Open-World Ecological Taxonomy Classification

Updated 29 December 2025

Open-world ecological taxonomy classification is a framework that automates the hierarchical identification of organisms by addressing challenges like novel species, long-tailed distributions, and domain shifts.
Models leverage embedding-based classifiers, vision-language approaches, and hyperbolic multimodal encoders to achieve structure-aware taxonomy with open-set and fine-grained recognition.
Practical applications include reliable biodiversity monitoring and conservation, supported by rigorous evaluation protocols from benchmarks such as TerraIncognita and GlobalGeoTree.

Open-world ecological taxonomy classification concerns the automated, hierarchical identification of organisms under realistic ecological sampling, which includes the presence of previously unobserved (novel) taxa, long-tailed distributions, fine-grained morphological variation, and inconsistent domain conditions. The goal is to build models and workflows that not only recognize and attribute images or other sensor data to known taxa (e.g., family, genus, species), but also abstain, discover, or cluster samples belonging to novel organisms, all with sufficient interpretability and rigor to support biodiversity monitoring, conservation, and scientific discovery. This paradigm moves beyond conventional closed-set supervised learning, requiring robust open-set recognition, hierarchical reasoning, uncertainty handling, and adaptive evaluation in the face of taxonomic, geographic, and data-driven drift.

1. Problem Formulation and Motivation

Classical taxonomic classifiers assume a fixed, closed set of target categories and a balanced label distribution. However, ecological reality dictates extreme class imbalance (a handful of common species, many rare or undescribed), dynamic spatiotemporal shifts (seasonality, geography, sensor modality), and frequent encounters with unknown taxa, particularly at fine-grained (genus/species) levels. Open-world taxonomy classification is thus characterized by four key, co-occurring challenges (Low et al., 22 Dec 2025):

Long-tailed class distributions: A few overrepresented taxa, many underrepresented (rare) taxa.
Fine-grained morphological distinction: Many different taxa are visually or acoustically similar, especially in high-diversity groups.
Open-set recognition: Models must reject or appropriately flag samples from taxa not seen in training.
Domain shift: Real-world deployments span different environments, seasons, and acquisition protocols.

Formally, if training data $\mathcal{D} = \{(x_i, y_i)\}_{i=1}^N$ , $x_i$ are modality inputs (images, audio, etc.), $y_i$ are labels for a known set of $c$ taxa, the challenge is to learn a mapping $f:x \mapsto \{y_1, ..., y_c\}\cup \{\textrm{unknown}\}$ , where "unknown" encompasses both unseen members of the hierarchy and truly novel species (Low et al., 22 Dec 2025, Chiranjeevi et al., 29 May 2025). Novelty arises at any taxonomic rank.

2. Model Architectures and Learning Paradigms

Approaches to open-world taxonomy span encoder-based, vision-language, and generative frameworks, with increasing emphasis on hierarchical structure, multimodality, and metric-based open-set strategies.

Embedding-based classifiers: TaxoNet employs a ResNet-101 encoder mapping images to normalized vectors, with classification prototypes assigned per class (unit sphere). Dual-margin penalization loss accentuates separation between rare and common classes, incentivizing intra-class compactness and inter-class margin adaptation based on the inverse class prior; see equations and details in (Low et al., 22 Dec 2025). At inference, the maximal logit below threshold flags samples as "unknown".
Vision-LLMs (VLMs): Systems like GeoTreeCLIP (Mu et al., 18 May 2025), TaxaBind (Sastry et al., 2024), and OpenWildlife (Patel et al., 24 Jun 2025) use paired image–text or multimodal encoders (typically ViT-B/16 for images, CLIP- or BERT-style transformer for text), trained with contrastive losses. Such models enable zero-shot and few-shot predictions by computing similarities with text-derived taxonomic prototypes, allowing open-vocabulary querying and flexible OOD handling. Text prompts can encapsulate taxonomic descriptions or functional traits.
Hyperbolic multimodal encoders: "Hyperbolic Multimodal Representation Learning" explicitly embeds images, DNA, and text into hyperbolic space, where hierarchical entailment is geometrically encoded through entailment cones and stacked entailment losses (SEL) that enforce is-a relationships (Gong et al., 22 Aug 2025).
Retrieval-Augmented Generation (RAG): For enhanced interpretability and long-tail robustness, LLM-driven pipelines combine dense image captioning with text corpus retrieval (Wikipedia, Wikispecies), fusing visual evidence with explicit taxonomic reasoning. The RAG model refrains from fine-grained/species classification unless strong context is retrieved, reducing overconfidence at genus/species levels (Lesperance et al., 13 Mar 2025).

