Landscape Ecol (2015) 30:1473–1482 DOI 10.1007/s10980-015-0201-9
RESEARCH ARTICLE
Effects of historical and current disturbance on forest biomass in Minnesota Brice B. Hanberry . Hong S. He
Received: 28 August 2014 / Accepted: 10 April 2015 / Published online: 21 April 2015 Ó Springer Science+Business Media Dordrecht 2015
Abstract Context Historical carbon storage prior to widespread forest clearing is uncertain. We examined aboveground biomass in historical (1847–1908) and current (2004–2008) mixed and broadleaf forests of Minnesota. Objective Our objective was to compare aboveground forest biomass density and total aboveground carbon storage for two forest types with different historical and current disturbance regimes. Methods We used densities and diameter distributions from historical and current tree surveys and applied relationships between diameter and biomass to estimate biomass in historical and current forests for larger trees with diameters C12.7 cm. Results In the 8.5 million ha Northern Mixed Forest ecological division of Minnesota, historical forests ecosystems under a stand-replacing fire regime that produced high density forests contained greater aboveground biomass density (98 Mg/ha) than current forests (53 Mg/ha) disturbed by frequent tree cutting. Historical total carbon storage was 333 TgC and Electronic supplementary material The online version of this article (doi:10.1007/s10980-015-0201-9) contains supplementary material, which is available to authorized users. B. B. Hanberry (&) H. S. He Department of Forestry, University of Missouri, 203 Natural Resources Building, Columbia, MO 65211, USA e-mail:
[email protected] H. S. He e-mail:
[email protected]
current carbon storage was 158 TgC; estimates depended on diameter distribution and historical forested extent. In the 4.5 million ha Eastern Broadleaf Forest division, historical forests under a frequent surface fire regime that produced low density oak savannas contained less biomass density (54 Mg/ha) than current dense eastern broadleaf forests (93 Mg/ha). Historical total carbon storage was 79 TgC and current carbon storage was 31 TgC, depending on diameter distribution and forested extent. Conclusions Total carbon storage appears to be unrealized due to potential for tree diameter increases in both divisions, stem density increases in the Northern Mixed Forest, and forested extent increases in the Eastern Broadleaf Forest. Keywords Carbon Densification Fire Harvest Land use Management Introduction Forests have a relatively dynamic role in carbon storage because of changing land use. Many forests in the United States are recovering from widespread forest clearing and agricultural abandonment after Euro-American settlement and industrialization. In the eastern US, tree clearing, harvest, and grazing continue to disturb about half of forest area each decade, whereas natural disturbance from fire currently is rare due to effective fire exclusion (Pan et al. 2011).
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Disturbance alters forest ecosystems and affects carbon exchange between forests and the atmosphere. Fire exclusion may have increased carbon storage in forests as opposed to the past when fire removed biomass, releasing carbon dioxide to the atmosphere (Gough et al. 2007). Historically, fire regimes shaped the structure and composition of many forest ecosystems in the United States. Stand-replacing crown fires occurred in northern mixed (i.e., boreal forest species mixed with species of broadleaf forests and northern pine species), boreal, and subalpine forests (Schoennagel et al. 2004; Johnstone et al. 2010), terminating these forests about every 50–150 years, probably after drought in the humid eastern US (Heinselman 1973; Frelich and Reich 1995; White and Host 2008). Because the fire return interval was within the typical lifespan for tree species, few transitions in composition, density, and structural complexity occurred in fire-initiated and terminated forests (Frelich and Reich 1995; Johnstone et al. 2010). Postfire forests established in dense, evenaged cohorts similar to prefire forests within 5 years. Northern pine forest ecosystems composed of jack and red pines (Pinus banksiana and P. resinosa) probably experienced a mixed-severity fire regime that removed non-pine species and produced varying and contrasting structure (Wade et al. 2000). In contrast, a frequent surface fire regime stabilized open forest ecosystems of pine and oak savannas and woodlands, which covered at least the western edge of eastern broadleaf forests, millions of hectares across the Southeast, and western pine parklands of the United States. Fire frequency may have increased during initial Euro-American settlement, but as populations increased to the threshold where fire could be dangerous, fire exclusion became widespread during the first half of the twentieth century (Guyette et al. 2006; Fowler and Konopik 2007; McEwan et al. 2007; Stambaugh et al. 2009). Following EuroAmerican settlement and subsequent large scale logging, agricultural clearing, and eventual fire exclusion, most of the historically open forest ecosystems of the eastern US have increased in woody stem densities and transitioned to more fire-sensitive species (Hanberry et al. 2012a, 2013, 2014; Hutchison et al. 2012). The effect of fire disturbance on historical carbon stocks and extent to which forest biomass has changed
