Browsing by Author "Fieberg, John"

Now showing 1 - 5 of 5
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    R code and data supporting: Cattle exclusion increases encounters of wild herbivores in Neotropical forests
    (2024-05-30) Vélez, Juliana; McShea, William; Pukazhenthi, Budhan; Rodríguez, Juan D; Suárez, María F; Torres, José M; Barrera, César; Fieberg, John; julianavelezgomez@gmail.com; Vélez, Juliana; Fieberg Lab
    This repository contains R code and data supporting: Cattle exclusion increases encounters of wild herbivores in Neotropical forests. This study implements a BACI experimental sampling design to quantify the effect of cattle exclusion on encounter probability of the native community of browsers and fruit consumers, and percent ground cover in multifunctional landscapes of the Colombian Orinoquía. Wildlife-permeable fences were built along forest edges in four forest patches (i.e., blocks) containing control and fenced (treatment) sites. We installed 33 camera traps to obtain information about wildlife and cattle encounter probabilities, before and after the fences were constructed. We fit Bayesian generalized linear mixed effects models to quantify the effect of fences via the interaction between the time period (before and after the fences were built) and treatment (control or fenced sites).
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    R code and data supporting: Implications of the scale of detection for inferring co-occurrence patterns from paired camera traps and acoustic recorders
    (2023-09-05) Vélez, Juliana; McShea, William; Pukazhenthi, Budhan; Stevenson, Pablo; Fieberg, John; julianavelezgomez@gmail.com; Vélez, Juliana; University of Minnesota Fieberg Lab; Smithsonian's National Zoo and Conservation Biology Institute
    The objective of this study was to investigate the association between two measures of disturbance (poaching and livestock) and wild ungulates using data collected with camera traps and autonomous acoustic recording units. We quantified these associations using joint species distribution models (JSDMs) fit to data from multifunctional landscapes of the Orinoquía region of Colombia. We also evaluated the effect of the detection scale of camera traps and acoustic recorders for inferring co-occurrence patterns between wildlife and disturbance factors.
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    R Code and Output Supporting: Modeling individual variability in habitat selection and movement using integrated step-selection analyses
    (2024-03-11) Chatterjee, Nilanjan; Wolfson, David; Kim, Dongmin; Velez, Juliana; Freeman, Smith; Bacheler, Nathan; Shertzer, Kyle; Taylor, Chris; Fieberg, John; nchatter@umn.edu; Chatterjee, Nilanjan; Fieberg Lab
    This repository contains data and R code (along with associated output from running the code) supporting all results reported in: Chatterjee, Nilanjan; Wolfson, David; Kim, Dongmin; Vélez, Juliana; Freeman, Smith; Bacheler, Nathan; Shertzer, Kyle; Taylor, J.; Fieberg, John 2024. Modelling individual variability in habitat selection and movement using integrated step-selection analysis. Methods in Ecology and Evolution. The code demonstrates how to model the individual variation in habitat selection and movement parameters using integrated step-selection analysis.
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    R code associated with Evaluating goodness-of-fit of animal movement models using lineups
    (2023-10-02) Fieberg, John; Freeman, Smith; Signer, Johannes; Jfieberg@umn.edu; Fieberg, John; Fieberg lab
    This repository contains data and R code (along with associated output from running the code) presented in: Fieberg, J., Freeman, S. and J. Signer. Evaluating goodness-of-fit of animal movement models using lineups.
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    R Code, Data, and Output Supporting: A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring
    (2022-12-19) Bajcz, Alex W; Glisson, Wesley; Larkin, Daniel J; Fieberg, John; bajcz003@umn.edu; Bajcz, Alex W; Minnesota Aquatic Invasive Species Research Center; Department of Fisheries, Wildlife, and Conservation Biology
    These data and files support the published paper "A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring" | Scientific Reports (nature.com). It contains both input files and data as well as processed output files from the modeling effort described in the paper, which uses predictor variables to predict both occupancy and detection for within-lake locations by starry stonewort, an invasive aquatic macrophyte. The files being submitted include everything needed to fully replicate and further interpret our results and provide a framework for constructing similar models in similar contexts.