US Geological Survey, Lincoln, NE 68512, USA.
Environ Monit Assess. 2010 Sep;168(1-4):461-79. doi: 10.1007/s10661-009-1127-y. Epub 2009 Aug 15.
Stream metabolism was measured in 33 streams across a gradient of nutrient concentrations in four agricultural areas of the USA to determine the relative influence of nutrient concentrations and habitat on primary production (GPP) and respiration (CR-24). In conjunction with the stream metabolism estimates, water quality and algal biomass samples were collected, as was an assessment of habitat in the sampling reach. When data for all study areas were combined, there were no statistically significant relations between gross primary production or community respiration and any of the independent variables. However, significant regression models were developed for three study areas for GPP (r(2) = 0.79-0.91) and CR-24 (r(2) = 0.76-0.77). Various forms of nutrients (total phosphorus and area-weighted total nitrogen loading) were significant for predicting GPP in two study areas, with habitat variables important in seven significant models. Important physical variables included light availability, precipitation, basin area, and in-stream habitat cover. Both benthic and seston chlorophyll were not found to be important explanatory variables in any of the models; however, benthic ash-free dry weight was important in two models for GPP.
我们在 33 条溪流中测量了水流代谢情况，这些溪流位于美国四个农业区，跨越了养分浓度梯度，旨在确定养分浓度和生境对初级生产力（GPP）和呼吸作用（CR-24）的相对影响。结合水流代谢估算，我们收集了水质和藻类生物量样本，并评估了采样河段的生境。当将所有研究区域的数据合并时，总初级生产力或群落呼吸作用与任何独立变量之间均无统计学显著关系。然而，我们为三个研究区域制定了重要的 GPP（r(2) = 0.79-0.91）和 CR-24（r(2) = 0.76-0.77）回归模型。在两个研究区域中，各种形式的养分（总磷和面积加权总氮负荷）对预测 GPP 非常重要，而在七个重要模型中，生境变量也很重要。重要的物理变量包括光照可用性、降水、流域面积和溪流栖息地覆盖度。在任何模型中，底栖生物和悬浮物叶绿素都不是重要的解释变量；然而，在两个 GPP 模型中，底栖无灰干重非常重要。
