Editorial: Monitoring and responding to global change to promote resilient and productive forests through innovative forest inventory
Randall S. Morin1* 
Sean P. Healey2 
Steve Prisley3 
KaDonna C. Randolph4 
James A. Westfall1 
Andrew N. Gray5- 1Department of Agriculture, Northern Research Station, Forest Service, York, PA, United States
- 2Department of Agriculture, Rocky Mountain Research Station, Forest Service, Riverdale, UT, United States
- 3National Council for Air and Stream Improvement, Cary, NC, United States
- 4Department of Agriculture, Southern Research Station, Forest Service, Knoxville, TN, United States
- 5Department of Agriculture, Pacific Northwest Research Station, Forest Service, Corvallis, OR, United States
Editorial on the Research Topic
Monitoring and responding to global change to promote resilient and productive forests through innovative forest inventory
1. Introduction
National Forest Inventories (NFIs) provide critical information about the distribution and composition of forest resources and help assess sustainable forest management. Data on important topics such as biodiversity, carbon sequestration, and forest products inform planning, policy, and national and international reporting. The USDA Forest Service's Forest Inventory and Analysis Program (FIA) is the primary source of strategic-level information about the status of and trends in forests in the United States. The program is built on a statistically robust sampling network of permanent forest inventory and monitoring plots that serve as the basis for a diverse array of scientific studies (Tinkham et al., 2018; Lister et al., 2020). The papers presented here highlight research presented at the 2022 FIA Science Stakeholder Meeting, the 15th such meeting held since 1999, e.g., Morin and Liknes (2012), Stanton and Christensen (2015), Healey and Berrett (2017), and Brandeis (2020). The sampling of papers in this Research Topic represents the broad topical, geographic, and applied scope of the ongoing research associated with the national FIA Program, highlighting the impact of innovative methods and uses of forest inventory data in forest research and management across the U.S. and globally.
2. Improving estimation techniques in urban environments
The FIA program has a history of continuous improvements to its sampling methods, while still maintaining consistency (Bechtold and Patterson, 2005; Westfall et al., 2022). Federal legislation from 2014 emphasized specific FIA activities, including initiation of urban forest inventories (Edgar, 2022). A particular focus area for improvements is survey non-response (Patterson et al., 2012; Corona et al., 2014), and FIA's urban inventory is particularly impacted due to plots crossing multiple ownerships. Westfall and Edgar found that non-response bias in FIA urban forest inventories exceeded 10% in some cases. Non-response bias is thus a critical research focus for FIA as urban inventories expand.
3. Forest dynamics, ecology, and health
The stability of the inventory across decades allows FIA to monitor trends in forest status. Many aspects of forest dynamics, ecology, and health have been addressed with NFI data including disturbance impacts (Gray and Whittier, 2014), fire modeling (Fried et al., 2017), pest vulnerability and susceptibility (Healey, 2020; Goeking and Windmuller-Campione, 2021), management and timber supply assessments (Prestemon and Wear, 2000; Wurtzebach et al., 2020), and many others.
Edgar and Westfall quantified the timing and extent of forest disturbance in the Laurentian Mixed Forest of northern Minnesota, Wisconsin, and Michigan. Results from repeated measures of forest inventory plots indicated a large increase in forest disturbance between 1999 and 2015.
Potter et al. developed indicators of non-native tree regeneration to identify the most highly invasive tree species and the U.S. forest regions with the highest diversity of non-native tree species. The metrics they describe can help prioritize management of non-native tree species by accounting for environmental variation and the invasion process while also providing broad-scale information across geopolitical borders.
Woodbridge et al. assessed environmental conditions driving mesophication in eastern U.S. forests. Change analyses with remeasured NFI plots identified conditions associated with higher rates of mesophication over time. Canopy mesophication, which was greatest in the midstory, may impact future trait conditions and composition across eastern U.S. forests.
Kralicek et al. compared projected climate change impacts to observed change for five tree species in the Pacific Northwest U.S. by associating mortality and net-growth estimates with areas of shifting suitability and a naïve division of habitat based on elevation and latitude. Results indicated that population decline in a species' core range was greatest for California black oak (Quercus kelloggii).
