This READ_ME.txt file was generated on December 13, 2020 by Fabiola Iannarilli ------------------- GENERAL INFORMATION ------------------- 1. Title: Data, R Code, and Output Supporting: Evaluating species-specific responses to camera-trap survey designs 2. Author Information Name: Fabiola Iannarilli Institution: Conservation Sciences Graduate Program Department of Fisheries, Wildlife, and Conservation Biology Address: University of Minnesota-Twin Cities, St. Paul, Minnesota, USA Email: ianna014 [at] umn.edu Name: John Erb Institution: Minnesota Department of Natural Resources Address: Minnesota DNR - Northeastern Region Headquarters, Grand Rapids, Minnesota, USA Email: john.erb [at] state.mn.us Name: Todd W. Arnold Institution: Department of Fisheries, Wildlife, and Conservation Biology Address: University of Minnesota-Twin Cities, St. Paul, Minnesota, USA Email: arnol065 [at] umn.edu Name: John R. Fieberg Institution: Department of Fisheries, Wildlife, and Conservation Biology Address: University of Minnesota-Twin Cities, St. Paul, Minnesota, USA Email: jfieberg [at] umn.edu 3. Date of data collection: May 2016 - July 2018 4. Geographic location of data collection: Northeastern Minnesota, USA 5. Information about funding sources that supported the collection of the data: This project was funded by the Minnesota Department of Natural Resources and the Wildlife Restoration Program (Pittman-Robertson). -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: These data are protected under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 license. 2. Links to publications that cite or use the data: Iannarilli, F., Erb, J., Arnold, T. W., and Fieberg, J. R. (in press). Evaluating species-specific responses to camera-trap survey designs. Wildlife Biology. Accepted Manuscript. doi: 10.2981/wlb.00726 3. Recommended citation for the data: Iannarilli, Fabiola; Erb, John; Arnold, Todd W; Fieberg, John R. (2019). Data, R Code, and Output Supporting: Evaluating species-specific responses to camera-trap survey designs. Retrieved from the Data Repository for the University of Minnesota. --------------------- DATA & FILE OVERVIEW --------------------- File List The compressed folder 'Iannarilli_et_al_2020_Wildlife_Biology_data_and_code' contains data, R scripts, and relative output files (figures and htmls files) to reproduce figures and results reported in Iannarilli F et al. (in press). We recommend users to unzip the folder in the desired working directory and use the R user interface RStudio (RStudio Team 2018) to reproduce the analysis. Clicking the 'Iannarilli_et_al_2020_data_and_code.Rproj' file will directly open the RStudio interface and will allow users to navigate through the different R scripts and select the code to reproduce the analysis. More information on how to use a project in RStudio are available at: https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects. Below, details on the files contained in the compressed folder: 1. The subfolder 'data_input' contains: A. Count_Indep_Events_30min_allSpecies_allSessions.csv: data set listing counts of independent events by species, day, and locations for camera-trap data collected at 100 locations in Northern Minnesota between May 2016 and July 2018 by Minnesota Department of Natural Resources. Observations events were considered independent when pictures of the same species at a certain camera-trap site were taken at least 30-minutes apart. This time interval was defined following Iannarilli et al. 2019, after analyzing lorelograms at 1-minute scale (Iannarilli F et al. (in press): figure A3-1 in Supplemental Materials). For additional details about data collection see Methods section in Iannarilli F et al. (in press). column 1: row counter. column 2: Site. Camera-trap location unique ID. column 3: Session. Sampling period to which the count value refers to. 5 levels: Spring2016, Fall2016, Spring2017, Fall2017, Spring2018. Spring sessions ran between the beginning of May and the end of July of each year; fall sessions started at the beginning of September and finished at the end of October. column 4: Species. column 5: Block. Sampling design attribute indicating to which block the camera-trap location belonged to. 20 levels, from 1 to 20. Each block contained 5 camera-trap sites. column 6: Site_selection: Sampling design attribute indicating the survey-design framework a site was assigned to. Sites assigned to the 'Lur_Rand' treatment were deployed at randomly selected locations at lured with either salmon or fatty acid scented oil (FAS); sites assigned to the 'Unl_Road' treatment were deployed without lure at locations along secondary, logging roads. column 7: Lure. Sampling design attribute indicating whether a site was lured with salmon oil or FAS. NAs indicate sites with no lure. Sites assigned to salmon oil in 2016 were assigned to FAS in 2017 and 2018 and viceversa. column 8: Deployment. Sampling design attribute indicating the placement strategy a site was assigned to. 