Details:

Journal Title:
Ecosphere
Personal Author:
Mimouni, El-Amine ; Ridal, Jeffrey J. ; Skufca, Joseph D. ; Twiss, Michael R. ;
Mimouni, El-Amine ; Ridal, Jeffrey J. ; Skufca, Joseph D. ; Twiss, Michael R. ; Less -
NOAA Program & Office:
Sea Grant ; OAR (Oceanic and Atmospheric Research) ;
Sea Grant ; OAR (Oceanic and Atmospheric Research) ; Less -
Sea Grant Program:
NYSGI/NYEXT (New York Sea Grant) ;
Description:
Monitoring select ecosystem variables across time and interpreting the collected data are essential components of ecosystem assessment supporting management. Increasingly affordable sensors and computational capacity have made very large dataset assembly more common. However, these datasets initiate analytical challenges by their size and theoretical challenges due to the scale of the processes they encompass. Multiscale assessment of high temporal resolution water quality sensor data (temperature, in vivo chlorophyll a, colored dissolved organic matter) collected year-round was conducted for the Upper St. Lawrence River. Using numerical methods that directly integrate the concept of scale, we show that consideration of scale-dependent processes can lead to increased predictive power and a clearer understanding of ecosystem function. These results suggest that multiscale methods are not only an alternative way of approaching long-term data assessment, but also a necessity in order to avoid spurious interpretation. Consequently, the concept of scale as described here can be consistently integrated into long-term data studies to assist in the interpretation of high-resolution data that help describe natural phenomena in aquatic systems.
Keywords:
[+]
Data Sets
Rivers
Water Quality

Source:
Ecosphere 11(2): e03014
DOI:
https://doi.org/10.
; 1002/ecs2.3014 ;
Sea Grant Document Number:
NYSGI-R-20-009
Document Type:
Journal Article ;
Place as Subject:
Saint Lawrence River ;
Rights Information:
CC BY
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:a961843809236e162cef6906f9771b5c1d08dc3189882150b3e820512e72ccbd
File Type:

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