Evaluating the method reproducibility of deep learning models in biodiversity research.

ABSTRACT: Artificial intelligence (AI) is revolutionizing biodiversity research by enabling advanced data analysis, species identification, and habitats monitoring, thereby enhancing conservation efforts. Ensuring reproducibility in AI-driven biodiversity research is crucial for fostering transparency, verifying results, and promoting the credibility of ecological findings. This study investigates the reproducibility of deep learning (DL) methods within the biodiversity research. We design a methodology for evaluating the reproducibility of biodiversity-related publications that employ DL techniques across three stages. We define ten variables essential for method reproducibility, divided into four categories: resource requirements, methodological information, uncontrolled randomness, and statistica

SUBMITTER: Ahmed W 

PROVIDER: S-EPMC11888858 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature