Ecologists Have Model Envy
General Circulation Models (GCM), which simulate the physics and chemistry of Earth's land, oceans and atmosphere, are an essential component of the pseudoscience behind the global warming scam. Using large, complex computer programs doing indecipherable things while burning up exa-cycles of supercomputer time allows climate scientists to lay down a fake science smokescreen few can penetrate. Naturally, other scientists, with lower funding, are envious of climate modelers and their play toys, including ecologists. In an impassioned plea published in Nature, a group of environmental scientists argue that it is high time that they get to build models of their own. General Ecosystem Models (GEMs) could radically improve understanding of the biosphere and inform policy decisions about biodiversity and conservation they claim. But are they really aware of what they are asking for?
In a commentary piece, “Ecosystems: Time to model all life on Earth,” Drew Purves, of the Environmental Science Group at Microsoft Research in Cambridge, UK, and colleagues have proposed that global models of Earth's ecosystem, analogous to global climate models, be developed to place environmental science on an even footing with climate science. The lack of such GEM programs causes the authors to opine:
No report from the Intergovernmental Panel on Climate Change would fail to mention global climate models. Yet the international bodies that are charged with addressing global challenges in conservation — including the Intergovernmental Platform on Biodiversity and Ecosystem Services, which holds its first plenary meeting next week in Bonn, Germany — cannot refer to analogous models of the world's ecosystems. Why? Because ecologists have not yet built them.
Currently, ecological assessments must be made by specific studies in the affected area—the impact on birds in a deforested area is given as an example. But with a GEM scientists “could capture the broad-scale structure and function of any ecosystem in the world by simulating processes—including feeding, reproduction and death—that drive the distribution and abundance of organisms within that ecosystem.”
In other words, they wouldn't have to mess with those troublesome, inconvenient on-site surveys. They could do as their climate scientist colleagues: avoid fieldwork and stay in the nice, clean computer center. But how would these magical models work? Climate models have all sorts of physical processes to simulate and link together, can the same be done for biological processes? After all, biological organisms range from the very small (bacteria, diatoms) to the very large (redwood trees, whales), and interact in many ways. Here is how the authors envision such a model being constructed:
Ecologists could apply a GEM to African savannas, for instance, to model the total biomass of all the plants, the grazers that feed on the plants, the carnivores that feed on the grazers and so on. Over time, the flows of energy and nutrients could be mapped between them. All of the organisms would be grouped not by species, but according to a few key traits such as whether they are plants, birds or mammals, cold blooded or warm blooded, diurnal or nocturnal. By encoding processes such as migration and predation into simple mathematical and computational forms, ecologists could model what happens to the various groups over time.
Purves et al. do recognize that the incredible diversity of living things poses a problem for would be eco-modelers. They cite work they have done at Microsoft Research and at the United Nations Environment Programme World Conservation Monitoring Centre. There a prototype GEM for terrestrial and marine ecosystems has been under construction. Called the Madingley model, it uses actual carbon flow data as a fundamental driving factor. “We have hit all sorts of computational and technical hurdles, and are expecting more as we develop the model,” they admit candidly.
The Madingley model encodes fundamental ecological processes into simple computational forms.
There have been eco-models constructed in the past, abet on more limited scales than climatologists' ambitious global models. Yet even models of limited scope have suffered from a dearth of data. The authors speak truthfully when they admit that nature can overwhelm any attempt at capturing everything in a model. “Obviously, modelling every organism within an ecosystem is impossible,” they state. “We estimate that it would take a standard laptop computer around 47 billion years to model for 100 years every multicellular animal within just one of the 1-degree grid cells covering Earth.”
Indeed, the biggest stumbling block to constructing GEMs is obtaining sufficient data to build, drive and validate them. As complex as the physical processes comprising Earth's ecology are, our planet's biota and their interactions are orders of magnitude more complex. Humans share the planet with as many as 8.7 million different forms of life, according to a recent estimate of life on Earth (see “How Many Species Are There on Earth and in the Ocean?”). An astonishing 86% of all plants and animals on land and 91% of those in the seas have yet to be named and catalogued, the study claims.
Climate modelers have stumbled repeatedly over processes we do not sufficiently understand. Having to account for each organism and its interactions will all other organisms verges on the impossible. Aside from the combinatorial problems, the rules governing interactions may not be cut and dried. “Ecological systems do not have the equivalent of the precise laws used by climate scientists,” the authors state.
How do you account for organisms you do not know exist? The authors imagine a new, world wide program to find and catalog new species using automated cameras. “Using automated cameras and image recognition, it should be possible to sample thousands of animals and determine their approximate size and what broad group they belong to: reptile or mammal, flying or non-flying,” they state. But such cameras will not identify the smallest of Earth's creatures, or species of plants, which often have greater impact on an area's ecology than large animals.
The number of unknown organisms in tropical rain forests is greater than the number known.
“[S]tored away in numerous research institutions are vast samples of insects collected in traps that suck them out of the air, and data from continuous plankton recorders towed beneath ships for millions of kilometres,” they protest, as if poring over existing archives will rectify their data problems. No, ecological studies are in an even more ignorant state than climate science, even though that bar is set fairly low. Collecting the data needed for the proposed GEMs will require a massive effort and that translates directly to large sums of money.
Naturally, a major new data-gathering programme would be costly. But globally, governments already spend billions of dollars on satellite observations of vegetation and habitat distribution, fisheries surveys, forest inventories and species surveys. Diverting a small fraction of these funds to gathering the data needed to develop and evaluate GEMs could pay dividends. A first step would be for governments around the world to support programmes similar to the National Ecological Observatory Network — an international cooperation funded by the US National Science Foundation to manage large-scale collection of ecological and climate data.
Perhaps the authors have been so engrossed in their studies that they have not noticed that current financial state of the world? Most scientifically advanced nations are struggling with stagnant economies, high unemployment and hemorrhaging budgets. Climate science has managed decades of high funding by spinning frightening stories of impending doom. Now, even their funding is under harsh scrutiny, and not just because the promised catastrophes have not materialized. To suggest a new, large-scale and expensive program in today's economic climate is the height of folly.
There are other, more telling factors to consider. Purves et al. recognize this themselves: “Constructing realistic GEMs is one thing. The real challenge is to produce models from which the predictions are trustworthy enough to guide the decisions of conservationists and policy-makers.” Are they really suggesting that models can be developed that will replace in-depth, on-site fieldwork? Will governments and the public accept the prognostications of computer programs when it comes to regulating land use and conservation programs? There is nothing wrong with constructing generalized models in the search for understanding how nature works, but qualitative models should not be used to make concrete predictions of future conditions. As Richard W. Hamming put it, “The purpose of computing is insight, not numbers.”
Computer climate models have proven to be climate science's Achilles heel. They have consistently over promised and under delivered results for decades and show no sign of improving significantly. If a program to create ecological models is launched the temptation to make similar, overreaching promises will be irresistible—particularly when government funding is at stake. “But just by attempting to build general models, ecologists will find out what they need to know to truly understand ecosystems,” the article summarizes. To propose a massive modeling project in order to discover what ecologists should be studying as a side effect is beyond inane. There is a way to figure out what ecologists truly need to understand—it is hard, time consuming and messy. That way is called science.
Be safe, enjoy the interglacial and stay skeptical.