Data and code to replicate "A dynamic occupancy model for interacting species with two spatial scales"doi:10.18710/ZLW59WDataverseNO2020-12-164Kleiven, Eivind Flittie; Barraquand, Frederic; Gimenez, Olivier; Henden, John-André; Ims, Rolf Anker; Soininen, Eeva M.; Yoccoz, Nigel Gilles, 2020, "Data and code to replicate "A dynamic occupancy model for interacting species with two spatial scales"", https://doi.org/10.18710/ZLW59W, DataverseNO, V4Data and code to replicate "A dynamic occupancy model for interacting species with two spatial scales"doi:10.18710/ZLW59WKleiven, Eivind FlittieBarraquand, FredericGimenez, OlivierHenden, John-AndréIms, Rolf AnkerSoininen, Eeva M.Yoccoz, Nigel GillesBöhner, HannaUiT The Arctic University of NorwayR245638245638DataverseNOUiT The Arctic University of NorwayKleiven, Eivind FlittieKleiven, Eivind Flittie2020-12-15Earth and Environmental SciencesEcologyStatistical modellingSpatial Occupancy<p>In this dataset you will find code and data to run a dynamic occupancy model for interaction species with two spatial scales. There is code and data to conduct a simulation study to investigate bias in any of the estimated parameters under different data scenarios. Also there is data and code to analyze a case study. This is real world data from an long term monitoring program, COAT(www.coat.no), of small mammals on the arctic tundra. All codes are run in R version 4.0.3.</p>
<p>Background:</p>
<p>Occupancy models have been developed independently to account for multiple spatial scales and species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant in models of interacting species. Here we bridge these two model frameworks by developing a multi-scale two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities - including probabilities conditional to the other species' presence. With a simulation study, we demonstrate that the model is able to estimate parameters without bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities. We further show the model's ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator-prey system. The field study illustrates that the model allows estimation of species interaction effects on colonization and extinction probabilities at two spatial scales. This creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasted movement ranges with camera traps.</p>2015-09-012019-06-302015-09-012020-06-01NorwayVarangerTroms and FinnmarkpeninsulaSurvey dataProgram source code00_ReadMe.txtreadme filetext/plain1_Sim50data_8b_hig_det.RCode to simulate data under the high detection scenario.type/x-r-syntax1_Sim50data_8b_hig_occ.RCode to simulate data under the high occupancy scenario.type/x-r-syntax1_Sim50data_8b_low_det.RCode to simulate data under the low detection scenario.type/x-r-syntax1_Sim50data_8b_low_occ.RCode to simulate data under the high occupancy scenario.type/x-r-syntax1_Sim50data_8b_mid_det.RCode to simulate data under the mid detection scenario.type/x-r-syntax1_Sim50data_8b_mid_occ.RCode to simulate data under the mid occupancy scenario.type/x-r-syntax1_va_mustela_rodent_nested_loop_temp_prior1_redused2_2021.RScript to run the dynamic occupancy model with two spatial scales for the case study with prior set 1type/x-r-syntax1_va_mustela_rodent_nested_loop_temp_prior2_redused2_2021.RScript to run the dynamic occupancy model with two spatial scales for the case study using prior set 2type/x-r-syntax1_va_mustela_rodent_nested_loop_temp_prior3_redused2_2021.RScript to run the dynamic occupancy model with two spatial scales for the case study with prior set 3type/x-r-syntax2_GOF_diagnostics_case_study.RScript to assess Goodness-of-fit for the case studytype/x-r-syntax2_plotting_violin_plot.RPlotting the results from the case study.type/x-r-syntax2_plotting_violin_plot_priorsens.RR-Script to plot results for the prior sensitivity analysis. type/x-r-syntax2_sim_hig_det_4stpm.RCode to analyse data from the high detection scenario with the multi-scale occupancy model.type/x-r-syntax2_sim_hig_occ_4stpm.RCode to analyse data from the high occupancy scenario with the multi-scale occupancy model.type/x-r-syntax2_sim_low_det_4stpm.RCode to analyse data from the low detection scenario with the multi-scale occupancy model.type/x-r-syntax2_sim_low_occ_4stpm.RCode to analyse data from the low occupancy scenario with the multi-scale occupancy model.type/x-r-syntax2_sim_mid_det_4stpm.RCode to analyse data from the mid detection scenario with the multi-scale occupancy model.type/x-r-syntax2_sim_mid_occ_4stpm.RCode to analyse data from the mid occupancy scenario with the multi-scale occupancy model.type/x-r-syntax3_plotting_sim_4stpm.RCode to plot the results from the simulation studytype/x-r-syntaxCase_study_data.rdaR data file containing the multi-state occupancy dataset. application/x-rlang-transportseas_2021.rdaR data file containing data for the season covariate used in the analysis.application/x-rlang-transportSimData.RDataapplication/x-rlang-transport