Journal of Forestry

Butler, Brett J.

doi: 10.1007/s44392-026-00090-zpmid: N/A

An estimated 37% of the forestland across the United States, excluding Interior Alaska, is controlled by family forest ownerships. This paper examines trends in family forest ownerships with 4 + ha (10 + ac) based on data from the 2013, 2018, and 2023 iterations of the USDA Forest Service’s National Woodland Owner Survey (NWOS). The overall profile of family forest ownerships remained relatively consistent between 2013 and 2023, but there were some significant changes. The percentage of ownerships with a primary decision-maker older than 68 years (the median age in 2023) increased from 32 to 46%. Several reasons for owning forestland and concerns for their land decreased, but the only ones that were significantly different were timber production as a reason for owning, which went from 23 to 21%, and concern about wildfire, which went from 64 to 57%. Harvesting trees for sale and for personal use significantly decreased from 18 to 14% and 41 to 35%, respectively. Participation in preferential property tax, conservation easement, and green certification programs increased from 15 to 18%, 2 to 4%, and 1 to 2%, respectively. These results provide important information for understanding the motivations and needs of family forest ownerships and are essential for monitoring the impacts of existing programs and services, as well as informing future programs and services.

Wagle, Samjhana; Yang, Sheng-I; Brandeis, Thomas J.; Marcano-Vega, Humfredo; Bullock, Bronson

doi: 10.1007/s44392-026-00095-8pmid: N/A

Puerto Rico (PR) and the US Virgin Islands (VI), two archipelagos located in the Caribbean region, possess forest ecosystems with high species diversity, which remain largely underrepresented in forest modeling research compared to temperate forests. Accurately characterizing tree allometry is essential for monitoring forest status over time and predicting tree growth and yield in sustainable forest management. Height-diameter relationship models, denoted as H–D models, are widely used in forestry practice since they effectively reduce the time and cost of field data collection and provide quick estimates of the height of trees based on tree diameter at breast height. For diverse forests, mixed-effects models have often been proposed. In these models, fixed effects provide the predicted H–D curve for the population average (all species) while a calibrated prediction of H–D relationship for each species can be made with the inclusion of both fixed and random effect terms. This study developed species-specific H–D relationship models for different forest types across PR and VI. A total of 25 parametric model candidates published in the literature were examined for each forest type on each archipelago, and the best-fit model in each forest type was selected to build a mixed model for a given forest type and archipelago. The mixed model was then used to generate species-specific predictions of total tree heights. Tree data used were collected from the permanent plots of the USDA Forest Service’s Forest Inventory and Analysis (FIA) program. A total of 419 species with 51,166 observations were collected from eight forest types across the two archipelagos. The results highlight variations in H–D relationships across forest types and archipelagos, emphasizing the need for forest type–specific models to capture their unique ecological traits of species. Results show that three-parameter H–D models provide more accurate predictions than two-parameter ones. Carefully examining all possible combinations of placing the random effects when constructing mixed models is suggested, which can improve model predictability for capturing the H–D relationships for diverse species forests.

Klock, Angela M.; Vogt, Kristiina A.; Vogt, Daniel J.; Gordon, John C.; Mukumoto, Cal T.

doi: 10.1007/s44392-026-00099-4pmid: N/A

Accurate forest carbon accounting is critical for climate mitigation and ecosystem service valuation, yet many current approaches exclude below-ground productivity, particularly fine roots. To test this assumption, we compared two carbon accounting methodologies for Douglas-fir (Pseudotsuga menziesii), Lodgepole pine (Pinus contorta), Western hemlock (Tsuga heterophylla), and fir (Abies spp.)-dominated stands across Washington State, Oregon, and British Columbia. The standard approach uses look-up tables based solely on above-ground biomass, ignoring local site conditions. In contrast, we applied a field-calibrated method incorporating total net primary productivity (tNpp) that included both above- and below-ground biomass linked to climatic and edaphic variables. Results show that conventional accounting underestimates carbon sequestration by 38–68% due to exclusion of fine-root productivity. Across different species dominated stands, estimated carbon credit values ranged from $10–$87 ha⁻1 yr⁻1 under the standard method versus $279–$1,353 ha⁻1 yr⁻1 using the proposed approach. These findings demonstrate that inclusion of fine-root productivity substantially alters regional forest carbon estimates and has important implications for landowners managing small parcels, whose forests may be systematically undervalued under existing carbon market frameworks.