ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, Australia.
Glob Chang Biol. 2013 Feb;19(2):352-63. doi: 10.1111/gcb.12064. Epub 2012 Nov 30.
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.
许多研究都探讨了采用更复杂的建模技术或空间数据来提高我们准确预测生态系统对全球变化响应能力的好处。然而，我们目前还不太清楚，一旦考虑到获得改进模型或数据的成本，改进后的预测是否真的会带来更好的保护结果。这严重限制了我们根据全球压力做出战略决策的能力，尤其是在那些像沿海地区那样受到动态变化影响的景观中。在这些景观中，海平面上升这一全球现象是保护生物多样性的一个关键考虑因素。在这里，我们在考虑用有限的预算来确定保护区系统的位置以保护沿海生物多样性的背景下解决了这个问题。具体来说，我们确定了在澳大利亚昆士兰州东南部沿海地区投资于高分辨率高程数据和基于过程的模型来预测湿地变化的成本效益。我们评估了由此产生的保护区选择优先区域，以量化投资于更好地量化生物和物理过程的成本效益。我们表明，在这种情况下，使用基于过程的模型和高分辨率高程数据要合算得多，即使这需要项目预算的很大一部分（在一种情况下高达 99%）。不太准确的模型和数据集未能确定具有高保护价值的区域，降低了保护计划的成本效益。这表明，在海平面上升威胁生物多样性的地区制定保护计划时，投资于高分辨率高程数据和基于过程的模型来预测沿海生态系统的变化可能具有很高的成本效益。未来的研究重点是确定这种成本效益在全球不同地区的差异。
