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Status of Sassafras albidum (Nutt.) Nees in the Presence of Laurel Wilt Disease and Throughout the Eastern United States
KaDonna C. Randolph

Southeastern Naturalist, Volume 16, Issue 1 (2017): 37–58

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Southeastern Naturalist 37 K.C. Randolph 22001177 SOUTHEASTERN NATURALIST Vo1l6.( 116):,3 N7–o5. 81 Status of Sassafras albidum (Nutt.) Nees in the Presence of Laurel Wilt Disease and Throughout the Eastern United States KaDonna C. Randolph* Abstract - Sassafras albidum (Sassafras) is an ecologically important tree species that is widely distributed throughout the eastern United States. Sassafras is presently threatened by Raffaelea lauricola, a fungus vectored by Xyleborus glabratus (Coleoptera: Curculionidae: Scolytinae; Redbay Ambrosia Beetle), which causes a lethal vascular wilt known as laurel wilt disease (LWD). This study summarizes the status of Sassafras across the entire eastern United States and in areas with LWD in particular, so that LWD-induced changes in the Sassafras resource may be properly understood. Inventory data collected by the Forest Inventory and Analysis (FIA) Program of the US Department of Agriculture Forest Service indicated that as of 2013–2014 there were 1.9 billion live Sassafras trees and saplings across 28 states, 53 ecoregion sections, and 69 forest types in the eastern United States. Only 1.7% of Sassafras trees ≥2.5 cm diameter at breast height occurred in counties with LWD; an additional 2.8% occurred in neighboring counties. To date, LWD has not reached the heart of the Sassafras range, yet discontinuous jumps of the disease beyond its advancing front suggest that future introductions may be possible. Landowners and forest managers within the range of Sassafras should be diligent to watch for LWD symptoms and consider the changes that may occur in their forests if the disease becomes established. Introduction Non-native phytophagous forest insects disrupt the economic, social, and ecosystem services and benefits provided by forest and urban trees, costing governments and homeowners millions and even billions of dollars annually (Aukema et al. 2011). Over 150 such insects have established themselves in the continental United States since 1930, and many have become major pests (Aukema et al. 2011, Moser et al. 2009). Included among the more recent and most detrimental introductions to the eastern United States is Xyleborus glabratus Eichhoff (Coleoptera: Curculionidae: Scolytinae; Redbay Ambrosia Beetle [RAB]), a native to Southeast Asia, which was first discovered at Port Wentworth near Savannah, GA in 2002 (Fraedrich et al. 2008). The RAB carries spores of the fungus Raffaelea lauricola T.C. Harr., Fraedrich, & Aghayeva in its mandibular mycangia. The fungus is thought to be initially transmitted into healthy trees when the beetle excavates but then abandons the tunnels before laying eggs (Fraedrich et al. 2008). Once infected with R. lauricola, the trees become suitable for RAB brood production and typically die quickly, sometimes within a few weeks (Hughes et al. 2015). As observed on dead and dying Persea *US Department of Agriculture, Forest Service, 4700 Old Kingston Pike, Knoxville, TN 37919; krandolph@fs.fed.us. Manuscript Editor: Richard Baird Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 38 borbonia (L.) Spreng. (Redbay) trees, R. lauricola causes a lethal vascular wilt known as laurel wilt disease (LWD), so named because other members of the family Lauraceae, including Sassafras albidum (Nutt.) Nees (Sassafras), are also susceptible (Fraedrich et al. 2008, Hughes et al. 2015). Following its introduction into the United States, LWD spread rapidly among Redbay trees along the southern Atlantic Coast (Fig. 1; Koch and Smith 2008). Early predictions suggested that the disease would not reach as far west as Texas until 2030 (Koch and Smith 2008); however, such predictions were unable to account for human-assisted spread and the ability of RAB to persist on Sassafras in the absence of Redbay. In addition to Georgia, LWD is now found in Alabama (Bates et al. 2013), Arkansas (Olatinwo et al. 2016), Florida (Smith et al. 2009), Louisiana (Fraedrich et al. 2015), Mississippi (Riggins et al. 2011), North Carolina (North Carolina Forest Service 2012), South Carolina (Fraedrich et al. 2008, Smith et al. 2009), and Texas (Menard et al. 2016). LWD progression beyond the coastal forest and on Sassafras, in particular, is concerning to individual landowners, municipalities, and others because of the cultural, ecological, and economic significance of lauraceous species (Hughes et al. 2015). Like Redbay, Sassafras is an ecologically important tree species in the eastern United States. It is an early successional species that colonizes abandoned fields Figure 1. Distribution of laurel wilt disease (LWD) as of 7 April 2016, by year of initial detection (Southern Regional Extension Forestry 2016). Analyses of Sassafras in counties with LWD included only the counties where LWD was discovered prior to 2015 and where stated, their neighboring counties. Southeastern Naturalist 39 K.C. Randolph 2017 Vol. 16, No. 1 and often forms dense thickets through root suckering (Griggs 1990). Large Sassafras trees can also persist in later successional stands. Its bark, twigs, and leaves provide food for wildlife and insects (Griggs 1990). Two butterflies, Papilio palamedes Drury (Palamedes Swallowtail Butterfly) and P. troilus L. (Spicebush Swallowtail), preferentially utilize Laureaceae species, including Sassafras, for oviposition and larval feeding (Gramling 2010, Lederhouse et al. 1992). Sassafras wood is used for specialty wood products, and extracts from the leaves, bark, and roots are used for tea and perfume (Griggs 1990). With a range extending from the Great Lakes to the Gulf Coast and from the Atlantic Coast to Texas and Oklahoma (Fig. 2), the loss of Sassafras would affect much of the eastern forest ecosystem. Most of the studies examining the effects of LWD have focused on Redbay at the local or subregional level, e.g., Cameron et al. (2015), Fraederich et al. (2008), and Shields et al. (2011). Exceptions to this include simulated responses to Sassafras mortality in central Kentucky forests (Nielsen and Rieske 2015), modeled spatiotemporal spread of LWD across the entire eastern United States using the ranges Figure 2. Historical range of Sassafras (Little 1971) and the 37 states in the eastern United States that were included in the analysis (shaded in inset map). The area of central and west Texas was not included in the study. Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 40 of both Redbay and Sassafras (Koch and Smith 2008), and reported regional, i.e., population-level, responses of Redbay to LWD (Shearman et al. 2015). Both Koch and Smith (2008) and Shearman et al. (2015) found data from the Forest Inventory and Analysis (FIA) Program of the US Department of Agriculture Forest Service suitable for their regional studies but neither focused specifically on Sassafras. Thus, the objective of this study was to describe the status of Sassafras in the presence of LWD and across the eastern United States based on data collected by FIA so that future changes in the resource may be properly understood. Methods Data source Field inventory. The FIA Program samples ground plots that are permanently located across the United States at a sampling intensity of 1 plot/~2400 ha (McRoberts 2005). Each plot is georeferenced and assigned to 1 of several spatially balanced “panels” whereby 1 panel of plots is measured every year so that each state is completely measured once every 5–10 years on an ongoing basis. Each plot consists of four 7.32-m fixed-radius subplots. Trees ≥12.7 cm in diameter at breast height (1.37 m above the ground; DBH) are measured on each subplot and trees less than 12.7 cm DBH are measured on a 2.07-m fixed-radius microplot located within each subplot. These data are available to the public through the FIA Program’s online database (http://apps.fs.fed.us/fiadb-downloads/datamart.html [Accessed 26 February 2016]) (O’Connell et al. 2015). Of the multitude of variables collected by FIA, only a few were pertinent to this study: forest type, tree status, species, standing-dead status, and cause of death (USDA Forest Service 2014). Forest type describes the species composition of the community in which trees are growing and is derived by FIA using a computer algorithm (Arner et al. 2003). Of particular interest for this study was the Sassafras/Eastern Persimmon forest type. For this forest type, Sassafras or Diospyros virginiana L. (Eastern Persimmon) forms the plurality of stocking for all live trees. Other species such as Ulmus spp. (elm), Juniperus virginiana L. (Eastern Redcedar), Carya spp. (hickory), Fraxinus spp. (ash), Acer saccharum Marsh. (Sugar Maple), Liriodendron tulipifera L. (Yellow-poplar), Sophora affinis Torr. & A. Gray (Texas Sophora), and Quercus spp. (oak) frequently occur in this forest type (USDA Forest Service 2014). Tree status code describes whether a tree is alive, dead, or no longer present, i.e., has been cut and removed since the previous inventory (USDA Forest Service 2014). A dead tree qualifies as “standing-dead” if the bole has an unbroken actual length ≥1.37 m and leans less than 45° from vertical as measured from the base of the tree to 1.37 m up from the ground (USDA Forest Service 2014). Cause of death is estimated by the inventory crew for trees that die between plot visits, i.e., have a status change from live to dead. Possible causes of death include insect, disease, fire, animal, weather, suppression or competition from other vegetation, silvicultural or land clearing activity, and unknown or other. Population estimates. Population estimates for various forest descriptors, e.g., forest land area and total number of live trees, are provided by the FIA Program Southeastern Naturalist 41 K.C. Randolph 2017 Vol. 16, No. 1 through the online estimation tool EVALIDator (Miles 2016). Population estimates for any given year are calculated as a moving average, i.e., an equally weighted sum, of the most recently collected panel of data (~20% of all plots in each state) and the remaining panels collected in previous years (~80% of all plots in each state). Although measurements are spread over multiple years, the estimates typically are dated with the year of the most recently collected panel of data. For example, the estimate of total forest land area in Florida for the year 2014 is based on plots measured in 2010, 2011, 2012, 2013, and 2014. Because the data are collected over multiple years, it is best to view the estimates as representing the attribute of interest at some point between the first and last year of panel data collection (Patterson and Reams 2005). Data analysis Current status in the eastern United States. I obtained estimates from EVALIDator, by ecoregion section (Cleland et al. 2007) for 37 states in the eastern United States (Fig. 2), of (1) forest land area of the Sassafras/Eastern Persimmon forest type; (2) number of live Sassafras trees (DBH ≥ 12.7 cm), saplings (DBH ≥ 2.5 cm and DBH < 12.7 cm), and seedlings (DBH < 2.5 cm) on forest land; and (3) number of standing-dead Sassafras trees (DBH ≥ 12.7 cm) on forest land. The most recent year of data collection included in these estimates was 2013 for Kentucky, Louisiana, Tennessee, and Virginia, and 2014 for all other states. Data from central and west Texas (Fig. 2) were not included. The distribution of the number of live Sassafras stems (DBH ≥ 2.5 cm)/ha across the eastern United States was generated by Koch and Smith (2008). Instead of repeating their analysis, I divided the distributions of the number of Sassafras trees (live and dead), saplings, and seedlings/ha of forest land across the ecoregion sections by quartiles and classified each ecoregion section according to whether the number of stems/ha, i.e., Sassafras density, in the ecoregion section was in the first, second, third, or fourth quartile. In addition, I calculated Sassafras percent standing-dead for each ecoregion section as the ratio of the number of standing-dead trees in the ecoregion section to the total number of live trees plus standing-dead trees in the ecoregion section expressed as a percentage. I obtained estimates of the number of live and standing-dead Sassafras trees in counties with LWD and in their neighboring counties (Fig. 1) from EVALIDator. I calculated Sassafras percent standing-dead for each group of counties as the ratio of the number of standing-dead trees in the group of counties to the total number of trees (live plus standing-dead) in the group of counties expressed as a percentage. To match the timeframe of the data available in the FIA database at the time of this analysis, only counties where LWD discoveries were made prior to 2015 (Hoyle 2016) were used to delineate the 2 sets of counties. Recent mortality in counties with laurel wilt disease. I assigned all trees and saplings measured by FIA in counties with LWD (Fig. 1) a “year of LWD” according to the year of LWD detection (Hoyle 2016). For trees and saplings measured more than once, I included in the analysis only the most recent assessment prior to Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 42 LWD confirmation (“before LWD”) and only the most recent assessment following LWD confirmation (“after LWD”), i.e., only 2 assessments/tree or sapling. Trees and saplings measured in the same year as LWD discovery were retained for the “after” assessment regardless of when, i.e., what month, they were assessed. Assignments of trees and saplings to 1 of 2 categories (either “survivors” or “mortality”) were made on the basis of tree status code. I labeled trees or saplings with a status code of live at both assessments as survivors and trees or saplings with a status code of live before LWD and dead after LWD as mortality. I assigned to the survivor class trees or saplings that were alive after LWD but had no status before LWD (missed or ingrowth stems). Trees or saplings with a status code of live before LWD and removed after LWD were not included in the analysis because it was not known if they were alive or dead at the time of removal. I also excluded trees and saplings that were alive before LWD but had no status after LWD. I built a 2 x 2 contingency table with the before and after dataset and used a Rao-Scott chi square test of independence to test for an association between species group, i.e., Sassafras or non-Sassafras (“other species”), and survivorship. So that the proportion of mortality for the non-Sassafras group would not be inflated, I excluded Persea species from the analysis. The test was performed using the SAS procedure SURVEYFREQ (SAS Institute, Inc. 2010) which takes into account the FIA sampling design whereby a correlation exists among trees growing on the same plot. In addition to the test of independence, I summarized causes of death for the mortality trees by species group. Results Current status in the eastern United States Area of the Sassafras/Eastern Persimmon forest type. Across the eastern United States, Sassafras was observed in 69 different forest types from both the softwood and hardwood forest type groups (Appendix 1; USDA Forest Service 2014). The Sassafras/Eastern Persimmon forest type covered an estimated 898,000 ha of forest land across 53 ecoregion sections (Fig. 3). The Sassafras/Eastern Persimmon forest type constituted as much as 5.1% of the total forest land area within a single ecoregion section, but overall the forest type covered less than 1% of the total forest land area in all 53 ecoregion sections (Table 1). Because the Sassafras/Eastern Persimmon forest type may be dominated by either Sassafras or Eastern Persimmon, forest land designated as this forest type may include only Sassafras or only Eastern Persimmon. Such was the case with the following ecoregion sections for which the Sassafras/Eastern Persimmon forest type included only Eastern Persimmon: 232G in Florida, 232L along the Gulf of Mexico, and 234E, 251E, 251F, 255A, 255D, 332F, and M231A throughout Arkansas, Kansas, Missouri, Oklahoma, and Texas (Fig. 3). Without these 9 ecoregion sections, the total forest land area of the Sassafras/ Eastern Persimmon forest type was estimated to be 769,000 ha (Table 1). Status across all forest types. Sassafras was most abundant in ecoregion section 223A where there were an estimated 273.3 million live trees and saplings and 2.39 billion seedlings (Table 2). There were also an estimated 4.4 million dead Sassafras Southeastern Naturalist 43 K.C. Randolph 2017 Vol. 16, No. 1 Figure 3. Ecoregion sections where Sassafras trees, saplings, seedlings, or the Sassafras/ Eastern Persimmon forest type were found (shaded). Table 1. Area of the Sassafras/Eastern Persimmon forest type in the eastern United States, by ecoregion section. SE = sampling error as a percent of the estimate. A 68.27% confidence interval for the estimate can be calculated as estimate ± estimate * (SE/100). Source: Miles (2016). Sassafras/Eastern Persimmon forest type All forest types Ecoregion sectionA Estimate (thousand ha) SE (%) Estimate (thousand ha) SE (%) 211F 5.7 71.3 2489.7 2.4 211G 2.7 101.8 1624.5 2.7 221A 3.6 79.1 3130.2 1.8 221B 2.4 99.0 669.7 5.3 221D 4.5 58.4 864.2 4.2 221E 78.7 17.1 5245.5 1.3 221F 9.5 45.1 1143.1 3.6 221H 8.7 50.2 2415.6 2.1 221J 11.2 41.3 814.8 4.1 222H 4.6 49.1 897.8 3.6 222J 17.9 34.3 1459.2 2.5 222U 6.5 57.6 362.8 7.4 223A 43.5 20.4 6349.3 1.0 Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 44 trees in this ecoregion section; only ecoregion sections 221E (13.6 million trees), 223E (5.7 million trees), M221C (5.6 million trees), and 223D (5.3 million trees) had more dead