4. Forest and habitat classification and mapping
Forest classifications based on ground collected and/or remotely sensed data have been used to address many topics, e.g., fire susceptibility (Shaw et al., 2017), vegetation types (Davis et al., 2022), wildlife habitat (Zielinski and Gray, 2018), and many others.
Lesmeister and Jenkins utilized forest inventory data and passive acoustic monitoring networks for northern spotted owls (Strix occidentalis caurina) to develop tools for predicting forest change impacts on wildlife populations. Results indicated that these data, along with complementary advancements in data computation and statistics, are effective for monitoring northern spotted owls. The next phase of the research will implement new technologies to expand the scope of inference from FIA-derived models.
Berdeen et al. assessed the change in abundance of potential nesting cavities for wood ducks (Aix sponsa) in Northern Minnesota U.S. forests across several decades. Total suitable stems and those of late-successional tree species generally increased at all scales during the analysis period. These results can inform research directions for future studies of nesting wood ducks and help forest management decisions.
Barnett et al. applied saturating, non-linear growth models to NFI plot data to model forest carbon accumulation over time and to classify and map mature and old-growth forests in the US. Approximately 6% of currently forested lands in the U.S. were classified as old growth and almost one-third as mature. Better integration of old-growth structural definitions and other improvements to forest development models are needed to help forest managers achieve old-growth retention and development targets.
5. Conclusion
These and other examples highlight ongoing research that relies heavily on FIA data and the cadre of scientists, managers, and other stakeholders that use FIA data to improve our understanding of the status of and trends in our nation's forest resources.
Author contributions
RM led the writing of this editorial with review and comments from SH, SP, KR, JW, and AG. All authors contributed to the article and approved the submitted version.
Acknowledgments
The authors thank all the participants who submitted to our Research Topic.
Conflict of interest
SP was employed by National Council for Air and Stream Improvement.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Author disclaimer
The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy.
References
Bechtold, W. A., and Patterson, P. L, . (eds) (2005). The Enhanced Forest Inventory and Analysis Program - National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS-80 (Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station), 85.
Brandeis, T. J. (comp). (2020). Celebrating Progress, Possibilities, and Partnerships: Proceedings of the 2019 Forest Inventory and Analysis (FIA) Science Stakeholder Meeting. November 19–21, 2019. Knoxville, TN. e-Gen. Tech. Rep. SRS-256 (Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station), 267.
Corona, P., Chirici, G., Franceschi, S., Maffei, D., Marcheselli, M., Pisani, C., et al. (2014). Design-based treatment of missing data in forest inventories using canopy heights from aerial laser scanning. Can. J. For. Res. 44, 892–902. doi: 10.1139/cjfr-2013-0521
Davis, R. J., Bell, D. M., Gregory, M. J., Yang, Z., Gray, A. N., Healey, S. P., et al. (2022). Northwest Forest Plan—The First 25 Years (1994-2017): Status and Trends of Late-successional and Old-growth Forests. PNW-GTR-1004 (Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station), 82. doi: 10.2737/PNW-GTR-1004
Edgar, C. B. (2022). “Urban forest inventory and analysis,” in Sampling and Estimation Documentation for the Enhanced Forest Inventory and Analysis Program: Gen. Tech. Rep. NRS-GTR-207, eds J. A. Westfall, J. W. Coulston, G. G. Moisen, and H.-E. Andersen (Madison, WI: U.S. Department of Agriculture, Forest Service, Northern Research Station), 129.
