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Mapping and modeling eelgrass Zostera marina distribution in the western Baltic Sea

机译：绘制和建模波罗的海西部鳗gra草的Zostera码头分布

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In the northern hemisphere, eelgrass Zostera marina L. is the most important and widespread seagrass species. Despite its ecological importance, baseline data on eelgrass distribution and abundance are mostly absent, particularly in subtidal areas with relatively turbid waters. Here, we report a combined approach of vegetation mapping in the Baltic Sea coupled to a species distribution model (SDM). Eelgrass cover was mapped continuously in the summers of 2010 and 2011 with an underwater towed camera along similar to 400 km of seafloor. Eelgrass populated 80% of the study region and occurred at water depths between 0.6 and 7.6 m at sheltered to moderately exposed coasts. Mean patch length was 128.6 m but was higher at sheltered locations, with a maximum of >2000 m. The video observations (n = 7824) were used as empiric input to the SDMs. Using generalized additive models, 3 predictor variables (depth, wave exposure, and slope), which were selected based on Akaike's information criterion, were sufficient to predict eelgrass presence/absence. Along with a very good overall discriminative ability (area under the receiver-operating characteristic curve ROC/AUC = 0.82), depth (as a proxy for light), wave exposure, and slope contributed 66, 29, and 5%, respectively, to the final model. The estimated total areal extent of eelgrass in the study region amounts to 140.5 km(2) and comprises about 11.5% of all known Baltic seagrass beds. The present work is, to the best of our knowledge, the largest study undertaken to date on vegetation mapping and the first to assess distribution of eelgrass quantitatively in the western Baltic Sea.
机译：在北半球，鳗草Zostera marina L.是最重要和分布最广的海草物种。尽管其具有生态重要性，但大多缺乏有关鳗草分布和丰度的基线数据，尤其是在水相对混浊的潮下带地区。在这里，我们报告结合波罗的海植被映射和物种分布模型（SDM）的组合方法。 Eelgrass的覆盖层在2010年和2011年夏季连续用水下拖曳相机沿约400公里海底进行了测绘。鳗草遍布研究区域的80％，发生在庇护至中度裸露的海岸的水深介于0.6至7.6 m之间。平均斑块长度为128.6 m，但在避难处较高，最大> 2000 m。视频观察结果（n = 7824）被用作SDM的经验输入。使用广义加性模型，根据Akaike的信息标准选择了3个预测变量（深度，波照射和坡度），足以预测鳗草的存在与否。除了具有非常好的总体判别能力（接收器工作特性曲线下的面积ROC / AUC = 0.82）外，深度（作为光的替代），波曝光和斜率分别对光强有66、29和5％的影响。最终模型。研究区域中鳗草的总面积估计为140.5 km（2），约占所有已知波罗的海草床的11.5％。据我们所知，目前的工作是迄今为止进行的最大的植被制图研究，也是第一个定量评估西波罗的海鳗草分布的研究。

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