SAVING the world used to be the job of superheroes. But now, with millions of
plant and animal species facing extinction, it’s down to us mere mortals. Where
do we begin? We don’t even know how many species are out there. And even if we
did, the numbers are meaningless until we know how the different species
interact.
Collecting all this detail is a Herculean task. Yet without the detail, how
can we know which human activities are most likely to have apocalyptic
consequences, let alone work out ways to avoid them?
What we need is a way to make predictions based on the information we already
have. But the fledgling science of ecology has struggled to describe the natural
world, let alone understand it. Like early astronomers, ecologists are faced
with a unique system that doesn’t lend itself to scientific methods such as
experimentation, replication or manipulation. However, just as stargazers learnt
to predict eclipses and alignments of planets, ecologists are now starting to
build models that can explain patterns in nature and help predict how ecosystems
will react to change.
Advertisement
One pioneer of this approach to understanding biocomplexity is Neo Martinez
from San Francisco State University in California. He has made a career of
studying food webs—in other words, who eats who within any ecosystem.
Charles Darwin once described such webs as intractable “tangled banks”. But
Martinez is starting to trace the strands that tie the beautiful mess together.
He has come up with a way to discover how the animals and plants in a given
community interact, without resorting to exhaustive observation and sampling.
His computer models open the door to a better understanding of how specific
extinctions affect biodiversity as a whole.
Martinez’s breakthrough came when he noticed curious patterns in the links
between trophic species in food webs. When it comes to understanding natural
relationships, trophic species—groups of organisms that share the same
predators—are more relevant than biological species. They also make up a
much larger fraction of the biodiversity under scrutiny. Working at Little Rock
Lake in Wisconsin, home to a notoriously complex food web containing 92 such
species, Martinez discovered that each species was either prey for or preyed
upon by 10 per cent of all the other species. He describes this in terms of
“connectance”—the fraction of predator links actually present out of all
those possible within a food web.
What’s more, this percentage of links between species—the
connectance—did not change as Martinez added new species to build up a
detailed picture of the system. “The constant-connectance hypothesis asserts
that a particular balance of food-web complexity exists in nature,” he says.
“This balance is between everything eating everything and everything eating
nothing.” And his trawl of the literature confirmed the pattern in a variety of
complex ecosystems, from deserts and islands to estuaries and lakes. Ten per
cent wasn’t a magic number, but whatever the connectance within a given
ecosystem, it remained curiously constant no matter how many new species were
added to the food web.
No single ecological or biological theory could predict or explain such a
pattern. What was going on? Martinez suspected the answer lay in the way new
links are created when a species enters the system, whether by migration or
evolution. To investigate what happens when a new species lands in the web, he
teamed up with Richard Williams from San Francisco State University’s Romberg
Tiburon Center for Environmental Studies. Their mathematical model started with
the notion that complex food webs arise from a simple pecking order. Then they
added two rules. First, that species higher up in a fixed pecking order tend to
eat those lower down. And secondly, that if an organism eats two species in a
web then it must also eat all the intervening species in the hierarchy. So, for
example, a shark that preys on large fish such as tuna and small fish including
mackerel will also eat cod and any other medium-sized fish.
By including just two parameters in the model—the number of species in
a given web and the connectance between these—Martinez and Williams can
accurately predict 12 ecological characteristics of that web. These include the
length of food chains, the number of omnivores and cannibals, the distribution
of specialists—those with a restricted diet—and
generalists—those that eat whatever’s going—and perhaps most
importantly from a conservation viewpoint, the vulnerability of various groups
of species.
When it comes to predicting the way complex natural communities interact,
this model is far superior to any other. “It was surprising to me, and I think
most people, that a model with such a simple conceptual basis would do such a
good job of predicting the properties of observed feeding networks,” says
Williams.
And a model that shows what’s already there has obvious potential to forecast
what might be. In any community of organisms, each species fits into its own
niche. The niche model devised by Martinez and Williams is novel because it
successfully predicts the niche any species will adopt in terms of its
“connectedness” to other organisms in the system.
Using the niche model you can build virtual versions of real food webs and
then see what happens when you simulate speciation or extinctions. “We can play
games in the computer and see if species at the bottom of the food web are more
important to maintaining diversity than those at the top. We can play similar
games comparing specialists and generalists,” says Martinez. The niche model
could also show how pollutants such as DDT and PCBs accumulate in food webs of
different sizes and complexities.
Martinez’s approach has been well received. “He has made a huge effort in
exploring the regularities displayed by ecological interactions,” says Ricard
Sole from the Santa Fe Institute in New Mexico. “His model with Williams will be
a classic reference in ecology.” This could be the leap that ecology has been
waiting for. But in some ways, Martinez’s thinking is quite conservative because
he has natural selection as the driving force behind ecological patterns. Other
students of biocomplexity are much more radical. They talk about food webs in
the language of the physical sciences, explaining the complexities of nature in
terms of “emergent properties” rather than biological principles.
