{
  "id": 17234703,
  "title": "Advancing Spatial Pattern Integration in Species Distribution Modelling With Computer Vision: How Grain Size and Extent Concepts Shape Predictions of Emerging Aquatic Insects",
  "url": "https://urgent.news/2026/10/30/advancing-spatial-pattern-integration-in-species-distribution",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-30T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762482v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "1. Capturing spatial patterns in ecology necessitates jointly accounting for the concepts of grain size (i.e., the spatial resolution of the input data) and extent (i.e., the area covered by the input data). Species distribution models (SDMs) that rely on traditional machine learning models (e.g., Random Forest, MaxEnt) only integrate the grain size through environmental variables derived from…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}