Improving the use of early timber inventories in reconstructing historical dry forests and fire in the western United States: Comment

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Abstract

Knowledge of historical forest conditions and disturbance regimes improves our understanding of landscape dynamics and provides a frame of reference for evaluating modern patterns, processes, and their interactions. In the western United States, understanding historical fire regimes is particularly important given ongoing climatic changes and their effects on fire regimes (Miller and Safford 2012, Westerling 2016, Abatzoglou et al. 2017). Yet, all methods used to reconstruct historical forest conditions have limitations. Confidence in the results generated by any single method increases when multiple studies, using diverse methods, converge on comparable results. Early timber inventories in western ponderosa pine and mixed-conifer forests (Collins et al. 2011, Hagmann et al. 2013, 2014, Collins et al. 2015, Stephens et al. 2015, Hagmann et al. 2017) document forest conditions that are consistent with other records and reconstructions of historical vegetation patterns and fire regimes on landscapes that experienced frequent low- to moderate-severity fires. In a recent assessment of early timber inventories, Baker and Hanson (2017) (hereafter B&H) concluded that these inventories of large forest landscapes in the Central and Southern Sierra Nevada in California and the eastern slopes and foothills of the Cascade Range in Oregon systematically underestimated historical tree density and were biased toward areas of large, merchantable trees. Here, we document serious errors in B&H due to the following: (1) biased estimates of historical tree density from land-survey data; (2) incorrect assumptions about the accuracy of early timber inventories; (3) inappropriate comparisons of studies of vastly different spatial scales, forest types, and diameter limits; (4) unsubstantiated criticism of bias in early timber inventories; and (5) inappropriate cross-referencing and misrepresentation of high-severity fire in historical records. The method used by B&H to estimate historical tree densities (column labeled “General Land Office” [GLO] in B&H Tables 1–3) overestimates known tree densities. In a recent study, Levine et al. (2017) evaluated the performance of the plotless density estimator (PDE, Williams and Baker 2011) used by B&H to calculate pre-management era forest composition from witness trees recorded during the GLO survey of public lands. In six forest stands with densities ranging from 159 to 784 trees/ha, the PDE used by B&H produced results that ranged from 1.2 to 3.8 times larger than the true tree density. A fundamental flaw in the method used by B&H, as applied in dry conifer forests, is reliance on crown radius to predict tree spacing. From an analysis of more than 6000 stem mapped trees, Levine et al. (2017) found only weak relationships between crown area and tree spacing. During revision of this response to B&H, Baker and Williams (2018) published a critique of Levine et al. (2017) addressing purported flaws in the methodology of Levine et al. (2017). These assertions are either irrelevant (e.g., issues with scale or site locations) or invalid (e.g., issues with the designation of neighborhood density; C. R. Levine, J. J. Battles, C. V. Cogbill et al., unpublished manuscript). Additionally, Baker and Williams (2018) suggest a correction in estimating tree diameter at stump height (dsh, 30 cm) from measurements of tree diameter at breast height (dbh, 137 cm), based on Baker and Williams’ unpublished, private data. By their calculation, this revised dsh-to-dbh ratio would bring estimates of tree density closer to the true density. A forthcoming response to this critique of Levine et al. (2017) reevaluates the importance of the dsh correction of dbh on plotless density estimates. New model runs demonstrate that increasing the dsh-to-dbh ratio by as much as 1.23, as recommended by Baker and Williams (2018), had little effect on reducing the overestimation of density inherent in the PDE they use. Thus, the estimates of historical density B&H compared with early timber inventories overestimate historical densities. Quality control records from two-chain timber inventories used by Collins et al. (2011, 2015) and Hagmann et al. (2013, 2014, 2017) refute assertions that these early timber inventories were unreliable due to “large underestimation errors.” In the early 1900s, agencies conducting the timber inventories performed “check cruises” on a subset of the area inventoried. We obtained check cruise data from the same archives as the original inventories. At the spatial scales at which these inventories are used, comparable tree densities were recorded in duplicate cruises of the same areas (Table 1). Mean differences of 4–11% between original inventories and check cruises reveal a slight tendency for overestimation of tree density in the original inventories. The interquartile range of differences in tree density measurements between the original inventory and check cruise was −6% to 19%; the range of the middle 80% of values was −29% to 39%. Negative values indicate lower tree densities (or volume) in the original cruise than the check cruise. To support the contention of inaccuracy of historical timber inventories, B&H (page 4) argued that crews had to work so fast that “…only a few minutes