Our Future: Hot n’ Tasty?

Climate change. It’s going to wreak no small amount of havoc on mother nature (and if you’re reading this but think all of this climate change stuff is poppycock, please visit Skeptical Science and then come back). How good of a guide is our intuition for what will happen?

This is a great question when it comes to predator-prey relationships and the food chain. One may well think that, heck, from first principles we know that adding heat to a system speeds things up. So, you know, things should continue just as they are – just faster, and maybe with some range-limits and annual timing moved around a bit.

This is quite a sensible proposition. It’s also wrong.

Biomass of phytoplankton and zooplankton under different temperature regimes. Images from Wikipedia.

Recent work from Mary O’Connor and colleagues in PLoS Biology as well as Oliver Beveridge and colleagues in the Journal of Animal Ecology points out some intriguing and non-intuitive effects of heating up food webs – in two completely different systems no less.

O’Connor et al. examine the relationship between phytoplankton and their predators under nutrient enriched conditions. While the little green guys grow more quickly (per capita primary production goes up with increasing temperature), this doesn’t matter to voracious copepods. Their metabolism is sped up even faster, leading to more and more copepods and less and less phytoplankton at higher temperatures.

But what about layering a little complexity on top of that? Beveridge et al. look at a three-level food chain, with bacteria at the bottom, a protozoan consumer, and a ciliate top predator. They cross a manipulation of number of links in the food chain against an increase in temperature and let things run for 6 weeks. While cranking up the heat leads bacteria activity to pick up markedly (and even moreso when their consumer is around), temperature crossed with food chain length does some funny things.

Density of different organisms under conditions of differing food chain length and temperature. Note, if you like the knitted bacteria, you can find the patterns at loxosceles - http://loxosceles.org/crafty/bacterium.html.

Depending on the number of links in the food chain, the relationship between temperature and bacterial density is positive, negative, or U-shaped. In contrast, their consumer, Colpidium, increases where it’s warm when there are no predators, but markedly declines with temperature when it’s predator is around. It’s predator, Didinium, increases in density only at intermediate temperatures. Again, shifts in metabolism and predation rates throughout the food chain appears to be key.

Together, the two studies suggest a dynamic interplay between metabolic activity, rates of predation, and population dynamics. Different levels of a food chain can be affected in very different ways. The simple faster-herbivore-kill-kill scenario is sadly discredited. Rather, we need an understanding of how warming will affect different types of organisms’ rates of growth, death, and predation. Only then can we determine climate change’s dynamic impact on food webs.

O’Connor, M., Piehler, M., Leech, D., Anton, A., & Bruno, J. (2009). Warming and Resource Availability Shift Food Web Structure and Metabolism PLoS Biology, 7 (8) DOI: 10.1371/journal.pbio.1000178

Beveridge, O., Humphries, S., & Petchey, O. (2010). The interacting effects of temperature and food chain length on trophic abundance and ecosystem function Journal of Animal Ecology, 79 (3), 693-700 DOI: 10.1111/j.1365-2656.2010.01662.x

The Map of Science

Why does it take so long for awesome cutting-edge statistical to make their way over to ecology? There are a myriad of techniques out there that have been around for 20, 30, 40, or more years that could help so many ecologists from banging their head into a wall over and over and over and…well, you get the point. But, it takes quite a while for them to percolate over to us. This is not for lack of user-friendly tools, often. Rather it has to do with the connectivity of disiciplines.

For example, I was having a lovely conversation with Jim Grace the other day about using Structural Equation Modeling for predictive purposes, and we ended up chatting a little about history. SEM as it is done currently – using maximum likelihood approaches to fit a model to a covariance or correlation matrix – really dates to the late 1960s and early 1970s. Before then, scientists in a number of disciplines used a wide variety of approaches to examine path models (a là Sewall Wright’s Path Analysis), or perform Factor Analysis, or approach other multivariate models that often included latent variables. These techniques were fairly heterogeneous, even though they attempted to do roughly similar things.

It took Karl Jöreskog‘s wonderful papers outlining his LISREL technique and software using maximum likelihood to really bring the whole enterprise together into modern SEM.

And yet, despite the fact that this seminal work was published in the 70s, there are Ecological papers well into 90s that use piecewise regression models to fit path analyses. Why?

The answer can be summed up by this beautiful diagram detailing the connectivity of science in 2004 from the ever-interesting eigenfactor.org (and hat-tip to Jim for pointing it out to me).

Orange circles represent fields, with larger, darker circles indicating larger field size as measured by Eigenfactor score™. Blue arrows represent citation flow between fields. An arrow from field A to field B indicates citation traffic from A to B, with larger, darker arrows indicating higher citation volume. Image from eigenfactor.org.

Basically, these methods were developed for economics, and saw their first heavy use there and and sociology, political science, education, and psychology. In terms of connectivity, Ecology & Evolution sites on the other side of a doughnut hole of communication (with the occasional exception of psychology). Historically, the fields where the newest techniques are being developed are rarely examined by ecologists, and it is to our loss. Fortunately, I think this is a historical trend. With the rise of search engines, message-boards, and copious mailing lists, I do wonder if a connectivity graph from 2004-2010 would be much tighter.

Connectivity can only be a boon for science. With environmental issues beginning to impinge on every endeavor, it has become more important than ever to survey the breadth of what is out there.

So, hey, sign-up for alerts for a journal that you think will have no relevance to you. Who knows what might drop into your inbox.