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Abstract Forested landscapes have the potential to help offset global carbon emissions. However, current global models do not, nor are they intended to, capture the fine‐scale variability of the distributions of carbon in aboveground or belowground stocks or their simultaneous variability. Regional investigations are necessary to resolve patterns in carbon that can guide policy and planning, but regional maps that quantify multiple carbon pools are scarce. We quantified the spatial relationships of aboveground and belowground carbon stocks to understand their simultaneous variability across the forested area of the perhumid ecoregion of the Pacific Coastal Temperate Rainforest. Further, we identified topo‐climatic contexts associated with unique patterns in both aboveground and belowground carbon stocks by conducting an overlay analysis across the entire ecoregion. We utilized previously published estimates of carbon stocks based on extensive governmental data and machine learning techniques to model simultaneous spatial relationships of aboveground and belowground carbon stocks and generate a map for a high carbon region. We employed Pearson's correlations as well as ANOVA and Tukey honestly significant difference (HSD) tests for comparisons of topography and climate. Approximately 25% (2.6 million ha) of the area across the perhumid ecoregion had similar trends in aboveground and belowground stocks (convergence). Likewise, 20% of the ecoregion had opposite trends of aboveground and belowground stocks (divergence), and 56% of the ecoregion experienced no relationship (moderate conditions) between aboveground and belowground stocks. Convergence areas consist of carbon hotspots associated with 1.3 million ha and 794 Mg C ha−1on average, or carbon cold spots associated with 1.2 million ha and 224 Mg C ha−1. Areas with convergence, divergence, and moderate carbon stocks all had unique associations with slope, elevation, aspect, mean annual precipitation, and annual mean temperature. High levels of aboveground carbon were associated with steeper slopes, while high levels of belowground carbon were associated with high levels of precipitation. The interactions between slope, precipitation, and temperature correspond with carbon convergence and divergence, likely due to water accumulation which impacts the decomposition of organic matter in soil. These data are critical to regional planning and carbon policy and inform expectations for future carbon storage as the climate changes.
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This content will become publicly available on November 5, 2026
Impacts and Legacies of Extreme Precipitation on Temperate Forests During Critical Ecological Windows
Within seasonal temperate forests, changes in precipitation structure—its form, duration, and seasonal timing—is a dominant characteristic of climate change. While past research has focused primarily on annual precipitation totals, emerging evidence shows that short-duration extreme precipitation can impact ecosystem carbon, water, and biogeochemical cycling when it coincides with key phenological and physiological transitions. These impacts are mediated by the responses of plant and microbial physiology, aboveground–belowground interactions, and lagged feedbacks as organisms and communities adjust to these extremes. This review focuses on shifts within ecosystem water cycling, within tree growth dynamics (carbon uptake and aboveground–belowground allocation and coordination), within soil biogeochemical cycling, from the loss of winter snow, and in forest structure and community composition. Together, these concepts highlight the urgent need to understand how changes in all aspects of precipitation structure reshape the functioning and resilience of mesic temperate forests.
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- Award ID(s):
- 2231681
- PAR ID:
- 10659321
- Publisher / Repository:
- ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS
- Date Published:
- Journal Name:
- Annual Review of Ecology, Evolution, and Systematics
- Volume:
- 56
- Issue:
- 1
- ISSN:
- 1543-592X
- Page Range / eLocation ID:
- 315 to 335
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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