BioAnalyst: A Foundation Model for Biodiversity

• URL: http://arxiv.org/abs/2507.09080v1
• Date: Fri, 11 Jul 2025 23:56:08 GMT
• Title: BioAnalyst: A Foundation Model for Biodiversity
• Authors: Athanasios Trantas, Martino Mensio, Stylianos Stasinos, Sebastian Gribincea, Taimur Khan, Damian Podareanu, Aliene van der Veen,
• Abstract summary: We introduce BioAnalyst, the first Foundation Model tailored for biodiversity analysis and conservation planning.<n>BioAnalyst employs a transformer-based architecture, pretrained on extensive multi-modal datasets.<n>We evaluate the model's performance on two downstream use cases, demonstrating its generalisability compared to existing methods.
• Score: 0.565395466029518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The accelerating loss of biodiversity presents critical challenges for ecological research and conservation strategies. The preservation of biodiversity is paramount for maintaining ecological balance and ensuring the sustainability of ecosystems. However, biodiversity faces numerous threats, including habitat loss, climate change, and the proliferation of invasive species. Addressing these and other ecology-related challenges, both at local and global scales, requires comprehensive monitoring, predictive and conservation planning capabilities. Artificial Intelligence (AI) Foundation Models (FMs) have gained significant momentum in numerous scientific domains by leveraging vast datasets to learn general-purpose representations adaptable to various downstream tasks. This paradigm holds immense promise for biodiversity conservation. In response, we introduce BioAnalyst, the first Foundation Model tailored for biodiversity analysis and conservation planning. BioAnalyst employs a transformer-based architecture, pre-trained on extensive multi-modal datasets encompassing species occurrence records, remote sensing indicators, climate and environmental variables. BioAnalyst is designed for adaptability, allowing for fine-tuning of a range of downstream tasks, such as species distribution modelling, habitat suitability assessments, invasive species detection, and population trend forecasting. We evaluate the model's performance on two downstream use cases, demonstrating its generalisability compared to existing methods, particularly in data-scarce scenarios for two distinct use-cases, establishing a new accuracy baseline for ecological forecasting. By openly releasing BioAnalyst and its fine-tuning workflows to the scientific community, we aim to foster collaborative efforts in biodiversity modelling and advance AI-driven solutions to pressing ecological challenges.

Related papers

• AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock [77.95897723270453]
  Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population.<n> Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI)<n>This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques, and recent vision-language foundation models.
  arXiv  Detail & Related papers  (2025-07-29T17:59:48Z)
• Continental scale habitat modelling with artificial intelligence and multimodal earth observation [0.0]
  Habitats integrate the abiotic conditions and biophysical structures that support biodiversity and sustain nature's contributions to people.<n>Current maps often fall short in thematic or spatial resolution because they must model several mutually exclusive habitat types.<n>Here, we evaluated how high-resolution remote sensing (RS) data and Artificial Intelligence (AI) tools can improve habitat classification over large geographic extents.
  arXiv  Detail & Related papers  (2025-07-13T18:11:26Z)
• Deep learning-based species-area models reveal multi-scale patterns of species richness and turnover [0.0]
  As the sampled area expands, species richness increases, a phenomenon described by the species-area relationship (SAR)<n>Here, we develop a deep learning approach that leverages sampling theory and small-scale ecological surveys to spatially resolve the scale-dependency of species richness.<n>Our model improves species richness estimates by 32% and delivers spatially explicit patterns of species richness and turnover for sampling areas ranging from square meters to hundreds of square kilometers.
  arXiv  Detail & Related papers  (2025-07-08T19:42:33Z)
• When Servers Meet Species: A Fab-to-Grave Lens on Computing's Biodiversity Impact [2.664633466640672]
  This paper presents the first end-to-end analysis of biodiversity impact from computing systems.<n>We introduce two new metrics--Embodied Biodiversity Index (EBI) and Operational Biodiversity Index (OBI)--to quantify biodiversity impact across the lifecycle.<n>We present FABRIC, a modeling framework that links computing workloads to biodiversity impacts.
  arXiv  Detail & Related papers  (2025-06-25T13:50:04Z)
• BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning [51.341003735575335]
  We find emergent behaviors in biological vision models via large-scale contrastive vision-language training.<n>We train BioCLIP 2 on TreeOfLife-200M to distinguish different species.<n>We identify emergent properties in the learned embedding space of BioCLIP 2.
  arXiv  Detail & Related papers  (2025-05-29T17:48:20Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
  We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model.<n>Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics.<n>This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• Feedforward Few-shot Species Range Estimation [61.60698161072356]
  Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts.<n> accurate range estimates are only available for a relatively small proportion of all known species.<n>We outline a new approach for few-shot species range estimation to address the challenge of accurately estimating the range of a species from limited data.
  arXiv  Detail & Related papers  (2025-02-20T19:13:29Z)
• Artificial Intelligence for Sustainable Urban Biodiversity: A Framework for Monitoring and Conservation [0.0]
  The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management.<n>This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation.
  arXiv  Detail & Related papers  (2024-12-28T03:18:56Z)
• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• Ensembles of Vision Transformers as a New Paradigm for Automated Classification in Ecology [0.0]
  We show that ensembles of Data-efficient image Transformers (DeiTs) significantly outperform the previous state of the art (SOTA) On all the data sets we test, we achieve a new SOTA, with a reduction of the error with respect to the previous SOTA ranging from 18.48% to 87.50%.
  arXiv  Detail & Related papers  (2022-03-03T14:16:22Z)
• Seeing biodiversity: perspectives in machine learning for wildlife conservation [49.15793025634011]
  We argue that machine learning can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies.
  arXiv  Detail & Related papers  (2021-10-25T13:40:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.