Tulloch Vivitskaia J, Klein Carissa J, Jupiter Stacy D, Tulloch Ayesha I T, Roelfsema Chris, Possingham Hugh P
ARC Centre of Excellence for Environmental Decisions, School of Biological Sciences, University of Queensland, St Lucia, QLD 4072, Australia.
School of Geography, Planning, and Environmental Management, University of Queensland, St Lucia, QLD 4072, Australia.
J Environ Manage. 2017 Mar 1;188:108-119. doi: 10.1016/j.jenvman.2016.11.070. Epub 2016 Dec 8.
Conservation planners must reconcile trade-offs associated with using biodiversity data of differing qualities to make decisions. Coarse habitat classifications are commonly used as surrogates to design marine reserve networks when fine-scale biodiversity data are incomplete or unavailable. Although finely-classified habitat maps provide more detail, they may have more misclassification errors, a common problem when remotely-sensed imagery is used. Despite these issues, planners rarely consider the effects of errors when choosing data for spatially explicit conservation prioritizations. Here we evaluate trade-offs between accuracy and resolution of hierarchical coral reef habitat data (geomorphology and benthic substrate) derived from remote sensing, in spatial planning for Kubulau District, Fiji. For both, we use accuracy information describing the probability that a mapped habitat classification is correct to design marine reserve networks that achieve habitat conservation targets, and demonstrate inadequacies of using habitat maps without accuracy data. We show that using more detailed habitat information ensures better representation of biogenic habitats (i.e. coral and seagrass), but leads to larger and more costly reserves, because these data have more misclassification errors, and are also more expensive to obtain. Reduced impacts on fishers are possible using coarsely-classified data, which are also more cost-effective for planning reserves if we account for data collection costs, but using these data may under-represent reef habitats that are important for fisheries and biodiversity, due to the maps low thematic resolution. Finally, we show that explicitly accounting for accuracy information in decisions maximizes the chance of successful conservation outcomes by reducing the risk of missing conservation representation targets, particularly when using finely classified data.
保护规划者必须权衡使用不同质量的生物多样性数据进行决策时所涉及的取舍。当精细尺度的生物多样性数据不完整或无法获取时，粗略的栖息地分类通常被用作设计海洋保护区网络的替代指标。尽管精细分类的栖息地地图提供了更多细节，但它们可能存在更多的错误分类，这是使用遥感影像时常见的问题。尽管存在这些问题，但规划者在选择用于空间明确的保护优先级的数据时，很少考虑错误的影响。在此，我们在斐济库布劳区的空间规划中，评估了从遥感获得的分层珊瑚礁栖息地数据（地貌和底栖基质）在准确性和分辨率之间的权衡。对于这两者，我们使用描述地图栖息地分类正确概率的准确性信息来设计实现栖息地保护目标的海洋保护区网络，并证明了在没有准确性数据的情况下使用栖息地地图的不足之处。我们表明，使用更详细的栖息地信息可确保更好地代表生物栖息地（即珊瑚和海草），但会导致更大且成本更高的保护区，因为这些数据存在更多错误分类，获取成本也更高。使用粗略分类的数据可能会减少对渔民的影响，如果考虑数据收集成本，这些数据在规划保护区时也更具成本效益，但由于地图的主题分辨率较低，使用这些数据可能会低估对渔业和生物多样性很重要的珊瑚礁栖息地。最后，我们表明，在决策中明确考虑准确性信息可通过降低错过保护代表性目标的风险，最大限度地提高成功实现保护成果的机会，特别是在使用精细分类数据时。
