated within nine broadly defined ecoregions, predictive relations largely disappeared. Only 3-PGS predictions fit a theorized unimodal function by being able to distinguish highly productive forests in the Pacific Northwest that support lower than expected tree diversity. Other models predicted a continuous steep rise in tree diversity with increasing productivity, and did so with generally better or nearly equal precision with fewer data requirements.
机译：在区域和大陆范围内，生态学家认为，生物多样性的空间变化可以解释为对气候差异的反应。为了验证这一理论，我们假设与当前气候条件（2000-2004年）相关的生态约束如果通过卫星衍生的初级总产值（GPP）来表示，则可能与树木丰富度最好相关，而不是更常用的，但较少持续衍生的净初级生产。为了评估美国各地的树木多样性现状，我们从美国农业部森林服务局的森林清单和分析计划中获取了有关树木组成的信息，该计划代表了174000多个调查地块。我们选择了2693个1000 km 2的单元，其中有足够的地块可用来估算每公顷树木的丰富度。我们对森林生产力的估计从指示植被在16 d间隔内被冠层截获的光的简单植被指数（中等分辨率成像光谱辐射计）的产品，到通过以下方法得出的8和10 d GPP产品不等：最低气候数据（MODIS）和SPOT-植被（Systeme Pour l'Observation de la Terre）到3-PGS（预测卫星生长的生理原理），这需要气候和土壤数据。在整个连续的美国，建模的总生产力预测占树木多样性记录的空间变化的51％至77％，树木多样性的变化范围为每公顷2至67种。当分析集中在九个定义广泛的生态区域内时，预测关系在很大程度上消失了。只有3-PGS预测能够区分西北太平洋的高产森林，这些森林的树木多样性低于预期，因此符合理论上的单峰函数。其他模型预测，随着生产率的提高，树木多样性将持续急剧上升，并且这样做的精度通常更高或几乎相等，而数据需求却更少。

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