Spatial and temporal monitoring of coastal water quality: refining the way we consider, gather, and interpret patterns

Aquatic Biology

Sean D. Connell¹  Travis S. Elsdon¹ 

关键词: Water quality;    Monitoring;    Impacts;    Management;    Chlorophyll a;    Turbidity;    Coastal eutrophication;   

DOI  :  10.3354/ab00146

学科分类：生物科学（综合）

来源: Inter-Research

【 摘 要 】

ABSTRACT: Environmental scientists are expected to provide interpretations about patterns across broad scales of space and time, but face the challenge that the environment can vary at smaller spatial scales than commonly recognised. We described spatial and temporal variability in nutrients and associated environmental parameters at sites associated with historical impacts to assess appropriate monitoring practices. Sites were selected on the basis of past research into nutrient-driven habitat change. Temporal variability was examined using a nested sampling design (i.e. days within weeks, weeks within months) that included monitoring nutrients and environmental parameters such as secchi depth, chlorophyll a, ammonia, total Kjeldahl nitrogen, total nitrogen (TN), and oxidised nitrogen. Impacted and control sites differed in nutrient concentration and Secchi depth, although sites did not differ on every sampling occasion. Impacted sites always ranked higher, based on means, in nutrient concentrations and were more turbid than control sites. In general, one impacted site had greater nutrient concentration and was more turbid than the other impacted site. Control sites typically had low and stable concentrations of TN. Variation over small time scales of days was large relative to variation at scales of weeks and months; these results warn that monitoring of long-term trends must be mindful of short-term variation and its capacity to confuse interpretations over broader time scales. In this regard, we make suggestions to improve the way we consider, gather, and interpret patterns in environmental data that almost always vary on small scales.

【 授权许可】

Unknown