Many studies have explored the benefits of adopting more sophisticated modelling techniques or spatial data in terms of our ability to accurately predict ecosystem responses to global change. However, we currently know little about whether the improved predictions will actually lead to better conservation outcomes once the costs of gaining improved models or data are accounted for. This severely limits our ability to make strategic decisions for adaptation to global pressures, particularly in landscapes subject to dynamic change such as the coastal zone. In such landscapes, the global phenomenon of sea level rise is a critical consideration for preserving biodiversity.
Here, we address this issue in the context of making decisions about where to locate a reserve system to preserve coastal biodiversity with a limited budget. Specifically, we determined the cost-effectiveness of investing in high-resolution elevation data and process-based models for predicting wetland shifts in a coastal region of South East Queensland, Australia. We evaluated the resulting priority areas for reserve selection to quantify the cost-effectiveness of investment in better quantifying biological and physical processes.
We show that, in this case, it is considerably more cost effective to use a process-based model and high-resolution elevation data, even if this requires a substantial proportion of the project budget to be expended (up to 99% in one instance). The less accurate model and data set failed to identify areas of high conservation value, reducing the cost-effectiveness of the resultant conservation plan. This suggests that when developing conservation plans in areas where sea level rise threatens biodiversity, investing in high-resolution elevation data and process-based models to predict shifts in coastal ecosystems may be highly cost effective. A future research priority is to determine how this cost-effectiveness varies among different regions across the globe.
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