Sassafras trees (Table 2). In ecoregion sections 232C and 232J, which encompass many of the counties in Georgia, North Carolina, and South Table 1, continued. Sassafras/Eastern Persimmon forest type All forest types Ecoregion sectionA Estimate (thousand ha) SE (%) Estimate (thousand ha) SE (%) 223B 10.4 39.5 633.3 4.4 223D 28.2 22.4 1492.9 2.5 223E 50.4 19.4 2717.3 1.9 223F 14.7 34.5 1050.5 2.9 223G 16.8 30.0 815.4 3.7 231AB 13.3 36.2 5143.8 1.5 231B 23.5 28.8 5577.9 1.5 231CB 9.0 47.9 1422.5 3.2 231D 4.3 61.3 1305.4 3.4 231E 38.4 22.7 6183.3 1.2 231G 28.3 27.2 1204.4 4.0 231H 25.1 26.4 3697.1 1.7 231I 16.3 32.6 4982.3 1.3 232A 4.6 66.3 735.1 4.2 232B 82.6 15.7 8113.7 1.1 232CB 7.8 46.3 5523.2 1.5 232F 1.2 70.7 4034.8 1.5 232GC 5.1 60.5 1014.1 4.4 232H 7.3 47.5 2308.7 2.2 232I 2.8 74.6 1392.2 3.0 232J 59.9 18.4 5334.5 1.7 232KB 5.4 60.4 1028.1 4.1 232LC 7.3 58.0 1915.3 3.0 234A 12.7 40.5 1069.3 3.9 234D 13.4 36.3 1095.4 3.6 234EC 4.7 57.2 462.9 6.2 251C 6.2 57.8 2385.3 2.2 251D 2.6 65.8 314.5 7.1 251EC 2.6 84.0 656.2 5.2 251FC 3.4 98.7 181.5 11.8 255AC 84.2 20.0 2161.0 2.8 255CB 8.9 44.9 654.5 5.5 255DC 4.8 71.4 94.6 15.0 332FC 5.0 93.4 483.5 7.7 M221A 22.2 33.0 4030.5 1.7 M221B 13.9 41.2 2048.8 2.7 M221C 10.8 46.3 2511.5 2.3 M221D 16.0 35.3 3452.1 1.8 M223AB 12.8 40.9 1234.7 3.3 M231AC 11.7 43.3 2336.3 2.0 AGeographic locations are shown in Figure 3. Descriptive names a re listed in Appendix 2. BOnly Sassafras seedlings (DBH < 2.5 cm) were observed. CPresence of the Sassafras/Eastern Persimmon forest type due to Eastern Persimmon. Southeastern Naturalist 45 K.C. Randolph 2017 Vol. 16, No. 1 Table 2. Number of live sassafras trees (DBH ≥ 12.7 cm), live saplings (DBH ≥ 2.5 and DBH < 12.7 cm), live seedlings (DBH < 2.5 cm), and standing-dead Sassafras trees (DBH ≥ 12.7 cm) in the eastern United States, by ecoregion section. SE = sampling error as a percent of the estimate. A 68.27% confidence interval for the estimate can be calculated as estimate ± estimate * (SE/100). Source: Miles (2016). Live trees Live saplings Live seedlings Standing-dead trees Ecoregion Estimate Estimate Estimate Estimate sectionA (thousands) SE (%) (thousands) SE (%) (millions) SE (%) (thousands) SE (%) 211F 4906 28.8 2272 58.6 54.8 46.1 146 54.0 211G 3409 34.8 6874 34.3 103.8 29.7 563 40.8 212H 1353 47.2 8862 34.1 110.9 19.6 168 47.8 221A 4090 17.7 21,362 22.3 153.3 19.7 892 29.8 221B 1349 51.0 564 94.3 5.7 50.9 236 59.7 221D 4215 17.2 11,896 26.4 61.4 22.3 1212 24.9 221E 38,073 8.3 168,332 9.8 1294.5 8.6 13,597 10.2 221F 3720 23.8 12,362 29.6 64.3 27.0 422 35.5 221H 8818 13.1 80,349 12.9 1188.6 7.6 3828 18.7 221J 6670 17.3 24,755 24.7 357.2 17.4 2002 35.3 222H 3891 24.9 7592 30.9 67.4 21.6 1145 27.4 222I 287 51.1 - - 8.3 61.3 39 100.0 222J 13,933 14.8 36,918 18.1 489.3 15.8 2404 20.2 222K 24 97.8 - - 0.3 97.8 - - 222U 1826 34.2 7224 40.9 34.1 37.6 37 104.0 223A 16,365 7.9 256,960 6.3 2394.7 4.1 4398 13.4 223B 7450 12.7 34,654 15.6 356.1 17.4 2354 17.0 223D 13,950 9.5 70,142 13.6 614.4 8.6 5267 11.1 223E 17,357 10.7 50,380 17.2 668.1 10.2 5698 13.8 223F 6567 14.5 22,636 19.2 127.8 15.5 1968 21.1 223G 11,954 12.6 43,609 16.8 217.8 12.7 1629 20.5 231A 642 23.6 27,818 19.8 115.2 14.6 138 50.0 231B 2501 15.7 62,672 12.1 338.2 9.1 598 26.1 231C 1687 25.8 17,591 18.8 205.9 18.2 577 32.9 231D 582 31.8 14,388 25.8 165.4 15.7 194 49.8 231E 3495 16.3 78,052 14.6 319.5 9.3 812 26.9 231G 684 42.4 5082 58.6 43.3 39.0 114 56.7 231H 6783 14.1 35,429 18.8 173.6 11.3 2057 20.6 231I 2021 31.6 18,869 19.7 209.5 12.2 493 32.0 232A 5511 18.1 21,518 25.5 37.1 41.1 946 27.2 232B 1911 16.2 37,757 14.7 491.9 12.1 249 38.0 232C 253 47.5 16,073 24.2 128.8 23.9 34 102.5 232E 39 97.2 446 101.0 2.3 52.8 - - 232F 1728 17.5 37,594 17.3 289.5 14.1 180 44.7 232H 2114 20.2 29,852 18.9 139.7 17.2 438 34.1 232I 599 87.3 5680 42.1 18.4 29.4 - - 232J 388 30.5 26,664 17.5 513.6 9.1 243 66.5 232K - - - - 5.0 54.4 - - 232L 71 71.7 - - 3.3 73.6 71 71.7 234A 179 53.4 460 100.2 5.3 48.1 - - 234D 1609 26.4 13,943 51.0 21.6 26.2 234 39.5 234E - - 4053 43.1 5.0 43.4 36 100.2 251C 3366 22.8 24,470 23.2 126.4 19.0 179 45.6 251D 2063 31.1 14,696 23.7 179.7 21.3 212 49.6 Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 46 Carolina with LWD, percent standing-dead was 11.8% and 38.5%, respectively. No live Sassafras trees or saplings were observed in 4 ecoregion sections and no standing-dead Sassafras trees were recorded in 9 ecoregion sections (Table 2). Only standing-dead Sassafras trees, i.e., no live Sassafras trees, saplings, or seedlings, were recorded in section 251E. Overall, the greatest abundance of Sassafras was located in the interior eastern United States. Nine ecoregion sections were in the top, i.e., fourth quartile, in terms of live Sassafras seedling, sapling, and tree density (Fig. 4). These ecoregion sections were located in 2 general geographical areas. First, ecoregion sections 222J, 223B, 223D, 223G, and 251D form a crescent shape from southern Michigan, through Illinois, and into southern Indiana and western Kentucky (Fig. 4). Secondly, ecoregion sections 221E, 221J, M221A, and M221C form a y-shape from Tennessee in the south to 2 endpoints in Pennsylvania, one through Ohio and West Virginia and the other through Virginia and Maryland (Fig. 4). Ecoregion sections in the top quartile in terms of standing-dead Sassafras trees/ha of forest land were concentrated in ecoregion sections spanning Indiana, Kentucky, Ohio, Tennessee, and West Virginia (Fig. 4). As expected, fewer Sassafras stems were found at the edges of its historic range (Fig. 2). Ecoregion sections in the bottom, i.e., first quartile, in terms of live Sassafras seedling, sapling, and tree density were located in northern Illinois, New Hampshire, eastern Oklahoma, and along the coast of the Gulf of Mexico (Fig. 4). Ecoregion sections in the bottom quartile in terms of dead Sassafras trees/ha of forest land were similarly located (Fig. 4). Sassafras was abundant as