'Y' indicates that the operator deploying the camera at that site actively looked for features that might enhanced the detection of the target species within a 90 meters radius of a previously randomly-selected points ('random, but feature-based sites', Feat). 'N' indicates sites in which cameras were deployed within a 5 meters radius of the previously randomly-selected location ('completely random sites', CR). column 9: day. Days since deployment of the camera at that specific site. column 10: count. Number of independent events of a specific species (column 4) detected at a specific site (column 2) during a specific day (column 9) and sampling session (column 3). 2. The subfolder 'figures' contains figures included in Iannarilli et al. (in press) and figure A3-1 in Supplemental Materials. The figures were created running the code reported in the R scripts files contained in the main folder. Each figure is in a .jpg format and labelled based on its reference in the manuscript (e.g. 'Iannarilli_et_al_fig1.jpg' is the Figure 1 in the manuscript). 3. The subfolder 'htmls' contains the outputs (including plots stored in the associated folder 'figures') associated with the R scripts included in the main folder. For a description of the content of each file, please refer to the description of the associated R script labelled using a similar name. 4. The subfolder 'output_p0' contains output produced running code available in the R script 'Fig4_Estimate_Daily_prob_of_encounter.R' for each species. We ran this script using resources at the Minnesota Supercomputing Institute (https://www.msi.umn.edu/). The name of the files indicate which species the data refer to. Each of the .csv files contains: column 1: row counter; column 2: Treatment. Treatment level. See Iannarilli et al. in press. column 3: Season. Either Fall or Spring. column 4: day. Day since camera deployment, divided by 30. column 5: Block. All NAs to return estimates at population-level. column 6: Site. All NAs to return estimates at population-level. column 7: mean. Mean estimate of daily probability of encounter the species at a site under the treatment listed in column 2 at a certain day and season (column 4 and 3, respectively). column 8: p0. Probability of encountering a species at least once during day and season specified in column 4 and 3 at a typical site under the treatment specified in column 2. column 9: boot_q025. Lower limit for the 95% confidence interval constructed using a parametric bootstrap around the estimate in column 8; each value was calculated as the 2.5% quantile of 2000 replicates. column 10: boot_q975. Upper limit for the 95% confidence interval constructed using a parametric bootstrap around the estimate in column 8; each value was calculated as the 97.5% quantile of 2000 replicates. 5. Fig2_Indep_events_per_day.R = R script containing code to reproduce figure 2 in Iannarilli et al. (in press). 5. Fig3_and_FigA3_1_Estimates_contrasts.R = R script containing code to reproduce figure 3 in Iannarilli et al. (in press) and figure A3-1 in its Supplemental Materials. 6. Fig4_Estimate_Daily_prob_of_encounter.R = R script containing code to reproduce results contained in 'output_p0' and used to build figure 4 in Iannarilli et al. (in press). 7. Fig4_Plot_Daily_prob_of_encounter.R = R script containing code to reproduce figure 4 in Iannarilli et al. (in press). ----------------------------------------- References Iannarilli, F., Erb, J., Arnold, T. W., and Fieberg, J. R. (in press). Evaluating species-specific responses to camera-trap survey designs. Wildlife Biology. Accepted Manuscript. doi: 10.2981/wlb.00726 Iannarilli F., T. W. Arnold, J. Erb, J. R. Fieberg. 2019. Using lorelograms to measure and model correlation in binary data: Applications to ecological studies. Methods in Ecology and Evolution. Accepted Author Manuscript. doi:10.1111/2041-210X.13308 RStudio Team (2018). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/ R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.