Research project: Integrating museum data to produce more effective biodiversity models and predictions, including those for climate change impacts

Dates

Start date:
2006

Museum investigators

Funded by

  • Australian Research Council network grant (ARC), Global Biodiversity Information Facility (GBIF), Terrestrial Ecosystems Research Network (TERN)

Description

Community-level methods provide a flexible, robust, biodiversity surrogates framework.
Community-level surrogates combine species and environmental data, and typically use dissimilarities and robust ordinations. We are developing and exploring a framework called "ED" that uses a general unimodal response model, generalised dissimilarity modeling (GDM), and variants of p-median optimisation.

ED calculations, including complementarity, and indices of species loss under climate change, mimic those made if we had carried out calculations using all species. ED calculations therefore reflect "overall" biodiversity, and serve management applications ranging from reserve selection to climate change impact assessments.

Our working group has used these methods to develop a biodiversity model for continental Australia. Future work will explore scenarios with varying implications for biodiversity losses.

GBIF is the Global Biodiversity Information Facility; it mobilises primary biological data from museums and elsewhere. Our GBIF 2010 Campaign is mobilising and applying GBIF data in order to address the globally recognised biodiversity target for 2010 (a significant reduction in the current rate of biodiversity loss at the global, regional and national levels). At the core of the campaign is better measurement of biodiversity patterns (by integrating GBIF and other data) and better support for the decision-making and planning needed to reduce biodiversity loss (by using systematic conservation planning). A reduced rate of biodiversity loss by 2010 is possible, based on the core idea of systematic conservation planning (SCP). The SCP approach depends on GBIF primary data as the basis for good measures of overall (wholesale) biodiversity. These data are integrated with environmental data to extend the predictive power of the biodiversity models.

We are extending these methods further in order to help develop a global biodiversity observation network (GEO BON). Our biodiversity models can act as a "lens" for interpreting remotely -sensed changes in the condition of land and water localities in various regions.


Dr Dan Faith , Principal Research Scientist
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