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is uncertain at landscape scales due to lack of detailed historical tree surveys, although recently Rhemtulla et al. (2009) calculated historical biomass for Wisconsin using historical tree surveys. Historical forest surveys are available for Minnesota and surveys probably were conducted before the major lumbering front, which began in the most accessible southeastern part of the state by 1880 (Williams 1992; Friedman and Reich 2005). In any event, Minnesota’s forests during the historical surveys appear to be representative of historical forest conditions and are different than current forests. In the Northern Mixed Forest division (Ecomap 1993; Fig. 1), stand-replacing crown fires initiated dense tree establishment; however, without frequent tree removal by land use, larger diameter trees developed over time (Hanberry et al. 2012a, 2013). Historical tamarack (Larix laricina)dominated mixed forests were more dense in tree number than current aspen (i.e., Populus tremulides)dominated mixed forests but aspen forestry may remove less biomass than high severity fires and the landscape remains forested. Conversely, in the Eastern Broadleaf Forest division, open oak savannas and woodlands resulted from frequent surface fires, which reduced stem density of small diameter trees (in any type of forests with a surface fire regime; North et al. 2009; Hutchinson et al. 2012), and larger diameter trees also developed over time without overstory canopy disturbance. Open forest ecosystems have densified into eastern broadleaf forests after fire exclusion (Hanberry et al. 2012a) but forested area has decreased. In Minnesota, two types of forest ecosystems with distinct historical fire regimes and current forest management provide a large range of potential effects of fire and management on carbon stocks. Current forests in both ecological divisions may differ in biomass per hectare and total aboveground carbon storage for the landscape compared to historical forests, due to differences in density and because historical tree diameters were greater without repeated tree harvest. Our objective was to compare aboveground forest biomass density and total aboveground carbon storage (for live trees with diameters C12.7 cm), using tree densities and diameter distributions from historical and contemporary forest inventories, for two forest types with different disturbance regimes.
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Fig. 1 Ecological subsections in the Northern Mixed Forest (shaded white, prefix of ‘212’) and Eastern Broadleaf Forest (shaded white, prefix of ‘222’) divisions of Minnesota, USA
Methods Tree surveys and forest ecosystem types We used pre-settlement tree records surveyed by the United States General Land Office (GLO) during 1847–1908 in Minnesota (http://deli.dnr.state.mn.us/ data_search.html). The GLO surveys divided public lands into square townships measuring 9.6 km 9 9.6 km and subdivided townships into 36–1.6 km2 sections. At survey points located at section corners and midpoints between section corners, surveyors
selected two to four trees, similar to a point-centered quarter sampling design, and recorded species, diameter, distance, and bearing. Recorded information was biased because GLO surveys were not complete or random forest inventories. Selected trees were of moderate diameter, rather than small and large diameter trees, to endure as section markers. We excluded trees with diameters \12.7 cm to maintain a consistent diameter threshold, because trees smaller than this diameter rarely were recorded, resulting in 179,340 trees in the Northern Mixed Forest and 74,711 trees in the Eastern Broadleaf Forest.
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For contemporary forests, we used the USDA Forest Service Forest Inventory and Analysis (FIA) database (FIA DataMart, www.fia.fs.fed.us/toolsdata). The FIA plots are surveyed in cycles of 5 years and we used the latest complete cycle from 2004 to 2008. We selected live trees with a DBH C12.7 cm and plots with at least two trees. We additionally selected plots that were 100 % forestland, which FIA defines as land at least 0.4 ha in size and about 37 m wide with at least 10 % cover by live trees of any size, including recently harvested land that is intended to be forest (‘‘formerly had such tree cover and that will continue to have forest use’’). Selection resulted in 33,464 trees in the Northern Mixed Forest and 5,220 trees in the Eastern Broadleaf Forest. The Northern Mixed Forest division was composed primarily of early-successional birch, tamarack, and aspen mixed forests, late-successional spruce, fir, and cedar forests, and pine forest ecosystems, which varied in composition over time. The Eastern Broadleaf Forest division was composed of oak forest ecosystems predominantly during historical surveys and mid-latesuccessional eastern broadleaf forests predominantly during current surveys. To describe ecosystems, we determined percent composition of forest ecosystem types by ecological subsection (Ecomap 1993; Fig. 1). Ecological subsections are smaller spatial units that share ecological characteristics within ecological divisions. Historical and current biomass estimates As a brief explanation of historical density estimates, we calculated density for large sample sizes of ecological subsection based on the Morista estimator (Morisita 1957; this estimator has greater accuracy than other estimators for point centered quarter survey methods; Bouldin 2008; Hanberry et al. 2012b) for (1) survey points with two trees and (2) survey points with three trees and the nearest three trees for survey points with four trees (the most distant tree was removed to reduce variability in range of density estimates; Hanberry et al. 2011; see Hanberry et al. 2012b for additional detail) for each ecological subsection. We then produced a low and high value based on adjustment for potential spatial patterning, such as clustering or regularity. We used these values to make corrections for surveyor bias in tree selection based on a rank-based method and bias-based method for nonrandom ratios (Hanberry et al. 2012b).
