Rugosity-based regional modeling of hard-bottom habitat
Rugosity-based regional modeling of hard-bottom habitat
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DOI：
10.3354/meps07839
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发表时间：
2009-01-01
影响因子：
2.5
通讯作者：
Halpin, Patrick N.
中科院分区：
文献类型：
--
作者：
Dunn, Daniel C.;Halpin, Patrick N.
系统的养护规划往往是针对海洋生物多样性的代表性和保护。然而，对海洋生物多样性的直接观察和取样极其耗时和昂贵。由于这些限制，海洋保护规划者寻求海洋生物多样性的替代品，用于他们的模型，硬底生境支持高水平的生物多样性，并经常被用作海洋空间规划的替代品。粗糙度（即海底的粗糙度）是硬底生境的一个指标。在本研究中，我们扩展了以前的粗糙度和硬底之间的关系的分析，并创建了第一个数据驱动的区域粗糙度模型来预测硬底生境。我们使用逻辑回归来创建一个经验模型，并将其与其他预先制定的粗糙度定义进行比较。我们的模型比所有其他模型表现更好，能够正确预测硬底栖息地的存在或不存在，准确率接近70%。这种模式提供了一个快速和廉价的替代更传统的调查方法，应该是有价值的区域保护规划人员和渔业管理人员作为硬底生境的初步预测。通过测试这个模型与低分辨率（90米）的测深数据，我们证明，这种类型的信息可以用于海洋保护计划在发展中国家等地区，高分辨率的数据是目前不可用的。此外，我们的模型提供了一个代理的海洋栖息地多样性在非沿海地区，一个代表性不足的部门在海洋保护规划。
Systematic conservation planning is most often directed at the representation and protection of marine biodiversity. However, direct observation and sampling of marine biodiversity is extremely time consuming and expensive. Due to these constraints, marine conservation planners have sought proxies for marine biodiversity to use in their models, Hard-bottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning. Rugosity (i.e. the roughness of the seafloor) is an indicator of hard-bottom habitat. In the present study, we expand on previous analyses of the relationship between rugosity and hard-bottom and create the first data-driven regional rugosity model to predict hard-bottom habitat. We used logistic regression to create an empirical model and compare it to other pre-formulated definitions of rugosity with receiver operator characteristic curves. Our model performed better than all other models and was able to correctly predict the presence or absence of hard-bottom habitat with similar to 70 % accuracy. This model offers a fast and inexpensive alternative to more traditional survey methods, and should be of value to regional conservation planners and fisheries managers as an initial predictor of hard-bottom habitat. By testing this model with low-resolution (90 m) bathymetry data, we demonstrate that this type of information may be used in marine conservation plans in regions such as developing countries, where high-resolution data is not currently available. Further, our model offers a proxy for marine habitat diversity in non-coastal areas, an under-represented sector in marine conservation planning.