Fried, J. S., Potts, L. D., Loreno, S. M., Christensen, G. A., and Barbour, R. J. (2017). Inventory-based landscape-scale simulation of management effectiveness and economic feasibility with BioSum. J. For. 115, 249–257. doi: 10.5849/jof.15-087
Goeking, S. A., and Windmuller-Campione, M. A. (2021). Comparative species assessments of five-needle pines throughout the western United States. For. Ecol. Manage. 496, 119438. doi: 10.1016/j.foreco.2021.119438
Gray, A. N., and Whittier, T. R. (2014). Carbon stocks and changes on Pacific Northwest national forests and the role of disturbance, management, and growth. For. Ecol. Manage. 328, 167–178. doi: 10.1016/j.foreco.2014.05.015
Healey, S. P. (2020). Long-term forest health implications of roadlessness. Environ. Res. Lett. 15, 104023. doi: 10.1088/1748-9326/aba031
Healey, S. P., and Berrett, V. M. (comps) (2017). Doing More with the Core: Proceedings of the 2017 Forest Inventory and Analysis (FIA) Science Stakeholder Meeting; 2017 October 24–26; Park City, UT. Proc. RMRS-P-75 (Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station), 56.
Lister, A. J., Andersen, H., Frescino, T., Gatziolis, D., Healey, S., Heath, L. S., et al. (2020). Use of remote sensing data to improve the efficiency of national forest inventories: a case study from the United States national forest inventory. Forests 11, 1364. doi: 10.3390/f11121364
Morin, R. S., and Liknes, G. C. (comps) (2012). Moving from Status to Trends: Forest Inventory and Analysis (FIA) Symposium 2012, 2012. December 4–6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105 (Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station) [CD-ROM], 478.
Patterson, P. L., Coulston, J. W., Roesch, F. A., Westfall, J. A., and Hill, A. D. (2012). A primer for nonresponse in the US forest inventory and analysis program. Environ. Monit. Assess. 184, 1423–1433. doi: 10.1007/s10661-011-2051-5
Prestemon, J. P., and Wear, D. N. (2000). Linking harvest choices to timber supply. For. Sci. 46, 377–389. doi: 10.1093/forestscience/46.3.37
Shaw, J. W., Goeking, S. A., Menlove, J., and Werstak, C. E. Jr. (2017). Assessment of fire effects based on forest inventory and analysis data and a long-term fire mapping data set. J. For. 11, 258–269. doi: 10.5849/jof.2016-115
Stanton, S. M., and Christensen, G. A. (comps) (2015). Pushing Boundaries: New Directions in Inventory Techniques and Applications: Forest Inventory and Analysis (FIA) symposium 2015, 2015. December 8–10; Portland, Oregon. Revised edition. Gen. Tech. Rep. PNW-GTR-931 (Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station), 384. doi: 10.2737/PNW-GTR-931
Tinkham, W. T., Mahoney, P. R., Hudak, A. T., Domke, G. M., Falkowski, M. J., Woodall, C. W., et al. (2018). Applications of the United States Forest Inventory and Analysis dataset: a review and future directions. Can. J. For. Res. 48, 1251–1268. doi: 10.1139/cjfr-2018-0196
Westfall, J. A., Coulston, J. W., Moisen, G. G., and Andersen, H.-E. (comps) (2022). Sampling and Estimation Documentation for the Enhanced Forest Inventory and Analysis Program: 2022. Gen. Tech. Rep. NRS-GTR-207 (Madison, WI: U.S. Department of Agriculture, Forest Service, Northern Research Station), 129. doi: 10.2737/NRS-GTR-207
Wurtzebach, Z., DeRose, R. J., Bush, R. R., Goeking, S. A., Healey, S. Menlove, et al. (2020). Supporting national forest system planning with forest inventory and analysis data. J. For. 118, 289–306. doi: 10.1093/jofore/fvz061
Keywords: sampling and estimation, forest disturbance, urban forestry, tree regeneration, invasive species, climate change, old-growth forests, wildlife habitat
Citation: Morin RS, Healey SP, Prisley S, Randolph KC, Westfall JA and Gray AN (2023) Editorial: Monitoring and responding to global change to promote resilient and productive forests through innovative forest inventory. Front. For. Glob. Change 6:1168453. doi: 10.3389/ffgc.2023.1168453
Received: 17 February 2023; Accepted: 10 March 2023;
Published: 22 March 2023.
Edited and reviewed by: Manfred J. Lexer, University of Natural Resources and Life Sciences Vienna, Austria
Copyright © 2023 Morin, Healey, Prisley, Randolph, Westfall and Gray. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Randall S. Morin, randall.s.morin@usda.gov