seedlings and saplings in some ecoregion sections without being highly abundant as trees. In 8 ecoregion sections, the density of seedlings and saplings was in the upper 2 quartiles, whereas the density of trees was Table 2, continued. Live trees Live saplings Live seedlings Standing-dead trees Ecoregion Estimate Estimate Estimate Estimate sectionA (thousands) SE (%) (thousands) SE (%) (millions) SE (%) (thousands) SE (%) 251E - - - - - - 36 101.8 255A 36 100.7 5020 98.7 15.3 61.2 36 100.7 255B - - - - 0.5 98.8 - - 255C 848 30.6 3271 46.7 33.5 31.0 295 42.6 255D - - 485 97.0 - - - - M211B 76 104.4 - - 0.6 96.0 - - M211C - - - - 6.6 101.5 - - M221A 21,224 9.7 83,572 11.4 922.4 10.1 4363 12.5 M221B 8556 16.2 45,491 18.6 486.4 16.9 2965 17.2 M221C 11,565 10.1 98,056 13.5 945.0 10.2 5636 12.3 M221D 8195 12.9 58,784 14.8 821.3 8.5 3980 14.1 M223A 1708 23.0 15,006 24.0 193.0 16.7 433 33.5 M231A 470 64.8 8675 30.0 29.2 24.3 142 101.6 AGeographic locations are shown in Figure 3. Descriptive names a re listed in Appendix 2. Southeastern Naturalist 47 K.C. Randolph 2017 Vol. 16, No. 1 in the lower 2 quartiles (Fig. 4). Many of these ecoregion sections were located in areas dominated by Pinus taeda L. (Loblolly Pine) and P. echinata Mill. (Shortleaf Pine) forest types (USDA Forest Service and USDI Geological Survey 2000), e.g., eastern Texas, western Louisiana, and a crescent along the coastal plain from Mississippi to Virginia. Status in counties with laurel wilt disease. Only 1.7% of the total live inventory of Sassafras trees and saplings was in counties where LWD was discovered prior to 2015; another 2.8% was in their neighboring counties. The estimated number of live Sassafras trees was 873,800 trees in the counties with LWD and 2 million trees in the neighboring counties. Percent standing-dead was higher in the counties with LWD (24.7%) than in the neighboring counties (20.2%). Figure 4. Quartile divisions of Sassafras (A) seedlings (DBH < 2.5 cm)/ha of forest land, (B) saplings (DBH ≥ 2.5 cm and DBH < 12.7 cm)/ha of forest land, (C) live trees (DBH ≥ 12.7 cm)/ha of forest land, and (D) standing-dead trees (DBH ≥ 12.7 cm)/ha of forest land by ecoregion section. Q1 = first quartile, Q2 = second quartile, Q3 = third quartile, Q4 = fourth quartile. Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 48 Recent mortality in counties with laurel wilt disease In the counties where LWD was discovered between 2002 and 2014, 62 plots containing Sassafras trees or saplings were assessed before and after the discovery of LWD. One plot was omitted from the analysis due to an excessively long time period between plot visits (19 years). The remaining 61 plots were measured, on average, 4.6 years before LWD discovery (range = 1–10 years) and 1.6 years after LWD discovery (range = 0–5 years). The mean time between measurements was 6.2 years (range = 3–11 years). Results of the Rao-Scott chi-square test of independence indicated that there was a significant (F = 43.5, P < 0.0001) relationship between species group and survivorship across the plots in the LWD counties. Of the Sassafras trees and saplings (n = 93), 36.6% died during the remeasurement period, compared to only 8.8% mortality among other species (n = 1783). Silvicultural/land-clearing activity and competition or suppression from other vegetation were the most frequently recorded causes of death for both Sassafras and other species (Fig. 5). Discussion With less than 5% of its total live inventory in counties with LWD and their neighboring counties, only a small portion of Sassafras is immediately threatened by the disease. However, discoveries of LWD on Sassafras in Alabama, Arkansas, and Louisiana (Bates et al. 2013, Fraedrich et al. 2015, Olatinwo 2016), where it was first thought that RAB would not be a threat due primarily to the absence of Redbay, suggest that the abundance of Sassafras located in the interior eastern United States is also at Figure 5. Cause of death for trees that were alive prior to the discovery of laurel wilt disease (LWD) and dead after LWD was discovered. Silvic. = Silviculture. n = number of trees. Southeastern Naturalist 49 K.C. Randolph 2017 Vol. 16, No. 1 risk. Climate-matching techniques employed by Koch and Smith (2008) predicted that RAB would be constrained to the southeastern coastal plain and perhaps a small area in the southern Appalachian Mountains. Yet recent cold-tolerance tests have indicated that RAB can survive temperatures as low as -24 °C (Formby et al. 2013); therefore, it is possible that RAB could survive farther north than previously anticipated. If this is indeed the case, the spread of LWD into the interior eastern United States could devastate Sassafras and alter many different forest types. Though mortality of Sassafras trees following establishment of LWD was greater than that observed for other tree species, results suggested that LWD was not the primary cause. Instead, estimated causes of death pointed to silvicultural/landclearing activities and competition or suppression from other vegetation. There are at least 2 possible explanations for the lack of Sassafras mortality attributed to LWD. First, LWD-mortality may have been incorrectly attributed to another factor or recorded as “unknown” by the inventory crews due to the relative novelty of the disease and complexity of identifying the causality of tree mortality (Franklin et al. 1987). Without a detailed inspection for LWD symptoms, e.g., sapwood discoloration or beetle frass tubes, LWD-induced mortality resembles typical Sassafras mortality and provides little reason to suspect LWD without a priori knowledge of its presence. Second, the after-LWD assessments may have been made too soon after the discovery of LWD. Sixty-one percent of the assessments were made within ~1 year after LWD was detected in the county. Whether LWD infections were localized or ubiquitous in the county was unknown. Thus, the plots with Sassafras could