------------------- GENERAL INFORMATION ------------------- 1. Title: R Code and Output Supporting: Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation 2. Author Information: Name: Stefanie Muff Affiliation: Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland and Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland Email: stefanie.muff@uzh.ch Name: Johannes Signer Affiliation: Wildlife Sciences, Faculty of Forest Science and Forest Ecology, University of Göttingen, Büsgenweg 3, 37077 Göttingen Email: jsigner@gwdg.de Name: John R. Fieberg Affiliation: Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota-Twin Cities Email: jfieberg@umn.edu 3. Description: This repository contains R code and associated output supporting the results reported in: Muff, S., Signer, J. and Fieberg, J., 2018. Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. bioRxiv, p.411801. -------------------------- Version History -------------------------- On 2021-11-29, "fisher_ssf_stan.html" and "fisher_ssf_stan.R" were added to the record On 2021-12-14, "Otters_SSF.R" and "Otters_SSF.html" were replaced with updated versions. The previous versions of these two files can be found at https://hdl.handle.net/11299/204737.1 -------------------------- 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: Muff, S., Signer, J. and Fieberg, J., 2018. Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. bioRxiv, p.411801. 3. Links to other publicly accessible locations of the data: The mountain goat data are available via the ResourceSelection package (Lele et al. 2017): https://cran.r-project.org/web/packages/ResourceSelection/index.html The marten data are available from Movebank: https://www.movebank.org/ and were originally described in LaPoint et al. (2013). 4. Recommended citation for this archive: Muff, S., Signer, J. and Fieberg, J., 2019. R Code and Output Supporting: Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. University of Minnesota Digital Conservancy. 10.13020/8bhv-dz98. 791. --------------------- FILE OVERVIEW --------------------- 1. fisher_data.csv = fisher location data collected by LaPoint et al. (2013). 2. d_otter.csv = otter data collected by Weinberger et al. (2016) 3. inla_emarginal.R = R function for getting the posterior means of variances from a fitted inla object. This function is called in Otters_SSF.R. 4. inla_mmarginal.R = R function for getting the posterior mode of variances from a fitted inla object. This function is called in Otters_SSF.R. 5. Goats_RSF.R and Goats_RSF.html = R code (and associated html file containing output from running the code). This code fits different mixed effects resource-selection functions to mountain goat data contained in the ResourceSelection R package (Lele et al. 2017). 6. Otters_SSF.R and Otters_SSF.html = R code (and associated html file containing output from running the code). This code fits different mixed effects step-selection functions to otter data first analyzed in Weinberger et al (2016). 7. fisher_rsf_and_ssf.R and fisher_rsf_and_ssf.html = R code (and associated html file containing output from running the code). This code fits different mixed effects resource-selection and step-selection functions to fisher data from LaPoint et al. (2013). 8. fisher_ssf_stan.R and fisher_ssf_stan.html = R code (and associated html file containing output from running the code). This code fits a mixed effects step-selection function using STAN to fisher data from LaPoint et al. (2013). 9 ssf_simulations_v17.R = file containing R code used to conduct the simulation studies in the paper. 10. landuse_study_area.tif = Landuse data as downloaded from https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover and cropped to the study area. -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Methods: For complete methodological details, please refer to Muff et al. (In Review). 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: fisher_data.csv ----------------------------------------- Number of variables: 4 Number of cases/rows: 18886 Variable List A. x = longitude of the location B. y = latitude of the location C. t = date and time of animal location D. id = identifier for animal ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: d_otter.csv ----------------------------------------- Number of variables: 8 Number of cases/rows: 41671 NA_ANIMAL NA_ID Loc Sohlenbrei NAT1 REST1 STAU1 Breaks_Dis Variable List: A. NA_ANIMAL = identifier for each unique otter B. NA_ID = numeric identifier for each stratum C. Loc = 1 if used location, 0 if available location D. Sohlenbrei = width of the river E. NAT1 = 1 if the point was in a natural habitat and 0 otherwise F. REST1 = 1 if the point was in the restwater of a reservoir and 0 otherwise G. STAU1 = 1 if the point was in a reservoir and 0 otherwise H. Breaks_Dis = step length (distance between previous and current location) ----------------------------------------- REFERENCES ----------------------------------------- LaPoint, S., Gallery, P., Wikelski, M. & Kays, R. (2013). Animal behavior, cost-based corridor models, and real corridors. Landscape Ecology, 28, 1615-1630. Lele, S.R., Keim, J L., and P. Solymos (2017). ResourceSelection: Resource Selection (Probability) Functions for Use-Availability Data. R package version 0.3-2. https://CRAN.R-project.org/package=ResourceSelection 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/. Weinberger, I. C., S. Muff, A. Kranz, and F. Bontadina (2016). Flexible habitat selection paves the way for a recovery of otter populations in the European Alps. Biological Conservation 199, 88-95.