首页> 美国卫生研究院文献>other >Mapping Habitats and Developing Baselines in Offshore Marine Reserves with Little Prior Knowledge: A Critical Evaluation of a New Approach
【2h】

Mapping Habitats and Developing Baselines in Offshore Marine Reserves with Little Prior Knowledge: A Critical Evaluation of a New Approach

机译:在很少了解先验知识的情况下绘制海洋海洋保护区中的栖息地并发展基线:对一种新方法的批判性评估

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

The recently declared Australian Commonwealth Marine Reserve (CMR) Network covers a total of 3.1 million km2 of continental shelf, slope, and abyssal habitat. Managing and conserving the biodiversity values within this network requires knowledge of the physical and biological assets that lie within its boundaries. Unfortunately very little is known about the habitats and biological assemblages of the continental shelf within the network, where diversity is richest and anthropogenic pressures are greatest. Effective management of the CMR estate into the future requires this knowledge gap to be filled efficiently and quantitatively. The challenge is particularly great for the shelf as multibeam echosounder (MBES) mapping, a key tool for identifying and quantifying habitat distribution, is time consuming in shallow depths, so full coverage mapping of the CMR shelf assets is unrealistic in the medium-term. Here we report on the results of a study undertaken in the Flinders Commonwealth Marine Reserve (southeast Australia) designed to test the benefits of two approaches to characterising shelf habitats: (i) MBES mapping of a continuous (~30 km2) area selected on the basis of its potential to include a range of seabed habitats that are potentially representative of the wider area, versus; (ii) a novel approach that uses targeted mapping of a greater number of smaller, but spatially balanced, locations using a Generalized Random Tessellation Stratified sample design. We present the first quantitative estimates of habitat type and sessile biological communities on the shelf of the Flinders reserve, the former based on three MBES analysis techniques. We contrast the quality of information that both survey approaches offer in combination with the three MBES analysis methods. The GRTS approach enables design based estimates of habitat types and sessile communities and also identifies potential biodiversity hotspots in the northwest corner of the reserve’s IUCN zone IV, and in locations close to shelf incising canyon heads. Design based estimates of habitats, however, vary substantially depending on the MBES analysis technique, highlighting the challenging nature of the reserve’s low profile reefs, and improvements that are needed when acquiring MBES data for small GRTS locations. We conclude that the two survey approaches are complementary and both have their place in a successful and flexible monitoring strategy; the emphasis on one method over the other should be considered on a case by case basis, taking into account the survey objectives and limitations imposed by the type of vessel, time available, size and location of the region where knowledge is required.
机译:最近宣布的澳大利亚联邦海洋保护区(CMR)网络覆盖了大陆架,斜坡和深渊生境总计310万km 2 。在该网络内管理和保护生物多样性价值需要了解其边界内的物理和生物资产。不幸的是,人们对网络内大陆架的栖息地和生物组合知之甚少,那里的多样性最丰富,人为压力最大。未来对CMR资产的有效管理要求有效,定量地填补这一知识空白。对于架子而言,这一挑战尤其艰巨,因为多波束回声测深仪(MBES)映射是一种用于识别和量化栖息地分布的关键工具,它在浅层深度上非常耗时,因此从中期来看,对CMR架子资产进行全覆盖图绘制是不现实的。在这里,我们报告了在弗林德斯联邦海洋保护区(澳大利亚东南部)进行的一项研究的结果,该研究旨在测试表征架子生境的两种方法的益处:(i)连续(约30 km 2 < / sup>),根据其潜力选择的区域包括一系列可能代表更广泛区域的海底栖息地, (ii)一种新颖的方法,该方法使用广义随机镶嵌细分分层样本设计,对大量较小但空间平衡的位置进行有针对性的映射。我们提出了弗林德斯自然保护区架子上生境类型和无柄生物群落的第一个定量估计值,前者基于三种MBES分析技术。我们对比了两种调查方法与三种MBES分析方法所提供的信息质量。 GRTS方法可实现基于设计的栖息地类型和无柄群落估计,还可以识别自然保护区IUCN IV区西北角以及靠近架子切割峡谷顶的位置中潜在的生物多样性热点。但是,基于设计的栖息地估算会因MBES分析技术的不同而大不相同,这凸显了保护区低调礁石的挑战性,以及在小型GRTS位置获取MBES数据时需要进行的改进。我们得出的结论是,这两种调查方法是相辅相成的,它们在成功且灵活的监测策略中均占有一席之地;应根据具体情况考虑一种方法相对于另一种方法的强调,并应考虑到调查目的和限制,这些限制是由船只类型,可用时间,需要知识的区域的大小和位置所施加的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号