A phenomenological spatial model for macro-ecological patterns in species-rich ecosystems
A phenomenological spatial model for macro-ecological patterns in species-rich ecosystems
复制标题
物种丰富的生态系统中宏观生态模式的现象学空间模型
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Azaele
中科院分区：
文献类型：
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作者：
F. Peruzzo;S. Azaele
在过去的几十年里，生态学家逐渐认识到，在相对较大的空间尺度上描述生态群落的关键生态格局不仅与尺度有关，而且密切交织在一起。物种的相对丰富度--它告诉我们物种的共性和稀缺性--将其形状从小空间尺度改变为大空间尺度。物种平均数量作为面积的函数在开始时急剧增加，然后在大尺度上降低坡度。最后，如果我们在一个给定的地点发现一个物种，我们更有可能发现一个相同物种的个体离我们很近，而不是更远。这种空间周转取决于物种的地理分布，而物种往往是在空间上聚集的。这会对一个区域内物种的丰富度和丰富度产生影响，但到目前为止，很难对这种关系进行量化。在一个中立的框架内--它认为所有的个体在竞争中是平等的--我们引入了一个空间随机模型，该模型从现象学的角度解释了个体的出生、死亡、移民和局部分散。我们计算配对关联函数--它封装了空间周转--以及在给定的圆形区域内找到具有特定种群的物种的条件概率。此外，我们还计算了上述宏观生态格局，并将解析公式与模型的数值积分进行了比较。最后，我们将模型预测与两个低地热带森林资源清查的经验数据进行了对比，显示出良好的一致性。
Over the last few decades, ecologists have come to appreciate that key ecological patterns, which describe ecological communities at relatively large spatial scales, are not only scale dependent, but also intimately intertwined. The relative abundance of species – which informs us about the commonness and rarity of species – changes its shape from small to large spatial scales. The average number of species as a function of area has a steep initial increase, followed by decreasing slopes at large scales. Finally, if we find a species in a given location, it is more likely we find an individual of the same species close-by, rather than farther apart. Such spatial turnover depends on the geographical distribution of species, which often are spatially aggregated. This reverberates on the abundances as well as the richness of species within a region, but so far it has been difficult to quantify such relationships. Within a neutral framework – which considers all individuals competitively equivalent – we introduce a spatial stochastic model, which phenomenologically accounts for birth, death, immigration and local dispersal of individuals. We calculate the pair correlation function – which encapsulates spatial turnover – and the conditional probability to find a species with a certain population within a given circular area. Also, we calculate the macro-ecological patterns, which we have referred to above, and compare the analytical formulæ with the numerical integration of the model. Finally, we contrast the model predictions with the empirical data for two lowland tropical forest inventories, showing always a good agreement.