{
  "abstract": "Background and Aims: Pollen germination and tube growth are critical stages of plant reproduction that are highly sensitive to temperature but remain labor-intensive to quantify. This study aimed to develop a deep learning-based approach for pollen phenotyping and to characterize the temperature dependence of pollen germination and elongation in Douglas-fir (Pseudotsuga menziesii). Methods: A convolutional neural network (CNN) was trained to segment pollen grains and classify germination status from microscopic images. Germination percentage and pollen length were quantified across a range of incubation temperatures from 5 to 40{degrees}C. Gamma functions were fitted to temperature response curves to estimate the optimal temperatures for pollen germination and elongation among Douglas-fir populations collected across an elevational gradient. Key Results: The CNN achieved high segmentation accuracy (intersection over union = 0.846), accurately distinguishing pollen grains from the background but showing moderate accuracy in separating germinated from ungerminated pollen during early elongation. Both pollen germination and elongation exhibited bell-shaped temperature response curves with distinct thermal optima. Pollen elongation consistently reached its optimum at higher temperatures than pollen germination. Estimated optimal temperatures fell within a narrow range, approximately 19 to 23{degrees}C. No significant relationship was detected between elevation and thermal optima, although some higher-elevation populations exhibited lower optimal temperatures. Comparisons with previous analyses of three western North American conifers showed that each species occupied a distinct reproductive thermal niche corresponding to the spring temperatures of its native habitat. Conclusions: Deep learning provides an efficient approach for high-throughput quantification of pollen germination and elongation from microscopic images. The narrow thermal range for reproductive performance suggests that Douglas-fir pollen is sensitive to temperature variation and that warming climates may alter reproductive success. These findings improve our understanding of the thermal sensitivity of conifer reproduction and provide a scalable framework for assessing impacts of climate warming on forest regeneration.",
  "abstract_shown": true,
  "affiliations": [
    "University of Washington"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "H.-W. Hsu"
    },
    {
      "name": "S. Zerrade"
    },
    {
      "affs": [
        0
      ],
      "name": "S.-H. Kim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/chsvd6raamg53.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.10.750703",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1.full",
  "id": "chsvd6raamg53",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "ecology"
    ],
    [
      "openalex",
      "1312|Plant Reproductive Biology"
    ]
  ],
  "openalex": "W7212458231",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/chsvd6raamg53.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:56Z",
      "native_id": "10.64898/2026.09.10.750703",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:ecology",
      "subfield": "ecology"
    }
  ],
  "title": "Temperature dependence of pollen germination in Douglas-fir (Pseudotsuga menziesii): A machine learning-based detection of pollen viability from microscopic images",
  "title_text": "Temperature dependence of pollen germination in Douglas-fir (Pseudotsuga menziesii): A machine learning-based detection of pollen viability from microscopic images",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:56Z",
  "url": "https://research.draconitic.com/paper/chsvd6raamg53",
  "venue": "bioRxiv",
  "versions": []
}