could be spent tallying the tree data” and therefore they did not have time for careful measurements. B&H based their estimate of time spent tallying trees on inventory methods substantially different from those described in the methods sections of Collins et al. (2011), Hagmann et al. (2013, 2014), Collins et al. (2015), Stephens et al. (2015), and Hagmann et al. (2017). The method B&H cited, the Vogel method, was used in the Southwestern Region (USFS Region 3) and entailed subsampling along the transect line. B&H derived their estimate of time available for tallying trees from a description of this method in which the cruiser customarily worked alone and completed 24 transects per day (Marsh 1969). On the one- and two-chain inventories critiqued by B&H, crews of two or three men divided the work of inventorying trees, mapping topography, navigating, and recording site conditions. Travel between transects was minimized as crews worked in consecutive strips. For the records used in Collins et al. (2011, 2015), cruisers completed an average of 8.4 transects per day (median 8, maximum 19). Crews completed a comparable number of transects per day (average 8.7, median 8, maximum 16) in the one-chain inventory records used by Stephens et al. (2015). Crews typically completed 16 transects per day (average 15.9, median 16, maximum 25) in the inventories used by Hagmann et al. (2013, 2014, 2017) based on 2072 transects in one randomly selected township from each reservation. Actual time spent tallying trees per transect is not known; however, given an 8-h day, an average of 8 or 16 transects per day, crews of two to three men, and minimal travel time between transects, we estimate cruisers spent roughly 30–60 min per transect tallying trees. Thus, time available to tally trees in these early timber inventories far exceeds B&H's estimate of a “few minutes” per transect. Differences in scale, sampling bias, minimum diameter, and site quality (B&H Tables 1–3; Table 2 in this paper) invalidate B&H's assertion that the timber inventories require correction multipliers for tree density. Early one- and two-chain timber inventories systematically sampled 10–20% of large landscapes (103 to 105 ha) across broad elevational and topographic gradients. Along these gradients, inherent variation in the growing environment and fire histories inevitably produced broadly varying tree densities. B&H reduced this variability in tree density to a single mean density, which they then compared to other studies without regard for similarity in site quality. B&H (page 17) advocated a bioclimatic envelope approach to “objectively identify appropriate” areas for comparisons. However, they failed to follow their own advice. B&H's more detailed analysis of the Greenhorn Mountains timber inventory contains additional errors. B&H incorrectly combined (1) records for trees of unknown size and (2) average values derived from different areas and vegetation classifications in their reassessment of a subset of the 1911 timber inventory of the Greenhorn Mountains (originally summarized by Stephens et al. 2015). Inconsistent documentation of smaller tree sizes in the inventory records analyzed by Stephens et al. (2015) precludes quantitative estimation of density for trees 30.5 cm derived by Stephens et al. (2015: Table 6) for the entire study area (221 mixed-conifer transects and 157 ponderosa pine transects), incorrectly combining averages for unmatched samples. The southern portion of the Stephens et al. (2015) study area that was reassessed by B&H (Fig. 2) included 199 transects. Using the vegetation classification from Stephens et al. (2015), 137 of these transects were ponderosa pine and 62 were mixed-conifer forest. B&H used a different classification for forest type and reported a sample size of 71 for both the ponderosa pine and mixed-conifer groups (B&H: pages 10–11). Due to both (1) uncertainty in tree diameters and (2) mismatch in average values derived from different areas and vegetation classifications, the tree densities calculated by B&H for the Greenhorn Mountains are meaningless. We find no evidence in either historical records or in the studies that used these early timber inventories (i.e., Collins et al. 2011, Hagmann et al. 2013, 2014, Collins et al. 2015, Stephens et al. 2015, Hagmann et al. 2017) to support criticisms (B&H: pages 1, 9–10, 13–15) of (1) bias toward areas of large, merchantable trees; (2) failure to include previously burned areas; (3) inclusion of areas logged prior to the inventory; or (4) lack of cross-validation with independent historical sources. The timber inventories in question comprised 10–20% samples of large landscapes (103–105 ha); used systematically located transects; and included areas the cruisers deemed capable of supporting tree cover, whether trees were present at the time of the inventory or not. Summaries of these early timber inventories show that sampled areas included transects with little to no tree cover in areas capable of supporting forest as well as in previously burned areas (Collins et al. 2011, Hagmann et al. 2013, 2014, Collins et al. 2015, Stephens et al. 2015, Hagmann et al. 2017). On the Klamath Reservation (hereafter Klamath), the inventory extends continuously from lower to upper treeline; on the Warm Springs Reservation (hereafter Warm Springs), the inventory extends continuously from lower treeline to above the limit of forest types typically