have been located in areas to which LWD had not yet spread or in stands where LWD was progressing slowly. In a study in southeastern Georgia, Cameron et al. (2015) observed 40% mortality among Sassafras 1 year after symptoms of LWD first appeared and >80% mortality 1.5 years later, i.e., 2.5 years after the first symptoms. Thus, subsequent assessments may show an increase in Sassafras mortality directly and more obviously attributable to LWD. Unlike in most stands of Redbay, studies have shown that the spread of LWD in Sassafras stands is inconsistent, moving rapidly and completely through some stands while abating before complete devastation in others (Cameron et al. 2015). This inconsistency, perhaps, favors the perpetuation of the species. Nevertheless, given that Sassafras trees generally succumb to competitive stress if they are unable to reach the upper (unshaded) canopy (Griggs 1990), the presence of the RAB, which preferentially attacks larger trees first (Cameron et al. 2015, Mayfield and Brownie 2013), adds pressure to the survivability of individual Sassafras trees. Continued region-wide monitoring of Sassafras by the FIA Program and the implementation of other localized studies will be important for assessing the loss of Sassafras as LWD progresses throughout the eastern United States. For example, Brandeis et al. (2016) already has noted that almost 41% of the Sassafras trees (DBH ≥ 2.5 cm) measured during the 2009 FIA survey of the state of Georgia had died by the time the 2014 survey was completed. In order to better understand future potential impacts, spatio-temporal models that take into account the ability of RAB to thrive in the absence of Redbay and that incorporate human-assisted spread Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 50 ahead of the advancing front of LWD should be developed. This need is especially important since eradication of LWD in the United States is no longer considered feasible (Hughes et al. 2015). Minimizing the effects of LWD in established areas and slowing its spread to new sites are the current recommended management actions (Hughes et al. 2015). Because R. lauricola has been observed to spread through the interconnected clonal root systems of Sassafras (Cameron et al. 2014), one possible way to minimize its spread would be to sever the root systems with heavy mechanical equipment (Hughes et al. 2015), although this hardly seems feasible in a forested setting. Likewise, the use of fungicides and insecticides in a forested setting would be costprohibitive due to the need for repeated applications (Hughes et al. 2015). Public awareness campaigns, such as dontmovefirewood.org, active monitoring of trees on the leading edge of the disease, and the removal of dead trees through sanitation cuts are among the management techniques most likely to help slow the spread of LWD throughout the broad range of ecological gradients and forest types in which Sassafras occurs in the eastern United States. LWD-induced Sassafras mortality will alter the forest both structurally and compositionally, and will also affect ecological interactions. With the exception of clonal thickets, Sassafras does not typically dominate a forest stand but is instead a minor to moderate component of a variety of forest types across the eastern United States (Griggs 1990). As such, the direct effects of Sassafras decline may be modest; however, the indirect effects, especially in terms of resources released for use by other species, will be measurable and likely complex (Nielsen and Rieske 2015). In a simulation study of the effects of LWD-induced Sassafras mortality in forests of central Kentucky, Nielsen and Rieske (2015) found that losses in Sassafras basal area can be offset by the increased growth of other species, particularly Yellowpoplar and Acer rubrum L. (Red Maple). The timing and extent of the response by other species will depend on the degree to which Sassafras disappears from the stand, as well as the size and species composition of the remaining forest. Dynamics within canopy gaps resulting from tree mortality are complex (Beckage et al. 2008, Oliver and Larson 1996); therefore, the effect of losing Sassafras in one forest type or stand may be very different than in another. Since the RAB was introduced in 2002, LWD has been moving gradually across the coastal plain (Fig. 1) where Sassafras is minimally abundant (Fig. 4). If and when LWD establishes itself in the more-centrally located forests where Sassafras is most abundant (Fig. 4), the impact of LWD on Sassafras will increase and studies similar to Shearman et al. (2015) can be completed. Landowners and forest managers in the heart of Sassafras’ range should be diligent to watch for LWD symptoms because discontinuous jumps of the disease may continue. Forest managers are encouraged to survey stands that include Sassafras and consider the changes that may occur if LWD becomes established and how such changes will affect their management objectives. Southeastern Naturalist 51 K.C. Randolph 2017 Vol. 16, No. 1 Acknowledgments Gratitude is extended to Sue Crocker, Anita Rose, and 2 anonymous reviewers for their comments on earlier drafts of this manuscript, and to the numerous forest inventory field crews who collected the data used in this study. Literature Cited Arner, S.L, S. Woudenberg, S. Waters, J. Vissage, C. MacLean, M. Thompson, and M. Hansen. 2003. National algorithms for determining stocking class, stand size class, and forest type for Forest Inventory and Analysis plots. US Department of Agriculture Forest Service, Forest Inventory and Analysis Program. 65 pp. Available online at http://www. fia.fs.fed.us/library/sampling/docs/supplement4_121704.pdf. Accessed 23 May 2016. Aukema, J.E., B. Leung, K. Kovacs, C. Chivers, K.O. Britton, J. Englin, S.J. Frankel, R.G. Haight, T.P. Holmes, A.M. Liebhold, D.G. McCollough, and B. Von Holle. 