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To determine biomass density for historical forests, we used density estimates and diameter distributions for each ecological subsection and then simulated stands and calculated biomass density using regression equations, similarly to Rhemtulla et al. (2009). Because surveyors only recorded 2–4 trees per historical survey point, it was not possible to determine accurate biomass, densities, or diameters for each historical survey point. In addition to non-random tree selection, diameter measurements were inexact; diameter distributions may help correct for bias (Bouldin 2010). By ecological subsection, we developed parameters for probability distribution functions of tree diameters truncated at 12.7 cm using lognormal, exponential, Weibull, and gamma distributions (i.e., Podlaski and Zasada 2008; SAS Proc Severity, SAS software, version 9.1, Cary, North Carolina). We then generated 10,000 stands of 1 ha using random diameters from parameters of the diameter distributions and random densities from density estimates and uncertainty (Runuran et al. http://statmath.wu.ac.at/ unuran/, http://cran.rproject.org/web/packages/Runuran/ index.html; R Development Core Team 2012). Using FIA surveys, we developed a regression equation based on the relationship of (the log transformation of) biomass to (the log transformation of) diameter that was unique for each ecological subsection. The FIA database supplied biomass estimates for each tree (aboveground stem biomass for trees C12.7 cm, excluding foliage) based on allometric equations. Because allometric equations were developed for trees in FIA forest surveys grown under current land use and management and not available for trees grown under different light and growing space conditions, we maintained a constant regression equation for both surveys in each subsection, which minimized variation between surveys. We summarized the biomass for each simulated stand and determined mean biomass and standard deviation per hectare by ecological subsection for simulated stands. To make certain that simulations were accurate, we also simulated FIA biomass density using the same method as for GLO biomass density. The FIA surveys provided density and biomass at each plot. Summation of the number of trees per plot and expanded to a hectare resulted in density estimates for each plot, while summation and expansion of individual tree biomass in plots resulted in biomass on a per hectare basis. We compared mean biomass density based on FIA plots to simulated FIA biomass density estimates,
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to determine which of the four distributions produced the smallest absolute difference from mean biomass. We then quantified, for each distribution, the difference between simulated biomass density estimates for historical and current forests. We selected the distribution that produced generally the least differences both between estimates for historical and current forests and mean biomass based on FIA plots to simulated FIA biomass estimates as the most conservative fit for comparisons. We produced areaweighted means for each region. We removed one ecological subsection in the Northern Mixed Forest due to limited current information (i.e., one FIA plot with biomass of 7 Mg/ha). Total aboveground carbon storage (live trees with diameters C12.7 cm) Currently, the Northern Mixed Forest is 67 % forested, including 29 % woody wetlands, and the Eastern Broadleaf Forest is 16 % forested, 56 % crops and pasture, and 11 % developed (Fry et al. 2011). We do not know the forested extent during 1847–1908; however, surveyors appeared to record trees in the entire systematic survey grid in the Northern Mixed Forest and 88 % of the systematic survey grid in the Eastern Broadleaf Forest, despite lower tree densities. Fire, wind, and ice disturbance decreased forested extent and additionally, Native Americans and European American settlers cleared forest. To calculate total carbon storage, we used forested extents ranging from 70 to 90 % in the Northern Mixed Forest for a landscape extent of 8.5 million ha (after exclusion of land cover water from study extent of 9.0 million ha without largest lakes) and forested extents ranging from 15 to 75 % in the Eastern Broadleaf Forest based on a landscape extent of 4.5 million ha (study extent of 4.8 million ha excluding water and high intensity residential use, which was not present during the historical period but land extent may be offset by loss of land cover water due to extensive drainage). We used a conversion factor of 0.5 to convert biomass to carbon.
Results For both divisions, the Weibull distribution produced the overall most similar and constant estimates to
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mean biomass density estimates of current forests. For the Northern Mixed Forest, mean absolute difference between simulated biomass density of current forests and mean biomass density estimates from FIA plots by ecological subsection ranged from of 4 Mg/ha for the Weibull distribution to 5 Mg/ha for the exponential distribution, indicating accurate simulated estimates (see below, area-weighted means from plot data and simulations are \1 Mg/ha different). For the Eastern Broadleaf Forest, mean absolute difference between simulated biomass density of current forests and mean biomass density estimates from FIA plots by ecological subsection ranged from of 8 Mg/ha for the exponential distribution and Weibull distribution to 14 Mg/ha for the lognormal distribution. Mean absolute difference between simulated biomass estimates for historical forests and simulated biomass of current forests by ecological subsection ranged from of 36 Mg/ha for the exponential distribution to 53 Mg/ ha for the lognormal distribution, with 45 Mg/ha for the Weibull distribution. Although in the Eastern Broadleaf Forest the exponential distribution was the most conservative, that is, produced least difference in biomass estimates between historical and current forests, biomass estimates from the Weibull distribution were more similar to biomass estimates produced by the other two distributions and also provided a constant distribution for comparison. In the Northern Mixed Forest, mean area-weighted biomass density for historical forests averaged about 98 Mg/ha (SD 20), ranging from 59 Mg/ha to 194 Mg/ha by ecological subsection (Appendix as ESM; Fig. 2). Mean area-weighted biomass density for current forests averaged about 53 Mg/ha (SD 7; compared to 53 Mg/ha from plot data), ranging from 37 to 72 Mg/ha by ecological subsection. Historical biomass density was about 1.8 times greater than current forests, ranging from 1.5 to 4.3 times greater by ecological subsection. In the Eastern Broadleaf Forest, mean area-weighted biomass density for historical forests averaged about 54 Mg/ha (SD 11), ranging from 18 to 82 Mg/ha by ecological subsection. Mean area-weighted biomass density for current forests averaged about 93 Mg/ha (SD 18; compared to 94 Mg/ha from plot data), ranging from 61 to 130 Mg/ha by ecological subsection. Historical biomass density was about 0.6 of current biomass, ranging from 0.2 to 1.0 of current biomass by ecological subsection.
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Fig. 2 Historical (a) and current (b) biomass density (Mg/ha; trees C12.7 cm) by ecological subsection in the Northern Mixed Forest and Eastern Broadleaf Forest divisions
Historical total carbon storage was 333 TgC (1 Tg = 1,000,000 Mg; SD 42) in the Northern Mixed Forest, given the Weibull distribution and 80 % forested extent assuming losses due to catastrophic fire and harvest (i.e., the midpoint between current forest extent and perhaps a 90 % maximum forest cover during historical disturbance regimes; Table 1). Current total carbon storage was 158 TgC (SD 13), or 50 % of historical carbon storage, given the Weibull distribution and 70 % forested extent. In the Eastern Broadleaf Forest, historical total carbon storage was 79 TgC (SD 16), given the Weibull distribution and 65 % forested extent historically. Current total carbon storage was 31 TgC (SD 6), or 40 % of historical carbon storage, given the Weibull distribution and 15 % forested extent.
Table 1 Total carbon storage (TgC; trees C12.7 cm), varying by forested extent, of the historical (GLO) and current (FIA) Northern Mixed Forest and Eastern Broadleaf Forest % Forested
GLO Mean
FIA SD
Mean
SD
Laurentian Mixed Forest 90
375
47
203
17
80
333*
42
180
15
70
292
36
158*
13
Eastern Broadleaf Forest 75 65
91 79*
19 16
157 136
30 26
55
67
14
115
22
45
55
11
94
18
35
43
9
73
14
25
30
6
52
10
15
18
4
31*
6
Discussion
* Representative value for forested extent
There is uncertainty involved with quantitative estimates from GLO surveys. Surveyors did not select trees randomly, diameter measurements were inexact,
and allometric equations vary in space and time (Boudin 2008, 2010). Additionally, historical forest cover is unknown and changed over time as the GLO
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data encompasses surveys from 1847 until 1908. However, because Minnesota was on the western edge of eastern forests, by 1880 only the southeastern part was lumbered (Sargent 1884; Williams 1992; Friedman and Reich 2005) and it was likely that surveyors surveyed ahead of the lumbering front. We were able to address these issues by correction of surveyor bias in density estimates (Hanberry et al. 2011, 2012b), large sample sizes (i.e., at the ecological subsection scale), use of diameter distributions (Bouldin 2010), and conservative selection of historical biomass (i.e., least difference with current biomass). In addition, historical composition, structure, and biomass corresponded to other similar work (Rhemtulla et al. 2009), and generally agreed with contemporaneous, coarse national forested cover and volume maps (Walker 1874; Sargent 1884; Liknes et al. 2013). Changes in disturbance have decreased biomass density in the Northern Mixed Forest division and increased biomass density in the Eastern Broadleaf Forest division in Minnesota. Our estimates of the capacity of different forest ecosystems to store biomass contrast with estimates by Rhemtulla et al. (2009) for forest ecosystems in the neighboring state of Wisconsin during the same time periods and over a landscape scale. There was close agreement between aboveground biomass of northern mixed forests in the two states and biomass may be generalizable to northern mixed forests in other states that experience stand-replacing fire regimes. Northern mixed forests historically stored about 94 (Rhemtulla et al. 2009) to 98 Mg/ha (this study) of biomass and currently store about 50 (Rhemtulla et al. 2009) to 53 Mg/ha (this study) of biomass. However, Wisconsin’s Eastern Broadleaf Forest division was very open, unlike in Minnesota, and accordingly 24 Mg/ha of biomass (Rhemtulla et al. 2009) only resembles the lower range of biomass estimates in Minnesota’s Eastern Broadleaf Forest division, in ecological subsections that contained fewer and smaller oak tree stems. Most of Minnesota’s Eastern Broadleaf Forest has increased in density of woody stems of midlate-successional eastern broadleaf forests, creating almost a doubling in biomass. Although other current disturbances (browsers, earthworms, CO2 fertilization; Frelich and Reich 2010) may have affected biomass estimates, it is unlikely that they were very influential because these disturbances typically produce unidirectional outcomes whereas the ecological divisions had two opposite trajectories in biomass density.
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Even though stand-replacing fires reduced biomass density and released carbon to the atmosphere, while moving charcoal to the soil, fire exclusion may not lead to an increase in biomass density because fires may have a mild impact on biomass compared to land uses that remove trees more frequently or more completely than fire. Over large ecological subsections in the Northern Mixed Forest division, forests generally with a stand-replacing fire regime on a 50–150 year cycle (Heinselman 1973; Frelich and Reich 1995; White and Host 2008) contained greater biomass density than forests under current land use with repeated removal of overstory trees. Net primary productivity returns to pre-fire levels within 10 years in boreal forests after fire (Hicke et al. 2003) and consequently, carbon was sequestered quickly in historical forests. Recovery of biomass after exclusion of the stand-replacing fire regime was not enough to offset tree removals in the Northern Mixed Forest division, where aspen is managed for forest products (Einspahr and Wyckoff 1990). Due to frequent tree cutting, stem densities and diameters are currently less than in the past. Some forest products such as structural lumber may extend carbon residence time in non-living material, but most of the aspen harvest is converted to pulp and particle board (Einspahr and Wyckoff 1990), and thus carbon may not be stored much longer than fuelwood. Conversely, current biomass density exceeded historical biomass density in the Eastern Broadleaf Forest division. Current biomass density estimates of 93 Mg/ha are similar to biomass estimates for most forests in the eastern United States, which are relatively the same age as Minnesota’s Eastern Broadleaf Forest division (for live trees with diameters C12.7 cm; Delcourt et al. 1981; Brown et al. 1999; Hanberry, University of Missouri, unpublished data; based on summarization and expansion of FIA plots; FIA DataMart, www.fia.fs.fed.us/tools-data). Current trees are small, and given time and small scale or low severity disturbance, potential forest biomass density may reach 200–300 Mg/ha (Ovington et al. 1963; Pregitzer and Euskirchen 2004; Bragg 2012). Historical biomass in the Eastern Broadleaf Forest division was reduced by frequent surface fires that removed fire-sensitive tree species and additionally, fire severity and/or frequency appeared to be great enough to reduce growth and limit tree size in even larger diameter fire-tolerant oak species in Minnesota;
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mean tree diameters by subsection were \30 cm in diameter (about 27 cm historically compared to 24 cm currently) and historical densities indicated land cover by oak savannas rather than closed woodlands. However, because the historical surveys occurred during settlement, land use disturbance may have contributed to reduced tree densities and diameters. Due to disturbance variation and resulting aboveground components (e.g., tree density, tree diameter, forested extent), historical biomass density and biomass recovery in Minnesota’s Eastern Broadleaf Forest division is not generalizable to other historical open oak ecosystems in eastern broadleaf forests. For example, in the Missouri Ozarks, historical open and closed oak woodlands had greater stem density and diameters than oak savannas and open woodlands in Minnesota’s Eastern Broadleaf Forest division (Hanberry et al. 2014). In Missouri, this resulted in historical biomass estimates under a surface fire regime reaching 180 Mg/ha, which was greater than current forest biomass (B. Hanberry, University of Missouri, unpublished data). Frequent surface fires, somewhat like silvicultural interventions such as thinning or herbicide applications, can reduce competition while also providing a short nutrient pulse, releasing tree growth and increasing carbon sequestration rates (Hurteau and North 2009; Gough et al. 2010). Disturbance may reduce site fertility through nutrient leaching (Gough et al. 2007; Pan et al. 2011), but large, dominant trees should be able to capture most available minerals and other resources. The Northern Mixed Forest and Eastern Broadleaf Forest divisions perhaps had shortfalls of 175 and 48 TgC in total carbon storage, respectively, compared to historical total carbon storage. Despite uncertainty in total carbon storage based on biomass estimates and historical forested extent, both divisions have the potential to continue as carbon sinks and still restore historical ecological characteristics. In Minnesota’s Northern Mixed Forest division, forests have not reached their potential to store carbon, based on historical biomass density, due to current tree removals, particularly from aspen forestry. In the Eastern Broadleaf Forest division, forests have accumulated biomass density beyond historical levels, due to fire exclusion followed by conversion from open oak ecosystems to dense, mid-late-successional eastern broadleaf forests, where the biomass ceiling may be greater than for open forest ecosystems.
