Relating the strength of density dependence and the spatial distribution of individuals
Relating the strength of density dependence and the spatial distribution of individuals
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DOI：
10.3389/fevo.2021.691792
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Micah Brush;J. Harte
中科院分区：
文献类型：
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作者：
Micah Brush;J. Harte
生态学中的空间格局包含了关于潜在机制和过程的有用信息。虽然有许多概括的统计数据用于量化这些空间格局，有更少的模型，直接联系明确的生态机制，观察到的模式很容易从现有的数据。我们提出了一个种内空间聚集模型，定量地将静态空间格局与负密度依赖联系起来。个体根据与生态学最大熵理论（METE）一致的定殖规则被放置，并且以与其丰度成正比的概率死亡，该概率被提高到幂α，这是指示密度依赖程度的参数。我们的模型定量地和一般地表明，增加密度的依赖性随机空间图案。α = 1恢复了与许多生态系统经验一致的强聚集METE分布，并且当α → 2时，我们的预测接近与随机放置一致的二项分布。在这两者之间，我们的模型预测的聚集比METE少，但比随机放置多。我们还将我们的机制参数α与负二项分布中的统计聚集参数k联系起来，在密度依赖的背景下对其进行生态学解释。我们使用我们的模型来分析两个对比的数据集，一个50公顷的热带森林和64平方米的蛇形草原阴谋。对于每个数据集，我们推断出单个物种的α以及群落α参数。我们发现，α一般是大的紧密包装的森林比稀疏的草地，密度依赖程度增加，在较小的尺度。这些结果是一致的，与目前的理解，在这两个生态系统，我们推断这种潜在的密度依赖性，仅使用经验的空间格局。我们的模型可以很容易地应用到其他数据集的空间显式数据。
Spatial patterns in ecology contain useful information about underlying mechanisms and processes. Although there are many summary statistics used to quantify these spatial patterns, there are far fewer models that directly link explicit ecological mechanisms to observed patterns easily derived from available data. We present a model of intraspecific spatial aggregation that quantitatively relates static spatial patterning to negative density dependence. Individuals are placed according to the colonization rule consistent with the Maximum Entropy Theory of Ecology (METE), and die with probability proportional to their abundance raised to a power α, a parameter indicating the degree of density dependence. Our model shows quantitatively and generally that increasing density dependence randomizes spatial patterning. α = 1 recovers the strongly aggregated METE distribution that is consistent with many ecosystems empirically, and as α → 2 our prediction approaches the binomial distribution consistent with random placement. In between our model predicts less aggregation than METE, but more than random placement. We additionally relate our mechanistic parameter α to the statistical aggregation parameter k in the negative binomial distribution, giving it an ecological interpretation in the context of density dependence. We use our model to analyze two contrasting datasets, a 50 ha tropical forest and a 64 m2 serpentine grassland plot. For each dataset, we infer α for individual species as well as a community α parameter. We find that α is generally larger in the tightly packed forest than the sparse grassland, and the degree of density dependence increases at smaller scales. These results are consistent with current understanding in both ecosystems, and we infer this underlying density dependence using only empirical spatial patterns. Our model can easily be applied to other datasets where spatially explicit data are available.