A survey of the LifeCLEF 2016 Plant Challenge highlights that even state-of-the-art CNN ensembles (VGG, GoogleNet, ResNet) require explicit unknown rejection mechanisms to maintain open-world precision (Goeau et al., 25 Sep 2025).

3. Hierarchical Recognition, Open-set Rejection, and Taxon Discovery

Hierarchical taxonomic trees (Order → Family → Genus → Species) are central to open-world taxonomy classification. Models may be architected to predict taxonomic levels sequentially or simultaneously, abstaining as necessary:

Hierarchical Cascades: Some pipelines deploy cascaded classifiers, where higher-level ranks (phylum, class, order, family) are predicted first, with the model abstaining or branching if confidence falls below rank-specific thresholds (Sinha et al., 24 Feb 2025, Chiranjeevi et al., 29 May 2025).
Open-set Rejection: Deployment demands robust "unknown" detection. Thresholding on maximal class logits, cosine similarity margins, or abstention tokens (in VLM prompting) is widely used. Models can be required to return the "Unknown" token if no confident assignment is possible at a given rank (as in TerraIncognita) (Chiranjeevi et al., 29 May 2025).
Class Discovery: Pairwise similarity sub-models (PCN) can be trained to distinguish same/different class membership and then applied to rejected examples for agglomerative (hierarchical) clustering, inferring the number and structure of hidden, unseen taxa (Shu et al., 2018).

An adaptation of (Shu et al., 2018) for ecological hierarchies involves multi-task PCN learning, where similarity is predicted at genus, family, and species levels, enabling taxonomically-constrained clustering of rejected samples.

4. Datasets, Benchmarks, and Evaluation Protocols

Rigorous open-world evaluation demands realistic, large-scale, and hierarchically annotated datasets, with explicit protocols for unknown taxa. Notable resources include:

TerraIncognita (Chiranjeevi et al., 29 May 2025): A dynamic entomological benchmark with known and novel subsets, stratified by four taxonomic ranks, with quarterly updates for longitudinal benchmarking, and strict abstention requirements to mirror real discovery scenarios.
GlobalGeoTree (Mu et al., 18 May 2025): A planetary-scale tree dataset with 6.3M occurrences, Sentinel-2 image series, environmental covariates, and four-level taxonomic labels, supporting zero-shot, few-shot, and open-world generalization.
EcoWikiRS (Zermatten et al., 28 Apr 2025): High-resolution aerial data over Swiss territory, with weakly supervised alignment of images to habitat text derived from Wikipedia and EUNIS hierarchical habitat classes for zero-shot classification.
LifeCLEF 2016 (Goeau et al., 25 Sep 2025): An open-set plant benchmark, with known/unknown splits, organ-specific views, and open-set mAP metrics.
TaxaBench-8k (Sastry et al., 2024): Multimodal, hierarchical test suite for species retrieval/classification tasks spanning six sensory modalities.

Metrics include per-rank accuracy, macro recall, mean average precision in open and closed settings (mAP-open, mAP-closed) (Goeau et al., 25 Sep 2025), hierarchical precision/recall, open-set TNR@95%TPR (Low et al., 22 Dec 2025), and attempt rate versus accuracy curves in abstaining LLMs (Lesperance et al., 13 Mar 2025).

5. Handling Long-tailed Distributions, Class Imbalance, and Rare Taxa

Long-tail and rare taxon performance is a primary bottleneck for open-world models, addressed by:

Class-balanced or margin-based losses: Dual-margin penalization (TaxoNet) directly assigns higher intra-class margin to rare classes, restraining overrepresented classes from dominating embedding space (Low et al., 22 Dec 2025). LDAM, class-balanced loss, and logit adjustment are common baselines for comparison.
Norm-guided/oversampling: Preferentially sampling low-norm or rare-class samples during training helps expose the network to underrepresented intra-class variance (Low et al., 22 Dec 2025).
Multimodal retrieval: Where image exemplars are scarce, models leveraging auxiliary text (trait descriptions, Wikipedia), audio (bioacoustics), or satellite/environmental data increase the robustness of rare-taxa assignments (Sastry et al., 2024, Lesperance et al., 13 Mar 2025).
Hierarchical evaluation: Macro-averaged metrics disproportionately weight recall at the tail, providing a more ecologically meaningful assessment (Low et al., 22 Dec 2025).

