A third mathematical challenge for forests is to understand how manyspecies can be removed before the functioning of the forest breaks down.
Northeast forests in the U.S., for example, have lost their elms and their cherry
trees, and ash trees are in danger of disappearing. So far, the forests have been
able to absorb these losses, but if too many more species die out, the ecosystem
will collapse. Mathematicians don’t yet have good ways of understanding the
resilience of a complex system like a forest.
Agriculture is a third area where mathematical scientists are having agrowing impact. Spatial planning provides one challenge. Optimization methods
can be used to determine the mixture of crops that is likely to be most productive
in a particular area, taking into account growth rates, susceptibility to disease,
economic value, etc. There’s an additional consideration that so far has been
little accounted for in such analyses: what crop grows where. One crop may be
hard to transport (like switchgrass for a biofuel plant, for example); another may
be susceptible to bacterial contamination from livestock (like spinach); a third
may be subject to invasive species. The mathematics to determine the best
spatial placement of crops is only now being developed.
To understand issues of sustainability, mathematical scientists need tounderstand issues of the social sciences and to bring social science issues and
methods into their models. Agriculture provides a prime example. One of the
greatest sustainability challenges comes from rising consumption and the need
for consumption to be divided more equitably around the world. This is
particularly true for agricultural products. Many different forces are coming
together to create an increasing demand for certain agricultural products:
population is rising; the developing world is growing economically; meat
consumption is increasing. Large models of the economy have been created that
take into account the different consumption levels in different areas, but there is
enormous uncertainty that influences their output: How fast will the developing
world grow? How much more meat will people eat? How fast will population rise?
How will decisions made by societies affect population growth patterns? All of
these things impact model output enormously. Improving these models requires
that mathematical scientists work with social scientists to understand the
dynamics of the developing and developed world and the impact of alternative
management plans.