Biases in lake water quality sampling and implications for macroscale research
Biases in lake water quality sampling and implications for macroscale research
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湖泊水质采样的偏差及其对宏观研究的影响
DOI：
10.1002/lno.11136
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发表时间：
2019
影响因子：
4.5
通讯作者：
Soranno, Patricia A.
中科院分区：
文献类型：
--
作者：
Stanley, Emily H.;Collins, Sarah M.;Lottig, Noah R.;Oliver, Samantha K.;Webster, Katherine E.;Cheruvelil, Kendra S.;Soranno, Patricia A.
宏观尺度湖沼学研究的增长伴随着从多个来源汇编的次要数据集的增加。我们研究了来自87个来源的数据集LAGOS‐NE的数据可用性模式，以确定湖泊水质数据可用性的偏差，并考虑这种偏差如何影响次大陆尺度上的感知模式。在8个常见的水质参数中，从总观测值、湖泊采样和长期记录来看，指示营养状态的变量（水分、叶绿素和总磷）最为丰富，而碳变量（真色和溶解有机碳）最为稀缺。大多数数据是在夏季从1-3年的较大（≥20公顷）湖泊收集的。每个变量的大约80%的数据来自~ 20%的采样湖泊。长期（≥20年）的记录很少，且在空间上聚集。数据可用性与主要的管理挑战（富营养化和酸雨）、公民科学以及量化碳和氮变量的一些项目有关。重新抽样练习表明，纠正表面积抽样偏差并没有实质性地改变8个变量的统计分布。此外，利用平均记录长度估算湖泊的长期中位数水分、叶绿素和总磷具有很高的不确定性，但适度增加样本量至50年产生的估计值具有可控误差。虽然抽样偏差的具体性质可能因地区而异，但我们预计它们是普遍存在的。因此，大型综合数据集可以而且应该用于确定湖泊研究的趋势，并作为大尺度湖沼学调查的一部分来解决这些偏差。
Growth of macroscale limnological research has been accompanied by an increase in secondary datasets compiled from multiple sources. We examined patterns of data availability in LAGOS‐NE, a dataset derived from 87 sources, to identify biases in availability of lake water quality data and to consider how such biases might affect perceived patterns at a subcontinental scale. Of eight common water quality parameters, variables indicative of trophic state (Secchi, chlorophyll, and total P) were most abundant in terms of total observations, lakes sampled, and long‐term records, whereas carbon variables (true color and dissolved organic carbon) were scarcest. Most data were collected during summer from larger (≥ 20 ha) lakes over 1–3 yr. Approximately 80% of data for each variable is derived from ~ 20% of sampled lakes. Long‐term (≥ 20 yr) records were rare and spatially clustered. Data availability is linked to major management challenges (eutrophication and acid rain), citizen science, and a few programs that quantify C and N variables. Resampling exercises suggested that correcting for the surface area sampling bias did not substantially change statistical distributions of the eight variables. Further, estimating a lake's long‐term median Secchi, chlorophyll, and total P using average record lengths had high uncertainty, but modest increases in sample size to > 5 yr yielded estimates with manageable error. Although the specific nature of sampling biases may vary among regions, we expect that they are widespread. Thus, large integrated datasets can and should be used to identify tendencies in how lakes are studied and to address these biases as part broad‐scale limnological investigations.
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影响因子：
9.8
作者：
Dugan HA;Summers JC;Skaff NK;Krivak-Tetley FE;Doubek JP;Burke SM;Bartlett SL;Arvola L;Jarjanazi H;Korponai J;Kleeberg A;Monet G;Monteith D;Moore K;Rogora M;Hanson PC;Weathers KC
通讯作者：
Weathers KC
DOI：
--
发表时间：
1992
期刊：
影响因子：
--
作者：
L. Håkanson
通讯作者：
L. Håkanson
DOI：
10.1002/2016jg003525
发表时间：
2017-04
期刊：
Journal of Geophysical Research: Biogeosciences
影响因子：
--
作者：
Jean‐François Lapierre;D. Seekell;Chris T. Filstrup;S. Collins;C. Emi Fergus;P. Soranno;K. Cheruvelil
通讯作者：
Jean‐François Lapierre;D. Seekell;Chris T. Filstrup;S. Collins;C. Emi Fergus;P. Soranno;K. Cheruvelil
影响因子：
10.1
作者：
Hughes, Brent B.;Beas-Luna, Rodrigo;Carr, Mark H.
通讯作者：
Carr, Mark H.
影响因子：
5
作者：
Read, Emily K.;Patil, Vijay P.;Weathers, Kathleen C.
通讯作者：
Weathers, Kathleen C.