Artificial Intelligence-Driven Biodiversity Conservation Framework

Abstract

Given the urgency of emerging loss of biodiversity in the world, new protection methods are essential for threatened species. This paper presents an Artificial Intelligence-based framework for supporting biodiversity conservation. We propose a structured approach to systematically incorporate artificial intelligence into conservation practice in order to realize stronger and more effective conservation outcomes. The approach introduces the Digital Nature platform that includes data collection, processing and management, as well as AI model development and deployment. Moreover it includes analytics and translation, decision making, intervention decisions, among other essential pieces.

Data acquisitionincludes the integration of environmental, species, and threat data from diverse sources such as remote sensing, in-situ sensors, and citizen science. Data processing and management focus on pre-processing multi-source data cleansing, formatting, and integration, and the use of cloud resources to store data. AI-based quality control enhances the quality of citizen-science data. AI model development and application employ machine learning, deep learning, and computer vision techniques for species identification, habitat mapping, pattern recognition, and ecological niche modeling.

The purpose of the framework is to generate AI-derived results that can be used to inform conservation planning, resource allocation, and policy advice. Targeted conservation strategies, such as anti-poaching patrols and habitat recovery projects, are becoming based on predictive models and AI-guided assessments. Continued monitoring of conservation outcomes and feedback loops enable model refinement and feedback to adaptive management.

Key concerns will include data quality, computational resource needs, ethical discussions, and the potential for interdisciplinary collaboration. The result is a more comprehensive framework for how AI for biodiversity can help, support, and track the effectiveness of these interventions – all in a rapidly changing world, and with little time left.