five

Marine Futures Project - Mount Gardner - biota

收藏
Research Data Australia2024-12-14 收录
下载链接:
https://researchdata.edu.au/marine-futures-project-gardner-biota/685744
下载链接
链接失效反馈
官方服务:
资源简介:
The Marine Futures Project was designed to benchmark the current status of key Western Australian marine ecosystems, based on an improved understanding of the relationship between marine habitats, biodiversity and our use of these values. Approximately 1,500 km2 of seafloor were mapped using hydroacoustics (Reson 8101 Multibeam), and expected benthic habitats "ground-truthed" using towed video transects and baited remote underwater video systems. Both sources of information were then combined in a spatial predictive modelling framework to produce fine-scale habitat maps showing the extent of substrate types, biotic formations, etc. Surveys took place across 9 study areas, including Mount Gardner, a site located just off Two People’s Bay, 30km east of the town of Albany. The area is host to a number of human uses, including recreational and commercial fishing, diving, surfing, recreational boat use and shipping and mining. The marine environment at this location is different to the other three study locations on the south coast, in that it encompasses the protected Two Peoples Bay with seagrass and invertebrate communities and the more exposed rocky and macroalgal reefs around the Mt Gardner peninsula itself.

海洋未来项目(Marine Futures Project)旨在基于对海洋生境、生物多样性与人类利用此类生态资源价值之间关系的深化认知,对西澳大利亚州重点海洋生态系统的当前现状开展基准测评。研究团队采用水声勘测(hydroacoustics)技术,搭载Reson 8101多波束声呐(Reson 8101 Multibeam),对约1500平方千米的海底开展测绘,并通过拖曳视频样带(towed video transects)与诱饵式远程水下视频系统(baited remote underwater video systems),对预判的底栖生境(benthic habitats)进行实地验证。随后将两类调查数据整合至空间预测建模框架(spatial predictive modelling framework)中,生成可展示基质类型、生物群落分布范围等信息的精细尺度生境地图。 本次勘测覆盖9个研究区域,其中包括位于奥尔巴尼(Albany)镇以东30公里处的双人湾(Two People’s Bay)近海的加德纳山(Mount Gardner)测点。该区域承载了多种人类活动,包括休闲与商业捕捞、潜水、冲浪、休闲游艇活动以及航运与矿业开发。该区域的海洋环境与南海岸其余3个研究点位有所不同:其既包含分布有海草与无脊椎动物群落的受保护双人湾,也涵盖加德纳山半岛周边暴露度更高的岩礁与大型藻礁。
提供机构:
Australian Ocean Data Network
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作