A novel generalized spatial mark-resight model that accounts for group associations

The number and distribution of animals in space form the basis of many wildlife studies. Yet, reliable estimation of population abundance remains challenging. Generalized spatial mark-resight (gSMR) models are widely applicable abundance estimators that do not require all individuals be uniquely identifiable. Despite their flexibility, gSMR models assume independence in distribution and detections of individuals throughout space. Group-living species that aggregate (i.e., share activity centers) and move cohesively (i.e., share detections) violate these assumptions, limiting the applicability of gSMR models to solitary species with limited home range overlap.
We developed a gSMR model that accounts for group associations to estimate spatial abundance. We treated groups as the measurable “units” and included a submodel that uses Poisson processes to estimate group size. We conducted a simulation study to compare our novel group-based gSMR to a gSMR that ignores group associations. We gen..., , # \"A novel generalized spatial mark-resight model that accounts for group associations\"

Repository for finalized code and data for submission to Methods in Ecology and Evolution
Prepared by Connor J. Meyer

All data to run models and simulations is included in the \"Data\" tab. Code for the simulation study is included in the \"Code\" and \"Simulation Study\" folder. Code to run the Wolf Case Study is included in the \"Code\" and \"Wolf Case Study Folders\". All code is mapped to data and results within this Rproj. file.

Metadata for all data necessary to recreate analyses discussed in the manuscript are included in the csv file *DataDescrption_MetaData.csv* located in the \"Data\" folder

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**Wolf Case Study R Code Files**

*0a. nimble_efficient_functions.R* - custom functions to calculate lambda and distributions of vectors.

*0b. group gSMR model code.R* - Model code for group-based g...,