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Although achievement of historical total carbon storage levels is unrealistic taking into account resource and land use, biomass maximization through diameter growth in both divisions and stem densification in the Northern Mixed Forest and increased treed extent in the Eastern Broadleaf Forest is possible and will increase similarity to historical ecosystem structure. Properly conducted prescribed burns and thinning of smaller diameter trees should reduce competition for resources and increase rate of diameter increase. Retention of large diameter overstory trees during clearing for land use and harvest will provide greater carbon storage than no retention. Some of these changes may occur more easily in the Eastern Broadleaf Forest division, where forestry economics are less important; however, the transition to eastern broadleaf forest species in the Northern Mixed Forest indicates that aspen forestry is becoming less influential (Hanberry et al. 2013). Reforestation through low density forests will increase biomass, compared to no biomass storage in trees, and will increase representation of historical tree densities in the Eastern Broadleaf Forest. Low density reforestation is possible and likely desirable in agriculturally marginal lands and low density urbanized areas, by planting widely spaced trees in pastures and lawns, along fence rows and roads, land and field boundaries, and other unused space. Due to current scarcity of open forest ecosystems that used to be abundant in the eastern US, scattered trees and trees retained after harvest provide high conservation value to wildlife (Manning et al. 2006; Hanberry et al. 2012c). We present only biomass of live aboveground trees (C12.7 cm in diameter). Generally, at least as much carbon is present in the other major carbon pool of soils (Turner et al. 1995; soils may be limited in the amount of carbon that they can hold but charcoal additions will add organic matter) and dead wood may represent[10 % of aboveground biomass (Rhemtulla et al. 2009). Although aboveground live tree biomass primarily is based on tree density, diameter, and height, composition may be influential though wood specific gravity and growing space or stocking (Ginrich 1967; Rogers 1983, Jenkins et al. 2003, Woodall et al. 2011). However, these characteristics probably are offsetting. That is, conifers have lower specific gravity than angiosperms, and therefore, less biomass, but their crowns take up less growing space and so conifers can grow more densely. Also, some conifers
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grow more quickly than angiosperms (thus, the prevalence of pine plantations), sequestering carbon more quickly, or grow to greater heights, thereby storing more carbon where growing space is not limited.
Conclusions Disturbance has changed due to fire exclusion and current forest management, resulting in decreased biomass density in the Northern Mixed Forest division and increased biomass density in the Eastern Broadleaf Forest division in Minnesota. Tree retention practices in forestry and land clearing will help increase tree diameters and carbon storage and compensate for loss of forested extent in the Eastern Broadleaf Forest division. Moving forward, these results about historical forests can provide critical information for managers because forest management for climate change includes carbon storage and maintenance of biodiversity. Increased temperature along with altered precipitation patterns will cause moisture stress on trees, leading to drought-related mortality. Selection for drought-tolerant species may help to maximize carbon storage while minimizing potential large scale tree mortality of achieved biomass storage during increased drought cycles of changed climate. Particularly, selection should favor oak and pine composition to provide a balance among carbon storage and management for drought, while providing foundation species for diversity because (1) of the greater heights, and thus biomass, of these species, compared to other drought-tolerant species, (2) these species were abundant historically during drier climates and restoration of the historically more open, large diameter, oak or pine forest structure and composition will reduce competition for water among numerous mesic tree species of dense eastern broadleaf forests, and (3) regional interest in pine and oak restoration for sustaining diversity (Hunter et al. 2001; Gilliam 2007). Acknowledgments We thank the anonymous reviewers for their help to improve the manuscript. This project was funded by the USDA Forest Service Northern Research Station and Eastern Region. Additional funds were provided by the Department of Interior USGS Northeast Climate Science Center. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the views of the United States Government.
