A phenomenological spatial model for macro-ecological patterns in species-rich ecosystems

TL;DR

This paper introduces a spatial stochastic model within a neutral framework to explain macro-ecological patterns in species-rich ecosystems, successfully matching empirical data from tropical forests.

Contribution

It presents a novel phenomenological spatial model that accounts for key ecological patterns and spatial turnover in species-rich ecosystems, validated against real-world data.

Findings

Model accurately predicts species abundance distributions

Captures spatial turnover and aggregation effects

Matches empirical data from tropical forests

Abstract

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 formul{\ae} 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.