Opening ecological data to reduce bias and strengthen AI in ecology

In 1985, Richard Stallman introduced copyleft, a licensing principle that allows source code to be used, modified, and redistributed provided that the same freedoms are preserved in derivative works (Stallman, 1985). Although developed for software, copyleft offers a useful analogy for ecological research, where data, metadata, and analytical code should be accessible and reusable under clear licenses. Similarly, our ability to understand and address ecological challenges such as biodiversity loss, species range shifts, land degradation, and climate change depends on ecological knowledge being open where ethically appropriate and otherwise responsibly accessible. Such knowledge should also be managed according to the FAIR principles (Findable, Accessible, Interoperable, and Reusable) and established domain standards such as Darwin Core and Ecological Metadata Language (Wieczorek et al. 2012, Wilkinson et al. 2016). Despite substantial progress in open science (Dwivedi et al. 2022), many key ecological datasets (e.g., national ecological inventories, biodiversity records, and high-spatial- and temporal-resolution remote-sensing data) remain proprietary or available only with restricted access (e.g., requiring formal approval). These gaps are substantial: Large-scale analyses of hundreds of millions of ecological records reveal persistent taxonomic, geographic, and temporal biases (Meyer et al. 2015, Daru and Rodriguez, 2023). Much of the scientific literature also remains behind paywalls, while open-access publication fees can create barriers for researchers with limited funding (Rocchini and Neteler 2012). This paradox, that is, publicly funded knowledge becoming inaccessible to the public, constrains who can participate in ecological research and which ecosystems, regions, and perspectives are ultimately represented. AI does not create this problem but can amplify it by embedding data biases in models reused at scale, while closed training data limit independent scrutiny (Koch et al. 2022). AI for ecology encompasses task-specific machine-learning and computer-vision models, as well as foundation models, including large language models (LLMs). These systems differ in scale, purpose, and accessibility, but all depend on the quality, documentation, and governance of the data used to train and evaluate them. By integrating diverse data sources, AI can transform ecological research. However, when data, code, or model-development processes cannot be independently inspected, existing knowledge gaps and biases may be transferred into ecological inference. Appropriately open, reliable, and interoperable ecological data, together with transparent and auditable AI infrastructures, are therefore essential for robust, equitable, and epistemically sound ecological research. The expansion of AI in ecological research intensifies the need for transparent and well-documented knowledge. Bias is not unique to AI: statistical, mechanistic, and machine-learning models can all inherit limitations from their data and assumptions. AI can increase the scale, opacity, and reach of these effects (Han et al. 2023). Currently, many applications in ecology use task-specific AI models trained for bounded prediction or classification problems (Pichler and Hartig 2023). These models are often trained on specialized aggregated ecological repositories (e.g., biodiversity data from the Global Biodiversity Information Facility [GBIF]), climatic variables (e.g., from WorldClim and ERA5), or land-use and land cover change mapping (e.g., from satellite imagery). In contrast, the rise of foundation models, including LLMs used for the automated synthesis of scientific literature, code generation, data processing and analysis, requires massive cross-domain training sets that are difficult to build from scratch. Consequently, models trained on skewed, geographically biased, or inaccessible datasets can systematically misrepresent under-sampled regions and reflect long-standing inequities in ecological knowledge. When training datasets are inaccessible, their composition is also difficult to inspect, making gaps in the representation of regions, languages, taxa, or perspectives harder to detect and evaluate. Thus, LLMs are not necessarily more biased than task-specific AI models, but their biases can be harder to trace and hence propagate across a wider range of users, tasks, and applications. In ecology, both task-specific and generic models inherit the limitations of the data used to train them. For example, species-recognition models inherit taxonomic imbalances in training images (Koch et al. 2022), whereas LLMs can process ecological information rapidly but may perform poorly on specific tasks. Gougherty and Clipp (2024) found that LLMs often struggled to extract pathogen-incidence values and sometimes inferred that all individuals were affected when no incidence value was reported. Similarly, Dorm et al. (2026) showed that LLMs performed poorly in generating species range maps and offered only limited improvement over random guessing in threat classification. Comparable effects have been observed in other AI domains, where imbalances in training data have produced systematically poorer model performance for underrepresented groups (Birhane, 2022). Together, these examples illustrate how unbalanced training data can transfer existing knowledge gaps into model outputs, highlighting the importance of identifying, documenting, and reducing such biases before models are applied beyond their validated domain. AI models can furthermore introduce subtler epistemic risks (Loru et al. 2025), including the confident fabrication of ecological information, the misrepresentation of species-trait responses to environmental gradients, and the generation of unsupported ecological interactions or spatial patterns. In ecological applications, documented limitations already include errors in quantitative information extraction and uneven performance across tasks (Gougherty and Clipp, 2024, Dorm et al. 2026). For these reasons, responsible openness and transparent governance of ecological data and metadata are vital, because the reliability of specialized models and large-scale synthesis depends on whether foundational ecological resources can be appropriately accessed, documented, and independently evaluated. Reproducible