------------------- GENERAL INFORMATION ------------------- 1. Title: R Code and Output Supporting: Resampling-Based Methods for Biologists 2. Author Information: Name: John R. Fieberg Affiliation: Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota-Twin Cities Email: jfieberg@umn.edu Name: Kelsey Vitense Affiliation: Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota-Twin Cities Name: Douglas H. Johnson Affiliation: Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota-Twin Cities 3. Description: This repository contains data, R code, and associated output supporting the results reported in: Fieberg, J., K. Vitense, and D. H. Johnson 2020. Resampling-Based Methods for Biologists. PeerJ -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data and code: Attribution-NonCommercial-ShareAlike 3.0 United States 2. Links to publications that cite or use the data: Fieberg, J., K. Vitense, and D. H. Johnson 2020. Resampling-Based Methods for Biologists. PeerJ 3. Links to other publicly accessible locations of the data: The longnosedace.csv data set can be downloaded from: http://www.biostathandbook.com/multipleregression.html The RIKZdat.csv data set is included in the romunov/AED R package and can be installed using instructions given here: https://rdrr.io/github/romunov/AED/ 4. Recommended citation for this archive: Fieberg, J., K. Vitense, and D. H. Johnson 2020. Resampling-Based Methods for Biologists. University of Minnesota Digital Conservancy. https://doi.org/10.13020/wn56-9y75. --------------------- FILE OVERVIEW --------------------- 1. bears.csv = data from Stapleton et al. (2014), containing counts of the number of polar bears within each of 164 roughly 3 x 3 km quadrats on Rowley Island in northern Foxe Basin, Nunavut. 2. costeff.csv = data from Zicus et al. (2006) used to evaluate 0he relative cost-effectiveness of single- and double-cylinder nesting structures for mallard (Anas platyrhynchos) ducks. 3. longnosedace.csv = abundance data of longnose dace (Rhinichthys cataractae) and in-stream variables collected from the Maryland Biological Stream Survey (downloaded from http://www.biostathandbook.com/multipleregression.html) 4. Pikedata.csv = data collected by the Minnesota Department of Natural Resources (MN DNR) to explore the potential impact of changing fishing regulations on the size distribution of northern pike (Esox lucius) in Medicine Lake, Beltrami County, MN. 5. RIKZdat.csv = marine benthic data collected by the Dutch institute RIKZ from nine inter-tidal areas along the Dutch coast during the summer of 2002. These data are described in Janssen and Mulder (2004, 2005) and Zuur et al. (2009). 6. CaseStudyI.R and CaseStudyI.html = R code (and associated html file containing output from running the code). This example demonstrates how: 1) a cluster-level bootstrap can be used for repeated measures data with equal-sized clusters and 2) how to use functions in the boot package to calculate different bootstrap confidence intervals, including the BCa interval, which has better statistical properties than percentile-based intervals. 7. CaseStudyII.R and CaseStudyII.html = R code (and associated html file containing output from running the code). This example demonstrates how: 1) the bootstrap can be used in applications that involve multiple response measures from the same set of cases; 2) the bootstrap can provide estimates of uncertainty for non-linear functions of model parameters. 8. CaseStudyIII.R and CaseStudyIII.html = R code (and associated html file containing output from running the code). This example demonstrates how the bootstrap can be used to explore model uncertainty. 9. MultipleLinearRegression.R and MultipleLinearRegression.html = R code (and associated html file containing output from running the code). This simulation example demonstrates how to conduct a permutation-based test for a partial regression coefficient in a multiple linear regression model. -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Methods: For complete methodological details, please refer to Fieberg et al. (In Press). 2. Instrument- or software-specific information needed to interpret the data: Programs were written for Program R (R Core Team 2018) and full session information, including packages used, are provided at the bottom of each example.html file. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: bears.csv ----------------------------------------- Number of variables: 2 Number of cases/rows: 164 Variable List A. Quadrat = quandrat number (ranging from 1 to 164) B. Num.Bears = a count of the number of bears in each quadrat Data source: Stapleton et al (2014). Requested from the authors. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: costeff.csv ----------------------------------------- Number of variables: 7 Number of cases/rows: 880 Variable List: A. STRTNO = structure ID (unique to each nesting structure) B. SIZE = size of the wetland where the structure was placed (hectares) C. year = year of study (1 through 8) D. yng = number of ducks produced E. DEPLY = indicator variable equal to 0 for single cylinders and 1 for double cylinders F. size2 = size of the wetland squared (i.e., SIZE x SIZE) G. surv = an indicator variable equal to 1 if the structure survived the winter without the need for additional maintanence (or 0 otherwise). Data source: Zicus et al (2006). ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: longnosedace.csv ----------------------------------------- Number of variables: 8 Number of cases/rows: 68 Variable List: A. stream = name of surveyed stream B. longnosedace = number of longnose dace (Rhinichthys cataractae) in each 75-meter surveyed stream section C. acreage = acreage drained by the stream (in acres) D. do2 = dissolved oxygen (mg/liter) E. maxdepth = maximum depth of the 75-meter surveyed section (cm) F. no3 = nitrate concentration (mg/liter) G. so4 = sulfate concentration (mg/liter) H. temp = water temperature on the data the stream was surveyed (degrees C). Data source: Maryland Biological Stream Survey. Downloaded from the http://www.biostathandbook.com/multipleregression.html ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: pikedata.csv ----------------------------------------- Number of variables: 2 Number of cases/rows: 154 Variable List: A. year = year fish was caught B. lenght.inches = lenght of the fish in inches Data source: Minnesota Department of Natural Resources (MN DNR). Requested from the DNR. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: RIKZdat.csv ----------------------------------------- Number of variables: 4 Number of cases/rows: 45 Variable List: A. week = week of sampling (1 through 4) B. exposure = index of exposure determined by the surf, slope, grain size, and depth of anearobic layer C. Beach = unique identifier for each beach that was sampled D. Richness = species richness (number of species observed) Data source: Janssen and Mulder (2004, 2005) and Zuur et al. (2009). The data can be downloaded as part of the romunov/AED R package and can be installed using instructions given here: https://rdrr.io/github/romunov/AED/ ----------------------------------------- REFERENCES ----------------------------------------- Janssen GM and S. Mulder. (2004) De ecologie van de zandige kust van Nederland (in Dutch). Ministerie van Verkeer en Waterstaat, Rijkswaterstaat / RIKZ / 033 Janssen GM and S. Mulder. (2005) Zonation of macrofauna across sandy beaches and surf zones along the Dutch coast. Oceanologia 47:265-282. Stapleton, S., LaRue, M., Lecomte, N., Atkinson, S., Garshelis, D., Porter, C., and Atwood, T. (2014). Polar bears from space: Assessing satellite imagery as a tool to track arctic wildlife. PLoS ONE, 9(7). R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/. Zicus, M. C., Rave, D. P., and Fieberg, J. R. (2006). Cost-effectiveness of single- versus double-cylinder over-water nest structures. Wildlife Society Bulletin, 34(3):647–655. Zuur, A., Ieno, E., Walker, N., Saveliev, A., and Smith, G. (2009). Mixed Effects Models and Extensions in Ecology with R. Springer.