Habitat requirements for submerged aquatic vegetation in Chesapeake Bay: Water quality, light regime, and physical-chemical factors
Habitat requirements for submerged aquatic vegetation in Chesapeake Bay: Water quality, light regime, and physical-chemical factors
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DOI：
10.1007/bf02803529
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发表时间：
2004-06
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通讯作者：
W. Kemp;R. Batiuk;Richard D. Bartleson;P. Bergstrom;V. Carter;C. Gallegos;W. Hunley;Lee Karrh;E. Koch;J. M. Landwehr;K. Moore;L. Murray;M. Naylor;Nancy B. Rybicki;J. C. Stevenson;David J. Wilcox
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作者：
W. Kemp;R. Batiuk;Richard D. Bartleson;P. Bergstrom;V. Carter;C. Gallegos;W. Hunley;Lee Karrh;E. Koch;J. M. Landwehr;K. Moore;L. Murray;M. Naylor;Nancy B. Rybicki;J. C. Stevenson;David J. Wilcox
我们开发了一种算法，用于计算沿海地点海草和相关沉水植被(SAV)的栖息地适宜性，这些沿海地点有五个水质变量的监测数据，这些变量控制叶表面的光可获得性。我们对SAV存活所需的最小光照进行了独立的估计，即穿过水柱到达SAV生长深度的表面光的百分比(PLWmin)和通过附生植物层到达叶片的光的百分比(PLLmin)。通过将与切萨皮克湾SAV存在阈值相对应的水质变量的统计减少值作为该算法的输入来计算。这些对PLWmin和PLLmin的估计值与文献回顾中确定的值相比很好。计算考虑了潮差，总的光衰减被划分为水柱和附生植物贡献。水柱衰减被进一步划分为叶绿素(Chla)、总悬浮固体(TSS)和其他物质的影响。我们使用这个算法来预测整个海湾地区潜在的SAV存在，在那里计算的植物叶片可用光超过PLLmin。预测结果与SAV分布的航空摄影监测调查结果非常吻合。预测和观测之间的一致性在中盐区和多苯区尤其强烈，这两个区域包含了该河口所有潜在SAV地点的75%-80%。该方法还允许对限制SAV生长和存活的光以外的物理和化学因素的影响进行独立评估。虽然这个算法是用切萨皮克湾的数据开发的，但它的一般结构允许对其进行校准，并将其用作应用水质数据的量化工具，以确定特定地点作为SAV在世界各地不同沿海环境中生存的适宜性。
We developed an algorithm for calculating habitat suitability for seagrasses and related submerged aquatic vegetation (SAV) at coastal sites where monitoring data are available for five water quality variables that govern light availability at the leaf surface. We developed independent estimates of the minimum light required for SAV survival both as a percentage of surface light passing though the water column to the depth of SAV growth (PLWmin) and as a percentage of light reaching reaching leaves through the epiphyte layer (PLLmin). Value were computed by applying, as inputs to this algorithm, statistically dervived values for water quality variables that correspond to thresholds for SAV presence in Chesapeake Bay. These estimates ofPLWminandPLLmincompared well with the values established from a literature review. Calcultations account for tidal range, and total light attenuation is partitioned into water column and epiphyte contributions. Water column attenuation is further partitioned into effects of chlorophylla(chla), total suspended solids (TSS) and other substances. We used this algorithm to predict potential SAV presence throughout the Bay where calculated light available at plant leaves exceededPLLmin. Predictions closely matched results of aerial photographic monitoring surveys of SAV distribution. Correspondence between predictions and observations was particularly strong in the mesohaline and polythaline regions, which contain 75–80% of all potential SAV sites in this estuary. The method also allows for independent assessment of effects of physical and chemical factors other than light in limiting SAV growth and survival. Although this algorithm was developed with data from Chesapeake Bay, its general structure allows it to be calibrated and used as a quantitative tool for applying water quality data to define suitability of specific sites as habitats for SAV survival in diverse coastal environments worldwide.