  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 domain. 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 statistical considerations. These categories
subsequently serve as the basis for defining different levels of
reproducibility. We manually extract the availability of these variables from a
curated dataset comprising 61 publications identified using the keywords
provided by biodiversity experts. Our study shows that the dataset is shared in
47% of the publications; however, a significant number of the publications lack
comprehensive information on deep learning methods, including details regarding
randomness.
