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Spatial Variation in Nutrient and Water Color Effects on Lake Chlorophyll at Macroscales

Overview of attention for article published in PLOS ONE, October 2016
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Title
Spatial Variation in Nutrient and Water Color Effects on Lake Chlorophyll at Macroscales
Published in
PLOS ONE, October 2016
DOI 10.1371/journal.pone.0164592
Pubmed ID
Authors

C. Emi Fergus, Andrew O. Finley, Patricia A. Soranno, Tyler Wagner

Abstract

The nutrient-water color paradigm is a framework to characterize lake trophic status by relating lake primary productivity to both nutrients and water color, the colored component of dissolved organic carbon. Total phosphorus (TP), a limiting nutrient, and water color, a strong light attenuator, influence lake chlorophyll a concentrations (CHL). But, these relationships have been shown in previous studies to be highly variable, which may be related to differences in lake and catchment geomorphology, the forms of nutrients and carbon entering the system, and lake community composition. Because many of these factors vary across space it is likely that lake nutrient and water color relationships with CHL exhibit spatial autocorrelation, such that lakes near one another have similar relationships compared to lakes further away. Including this spatial dependency in models may improve CHL predictions and clarify how well the nutrient-water color paradigm applies to lakes distributed across diverse landscape settings. However, few studies have explicitly examined spatial heterogeneity in the effects of TP and water color together on lake CHL. In this study, we examined spatial variation in TP and water color relationships with CHL in over 800 north temperate lakes using spatially-varying coefficient models (SVC), a robust statistical method that applies a Bayesian framework to explore space-varying and scale-dependent relationships. We found that TP and water color relationships were spatially autocorrelated and that allowing for these relationships to vary by individual lakes over space improved the model fit and predictive performance as compared to models that did not vary over space. The magnitudes of TP effects on CHL differed across lakes such that a 1 μg/L increase in TP resulted in increased CHL ranging from 2-24 μg/L across lake locations. Water color was not related to CHL for the majority of lakes, but there were some locations where water color had a positive effect such that a unit increase in water color resulted in a 2 μg/L increase in CHL and other locations where it had a negative effect such that a unit increase in water color resulted in a 2 μg/L decrease in CHL. In addition, the spatial scales that captured variation in TP and water color effects were different for our study lakes. Variation in TP-CHL relationships was observed at intermediate distances (~20 km) compared to variation in water color-CHL relationships that was observed at regional distances (~200 km). These results demonstrate that there are lake-to-lake differences in the effects of TP and water color on lake CHL and that this variation is spatially structured. Quantifying spatial structure in these relationships furthers our understanding of the variability in these relationships at macroscales and would improve model prediction of chlorophyll a to better meet lake management goals.

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Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 47 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 33%
Student > Ph. D. Student 11 23%
Student > Master 3 6%
Student > Bachelor 2 4%
Professor 2 4%
Other 3 6%
Unknown 11 23%
Readers by discipline Count As %
Environmental Science 13 27%
Earth and Planetary Sciences 8 17%
Agricultural and Biological Sciences 5 10%
Engineering 2 4%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 4 8%
Unknown 14 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 May 2017.
All research outputs
#18,349,015
of 23,577,761 outputs
Outputs from PLOS ONE
#154,643
of 202,084 outputs
Outputs of similar age
#230,565
of 321,474 outputs
Outputs of similar age from PLOS ONE
#2,986
of 3,988 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 202,084 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 321,474 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3,988 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.