Towards weather‐based forecasting of annual seed production in six European forest tree species
4 affiliations
- 1Department of Ecosystem Management, Climate and Biodiversity, Institute of Forest Ecology BOKU University Vienna Austria
- 2Austrian Research Centre for Forests Vienna Austria
- 3Department of Geography University of Liverpool Liverpool UK
- 4Department of Biology, Faculty of Science Kyushu University Fukuoka Japan
Abstract
1. Ecological forecasting is essential for addressing climate change adaptation and mitigation. In reforestation and habitat restoration, seed production forecasting will support planning and resource allocation, providing benefits for wildlife management and public health. 2. We hind- and forecast seed production using statistical models based on weekly weather and high-resolution seed data of six European tree species recorded in two Austrian old growth forest sites. Using a sliding-window approach and model selection, we model annual reproduction for three coniferous (Silver fir, European larch, Norway spruce) and three broadleaved species (Sycamore maple, European beech, European ash). We investigate the change of explained variance with decreasing time before seed rain and evaluate hindcasting proficiency as well as the potential forecast horizon based on quantitative and categorical measures useful to stakeholders in the tree seed sector. 3. Most models show unbiased but partly imprecise predictions with a broad range in explained variance (0.15 to 0.93) in the year prior to seed rain. Nevertheless, within this timeframe, hindcasting seed rain above 10% of the long-term maximum, a threshold relevant to practitioners, works well for all species. Previous seed rain explains a large proportion of the variation in seed rain of fir, ash, and maple. 4. We forecast seed rain for 2022 to 2025 for all six species. Regarding categorical one-year?out predictions, results for 2022 and 2023 were mostly correct for beech, maple and larch, mixed for spruce and fir, and incorrect for ash. 5. Synthesis and Applications: Seed production is predictable with a promising degree of accuracy for most studied species one year in advance. This holds value for seed harvesters, nurseries, forest and wildlife managers, and may also inform seed orchard management and public health risk anticipation. Seed forecasts will help address seed scarcity and thus support climate change adaptation and mitigation. Future efforts should prioritise species based on seed storability and support the harvesting of rare species. Understanding reproductive strategies and their responses to climate change points the way forward. Further collaboration with user groups and implementing multi-level seed monitoring schemes will allow for tailoring further seed forecasts that transform the field.
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BibTeX
@article{oberklammer2026towards,
title = {{Towards weather‐based forecasting of annual seed production in six European forest tree species}},
author = {Iris Oberklammer and Georg Gratzer and Silvio Schueler and Heino Konrad and Andrew J. Hacket‐Pain and Valentin Journé and Mario B. Pesendorfer},
journal = {Journal of Applied Ecology},
year = {2026},
month = jun,
doi = {10.1111/1365-2664.70437},
url = {https://doi.org/10.1111/1365-2664.70437},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Towards weather‐based forecasting of annual seed production in six European forest tree species
AU - Iris Oberklammer
AU - Georg Gratzer
AU - Silvio Schueler
AU - Heino Konrad
AU - Andrew J. Hacket‐Pain
AU - Valentin Journé
AU - Mario B. Pesendorfer
PY - 2026
DA - 2026/06/01
JO - Journal of Applied Ecology
DO - 10.1111/1365-2664.70437
UR - https://doi.org/10.1111/1365-2664.70437
AB - 1. Ecological forecasting is essential for addressing climate change adaptation and mitigation. In reforestation and habitat restoration, seed production forecasting will support planning and resource allocation, providing benefits for wildlife management and public health. 2. We hind- and forecast seed production using statistical models based on weekly weather and high-resolution seed data of six European tree species recorded in two Austrian old growth forest sites. Using a sliding-window approach and model selection, we model annual reproduction for three coniferous (Silver fir, European larch, Norway spruce) and three broadleaved species (Sycamore maple, European beech, European ash). We investigate the change of explained variance with decreasing time before seed rain and evaluate hindcasting proficiency as well as the potential forecast horizon based on quantitative and categorical measures useful to stakeholders in the tree seed sector. 3. Most models show unbiased but partly imprecise predictions with a broad range in explained variance (0.15 to 0.93) in the year prior to seed rain. Nevertheless, within this timeframe, hindcasting seed rain above 10% of the long-term maximum, a threshold relevant to practitioners, works well for all species. Previous seed rain explains a large proportion of the variation in seed rain of fir, ash, and maple. 4. We forecast seed rain for 2022 to 2025 for all six species. Regarding categorical one-year?out predictions, results for 2022 and 2023 were mostly correct for beech, maple and larch, mixed for spruce and fir, and incorrect for ash. 5. Synthesis and Applications: Seed production is predictable with a promising degree of accuracy for most studied species one year in advance. This holds value for seed harvesters, nurseries, forest and wildlife managers, and may also inform seed orchard management and public health risk anticipation. Seed forecasts will help address seed scarcity and thus support climate change adaptation and mitigation. Future efforts should prioritise species based on seed storability and support the harvesting of rare species. Understanding reproductive strategies and their responses to climate change points the way forward. Further collaboration with user groups and implementing multi-level seed monitoring schemes will allow for tailoring further seed forecasts that transform the field.
SN - 0021-8901
SN - 1365-2664
ER -