2011. Economic impacts of non-native forest insects in the continental United States. PLoS ONE 6(9):e24587. Bates, C.A., S.W. Fraedrich, T.C. Harrington, R.S. Cameron, R.D. Menard, and G.S. Best. 2013. First report of laurel wilt, caused by Raffaelea lauricola, on Sassafras (Sassafras albidum) in Alabama. Plant Disease 97(5):688. Beckage, B., B.D. Kloeppel, J.A. Yeakley, S.F. Taylor, and D.C. Coleman. 2008. Differential effects of understory and overstory gaps on tree regeneration. The Journal of the Torrey Botanical Society 135(1):1–11. Brandeis, T.J., J.M. McCollum, A.J. Hartsell, C. Brandeis, A.K. Rose, S.N. Oswalt, J.T. Vogt, and H.M. Vega. 2016. Georgia’s forests, 2014. Resour. Bull. SRS-209. US Department of Agriculture Forest Service, Southern Research Station, Asheville, NC. 78 pp. Cameron, R.S., C. Bates, and J. Johnson. 2014. Progression of laurel wilt disease in Georgia: 2009–2011 (Project SC-EM-08-02). Pp. 145–151, In K.M. Potter and B.L. Conkling (Eds.). Forest health monitoring: National status, trends, and analysis 2012. General Technical Report SRS-198. US Department of Agriculture Forest Service, Southern Research Station, Asheville, NC. 192 pp. Cameron, R.S., J. Hanula, S. Fraedrich, and C. Bates. 2015. Progression and impact of laurel wilt disease within Redbay and Sassafras populations in southeast Georgia. Southeastern Naturalist 14(4):650–674. Cleland, D.T., J.A. Freeouf, J.E. Keys, Jr., G.J. Nowacki, C. Carpenter, and W.H. McNab. 2007. Ecological subregions: Sections and subsections of the conterminous United States [1:3,500,000] [CD-ROM]. A.M. Sloan (Cartog.). General Technical Report WO- 76. US Department of Agriculture Forest Service, Washington, DC. Formby, J.P., N. Krishnan, and J.J. Riggins. 2013. Supercooling in the Redbay Ambrosia Beetle (Coleoptera: Curculionidae). Florida Entomologist 96(4):1530–1540. Fraedrich, S.W., T.C. Harrington, R.J. Rabaglia, M.D. Ulyshen, A.E. Mayfield III, J.L. Hanula, J.M. Eickwort, and D.R. Miller. 2008. A fungal symbiont of the Redbay Ambrosia Beetle causes a lethal wilt in Redbay and other Lauraceae in the southeastern United States. Plant Disease 92:215–224. Fraedrich, S.W., C.W. Johnson, R.D. Menard, T.C. Harrington, R. Olatinwo, and G.S. Best. 2015. First report of Xyleborus glabratus (Coleoptera: Curculionidae: Scolytinae) and laurel wilt in Louisiana, USA: The disease continues westward on Sassafras. Florida Entomologist 98(4):1266–1268. Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 52 Franklin, J.F., H.H. Shugart, and M.E. Harmon. 1987. Tree death as an ecological process: The causes, consequences, and variability of tree mortality. BioScience 37(8):550–556. Gramling, J.M. 2010. Potential effects of laurel wilt on the flora of North America. Southeastern Naturalist 9(4):827–836. Griggs, M.M. 1990. Sassafras albidum (Nutt.) Nees. In R.M. Burns and B.H. Honkala (Tech. Coords.). Silvics of North America: Vol 2. Hardwoods. Agriculture Handbook 654. US Department of Agriculture Forest Service, Washington, DC. 877 pp. Hoyle, Z. 2016. Laurel wilt continues to spread. CompassLive 9 February 2016. US Department of Agriculture Forest Service, Southern Research Station. Available online at http://www.srs.fs.usda.gov/compass/2016/02/09/laurel-wilt-continues-to-spread/. Accessed 12 February 2016. Hughes, M.A., J.A. Smith, R.C. Ploetz, P.E. Kendra, A.E. Mayfield III, J.L. Hanula, J. Huler, L.L. Stelinski, S. Cameron, J.J. Riggins, D. Carrillo, R. Rabaglia, J. Eickwort, and T. Pernas. 2015. Recovery plan for laurel wilt on Redbay and other forest species caused by Raffaelea lauricola and disseminated by Xyleborus glabratus. Plant Health Progress 16(4):173–210. Koch, F.H., and W.D. Smith. 2008. Spatio-temporal analysis of Xyleborus glabratus (Coleoptera: Circulionidae: Scolytinae) invasion in eastern US forests. Environmental Entomology 37(2): 442–452. Lederhouse, R.C., M.P. Ayres, J.K. Nitao, and J.M. Scriber. 1992. Differential use of lauraceous hosts by swallowtail butterflies, Papilio troilus and P. palamedes (Papilionidae). Oikos 63(2):244–252. Little, Jr., E.L. 1971. Atlas of United States trees. Volume 1. Conifers and important hardwoods. Miscellaneous Publication 1146. US Department of Agriculture. 9 pp. 200 maps. Available online at http://esp.cr.usgs.gov/data/little. Accessed 22 February 2016. Mayfield, A.E., III, and C. Brownie. 2013. The Redbay Ambrosia Beetle (Coleoptera: Curculionidae: Scolytinae) uses stem silhouette diameter as a visual host-finding cue. Environmental Entomology 42(4):743–750. McRoberts, R.E. 2005. The enhanced Forest Inventory and Analysis Program. Pp. 1–10, In W.A. Bechtold and P.L. Patterson (Eds.). The enhanced Forest Inventory and Analysis Program—national sampling design and estimation procedures. General Technical Report SRS-80. US Department of Agriculture Forest Service, Southern Research Station, Asheville, NC. 85 pp. Menard, R.D., S.R. Clarke, S.W. Fraedrich, and T.C. Harrington. 2016. First report of laurel wilt, caused by Raffaelea lauricola, on Redbay (Persea borbonia) in Texas. Plant Disease 100(7):1502. Miles, P.D. 2016. Forest Inventory EVALIDator web-application version 1.6.0.03. US Department of Agriculture Forest Service, Northern Research Station, St. Paul, MN. Available online at http://apps.fs.fed.us/Evalidator/evalidator.jsp. Accessed 20 May 2016. Moser, W.K., E.L. Barnard, R.F. Billings, S.J. Crocker, M.E. Dix, A.N. Gray, G.G. Ice, M. Kim, R. Reid, S.U. Rodman, and W.H. McWilliams. 2009. Impacts of nonnative invasive species on US forests and recommendations for policy and management. Journal of Forestry 107(6):320–327. Nielsen, A.M., and L.K. Rieske. 2015. Potential host and range expansion of an exotic insect-pathogen complex: Simulating effects of Sassafras mortality from laurel wilt disease invasion in the central hardwoods region. The Journal of the Torrey Botanical Society 142(4):292–301. Southeastern Naturalist 53 K.C. Randolph 2017 Vol. 16, No. 1 North Carolina Forest Service. 