A dynamical systems link between traits and ecosystems

| No Comments
Over the past few decades, ocean ecosystem modelers like myself have gone from a Nutrient-Phytoplankton-Zooplankton picture of the ocean that looks like this:
NPZ.jpg


...to one that looks more like this:

NNPPZZ.jpg

The motivation, I would say, has been that the simple paradigm misses important processes, like species life history, or important properties, like diversity and its consequences. (Not to mention the myriad of Z that do not consume P.) The more complex picture, although impressive looking, comes with baggage. Among others, there is also the problem of parameterizing these models. Lots of boxes and arrows means lots of rates and properties to be measured--increasing roughly with the square of the number of species (or state variables).

One approach to simplifying this mess is to organize the ocean according to certain traits. Trait-based perspectives are not new, and I think they might be able to clean up the NNNNPPPPPZZZZZZZZZZ spider web we're tinkering around with these days. For example, we can approximate the distribution of a trait across species with a curve. With some analysis, we can then look at the effect of the shape of this curve on the structure of the community. In the example below, a Gaussian distribution of growth rates (gamma) produces realistic rank-abundance curves in a zooplankton population. Different curves produce different community-level patterns.

Traits.jpg

This is an idealized analytical model. The real world is messier. Still, as long as we are modeling species by using collections of ecologically important traits, we can use the distributions of those traits to inform the model. 

As a messier example, I'll draw from a more complex copepod model. We included a number of traits, such as activation energies, development times, and diapause strategies (many copepods go into dormancy during certain stages). These traits have been painstakingly tabulated across many species by people who I assume have lots of coffee and live in cold, cloudy places. By drawing from these distributions in a sort of stochastic way, and plugging the model into different parts of the ocean, we get very different communities emerging at different places.

TraitModel.jpg


The next image shows preliminary output from a North Atlantic model. These are the results for the diapause trait. Basically, in northern latitudes, species that diapause make up the majority of the population. Closer to the equator, they don't fare well. On the right you can see an image of what the population looks like in terms of the size of the animals, and a distribution of the diapause trait as a function of life stage. In the north, there are large diapausing species with long development times. In the south, the opposite.

DiapauseMap.jpg

If you know the North Atlantic well, you'll recognize that this map is not perfect, and we are still a ways from describing the whole ecosystem this way. Still, by shifting the perspective away from the individual species, and towards properties of the community, we are able to make some more sense out of the NNNNNNPPPPPPPZZZZZZZZZZZZ spider web.

Nick Record, signing off


Hubbell SP (2001) The unified neutral theory of biodiversity and biogeography (MPB-32) (Vol. 32). Princeton University Press.

Record NR, Pershing AJ, Maps F (2013) Emergent copepod communities in an adaptive trait-structured model. Ecol Model

Record NR, Pershing AJ, Maps F (2013) The paradox of "the paradox of the plankton". ICES J. Mar. Sci. 

  

Leave a comment

About this Entry

This page contains a single entry by Nick Record published on February 24, 2014 6:19 PM.

The Gulf of Maine is Warming Fast! was the previous entry in this blog.

A closer look at Gulf of Maine warming is the next entry in this blog.

Find recent content on the main index or look in the archives to find all content.