This Readme.txt file was generated on 01112022 by Daniel Zielinski


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GENERAL INFORMATION
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1. Title of Dataset: Lock passage dataset for GLMM analysis


2. Author Information

Principal Investigator Contact Information:
Name: Peter Sorensen
Institution: University of Minnesota
Address: 135E Skok Hall, 2003 Upper Buford Circle, St. Paul, MN 55108
Email: soren003@umn.edu


Associate or Co-investigator Contact Information:
Name: Daniel Zielinski
Institution: Great Lakes Fishery Commission
Address: 310 D, 310 W. Front Street, Traverse City, MI
Email: dzielinski@glfc.org


3. Date of data collection:
07122017 to 07312018


4. Geographic location of data collection:
USACE Lock and Dam 8, Mississippi River mile 679.2, Genoa, WI


5. Information about funding sources that supported the collection of the data:
Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as well as the Minnesota Outdoor Heritage Fund via the MN DNR. A Twin Cities boy scout troop generously donated funds to pay for the speakers.


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DATA & FILE OVERVIEW
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1. File/Folder List

A. Filename: GLMM data.txt
GLMM_data.txt contains records of lock entrance, time spent near the lock, average discharge, average water temperature, status of the sound stimuli, fish ID, and date of the test period for trials at LD8.

Table headers descriptions are "Pass" - lock entrance (0 - no entrance, 1 - enterance), "Approaches" - numer of 15-min intervals the fish was detected near the lock entrance, "Qave (cms)" - daily discharge at LD8 in cubic meters per second, "Tave (degree C)" - Daily average water temperature in Celsius, "Sound" - Status of sound treatment (0 - off, 1 - on), "FishID" - Unique identification number of each fish, "Date" - Date fish were released to challenge the lock entrance.

2. Relationship between files:
Data in GLMM_data.txt were analyzed using a Generalize Linear Mixed Model to determine the effect of environmental conditions on time spent near the lock entrance and lock entrance.

3. Additional related data collected that was not included in the current data package:
Final data analysis of the raw data is detailed in a manuscript "Evaluation of a broadband sound projected from the gates of a navigation lock in the Mississippi River shows it to be a weak deterrent for common carp and unable to block passage" accepted in Management of Biological Invasions.

4. Are there multiple versions of the dataset? no


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METHODOLOGICAL INFORMATION
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1. Description of methods used for collection/generation of data:
Experimental methods for this project are provided in the manuscript "Evaluation of a broadband sound projected from the gates of a navigation lock in the Mississippi River shows it to be a weak deterrent for common carp and unable to block passage" accepted in Management of Biological Invasions.


2. Methods for processing the data:
The time that individual carp spent near (i.e. 15-min intervals when detected by receiver #1) the downstream lock chamber gates and the number lock entrances were analyzed using Generalized Linear Mixed Models (GLMM).  The time spent near the lock entrance was analyzed using mean river discharge and mean water temperature across each trial and sound (on/off) as fixed effects and carp identity (ID) as a random effect.  To meet the assumption of normality, the count of 15-min intervals that each carp spent near the lock entrance was root transformed.  To determine which model was most appropriate, an information theoretic approach was performed using Akaike’s information criterion (AIC), where the model with the lowest AIC value was the best model.  The best model was validated using the similar measures as Silva et al. (2015) by examining histograms of the normalized residuals, plotting the normalized residuals against fitted values, and by examining residual lag-plots to assess autocorrelation.  Lock entrance rates was analyzed using the same GLMM models except they included time spent near the lock entrance as an additional fixed effect and were modeled as logistic regressions with a binomial response variable.  Data were analyzed using the fitglme function in Matlab (Mathworks, MA, USA).

3. People involved with sample collection, processing, analysis and/or submission:

Data collected by: Andrew Riesgraf, Jean Finger, Clark Dennis III
Data processing by: Andrew Riesgraf, Clarck Dennis III, Jeff Whitty
Data analysis by: Daniel P. Zielinski, Andrew Riesgraf
Data submission by: Andrew Riesgraf