The devil isn't always in the details: how system properties can inform ecology

Selection on stability across ecological scales. Jonathan J. Borrelli, Stefano Allesina, Priyanga Amarasekare, Roger Arditi, Ivan Chase, John Damuth, Robert D. Holt, Dmitrii O. Logofet, Mark Novak, Rudolf P. Rohr, Axel G. Rossberg, Matthew Spencer, J. Khai Tran, Lev R. Ginzburg. 2015. Trends in Ecology & Evolution, http://dx.doi.org/10.1016/j.tree.2015.05.001.

This paper in TREE  on selection at higher level systems has been on my must-read list since it came out a few weeks ago, and it was worth the wait. It does what the best TREE papers do - makes you think a bit more deeply about a common topic. In this case, it develops an approach to understanding complex ecological systems (communities, ecosystems) that is blind to the details that ecologists often focus on.

The search for generalities and commonalities drives modern ecology. In short (though this paper deserves an in-depth read), this paper argues that we can learn much by considering stability and feasibility in complex ecological systems. That is, we can also study community structure or trophic webs by considered whether specific configurations of the system are stable. This is in contrast to a context-centric study of a system, where the usual list of proximate causes (productivity, niche availability, connectivity, etc, etc) may be used to understand why the system looks as it does.

The authors' premise is that nonadaptive (e.g. unstable) ecological systems will be unfavourable and selected against, and the resulting selective process “can produce many of those recurrent ecological patterns that have been observed in nature over large scales of space and time.” This requires that you accept a few underlying concepts: first, that large scale systems also experience selection (whether one prefers selection be in parentheses is up to the reader), in that unstable systems will be lost at faster rates leading to greater frequency of stable systems; and second, that this process of selection is determined by the properties of the system alone, not the specific conditions ecologists often focus on.

As an illustration, consider four possible food webs depicting intraguild predation that vary in their interaction strengths. All configurations are possible, but A-C are likely to lead to exclusion of the intraguild predator. D is most likely to be stable since the strong interaction between the resource and prey results in negative feedbacks between the densities of all species (i.e. when the resource is low, the prey should also be low, reducing the predator density as well) and thus more likely to be observed in natural systems. 

From Borrelli et al 2015.

A more specific example looks at attack rates and handling times in predator-prey interactions. When stability is considered, it seems that although predator-prey cycles may occur, it should be uncommon to have such extreme oscillations that populations reach dangerously low levels where stochastic extinctions may occur. Data suggests that oscillatory dynamics are less common in predator-prey relationships, but do occur particularly for specialist predator/prey pairings. Theory (Rosenzweig-MacArthur predator-prey models) predict that such pairings should be most stable if prey are weakly self-limited and predators have high attach rates/long handling times. Empirical evidence for this prediction supports it surprisingly well.

From Borrelli et al 2015.

 
Other related approaches consider feasibility across food webs, communities, and ecosystems. A community perspective might consider interactions across all species, perhaps using a network approach. Networks should tend towards formations that are the most stable – e.g. short chains rather than long ones. The commonness of nested network structures may reflect these constraints. 

Such an approach to ecology is not entirely new (Robert May's weak interactions comes to mind). But it provides perhaps the best potential explanation I’ve seen for ‘generality’ focused approaches in ecology, including ecological allometric relationships, macroevolutionary patterns, and network approaches. Macroecological patterns have often captured, rather than tidy linear relationships, occupied versus unoccupied parameter space. Thinking about feasibility as a macroecological ‘mechanism’ for ecological patterns at the system scale might lead to new research directions.