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Predictive habitat modelling of reef fishes with contrasting trophic ecologies

机译:具有相反营养生态的礁鱼预测生境建模

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摘要

The success of marine spatial management and, in particular, the zonation of marine protected areas (MPAs), largely depends on the good understanding of species' distribution and habitat preferences. Yet, detailed knowledge of fish abundance is often reduced to a few sampled locations and a reliable prediction of this information across broader geographical areas is of major relevance. Generalised additive models (GAMs) were used to describe species-environment relationships and identify environmental parameters that determine the abundance or presence-absence of 11 reef fishes with contrasting life histories in shallow habitats of the Azores islands, Northeast Atlantic. Predictive models were mapped and visualised in a geographic information system (GIS) and areas with potential single or multi-species habitat hotspots were identified. Schooling, pelagic species typically required presence-absence models, whereas abundance models performed well for benthic species. Depth and distance to sediment significantly described the distribution for nearly all species, whereas the influence of exposure to swell or currents and slope of the seafloor depended on their trophic ecology. Potential presence of single species was widespread across the study area but much reduced for multiple species. There were no habitats shared by high abundances of all species in a given trophic group, and areas shared by minimal abundances were smaller than expected. Potential habitat hotspots should be considered as priority sites for conservation, but were only partially included in the existing MPA network. These findings highlight the potential of this methodology to support scientifically sound conservation planning, including but not restricted to fragmented and constrained habitats, such as those of oceanic archipelagos.
机译:海洋空间管理的成功,尤其是海洋保护区的分区,在很大程度上取决于对物种分布和生境偏好的良好理解。但是,对鱼类丰度的详细了解通常会减少到几个采样位置,并且在更广泛的地理区域中对该信息的可靠预测具有重大意义。使用广义加性模型(GAM)来描述物种与环境的关系,并确定环境参数,这些参数确定11种礁鱼的丰度或不存在,并与东北大西洋亚速尔群岛浅层栖息地的生活史形成鲜明对比。在地理信息系统(GIS)中对预测模型进行了映射和可视化,并确定了具有潜在单物种或多物种栖息地热点的区域。上学的中上层物种通常需要存在模型,而底栖物种的丰度模型表现良好。到沉积物的深度和距离显着描述了几乎所有物种的分布,而暴露于海底的涨潮或水流和坡度的影响取决于它们的营养生态。单个物种的潜在存在遍及整个研究区域,但对于多个物种则大大减少。在给定的营养组中,没有所有物种高丰度共有的栖息地,而最低丰度共有的面积小于预期。潜在的栖息地热点应被视为优先保护区,但仅部分包含在现有的MPA网络中。这些发现凸显了这种方法在支持科学合理的保护规划方面的潜力,包括但不限于支离破碎和受约束的栖息地,例如海洋群岛。

著录项

  • 来源
    《Marine ecology progress series》 |2013年第31期|201-216|共16页
  • 作者单位

    Centre of IMAR/Department of Oceanography and Fisheries of the University of the Azores and LARSyS-Associated Laboratory, 9901-862 Horta (Azores), Portugal;

    Centre of IMAR/Department of Oceanography and Fisheries of the University of the Azores and LARSyS-Associated Laboratory, 9901-862 Horta (Azores), Portugal;

    Centre of IMAR/Department of Oceanography and Fisheries of the University of the Azores and LARSyS-Associated Laboratory, 9901-862 Horta (Azores), Portugal;

    Centre of IMAR/Department of Oceanography and Fisheries of the University of the Azores and LARSyS-Associated Laboratory, 9901-862 Horta (Azores), Portugal;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    spatial predictive modelling; habitat mapping; generalised additive model; GAM; multi-species; abundance; GIS; azores;

    机译:空间预测建模;栖息地测绘;广义加性模型GAM;多物种丰富;地理信息系统亚速尔群岛;

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