A review and analysis of the experimental literature on seagrass shading evaluated the relationships among experimental light reduction, experimental duration, additional modifying factors and common meadow-scale seagrass response metrics to determine whether there were consistent statistical relationships. Modifying factors included study latitude, field site depth, season of experiment initiation, rhizome connectivity (severed, intact), experiment type (field, mesocosm), and seagrass life history strategy. Highly significant, best fit linear regression models were found for both biomass and shoot density reduction that included light reduction, duration and other modifying variables, although unexplained variation in the data were high. Duration of light limitation affected extent of response for both metrics, and unexplained variance was greatly reduced by analysis of data from durations >60 d for shoot density and for >60 d < 120 d for biomass. Life history strategy was also a significant factor in three of four regression models. While the slopes of the responses were relatively similar for biomass and shoot density, unexplained variation was generally greater for shoot density than biomass in models for data pooled across species. There were highly significant, best fit regression models found for both biomass and shoot density for both genus and species level analyses, with the extent and duration of light reduction the most important model factors. Season of experiment, rhizome status, latitude, and experiment type all were also included in multiple models. Biomass regression models again tended to have lesser unexplained variation than shoot density models. Life history was invariant within genus and species, and separate analyses for data divided among Colonizing, Opportunistic, and Persistent strategies found relatively similar, best fit regression models among strategies. However, the mean percent reduction of both biomass and shoot density was generally lower for the Persistent strategy than for the other two life histories, suggesting a greater buffering capacity against effects of light reduction for such species. Overall, biomass based models explained more of the variance in seagrass response to light reduction than shoot density, and may be the preferred response variable for meadow-scale impact assessments. The relationships observed may inform management decisions by helping define the scope of expected responses of seagrasses in general to the range of factors that may reduce light availability to seagrasses.

对海草遮光实验文献的回顾和分析评估了实验减光、实验持续时间、附加修改因素和常见草甸规模海草响应指标之间的关系，以确定是否存在一致的统计关系。修改因素包括研究纬度、野外地点深度、实验开始季节、根茎连接性（切断、完整）、实验类型（野外、中生态）和海草生活史策略。尽管数据中无法解释的变化很大，但对于生物量和芽密度减少，包括光照减少、持续时间和其他修改变量，发现了高度显着的最佳拟合线性回归模型。光限制的持续时间影响这两个指标的响应程度，通过分析芽密度持续时间 > 60 天和生物量 > 60 天 < 120 天的数据，大大减少了无法解释的方差。生活史策略也是四个回归模型中三个的重要因素。虽然生物量和枝条密度的响应斜率相对相似，但在跨物种数据汇集的模型中，枝条密度的不明原因变化通常大于生物量。对于属和物种水平的分析，发现了生物量和芽密度的高度显着的最佳拟合回归模型，其中光照减少的程度和持续时间是最重要的模型因素。多个模型中还包括实验季节、根茎状态、纬度和实验类型。生物量回归模型再次倾向于比芽密度模型具有更少的无法解释的变化。属和种内的生活史是不变的，对殖民、机会和持久策略的数据进行单独分析，发现策略之间相对相似、最适合的回归模型。然而，持久性策略的生物量和芽密度的平均减少百分比通常低于其他两种生活史，这表明此类物种对光减少影响的缓冲能力更强。总体而言，基于生物量的模型比芽密度更多地解释了海草对光减少响应的变化，并且可能是草甸规模影响评估的首选响应变量。观察到的关系可以通过帮助定义海草对可能减少海草光可用性的因素范围的预期反应范围来为管理决策提供信息。