---
title: "Temperature Dependence of Douglas-fir Pollen Germination and Machine Learning Detection of Viabili"
id: "biorxiv-0-temperature-dependence-of-pollen-germination-in-douglas-fir-pseudotsuga"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-0-temperature-dependence-of-pollen-germination-in-douglas-fir-pseudotsuga"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1?rss=1"
published_at: "2026-09-13T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Temperature Dependence of Douglas-fir Pollen Germination and Machine Learning Detection of Viabili
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-0-temperature-dependence-of-pollen-germination-in-douglas-fir-pseudotsuga
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1?rss=1)
- **Published At:** 2026-09-13T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- This preprint describes a study of **pollen germination** and pollen tube elongation in Douglas-fir (Pseudotsuga menziesii) across incubation temperatures from 5 to 40°C, using a convolutional neural network to quantify outcomes from microscopic images. - A **convolutional neural network (CNN)** was trained to segment pollen grains and classify germination status; segmentation performance reached an intersection-over-union (IoU) of 0.846. The model distinguished pollen from background well but had only moderate accuracy for separating germinated from ungerminated pollen during early elongation. - The authors quantified germination percentage and pollen length across temperature treatments and fitted **gamma functions** to temperature response curves to estimate thermal optima for both germination and elongation. - Both germination and elongation displayed bell-shaped temperature responses with distinct thermal optima; pollen elongation consistently peaked at higher temperatures than pollen germination. - Estimated optimal temperatures for reproductive performance across sampled Douglas-fir populations clustered in a narrow range, approximately **19–23°C**. - No statistically significant relationship between collection elevation and thermal optima was detected, though some higher-elevation populations showed lower optimal temperatures. - Comparative analysis with previously studied western North American conifers indicated each species occupies a distinct **reproductive thermal niche** matching the spring temperatures of its native range. - The study concludes that deep learning offers an efficient, scalable method for high-throughput quantification of pollen germination and elongation from images, and that the narrow thermal window for Douglas-fir reproductive performance implies **sensitivity to temperature variation** with potential implications under climate warming. - The article is a preprint and has not undergone peer review; authors declared no competing interests.
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Hsin-Wu Hsu University of Washington * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Hsin-Wu%2BHsu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Hsu%20H&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AHsin-Wu%2BHsu%2B) * [ORCID record for Hsin-Wu Hsu](http://orcid.org/0000-0002-6347-4540 "Open in new tab") Sabrina Zerrade University of Washington * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Sabrina%2BZerrade%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Zerrade%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASabrina%2BZerrade%2B) Soo-Hyung Kim University of Washington * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Soo-Hyung%2BKim%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Kim%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASoo-Hyung%2BKim%2B) * [ORCID record for Soo-Hyung Kim](http://orcid.org/0000-0003-3879-4080 "Open in new tab") * For correspondence: soohkim@uw.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5779184/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5779184/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5779184/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5779184/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5779184/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## 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°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°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. ### Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-ND 4.0 International license](http://creativecommons.org/licenses/by-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. [Donate to openRxiv ](https://www.zeffy.com/en-US/donation-form/donate-to-make-a-difference-10981) [ Back to top](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1?rss=1#page) [ Previous](https://www.biorxiv.org/content/10.64898/2026.09.10.750615v1 "Managing the genetic diversity of Dioscorea yams for adaptation to climate change") Posted September 13, 2026. [ Download PDF](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/09/13/2026.09.10.750703.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.09.10.750703v1.supplementary-material) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Temperature dependence of pollen germination in Douglas-fir (Pseudotsuga menziesii): A machine learning-based detection of pollen viability from microscopic images Hsin-Wu Hsu, Sabrina Zerrade, Soo-Hyung Kim bioRxiv 2026.09.10.750703; doi: https://doi.org/10.64898/2026.09.10.750703 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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