Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities
Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities
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DOI：
10.1111/1365-2745.13067
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发表时间：
2019-03-01
影响因子：
5.5
通讯作者：
Asner, Gregory P.
中科院分区：
文献类型：
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作者：
Draper, Frederick C.;Baraloto, Christopher;Asner, Gregory P.
亚马逊森林是地球上生物多样性最丰富的森林之一，但准确地量化了物种组成在空间中的变化（即，β多样性）仍然是一个重大挑战。在这里，我们使用高保真机载成像光谱从卡内基机载天文台量化β多样性的一个关键组成部分，通过空间的物种相似性的距离衰减，在北方秘鲁的三个景观。然后，我们比较了我们推导出的距离衰减关系，从泊松聚类过程，已知匹配以及经验的距离衰减关系，在当地的规模获得的理论预期。我们使用了一种无监督的机器学习方法来估计成像光谱数据中物种组成的空间周转。我们首先在两个景观中使用49个森林普查样地（0.1-1.5公顷）的森林组成的独立数据集验证了这种方法。然后，我们将我们的方法应用于三个景观，它们共同代表了陆地坚实的粘土森林，季节性洪水森林和白沙森林。最后，我们用我们的方法来量化的规模的距离衰减关系，并比较这些理论距离衰减关系来自泊松聚类过程。我们发现光谱数据和森林样地数据之间的相似性度量的显着相关性，这表明β-多样性内和森林类型之间可以准确地估计从空中光谱数据使用我们的无监督的方法。我们还发现，估计的距离衰减的物种相似性不同的森林类型，季节性洪水的森林表现出更强的距离衰减比白沙和陆地firme森林。最后，我们证明了距离衰减关系来自理论泊松集群过程比较差，我们的经验关系。合成.我们的研究结果表明，使用高保真成像光谱估计β多样性和连续的距离衰减在低地热带森林的有效性。此外，我们的研究结果表明，距离衰减关系在森林类型之间存在很大差异，这对保护这些宝贵的生态系统具有重要意义。最后，我们证明了一个理论泊松聚类过程预测距离衰减物种相似性同种聚集发生在一系列嵌套尺度内较大的景观。
The forests of Amazonia are among the most biodiverse on Earth, yet accurately quantifying how species composition varies through space (i.e., beta-diversity) remains a significant challenge. Here, we use high-fidelity airborne imaging spectroscopy from the Carnegie Airborne Observatory to quantify a key component of beta-diversity, the distance decay in species similarity through space, across three landscapes in Northern Peru. We then compared our derived distance decay relationships to theoretical expectations obtained from a Poisson Cluster Process, known to match well with empirical distance decay relationships at local scales. We used an unsupervised machine learning approach to estimate spatial turnover in species composition from the imaging spectroscopy data. We first validated this approach across two landscapes using an independent dataset of forest composition in 49 forest census plots (0.1-1.5 ha). We then applied our approach to three landscapes, which together represented terra firme clay forest, seasonally flooded forest and white-sand forest. We finally used our approach to quantify landscape-scale distance decay relationships and compared these with theoretical distance decay relationships derived from a Poisson Cluster Process. We found a significant correlation of similarity metrics between spectral data and forest plot data, suggesting that beta-diversity within and among forest types can be accurately estimated from airborne spectroscopic data using our unsupervised approach. We also found that estimated distance decay in species similarity varied among forest types, with seasonally flooded forests showing stronger distance decay than white-sand and terra firme forests. Finally, we demonstrated that distance decay relationships derived from the theoretical Poisson Cluster Process compare poorly with our empirical relationships. Synthesis. Our results demonstrate the efficacy of using high-fidelity imaging spectroscopy to estimate beta-diversity and continuous distance decay in lowland tropical forests. Furthermore, our findings suggest that distance decay relationships vary substantially among forest types, which has important implications for conserving these valuable ecosystems. Finally, we demonstrate that a theoretical Poisson Cluster Process poorly predicts distance decay in species similarity as conspecific aggregation occurs across a range of nested scales within larger landscapes.