associated with frequent-fire (Hagmann et al. 2014). As clearly illustrated in Hagmann et al. (2014) by Fig. 1 (extent of mixed-conifer forest types) and Fig. 5 (transect locations), essentially all of the area classified as dry and moist mixed conifer on the Warm Springs was both inventoried and included in summary statistics. As systematic samples across these extensive forested areas, there is no evidence to support criticism that the inventories were biased toward areas of large, merchantable trees within the sampled area. If B&H meant to suggest that selection of these two reservations in toto represents a bias toward areas of large, merchantable trees, no evidence has been presented to support this assertion. Similarly, the timber survey datasets analyzed in Collins et al. (2015) and Stephens et al. (2015) are systematic samples that included non-timber areas and show no bias toward areas with more merchantable timber. The dataset from Stanislaus National Forest used by Collins et al. (2015) includes transects in the rugged Tuolumne River Canyon where no merchantable timber was present. Rather than omitting these areas, the surveyors noted “Broken mountain, brushland, no timber” on the associated datasheets. In the survey data from the Greenhorn Mountains used by Stephens et al. (2015), the transects located within quarter-quarter sections at the edge of the surveyed area often ended in chaparral, and in many instances, surveyors noted the distance along the transect at which they hit the timber line (Stephens et al. 2015: Table A1). Since transects extended past timbered areas, the assertion that “younger, denser forests” were present outside of and intentionally omitted from the surveyed area is unfounded. Vegetation in California is strongly controlled by elevation; on all sides of the surveyed area in the Greenhorn Mountains, the landscape generally decreases in elevation, transitioning into vegetation types not dominated by conifers. B&H note 17 quarter-quarter sections (roughly 275 ha) adjacent to the study area that today are at least partially categorized as conifer under the California Wildlife Habitat Relationships (CWHR) vegetation database (www.dfg.ca.gov/biogeodata/cwhr/). However, use of these CWHR maps to identify small areas of contemporary conifer forest is inappropriate because the models were developed to predict vegetation types at an extremely coarse spatial scale (1:1,000,000; Collins et al. 2016). Other adjacent lands omitted from the timber survey were private lands, denoted as “patented” on maps drawn by surveyors. As described by Collins et al. (2016), these non-surveyed areas were incorrectly interpreted as evidence of extensive high-severity fire by Hanson and Odion (2016). Hagmann and colleagues made no attempt to exclude burned areas, and, as illustrated in the following examples, reported on the evidence of high-severity fire effects, despite assertions to the contrary made by B&H (pages 13–14). Hagmann et al. (2013): “Stand-replacing fire effects (“no timber, old burn”) were noted on only five BIA timber inventory transects (8 ha) in this area and these were in and adjacent to sites classified [by the Integrated Landscape Assessment Project] as dry and moist Shasta red fir (Abies magnifica) habitat types, not ponderosa pine or mixed-conifer sites.” Hagmann et al. (2014): “High-severity fire effects were documented at the upper elevation boundary of moist mixed-conifer habitat adjacent to colder, wetter habitat types.” Hagmann et al. (2017), Appendix B: evidence of fire in three independent historical records (one of which is the early timber inventory) was compared for 39,000 ha. B&H (pages 13–14) suggested that conclusions about the dominant influence of frequent, low-to moderate-severity fire on ponderosa pine and mixed-conifer forests by Hagmann et al. (2013, 2017) misrepresented historical conditions due to the exclusion of areas burned at high-severity in 1918 fires. However, these conclusions are consistent with Weaver's description of the by which fire effects from extensive ha) in is known of the 1918 that of the portion of the and that in did not where pine stands and in the of and in of ponderosa stands of this there to the 1918 A study of the 1918 (Hagmann et al., unpublished using of fire and timber inventory records the of these of crown conclusions about the dominant fire is consistent with Weaver's and results presented in Hagmann in the time between GLO and early timber inventories not for the in tree density between the inventories and the estimates of tree density used by B&H, despite to the contrary (B&H: densities recorded in other early timber inventories (Collins et al. 2011, Hagmann et al. 2013, Collins et al. 2015, Stephens et al. 2015, Hagmann et al. 2017) are consistent with those recorded in a comparable dataset for the Warm Springs on which did not the the timber inventory of the (Hagmann et al. 2014). The for a timber on the Warm Springs was into in However, no a of was in and the were A from the of the Warm Springs to the of the at the lack of in for due to the lack of in the of and in the of the of fire and that tree density these areas were inventoried have been previously (Hagmann et al. 2013, 2014, 2017). In their criticism that early timber inventories lack cross-validation with other independent of B&H (page published that similarity between timber inventories with early records and reconstructions of historical