As global climate change accelerates, one of the most urgent tasks for the coming decades is to develop accurate predictions about biological responses to guide the effective protection of biodiversity. Predictive models in biology provide a means for scientists to project changes to species and ecosystems in response to disturbances such as climate change.
However, current models are not performing very well and don't provide accurate predictions. One reason is that most of them do not include biological mechanisms such as demography, dispersal, evolution, and species interactions although these have been shown to be connected to climate change responses. Most models are descriptive, based on statistical correlations and observations, and fail to capture the underlying processes that produce observed changes. For example, a descriptive model might show that lynx in the northern U.S. are declining while bobcat populations in the same region are on the rise. Understanding what is driving this change requires a different sort of model, one that incorporates biological mechanisms. A mechanistic model that accounts for how warming temperatures affect snow depth, for instance, could provide insights into why bobcats - better adapted to habitats with less snow - are gaining a competitive edge over lynx.
Another challenge is that as models have grown in sophistication, they have far outpaced data collection. Put another way, a model is like a state-of-the-art kitchen, but the cupboards are bare.
We can now build videogame-like environments with computers where we can create multiple versions of Earth and ask what the implications under different scenarios are. But our ability to learn from these tools is constrained by the kinds of data we have.
An international group of researchers identified six biological mechanisms that influence wildlife's responses to climate change:
- demography and life history
- evolutionary potential and adaptation
- interactions between specie
- movement over land or water
- responses to changes in the environment.
They ranked the information needed to account for these mechanisms in models and suggested proxies for data that are missing or hard to collect. A globally coordinated effort to fill data gaps could greatly advance improvements in models and informed conservation approaches and local and regional conservation groups need not wait for a global body to coalesce to start using a mechanistic approach in their own region,.
If the ideas put forth in this paper start to be adopted and integrated into climate change work in a grass roots way, that could make a big difference in a region and could scale up over time.Working with citizen scientists offers an opportunity to get huge amounts of data, and it's foolish not to take advantage of it, The data might not be as rigorous and needs to be treated differently, but it's one more source of valuable information.