...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ICES Journal of Marine Science >Benthos distribution modelling and its relevance for marine ecosystem management
【24h】

Benthos distribution modelling and its relevance for marine ecosystem management

机译：Benthos分布模型及其对海洋生态系统管理的意义

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Marine benthic ecosystems are difficult to monitor and assess, which is in contrast to modern ecosystem-based management requiring detailed information at all important ecological and anthropogenic impact levels. Ecosystem management needs to ensure a sustainable exploitation of marine resources as well as the protection of sensitive habitats, taking account of potential multiple-use conflicts and impacts over large spatial scales. The urgent need for large-scale spatial data on benthic species and communities resulted in an increasing application of distribution modelling (DM). The use of DM techniques enables to employ full spatial coverage data of environmental variables to predict benthic spatial distribution patterns. Especially, statistical DMs have opened new possibilities for ecosystem management applications, since they are straightforward and the outputs are easy to interpret and communicate. Mechanistic modelling techniques, targeting the fundamental niche of species, and Bayesian belief networks are the most promising to further improve DM performance in the marine realm. There are many actual and potential management applications of DMs in the marine benthic environment, these are (i) early warning systems for species invasion and pest control, (ii) to assess distribution probabilities of species to be protected, (iii) uses in monitoring design and spatial management frameworks (e.g. MPA designations), and (iv) establishing long-term ecosystem management measures (accounting for future climate-driven changes in the ecosystem). It is important to acknowledge also the limitations associated with DM applications in a marine management context as well as considering new areas for future DM developments. The knowledge of explanatory variables, for example, setting the basis for DM, will continue to be further developed: this includes both the abiotic (natural and anthropogenic) and the more pressing biotic (e.g. species interactions) aspects of the ecosystem. While the response variables on the other hand are often focused on species presence and some work undertaken on species abundances, it is equally important to consider, e.g. biological traits or benthic ecosystem functions in DM applications. Tools such as DMs are suitable to forecast the possible effects of climate change on benthic species distribution patterns and hence could help to steer present-day ecosystem management.
机译：海洋底栖生态系统难以监测和评估，这与基于现代生态系统的管理要求在所有重要的生态和人为影响水平上都有详细信息形成鲜明对比。生态系统管理需要考虑到潜在的多用途冲突和在大空间范围内的影响，以确保海洋资源的可持续开发以及对敏感生境的保护。对底栖物种和群落的大规模空间数据的迫切需求导致分布模型（DM）的应用不断增加。 DM技术的使用能够利用环境变量的完整空间覆盖数据来预测底栖空间分布模式。特别是，统计决策模型为生态系统管理应用程序开辟了新的可能性，因为它们简单明了且输出易于解释和交流。针对物种基本生态位的机械建模技术和贝叶斯信念网络是最有希望进一步改善海洋领域DM性能的技术。 DM在海洋底栖环境中有许多实际和潜在的管理应用，它们是（i）物种入侵和害虫控制的预警系统，（ii）评估受保护物种的分布概率，（iii）监测中的用途设计和空间管理框架（例如，指定MPA），以及（iv）建立长期的生态系统管理措施（考虑未来气候驱动的生态系统变化）。同样重要的是，还要认识到在海洋管理环境中与DM应用相关的局限性，以及为将来的DM发展考虑新的领域。解释变量的知识，例如，为DM建立基础，将继续得到发展：这包括生态系统的非生物（自然和人为因素）和更紧迫的生物（例如物种相互作用）方面。另一方面，虽然响应变量通常集中在物种的存在和对物种丰度的研究上，但同样重要的是要考虑例如DM应用中的生物学特性或底栖生态系统功能。 DM等工具适合预测气候变化对底栖物种分布模式的可能影响，因此可以帮助指导当今的生态系统管理。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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