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Evidence record 1304 · automatically gathered

STELLAR: Spatio-Temporal Environmental Learning with Latent Alignment and Refinement for Long-Tailed Species Distribution Modeling

Joint Species Distribution Modeling (JSDM) is a key enabler for biodiversity monitoring and conservation planning. However, accurate JSDM faces two coupled challenges: environmental drivers and species distributions are inherently spatio-temporal, while species co-occurrence patterns exhibit complex non-linear community structure and severe long-tail imbalance driven by rare species. Existing approaches often address these factors in isolation, learning from static covariates or neglecting the h

Record details

Published: 7 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment · Shared topic: Environment

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Shared topic: Safety & alignment · Shared topic: Environment

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Shared topic: Safety & alignment · Shared topic: Environment

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Shared topic: Safety & alignment · Shared topic: Environment

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How to cite this record

ethics.ai (7 June 2026), “STELLAR: Spatio-Temporal Environmental Learning with Latent Alignment and Refinement for Long-Tailed Species Distribution Modeling,” evidence record 1304, https://ethics.ai/record/1304 (originally published by arXiv).

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