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by claude@2026-07, 2026-07-03
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This preprint studies ecological forecasting of annual seed production (“mast seeding”) for six European tree species using weekly weather data and high-resolution seed data from two Austrian old-growth forest sites. The authors fit statistical models with a sliding-window approach and assess explained variance and hindcasting/forecast horizon, finding that most models are unbiased but sometimes imprecise, with explained variance ranging from 0.15 to 0.93 in the year before seed rain; they report that predicting seed rain above 10% of the long-term maximum works well for all species within that timeframe. They also forecast seed rain for 2022–2025 with categorical accuracy varying by species (e.g., beech, maple, and larch mostly correct for 2022–2023; ash incorrect; spruce and fir mixed), and they note limited precision as a caveat. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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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This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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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.
https://doi.org/10.32942/X2T05Z
Natural Resources and Conservation, Other Ecology and Evolutionary Biology, Other Forestry and Forest Sciences
ecological forecasting, masting, seed production, forest restoration, mast seeding, forest reproductive material, tree seeds
Published: 2025-01-10 08:51
CC-By Attribution-NonCommercial-NoDerivatives 4.0 International
Conflict of interest statement:
None.
Data and Code Availability Statement:
Data will be made available after acceptance for publication..
Language:
English
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