10
C H A R L E S J . K R E B S
are higher-level questions that involve value judgments, not objective probability statements.
Recommendation 5: Use statistical estimation more than statistical inference.
There is more to life than p-values.
These cautionary notes should not be misinterpreted to indicate that you do
not need to learn statistics to be an ecologist. You should learn statistics well
and then learn to recognize the limits of statistics as a tool for achieving knowledge. Every good study needs explicit null hypotheses and the appropriate statistical testing.
Hypotheses and Prediction
Hypotheses, once tested and confirmed, lead us to understanding but not necessarily to predictions that will be useful in applied ecology. Prediction is often
used to mean forecasting in a temporal sense: What will happen to Lake Superior after zebra mussels are introduced? At present, applied ecologists can make
only qualitative predictions in the medium term and quantitative predictions
in the short term. We should focus on these strengths for the present and not
berate ourselves for an inability to predict in the long term how disturbed populations and communities will change.
Short-term quantitative predictions are of enormous practical utility. If you
know the number of aphids now, the numbers of their predators, and the temperature forecast for the next 2 weeks, you can predict aphid damage in the
short term (Raworth et al. 1984). Ecologists should exploit the vast store of
natural history data to develop these simple predictive models. This is not the
route to the Nobel Prize, but it is still one of the most important contributions
ecologists can make to society.
Medium-term predictions are more difficult, and ecologists often have to
settle for qualitative predictions. A good example is provided by the search for
habitat models that can be used in conservation planning. Not all habitat
patches are occupied by all species, and metapopulation theory builds on this
observation. But a habitat can be declared suitable only if it has the food and
shelter a species requires and if the species can disperse there. Suitable habitats
may have all the structural features needed but become unsuitable if a predator takes up residence (Doncaster et al. 1996). The scale of the difficulty in
achieving medium-term predictions can be seen by work on the spotted owl in
C H A R L E S J . K R E B S
are higher-level questions that involve value judgments, not objective probability statements.
Recommendation 5: Use statistical estimation more than statistical inference.
There is more to life than p-values.
These cautionary notes should not be misinterpreted to indicate that you do
not need to learn statistics to be an ecologist. You should learn statistics well
and then learn to recognize the limits of statistics as a tool for achieving knowledge. Every good study needs explicit null hypotheses and the appropriate statistical testing.
Hypotheses and Prediction
Hypotheses, once tested and confirmed, lead us to understanding but not necessarily to predictions that will be useful in applied ecology. Prediction is often
used to mean forecasting in a temporal sense: What will happen to Lake Superior after zebra mussels are introduced? At present, applied ecologists can make
only qualitative predictions in the medium term and quantitative predictions
in the short term. We should focus on these strengths for the present and not
berate ourselves for an inability to predict in the long term how disturbed populations and communities will change.
Short-term quantitative predictions are of enormous practical utility. If you
know the number of aphids now, the numbers of their predators, and the temperature forecast for the next 2 weeks, you can predict aphid damage in the
short term (Raworth et al. 1984). Ecologists should exploit the vast store of
natural history data to develop these simple predictive models. This is not the
route to the Nobel Prize, but it is still one of the most important contributions
ecologists can make to society.
Medium-term predictions are more difficult, and ecologists often have to
settle for qualitative predictions. A good example is provided by the search for
habitat models that can be used in conservation planning. Not all habitat
patches are occupied by all species, and metapopulation theory builds on this
observation. But a habitat can be declared suitable only if it has the food and
shelter a species requires and if the species can disperse there. Suitable habitats
may have all the structural features needed but become unsuitable if a predator takes up residence (Doncaster et al. 1996). The scale of the difficulty in
achieving medium-term predictions can be seen by work on the spotted owl in