Predicting Spatial Similarity of Freshwater Fish Biodiversity - ISSNAF - Italian Scientists and Scholars in North America Foundation

Predicting Spatial Similarity of Freshwater Fish Biodiversity

ISSNAF member and Postdoctoral Research Associate, Civil and Environmental Engineering at Princeton University

Sandro Azaele received a degree in Physics from the University of Trieste in 2002, and a PhD in Physics from Padua University in 2006. His work has mostly focused on stochastic processes and equilibrium/non-equilibrium statistical mechanics applied to complex systems.

Azaele’s association with Princeton as a Postdoctoral Research Associate, Civil and Environmental Engineering, dates back to 2007. Sandro Azaele works in the area of complex systems, with a focus on theoretical and quantitative ecology, analytical models for biodiversity on river systems and tropical forests, spreading of infectious diseases and stochastic equations for gene expression.

Abstract
A major issue in modern ecology is to understand how ecological complexity at broad scales is regulated by mechanisms operating at the organismic level. What specific underlying processes are essential for a macroecological pattern to emerge? Here, we analyze the analytical predictions of a general model suitable for describing the spatial biodiversity similarity in river ecosystems, and benchmark them against the empirical occurrence data of freshwater fish species collected in the Mississippi–Missouri river system. Encapsulating immigration, emigration, and stochastic noise, and without resorting to species abundance data, the model is able to reproduce the observed probability distribution of the Jaccard similarity index at any given distance. In addition to providing an excellent agreement with the empirical data, this approach accounts for heterogeneities of different subbasins, suggesting a strong dependence of biodiversity similarity on their respective climates. Strikingly, the model can also predict the actual probability distribution of the Jaccard similarity index for any distance when considering just a relatively small sample. The proposed framework supports the notion that simplified macroecological models are capable of predicting fundamental patterns—a theme at the heart of modern community ecology.

Full text: Predicting spatial similarity of freshwater fish biodiversity, PNAS >>>

Related reading

Dynamical evolution of ecosystems, Nature >>>
Neutral metacommunity models predict fish diversity patterns in Mississippi–Missouri basin, Nature >>>

Participate

Join

Make a donation

Support ISSNAF programs and operations.

Donations to ISSNAF are tax deductible.

Donate now!

Network

Find out who works in your area of expertise or geographic area. ISSNAF Database Search engine allows you to easily view and export contact selections.