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References Bouldin J (2008) Some problems and solutions in density estimation from bearing tree data: a review and synthesis. J Biogeogr 35:2000–2011 Bouldin J (2010) Issues in estimates of relative metrics of historic forest conditions from bearing tree data. Ecol Appl 20:1183–1187 Bragg DC (2012) Developing contemporary and historical live tree biomass estimates for old pine-hardwood stands of the Midsouth, USA. For Ecol Manage 281:32–40 Brown SL, Schroeder P, Kern JS (1999) Spatial distribution of biomass in forests of the eastern USA. For Ecol Manage 123:81–90 Delcourt HR, West DC, Delcourt PA (1981) Forests of the southeastern United States: quantitative maps for aboveground woody biomass, carbon, and dominance of major tree taxa. Ecology 62:879–887 Development Core Team R (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna Ecomap (1993) National hierarchical framework of ecological units. USDA Forest Service, Washington, DC Einspahr DW, Wyckoff GW (1990) North American aspen: timber supply, utilization, and research. N J Appl For 7:168–171 Fowler C, Konopik E (2007) The history of fire in the southern United States. Hum Ecol Rev 14:165–176 Frelich LE, Reich PB (1995) Spatial patterns and succession in a Minnesota southern-boreal forest. Ecol Monogr 65:325–346 Frelich LE, Reich PB (2010) Will environmental changes reinforce the impact of global warming on the prairie–forest border of central North America? Front Ecol Environ 8:371–378 Friedman SK, Reich P (2005) Regional legacies of logging: departure from presettlement forest conditions in northern Minnesota. Ecol Appl 15:726–744 Fry J, Xian G, Jin S, Dewitz J, Homer C, Yang L, Barnes C, Herold N, Wickham J (2011) Completion of the 2006 National Land Cover Database for the conterminous United States. PE&RS 77:858–864 Gilliam FS (2007) The ecological significance of the herbaceous layer in temperate forest ecosystems. Bioscience 57:845–858 Ginrich SF (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. For Sci 13:38–53 Gough CM, Vogel CS, Harrold KH, George K, Curtis PS (2007) The legacy of harvest and fire on ecosystem carbon storage in a north temperate forest. Glob Change Biol 13:1935–1949 Gough CM, Vogel CS, Hardiman B, Curtis PS (2010) Wood net primary production resilience in an unmanaged forest transitioning from early to middle succession. For Ecol Manage 260:36–41 Guyette RP, Spetich MA, Stambaugh MC (2006) Historic fire regime dynamics and forcing factors in the Boston Mountains, Arkansas, USA. For Ecol Manag 234:293–304 Hanberry BB, Fraver S, He HS, Yang J, Dey DC, Palik BJ (2011) Spatial pattern corrections and sample sizes for
123
1482 forest density estimates of historical tree surveys. Landscape Ecol 26:59–68 Hanberry BB, Palik BJ, He HS (2012a) Comparison of historical and current forest surveys for detection of homogenization and mesophication of Minnesota forests. Landscape Ecol 27:1495–1512 Hanberry BB, Yang J, Kabrick JM, He HS (2012b) Adjusting forest density estimates for surveyor bias in historical tree surveys. Am Midl Nat 167:285–306 Hanberry BB, Hanberry P, Demarais S, Jones JC (2012c) Importance of residual trees to birds in regenerating pine plantations. iForest 5:108–112 Hanberry BB, Palik BJ, He HS (2013) Winning and losing tree species of reassembly in Minnesota’s mixed and broadleaf forests. PLoS ONE 8:e61709 Hanberry BB, Kabrick JM, He HS (2014) Densification and state transition across the Missouri Ozarks landscape. Ecosystems 17:66–81 Heinselman ML (1973) Fire in the virgin forests of the Boundary Waters Canoe Area, Minnesota. Quat Res 3:329–382 Hicke JA, Asner GP, Kasischke ES, French NHF, Randerson JT, Collatz GJ, Stocks BJ, Tucker CJ, Los SO, Field CB (2003) Postfire response of North American boreal forest net primary productivity analyzed with satellite observations. Glob Change Biol 9:1145–1157 Hunter WC, Buehler DA, Canterbury RA, Confer JL, Hamel PB (2001) Conservation of disturbance-dependent birds in eastern North America. Wildl Soc Bull 29:440–455 Hurteau M, North M (2009) Fuel treatment