Review: Pascual M (2005). Computational Ecology: From the complex to the simple and back. PLoS Computational Biology, 1(2):e18. doi:10.1371/journal.pcbi.0010018

Feature Paper: Pascual M (2005). Computational Ecology: From the complex to the simple and back. PLoS Computational Biology, 1(2):e18. doi:10.1371/journal.pcbi.0010018

Author Abstract: In 1958, when ecology was a young science and mathematical models for ecological systems were in their infancy, Elton  wrote of the ‘‘neolithic days of animal ecology, that is to say about twenty-five years ago.’’ Acknowledging the influence of Lotka and Volterra, he noted, ‘‘Being mathematicians, they did not attempt to contemplate a whole food-chain with all the complications of five stages. They took two: a predator and its prey.’’


Note to Readers: Follow links above for author email, full article text, or the publishing scientific journal. Author notes in my review are in quotes.


Review: Sorry for being a day late with this week's review article. This article goes through the history of how the science of ecology developed to include mathematics from the classic days of Lotka and Volterra and the progress since then. For those who've taken a basic university ecology class, the Lotka-Volterra Predator-Prey model is key. Today, from the basic interactions outlined by those pioneering ecologists (not too long ago, by the way, to put the development of ecology and many other sciences in perspective), ecologists are using mathematical models to describe even complex "transmission of infectious diseases often [using] spatial or social networks [that] can span from local to global distances." 

The author notes though that instead of getting caught up on how complex nature can truly be, "an alternative and more useful role of computation is to address questions on the relationship between dynamics at different temporal, spatial, and organizational scales." 

The author notes how effects at local scales can be used to infer results at the global level. Local populations can either be well-mixed, in which case "mean-field equations" are appropriate, or else "stochastic models such as interacting particle systems" can be used for more complex local population dynamics. 

The author mentions how a difficulty with large-scale or global models is that they often rely on assumptions about knowledge at "microscopic" or local levels. One must be careful with models to ensure that they can account for local effects as well as large-scale effects.

The author then describes how ecology has expanded into the field of "global change ecology." As more data are collected from more locations worldwide, scientists are now able to track changes on large (or global) scales over time. A specific example given by the author is the case of phytoplankton, which are now monitored through remote sensing globally in terms of net primary productivity. 

The author further discusses how the larger a network becomes, the more non-linear the dynamics of interactions within the network become. In ecology, the classic example of "food webs" is receiving renewed interest as scientists are able to collect and analyze data at higher resolutions than ever before. 

Ecologists now also recognize that besides predator and prey interactions, other interactions need to be accounted for, such as "mutualism and parasitism, which can play an important role in ecosystem persistence and bioenergetics." 

The author concludes the article with the take-home message that "stochastic assembly models are prehaps the best candidates to develop a general dynamic theory not only to address open questions on the relationship between structure and dynamics, but also to generate the macroscopic community patterns that ecologists observe in nature and characterize diversity (such as species-area curves and species-rank abundance curves)."

In the last 25 years, ecology has come a long way and now as we enter the era of macroecology, where now scientists are trying to "explore the relationship between dynamics across scales," particularly those at regional and global levels. These interactions become increasingly vital to ascertain as the threat of various kinds of ecosystem collapse (or significant changes in interactions as species extinctions occur more frequently) from human-induced climate change may very well occur in the coming 25 years.