forest conditions. Stephens et al. (2015) found similarity in forest between early timber inventory records from the Sierra Nevada and (1) historical forest reconstructions from frequent-fire in California and in the and (2) data from mixed-conifer forests in the Sierra which have not experienced fire exclusion or and Safford 2016, et al. 2016, et al. The historical densities B&H (Table compared with Hagmann et al. (2014) all within the range of variability recorded in early timber inventories for and Oregon (Hagmann et al. 2013, 2014). Hagmann et al. (2017) found that the early timber inventory for 39,000 in Oregon is consistent with independent records of historical forest conditions for the same area United and in both by ponderosa pine and of and large ponderosa As noted by B&H, and found 1911 timber inventory records comparable to reconstructions of historical forest density. B&H suggested that this of accuracy could indicate a However, the more for similarity in tree density in this is the of in sampled areas, the comparisons made by B&H comparisons of studies and Table B&H cross-validation of about historical fire derived from estimates of tree density. errors in B&H (page 2) as cross-validation have been et al. documented errors in about historical fire regimes based on modern Forest and and Collins et al. documented errors to inappropriate use of habitat range maps as well as of early timber inventories. Due to we errors in cross-validation with only two additional 1911 timber inventory data for the Greenhorn Mountains (Stephens et al. 2015) and available et al. that B&H (page 2) use the of high-severity fire by tree area B&H high-severity fire from of vegetation conditions in the 1911 inventory records for the Greenhorn B&H high-severity fire from notes on and trees as well as the timber notes by Stephens et al. (2015). B&H the following conditions as evidence of high-severity trees with fire and within the conifer immature conifer regeneration in the with The that this high-severity fire is as the of evidence trees with fire are associated with frequent, low- to moderate-severity which tree density in dry ponderosa and pine forests and many areas of mixed-conifer forest et al. 2014, et al. 2016, Safford and et al. B&H that the of and or in the as a of high-severity Yet, an with a was in frequent-fire forests of the Sierra The of fire only a subset of trees and that to et al. analyzed data in prior to any on three in the Sierra These had a median fire of had no evidence of high-severity fire in the fire and had not burned trees cm were on these of the stem density. A survey of cover was in for these cover the of trees and not indicate high-severity B&H high-severity fire from the of and However, high-severity fire had the number of trees noted by cruisers would be to be much than the number of trees. was not the for any transects surveyed in when timber was the surveyors typically as the and noted fire of timber survey in Table in Stephens et al. 2015). with Stephens et al. (2015), B&H included the of chaparral, immature stands with no fire and a of trees as evidence of high-severity However, as noted by Stephens et al. (2015), are within the conifer particularly in areas with and and therefore not indicate the of high-severity B&H compared of high-severity fire in their studies, which as of tree area with et al. which as of the dominant (e.g., or tree from these studies are and any between et al. that pre-management era in dry mixed conifer were strongly dominated their Fig. which about of the dry mixed-conifer vegetation type across three their Fig. on and cover types, in areas capable of supporting forests their Table 1, where tree cover or forested In areas where the dominant vegetation cover was either or the high-severity the of the dominant or that of the tree were where were this the of of on of sampled area, et al. We refute that these early timber inventories are biased and require correction multipliers for tree density. We find no evidence of systematic bias in the density estimates (Table or of bias toward large merchantable timber and denser forests with timber or burned errors of and limit the of this B&H to a understanding of historical forest conditions and the that have documented errors in methodology or misrepresentation of the work of in published by Baker Hanson et al. Safford et al. et al. et al. 2014, Safford et al. 2015, Collins et al. 2016, et al. 2016, Hagmann et al. Levine et al. and Safford et al. 2017). The of these early timber inventories exceeds the they of historical tree density. is in the of landscape conditions that were under the variability of fire regimes and the of those conditions with other records and reconstructions of historical forest conditions and fire records and reconstructions are to understanding variability in historical fire and spatial as well as variability in the forest conditions that from historical fire work is to the of of early timber inventories and of other records and reconstructions of historical forest conditions. comparisons with these historical timber inventories could be by sampling the inventories to comparable growing or sampling understanding of historical fire regimes and conditions is to and to forest conditions that be under the variability of fire regimes the western United States, as forests to a We from two and criticisms the quality of this

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