Sole is one such thinker. Working with Jose Montoya of the Complex Systems
Research group at the Polytechnic University of Catalonia, Barcelona, he claims
to have found the “small worlds” phenomenon in food webs. Small worlds are big
news among mathematicians and modellers trying to understand all sorts of
complex networks from the Internet to the nervous system of worms. This is the
science behind the folklore that there are no more than six degrees of
separation between any two people, and that Kevin Bacon can be linked to any
other movie actor in just a few moves (New Scientist, 4 December 1999, p 24).
In essence, a small world is any network containing many nodes that have a
few links, together with a few nodes—or hubs—with many links. This
arrangement, known as a power law distribution, greatly reduces the number of
steps needed to link any two nodes compared with a completely regular network.
The distribution of species in food webs fit the pattern, say Sole and Montoya,
because most species are connected to a small proportion of others, while a few
hub species have many connections. These “keystone species” have long been
recognised by some ecologists as the linchpins of ecosystems, essential to the
stability of the community.
Seeing food webs in this way has advantages, because networks exhibiting
small-world phenomena show predictable responses to change. “The very topology
of these networks plays a crucial role in how these systems behave,” says
Albert-László Barabási of the University of Notre Dame,
Indiana. “Finding that food webs follow a power law distribution, just like we
see for the cell, and the Internet, suggests that nature displays a high degree
of economy when it spins its various networks—it uses the same blueprint
for most of them.”
Barabási’s own work reveals that such complex systems have emergent
properties—they are more than the sum of their parts, having properties
that emerge from the network as a whole. He has found, for example, that the
Internet is inherently stable and well protected against random removal of
sites, because most have very few links, and only a concentrated attack could
remove the rarer hubs and bring down a large part of the structure. “We have
shown that these networks are highly robust against random node removal but
fragile against concentrated attacks,” says Barabási.
In a new study, Sole and Montoya found that food webs can tolerate random
removal of species, but that directed removal of keystone species could bring
about the collapse of the entire ecosystem. “Our analysis shows that removal of
around 5 to 10 per cent, a small fraction of highly connected species, can lead
to ecosystem collapse,” says Sole.
The implications for conservation are obvious. A high level of close
interconnection between organisms in a shared ecosystem means that biodiversity
loss and species invasions may affect many more species than we had anticipated.
“It might be the case that some human-driven perturbations could target some of
the highly connected species, and thus eventually promote a cascade of
extinctions through the system,” says Sole. “If true, then current estimates of
biodiversity loss might be much lower than we expected.”
But can such a theoretical approach really help at ground level? Stuart Pimm,
an ecologist and complexity theorist at the University of Tennessee in Knoxville
believes it can. “Finding these broad-scale patterns is a fascinating insight
into the way nature works, helping us to rethink old ideas,” he says. Pimm says
that modelling extinctions has revealed some original insights. “Extinctions are
caused by habitat destruction, introductions of new species and hunting.
Secondary extinctions—when a species goes extinct because of the removal
of a closely connected neighbour—are the last part of the deadly quartet.”
And, says Pimm, the modelling reveals that secondary extinctions are much more
important than ecologists suspected.
In the real world, this means that many more species might need to be
protected. On a positive note, small-worlds modelling could help ecologists
identify those organisms whose continued existence is crucial to entire
ecosystems. That way, conservationists have their best chance of preventing the
runaway collapse of ecosystems through secondary extinctions. But we are running
out of time. “The speed of species loss is, sadly, very fast,” says Sole. “Many
species with low populations are simply going down slowly but inevitably to
their extinction.”
Barabási is more optimistic. He believes that in the next few decades,
biocomplexity will take centre stage. “We will learn more and more about the
structure, origin and evolution of these systems,” he says. “Eventually, we all
hope that this will culminate in large-scale modelling, allowing us to develop
the same mathematically rigorous framework for living systems that was so
successful in the physical sciences.”
“In the early nineties, there was great excitement about the possibility of
formulating a general theory of complex systems,” says Ricard Sole from the
Santa Fe Institute in New Mexico. But there were some major gaps in the overall
picture. Improvements in biocomplexity and network thinking have helped fill
these. “A new wave of theoretical results is changing many of our previous ideas
now, and the possibility of formulating a general theory of complexity seems
much more feasible.”
The discovery that real ecological networks share common traits with some
other systems, both biological and technological, has important implications. It
suggests that patterns in nature are not derived from biological processes
alone. Distributions of species, the World Wide Web and metabolic networks all
seem to show robust self-organisational phenomena. It is the global features of
such webs that matter, not the individual characteristics of the units that make
them up.
But while it is tempting to describe all systems along similar lines, the
details in each discipline matter. And, Sole cautions, although many complex
networks share common patterns, it is still unclear if, and how, similar
dynamics shape their order and evolution. As a result, using complexity theory
to come up with practical solutions such as conservation policy is still a
daunting challenge.
A theory of everything?
-
Further reading:
Signs of Life
by Ricard Sole and Brian Goodwin, Basic Books (2000) -
Simple Rules Yield Complex Food Webs
by Richard Williams and Neo Martinez, Nature, vol 404, p 180 (2000) - http://online.sfsu.edu/~webhead