effects on tree-based forest carbon storage and emissions under modeled wildfire scenarios. Front Ecol Environ 7:409–414 Hutchinson TF, Yaussy DA, Long RP, Rebbick J, Sutherland EK (2012) Long-term (13-year) effects of repeated prescribed fires on stand structure and tree regeneration in mixed-oak forests. For Ecol Manag 286:87–100 Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National scale biomass estimators for United States tree species. For Sci 49:12–35 Johnstone JF, Hollingsworth TN, Chapin FS III, Mack MC (2010) Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob Change Biol 16:1281–1295 Liknes GC, Nelson MD, Kaisershot DJ (2013) Net change in forest density, 1873-2001: using historical maps to monitor long-term forest trends, RMAP-NRS-4. USDA Forest Service, Northern Research Station, Newtown Square Manning AD, Fischer J, Lindenmayer DB (2006) Scattered trees are keystone structures—implications for conservation. Biol Conserv 132:311–321 McEwan RW, Hutchinson TF, Long RP, Ford DR, McCarthy BC (2007) Temporal and spatial patterns in fire occurrence during the establishment of mixed-oak forests in eastern North America. J Veg Sci 18:655–664 Morisita M (1957) A new method for the estimation of density by the spacing method, applicable to non-randomly distributed populations. Seiri Seitai 7:134–144 (in Japanese)
123
Landscape Ecol (2015) 30:1473–1482 North M, Hurteau M, Innes J (2009) Fire exclusion and fuels treatment effects on mixed-conifer carbon stocks and emissions. Ecol Appl 19:1385–1396 Ovington JD, Haitkamp D, Lawrence DB (1963) Plant biomass and productivity of prairie, savanna, oakwood, and maize field ecosystems in central Minnesota. Ecology 44:52–63 Pan Y, Chen JM, Birdsey R, McCullough K, He L, Deng F (2011) Age structure and disturbance legacy of North American forests. Biogeosciences 8:715–732 Podlaski R, Zasada M (2008) Comparison of selected statistical distributions for modelling the diameter distributions in near-natural Abies-Fagus forests in the S´wie˛tokrzyski National Park (Poland). Eur J For Res 127:455–463 Pregitzer KS, Euskirchen ES (2004) Carbon cycling and storage in world forests: biome patterns related to forest age. Glob Change Biol 10:2052–2077 Rhemtulla JM, Mladenoff DJ, Clayton MK (2009) Historical forest baselines reveal potential for continued carbon sequestration. PNAS 106:6082–6087 Rogers R (1983) Guides for thinning shortleaf pine, GTR SE-24. USDA Forest Service, Southeastern Forest Experiment Station, Asheville Sargent CS (1884) Report on the forests of North America (exclusive of Mexico). US Department of Interior Census Office, Government Printing Office, Washington, D.C. Schoennagel T, Veblen TT, Romme WH (2004) The interaction of fire, fuels, and climate across Rocky Mountain forests. Bioscience 54:661–676 Stambaugh MC, Guyette RP, Godfrey R, McMurry ER, Marschall JM (2009) Fire, drought, and human history near the western terminus of the Cross Timbers, Wichita Mountains, Oklahoma, USA. Fire Ecol 5:51–65 Turner DP, Koerper GJ, Harmon ME, Lee JJ (1995) A carbon budget for forests of the conterminous United States. Ecol Appl 5:421–436 Wade DD, Brock BL, Brose PH, Grace JB, Hoch GA, Patterson WA III (2000) Fire in eastern ecosystems. In: Brown JK, Smith JK (eds) Wildland fire in ecosystems: effects of fire on flora, RMRS-GTR-42, vol 2. USDA Forest Service, Rocky Mountain Research Station, Ogden, pp 53–96 Walker FA (1874) Statistical atlas of the United States based on the results of the ninth census 1870 with contributions from many eminent men of science and several departments of the government. J Bien, New York White MA, Host GE (2008) Forest disturbance frequency and patch structure from pre-European settlement to present in the Mixed Forest Province of Minnesota, USA. Can J For Res 38:2212–2226 Williams M (1992) Americans and their forests: a historical geography. Cambridge University Press, New York Woodall CW, D’Amato AW, Bradford JB, Finley AO (2011) Effects of stand and inter-specific stocking on maximizing standing tree carbon stocks in the eastern United States. For Sci 57:365–378
