Thom Ronald, Gaeckle Jeffrey, Buenau Kate, Borde Amy, Vavrinec John, Aston Lara, Woodruff Dana, Khangaonkar Tarang, Kaldy James
Coastal Sciences Division, Pacific Northwest National Laboratory, Sequim, Washington 98382, USA.
Address correspondence to R. Thom,
Restor Ecol. 2018;26(6):1066-1074. doi: 10.1111/rec.12702.
The restoration of eelgrass ( L.) is a high priority in Puget Sound, Washington, United States. In 2011, the state set a restoration target to increase eelgrass area by 4,200 ha by 2020, a 20% increase over the 21,500 ha then present. In a region as large, dynamic and complex as Puget Sound, locating areas to restore eelgrass effectively and efficiently is challenging. To identify potential restoration sites we used simulation modeling, a geodatabase for spatial screening, and test planting. The simulation model of eelgrass biomass used time series of water properties (depth, temperature, and salinity) output from a regional hydrodynamic model and empirical water clarity data to indicate growth potential. The GIS-based analysis incorporated results from the simulation model, historical and current eelgrass area, substrate, stressors, and shoreline manager input into a geodatabase to screen sites for field reconnaissance. Finally, we planted eelgrass at test sites and monitored survival. We screened 2,630 sites and identified 6,292 ha of highly to very highly suitable conditions for eelgrass-ample area for meeting the 20% target. Test plantings indicated fine-scale data needs to improve predictive capability. We summarized the results of our analysis for the majority of the ~3,220 km of shoreline in Puget Sound on maps to support restoration site selection and planning. Our approach provides a process for identifying and testing potential restoration sites and highlights information needs and management actions to reduce stressors and increase eelgrass area to meet restoration objectives.
在美国华盛顿州普吉特海湾，大叶藻（L.）的恢复是一项高度优先的任务。2011年，该州设定了一个恢复目标，到2020年将大叶藻面积增加4200公顷，比当时的21500公顷增加20%。在像普吉特海湾这样广阔、动态且复杂的区域，有效且高效地确定大叶藻恢复区域具有挑战性。为了确定潜在的恢复地点，我们使用了模拟建模、用于空间筛选的地理数据库以及试种。大叶藻生物量模拟模型利用区域水动力模型输出的水属性（深度、温度和盐度）时间序列以及经验性的水体透明度数据来指示生长潜力。基于地理信息系统（GIS）的分析将模拟模型的结果、历史和当前的大叶藻面积、基质、压力源以及海岸线管理者的输入整合到一个地理数据库中，以筛选实地勘察的地点。最后，我们在试验地点种植大叶藻并监测其成活率。我们筛选了2630个地点，确定了6292公顷非常适合到极其适合大叶藻生长的区域——有足够的面积来实现20%的目标。试种表明需要精细尺度的数据来提高预测能力。我们将对普吉特海湾约3220千米海岸线大部分区域的分析结果汇总到地图上，以支持恢复地点的选择和规划。我们的方法提供了一个确定和测试潜在恢复地点的过程，并突出了信息需求以及减少压力源和增加大叶藻面积以实现恢复目标的管理行动。