evaluation therefore requires practical commitments. Ecological AI studies should provide standardized documentation describing where their training data came from, which taxa, regions, and environmental conditions they cover, how the model is intended to be used, and its known limitations. Established tools such as datasheets for datasets and model cards can support this documentation (Gebru et al. 2021). Models should also be evaluated across taxa, regions, languages, and environmental conditions, and data, code, prompts, model weights, and independent evaluation sets should be released where ethically, legally, and technically possible (Maasri et al. 2022, Smith et al. 2024). In order to improve data exchange and utilization, general principles and standards for the creation, verification, and sharing of data should be established while respecting the owners and creators of specific databases. Any restrictions and access procedures should also be documented. Public initiatives such as the Minerva LLM (Orlando et al. 2024) illustrate the value of transparent, language-specific model development, but openness alone does not ensure ecological or cultural validity across regions. AI also changes the politics of openness by creating a risk that openly shared data and open-source code are incorporated into proprietary models without reciprocal access, attribution, credit, or participation for the researchers, developers, and communities who produced them. In public and public/private programs such as GenAI4EU (2025), adequate funding should mean sustained support for data curation, digitization of legacy records, targeted collection in documented gaps, local skills and infrastructure, and independent auditing, not only model development. Funders, publishers, scientific societies, and research institutions should recognize data curation as a research output and require transparent documentation and evaluation. Collaboration should connect local communities and data holders to national infrastructures, international repositories, and standards bodies. Identifying and reducing bias in AI development, enabling robust validation, and sustaining open infrastructures all depend fundamentally on the availability of high-quality, representative, and well-documented ecological data. Where data are scarce, uneven, or unreliable, transparency becomes especially important for identifying gaps and assessing their consequences (Titley et al. 2017). Open data and governance policies must therefore be accompanied by dedicated funding for targeted data collection, the digitization of legacy records, curation, metadata preparation, quality control, local computational infrastructure, training, and equitable collaboration, particularly in under-sampled regions. Such investments are necessary to prevent openness from disproportionately benefiting institutions that already possess the resources needed to exploit shared datasets. Openness should not, however, imply unrestricted public release. Precise locations of threatened species, Indigenous and local knowledge, and data governed by consent or legal agreements may require spatial generalization, controlled access, embargoes, or community authority (Tulloch et al. 2018). Data should instead be as open as possible and as protected as necessary, combining FAIR data stewardship with the CARE principles (Collective Benefit, Authority to Control, Responsibility, and Ethics) where Indigenous or community-governed data are involved. International coordination among funders, ecological societies, repositories, publishers, data providers, and model developers is also needed to establish interoperable standards for data collection, annotation, documentation, sharing, and controlled access. Differences in the quality, resolution, and accessibility of ecological datasets should be explicitly documented and considered during model development and evaluation rather than remaining hidden (Dwivedi et al. 2022). Without shared standards and a commitment to responsible openness, ecological AI risks becoming fragmented, opaque, and shaped primarily by actors with the greatest computational and data resources. The practical priorities that we highlight are the following. Ecologists, AI researchers, funders, journals, repositories, and ecological societies should: audit geographic, taxonomic, temporal, and, where relevant, linguistic gaps in AI training and evaluation data, and quantify their effects on model performance; fund targeted data collection and the digitization of legacy records where these gaps have meaningful consequences; require machine-readable metadata and licenses, provenance records, uncertainty documentation, datasheets for datasets, and model cards; maintain independent benchmark datasets stratified across regions, taxa, sensors, and environmental conditions; and ensure that data-producing communities have access to computational infrastructure, training, credit, and decision-making roles rather than being treated solely as data providers (Wilkinson et al. 2016, Smith et al. 2024). Ecological research stands at a decisive crossroad: As AI reshapes how we observe and interpret the natural world, the openness and responsible governance of our data, models, and publications will determine whether this transformation is equitable and scientifically robust. Unrepresentative datasets, or data and models that cannot be independently evaluated, risk narrowing ecological insight, reinforcing global disparities, and introducing epistemic distortions that degrade our collective understanding of ecological systems. A well-governed, participatory, and inclusive ecological knowledge system, open where possible and protected where necessary, supported by public LLM research, auditable domain-specific models, and genuinely accessible publishing practices, is crucial to ensure that AI becomes a tool for discovery and justice rather than distortion. Just as copyleft preserved the freedoms to use, modify, and share software, responsible openness can free environmental science from informational silos while respecting legitimate restrictions. In the age of AI, openness is not only a technical requirement but an ethical obligation: Shared and responsibly governed knowledge is the foundation of collective solutions. There are no new data associated with this article. The authors declare no competing interests.