Diameter limits impede restoration of historical conditions in dry mixed‐conifer forests of eastern Oregon, USA

Abstract

The U.S. Forest Service is reconsidering policies that limit the size of trees that can be removed in the course of restoration treatments in dry forests of eastern Oregon. To evaluate the effects of diameter limits on the ability of managers to meet restoration objectives, we used an existing network of long-term research plots to summarize historical and contemporary structure and composition of mixed-conifer forests within a one million-ha study area in eastern Oregon. Then, we used a novel thinning simulation procedure to quantify the degree to which thinning using different diameter limits restored stands to historical conditions. Contemporary mixed-conifer forests within the study area are significantly denser, have more basal area, and have a greater proportion of shade-tolerant species than historical conditions. Our simulations of thinning under current policy that prohibits cutting of trees ≥53 cm show that a quarter of mixed-conifer stands cannot be restored to within the historical range of basal area or density. Those stands that could be restored to within historical basal area ranges still had a substantially higher component of shade-tolerant trees than historical stands. Permitting larger shade-tolerant trees to be removed allowed restoration of all or most of stands to within historical structural and compositional ranges. Forest conditions in the late 1800s may not necessarily provide the best template for management because climate and disturbance projections suggest that eastern Oregon forests will be less well suited to shade-tolerant species in the future. Adapting stands to future conditions will require robust monitoring of forest structural and compositional response to restoration treatments.

21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West

“…Vegetation types across this landscape include dry grasslands, shrubs, and pine and mixed conifer forest types [37]. This research utilized data from a long-term multiparty monitoring program, the Forest Vegetation and Fuels (FVF) program, led by Forest Service managers, Oregon State University researchers, and the Blue Mountains Forest Partners, a stakeholder group based in John Day, Oregon that convenes diverse stakeholders to help plan and implement restoration treatments on Forest Service lands [32]. The FVF program provides data that informs accelerated restoration in an adaptive management framework [38].…”

“…Reducing forest density to facilitate reintroduction of low-severity fire is a major goal of accelerated restoration funded by the CFLRP [44]. [32].…”

“…To address various grain sizes of the imagery and elevation datasets, all raster surface metrics used as predictor variables for modeling were resampled to a 30 m spatial resolution based on the environmental research institute's (ESRI's) nearest neighbor algorithm [46]. Primary vector datasets used included 2019 TIGER/Line files acquired from the US census bureau [33], Flowline and Waterbody line and polygon features from the National Hydrography Dataset (NHD) [47], FVF monitoring plots [32], POD polygons [11], and the location of the Malheur Lumber Company (Table 1).…”

“…This is not to imply that little is known with regards to the grass, shrub, forested vegetative communities [26], fuels [27], fire spread [12], fire risk [28][29][30], and the anthropogenic infrastructure within the area. Instead, it highlights that while inventory efforts such as Forest Inventory Analysis (FIA) [31], multiparty monitoring plots [32], Topologically Integrated Geographic Encoding and Referencing (TIGER) road inventories [33], and derived products such as those produced by the Landfire project and Wildfire Risk Simulation Software (FSim) exist, these resources alone do not provide the wall-to-wall information needed, at the appropriate scales, to address the finely grained spatially explicit questions or the justification for related prescriptions designed to restore the landscape to a fire-resilient condition.…”

“…However, coupled with finely grained remotely sensed information, such as Sentinel 2 imagery [34] and the National Elevation Dataset (NED) [35], data such as multiparty monitoring plots and TIGER road networks and statistical and machine-learning relationships can be leveraged to produce finely grained surfaces depicting the existing forest condition [23] and the costs to move biomass from the forest to a given sawmill [24]. In this study, we demonstrate how existing forest monitoring plots [32], TIGER line files [33], Sentinel 2 imagery [34], and PODs derived from fire managers and common fire modeling tools [11] were used to quantify various forest metrics and treatment costs which in turn were used to inform, justify, and improve restoration decisions. Due to the variety of data sources used, the methodological steps outlined, and the specialized language of the fire, forest, geospatial, and statistical and machine-learning modeling communities, many acronyms are presented in this study.…”