Data and code to replicate "A dynamic occupancy model for interacting species with two spatial scales" (doi:10.18710/ZLW59W)

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Data and code to replicate "A dynamic occupancy model for interacting species with two spatial scales"

doi:10.18710/ZLW59W

DataverseNO

2020-12-16

4

Kleiven, 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, V4

Data and code to replicate "A dynamic occupancy model for interacting species with two spatial scales"

doi:10.18710/ZLW59W

Kleiven, Eivind Flittie (UiT The Arctic University of Norway)

Barraquand, Frederic (CNRS, Institute of Mathematics of Bordeaux, Talence, France and Integrative and Theoretical Ecology, LabEx COTE, University of Bordeaux, Pessac, France)

Gimenez, Olivier (CEFE, Université Montpellier, CNRS, EPHE, IRD, Universite Paul Valéry Montpellier 3, Montpellier, France)

Henden, John-André (UiT The Arctic University of Norway)

Ims, Rolf Anker (UiT The Arctic University of Norway)

Soininen, Eeva M. (UiT The Arctic University of Norway)

Yoccoz, Nigel Gilles (UiT The Arctic University of Norway)

Böhner, Hanna

UiT The Arctic University of Norway

R

245638

245638

DataverseNO

UiT The Arctic University of Norway

Kleiven, Eivind Flittie

Kleiven, Eivind Flittie

2020-12-15

https://doi.org/10.18710/ZLW59W

Earth and Environmental Sciences, Ecology, Statistical modelling, Spatial 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>

2015-09-01-2019-06-30

2015-09-01-2020-06-01

Norway

Varanger, Troms and Finnmark

peninsula

Survey data

Program source code

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00_ReadMe.txt

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readme file

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1_Sim50data_8b_hig_det.R

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Code to simulate data under the high detection scenario.

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1_Sim50data_8b_hig_occ.R

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Code to simulate data under the high occupancy scenario.

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1_Sim50data_8b_low_det.R

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Code to simulate data under the low detection scenario.

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1_Sim50data_8b_low_occ.R

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Code to simulate data under the high occupancy scenario.

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1_Sim50data_8b_mid_det.R

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Code to simulate data under the mid detection scenario.

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1_Sim50data_8b_mid_occ.R

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Code to simulate data under the mid occupancy scenario.

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1_va_mustela_rodent_nested_loop_temp_prior1_redused2_2021.R

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Script to run the dynamic occupancy model with two spatial scales for the case study with prior set 1

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1_va_mustela_rodent_nested_loop_temp_prior2_redused2_2021.R

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Script to run the dynamic occupancy model with two spatial scales for the case study using prior set 2

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1_va_mustela_rodent_nested_loop_temp_prior3_redused2_2021.R

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Script to run the dynamic occupancy model with two spatial scales for the case study with prior set 3

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2_GOF_diagnostics_case_study.R

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Script to assess Goodness-of-fit for the case study

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2_plotting_violin_plot.R

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Plotting the results from the case study.

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2_plotting_violin_plot_priorsens.R

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R-Script to plot results for the prior sensitivity analysis.

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2_sim_hig_det_4stpm.R

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Code to analyse data from the high detection scenario with the multi-scale occupancy model.

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2_sim_hig_occ_4stpm.R

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Code to analyse data from the high occupancy scenario with the multi-scale occupancy model.

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2_sim_low_det_4stpm.R

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Code to analyse data from the low detection scenario with the multi-scale occupancy model.

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2_sim_low_occ_4stpm.R

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Code to analyse data from the low occupancy scenario with the multi-scale occupancy model.

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2_sim_mid_det_4stpm.R

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Code to analyse data from the mid detection scenario with the multi-scale occupancy model.

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2_sim_mid_occ_4stpm.R

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Code to analyse data from the mid occupancy scenario with the multi-scale occupancy model.

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3_plotting_sim_4stpm.R

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Code to plot the results from the simulation study

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Case_study_data.rda

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R data file containing the multi-state occupancy dataset.

application/x-rlang-transport

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seas_2021.rda

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R data file containing data for the season covariate used in the analysis.

application/x-rlang-transport

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SimData.RData

application/x-rlang-transport