PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 30, 20250 citationsOpen Access

The camtrapR R package: From data management to interactive ecological analysis of camera trap data

View Full Paper
JNJürgen NiedballaRSRahel SollmannAWAndreas Wilting

Key Points

  • The updated camtrapR R package now transforms camera trap data analysis with an interactive dashboard that simplifies workflows.
  • Enhanced functionalities allow for fitting community occupancy models, significantly improving ecological data management and analysis.
  • A code-free interface supports exploratory analyses, spatial predictions, and posterior predictive checks for Bayesian models.
  • This comprehensive update positions camtrapR as an essential tool for ecologists, offering robust and reproducible analytical capabilities.

Abstract

Camera trapping has become an indispensable tool in wildlife ecology, generating vast datasets that require efficient and robust analytical workflows. The R package camtrapR was originally developed for preparing and managing camera trap data for subsequent analysis in external modeling packages like unmarked. It has since become a standard tool in the field for this purpose. 2. Here, we introduce a major update that transforms camtrapR from a data preparation tool into a comprehensive, end-to-end analytical platform. The centerpiece of this evolution is the surveyDashboard(), a novel code-free graphical user interface that guides users through the entire analysis pipeline, from data import to final predictions. This update also incorporates enhanced data import functionalities for major standards like Wildlife Insights and Camtrap DP, a complete workflow for fitting community occupancy models, and streamlined tools for environmental covariate extraction. 3. The interactive dashboard provides an integrated environment for the entire analytical process. Users can perform essential exploratory analyses, such as generating species accumulation curves and mapping species detections, before proceeding to model fitting. The interface supports the interactive construction of both single-species and multi-species (community) occupancy models. The dashboard's covariate preparation tools generate inputs for both model fitting and for creating spatial predictions of species occupancy. 4. Furthermore, the update introduces a comprehensive workflow for fitting Bayesian community occupancy models using JAGS or NIMBLE. This allows for hierarchical modeling of species- and community-level responses to environmental drivers, providing deeper insights into wildlife communities. The workflow includes tools for model assessment, such as convergence diagnostics and posterior predictive checks for goodness-of-fit. 5. By integrating a powerful, code-free interface with advanced backend modeling functions, this major update to camtrapR aims to make robust and reproducible camera trap data analysis accessible to a wider audience, including ecologists, wildlife managers, and students. This paper serves as the new definitive reference for the expanded functionality of camtrapR as a comprehensive tool for modern camera trap studies.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Niedballa et al. (2025) studied this question.

synapsesocial.com/papers/68dc1e3f8a7d58c25ebb214fhttps://doi.org/10.1101/2025.09.26.678697
Ask AI
Helpful
Bookmark
Share
View Full Paper