Zero-shot retrieval using prototypes built from natural language descriptions (taxonomic strings) and explicit RAG over biodiversity knowledge graphs further ameliorate the rare-taxa bottleneck, especially at higher ranks (Sastry et al., 2024, Lesperance et al., 13 Mar 2025).

6. Multimodal, Hierarchical, and Structure-Aware Representations

Recent advances emphasize joint embedding of diverse modalities (image, audio, text, genomic, environmental variables) with explicit alignment or entailment structure.

Multimodal unification: TaxaBind's six-modality 512-d embedding enables direct comparison and cross-retrieval across all ecological evidence streams. Multimodal patching and supervised-contrastive losses preserve class information and support open-set, zero-shot applications (Sastry et al., 2024).
Hyperbolic geometry: Embedding hierarchies in hyperbolic space ensures exponentially growing volume that matches taxonomic branching, while stacked entailment losses enforce explicit inclusion of child taxa within parent cones (Gong et al., 22 Aug 2025). Open-world generalization benefits in DNA-based tasks, with moderate impact on image-based rare taxa.
Hierarchical prototypes and heads: Lightweight classifiers or retrievers can be attached for multiple taxonomic levels, reusing shared representations and enabling multi-level querying or thresholded open-set rejection (Sastry et al., 2024, Mu et al., 18 May 2025).

However, fine-grained open-world recognition at genus/species remains low in absolute terms for most architectures, especially for unseen taxa or those severely underrepresented in training (Chiranjeevi et al., 29 May 2025, Gong et al., 22 Aug 2025).

7. Impact, Current Limitations, and Future Directions

Open-world ecological taxonomy classification underpins scalable biodiversity monitoring, conservation, and ecosystem management, enabling practitioners to triage and flag novel species, efficiently process large-scale ecological imagery, and adapt to evolving taxonomies and distributions.

Key limitations persist:

Coarse-level accuracy (Order/Family) is high (F1 $>$ 90%), but sharp performance drops are observed at fine-grained levels (F1 $<$ 2% at Species on TerraIncognita, macro recall $<$ 25% for rare classes in standard losses) (Chiranjeevi et al., 29 May 2025, Low et al., 22 Dec 2025).
Open-set rejection remains largely threshold-based; more principled distributional tail modeling (e.g., OpenMax), distance-based scoring, or uncertainty-aware representations are required for full reliability (Goeau et al., 25 Sep 2025, Low et al., 22 Dec 2025).
Large foundation models (LLMs, VLMs) lag in expert-level fine-grained classification, often due to lack of taxon-specific tuning and limited access to context-rich, curated biodiversity text (Low et al., 22 Dec 2025, Lesperance et al., 13 Mar 2025).
Datasets, though increasingly comprehensive, still exhibit geographic and taxonomic biases, with ongoing need for regular expansion and cross-domain evaluation (Chiranjeevi et al., 29 May 2025, Mu et al., 18 May 2025).

Active research directions include incorporating phylogenetic priors (tree-structured loss regularization), adapter modules tuned per rank, continual learning for taxonomic drift, uncertainty quantification, and expansion to global, multi-year, and multi-language resources (Sastry et al., 2024, Mu et al., 18 May 2025, Zermatten et al., 28 Apr 2025).

Open-world ecological taxonomy classification is converging on unified, structure-aware, and interpretability-focused foundations essential for next-generation biodiversity discovery and monitoring (Low et al., 22 Dec 2025, Chiranjeevi et al., 29 May 2025, Sastry et al., 2024, Lesperance et al., 13 Mar 2025, Mu et al., 18 May 2025, Gong et al., 22 Aug 2025, Goeau et al., 25 Sep 2025, Zermatten et al., 28 Apr 2025).