2012. Laurel wilt continues to spread in southeastern North Carolina. Forest Health Notes Vol. 201201-LW. Available online at http://ncforestservice. gov/forest_health/pdf/FHN/FHN1201LW.pdf. Accessed 19 December 2016. O’Connell, B.M., E.B. LaPoint, J.A. Turner, T. Ridley, S.A. Pugh, A.M. Wilson, K.L. Waddell, and B.L. Conkling. 2015. The Forest Inventory and Analysis database: Database description and user guide for phase 2 (version 6.02). Available online at http://www.fia. fs.fed.us/library/database-documentation/index.php. Accessed 18 May 2016. Olatinwo, R., C. Barton, S.W. Fraedrich, W. Johnson, and J. Hwang. 2016. First report of Laurel Wilt, caused by Raffaelea lauricola, on Sassafras (Sassafras albidum) in Arkansas. Plant Disease 100(11):2331. Oliver, C.D., and B.C. Larson. 1996. Forest Stand Dynamics. John Wiley and Sons, Inc., New York, NY. 520 pp. Patterson, P.L., and G.A. Reams. 2005. Combining panels for forest inventory and analysis estimation. Pp. 69–74, In W.A. Bechtold and P.L Patterson (Eds.). The enhanced Forest Inventory and Analysis Program: National sampling design and estimation procedures. General Technical Report SRS-80. US Department of Agriculture Forest Service, Southern Research Station, Asheville, NC. 85 pp. Riggins, J.J., S.W. Fraedrich, and T.C. Harrington. 2011. First report of laurel wilt caused by Raffaelea lauricola on Sassafras in Mississippi. Plant Disease 95(11):1479. SAS Institute, Inc. 2010. SAS v. 9.3. Cary, NC. Shearman, T.M., G.G. Wang, and W.C. Bridges. 2015. Population dynamics of Redbay (Persea borbonia) after laurel wilt disease: An assessment based on forest inventory and analysis data. Biological Invasions 17:1371–1382. Shields, J., S. Jose, J. Freeman, M. Bunyan, G. Celis, D. Hagan, M. Morgan, E.C. Pieterson, and J. Zak. 2011. Short-term impacts of laurel wilt on Redbay (Persea boronia (L.) Spreng.) in a mixed evergreen–deciduous forest in northern Florida. Journal of Forestry 109(2):82–88. Smith, J.A., T.J. Dreaden, A.E. Mayfield III, A. Boone, S.W. Fraedrich, and C. Bates. 2009. First report of laurel wilt disease, caused by Raffaelea lauricola on Sassafras in Florida and South Carolina. Plant Disease 93(10):1079. Southern Regional Extension Forestry. 2016. Distribution of counties with laurel wilt as of April 7, 2016. Available online at http://southernforesthealth.net/fungi/laurel-wilt/ distribution-map. Accessed 19 December 2016. US Department of Agriculture (USDA) Forest Service, and US Department of Interior (USDI) Geological Survey. 2000. Forest cover types [map]. Available online at http:// www.fia.fs.fed.us/library/maps/docs/forestcover.pdf. Accessed 5 August 2016. USDA Forest Service. 2014. Forest Inventory and Analysis national core field guide. Volume 1: Field data collection procedures for phase 2 plots. Ver. 6.1. Available online at http://www.fia.fs.fed.us/library/field-guides-methods-proc/index.php. Accessed 18 May 2016. 433 pp. Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 54 Appendix 1. Forest types where Sassafras live trees, live saplings, live seedlings, or standing- dead trees are found in the eastern United States. x = present. Source: Miles (2016). Trees: DBH ≥ 12.7 cm, saplings: DBH ≥ 2.5 cm and DBH < 12.7 cm, and seedlings: DBH < 2.5 cm. Trees Forest type group/forest type Live Dead Saplings Seedlings White/Red/Jack Pine Pinus resinosa Sol. es Aiton (Red Pine) x x x Pinus strobus L. (Eastern White Pine) x x x x Eastern White Pine/Eastern Hemlock x Tsuga canadensis (L.) Carrière (Eastern Hemlock) x x Longleaf/Slash Pine Pinus palustris Mill. (Longleaf Pine) x x x Pinus elliottii Engelm. (Slash Pine) x x x x Loblolly/Shortleaf Pine Pinus taeda L. (Loblolly Pine) x x x x Pinus echinata Mill. (Shortleaf Pine) x x x x Pinus virginiana Mill. (Virginia Pine) x x x x Pinus clausa (Chapm. ex Engelm.) Sarg. (Sand Pine) x Pinus serotina Michx. (Pond Pine) x Pinus rigida Mill. (Pitch Pine) x x x x Pinus glabra Walter (Spruce Pine) x Other eastern softwoods Juniperus virginiana L. (Eastern Redcedar) x x x x Exotic softwoods Pinus sylvestris L. (Scots Pine) x x x Other exotic softwoods x Picea abies (L.) H. Karst (Norway Spruce) x x Introduced Larix (larch) x x x x Oak/pine Eastern White Pine/Northern Red Oak/ x x x x Fraxinus americana L. (White Ash) Eastern Redcedar/hardwood x x x x Longleaf Pine/oak x x x x Shortleaf Pine/oak x x x x Virginia Pine/Quercus falcata Michx.(Southern Red x x x x Oak) Loblolly Pine/hardwood x x x x Slash Pine/hardwood x x x Other pine/hardwood x x x x Oak/hickory Quercus stellata Wangenh. (Post Oak)/Quercus x x x x marilandica Muenchh.(Blackjack Oak) Southeastern Naturalist 55 K.C. Randolph 2017 Vol. 16, No. 1 Quercus montana Willd. (Chestnut Oak) x x x x White Oak/red oak/hickory x x x x Quercus alba L. (White Oak) x x x x Quercus rubra L. (Northern Red Oak) x x x x Yellow-poplar/White Oak/Northern Red Oak x x x x Sassafras/Eastern Persimmon x x x x Liquidambar styraciflua L. (American Sweetgum)/ x x x x Yellow-poplar Quercus macrocarpa Michx. (Bur Oak) x Quercus coccinea Muenchh. (Scarlet Oak) x x x x Liriodendron tulipifera L. (Yellow-poplar) x x x x Juglans nigra L. (Eastern Black Walnut) x x x Robinia pseudoacacia L. (Black Locust) x x x x Southern scrub oaks (various spp. of small oaks) x x x Chestnut Oak/Quercus velutina Lam. (Eastern Black x x x x Oak)/Scarlet Oak Cherry/White Ash/Yellow-poplar x x x x Elm/ash/Black Locust x x x x Red Maple/oak x x x x Mixed upland hardwoods x x x x Oak/gum/cypress Quercus michauxii Nutt. (Swamp Chestnut Oak)/ x x x x Quercus pagoda Raf. (Cherrybark Oak) Sweetgum/Quercus texana Buckley (Nuttall’s Oak)/ x x x x Quercus phellos L. (Willow Oak) Quercus lyrata Walter (Overcup Oak)/Carya aquatica x x x x (F. Michx.) Nutt. (Water Hickory) Chamaecyparis thyoides (L.) Britton, Sterns, & x Poggenb. (Atlantic White Cedar) Taxodium distichum (L.) Rich. (Baldcypress)/Nyssa x x aquatica L. (Water Tupelo) Magnolia virginiana L. (Sweetbay)/Nyssa biflora x x x x Watler (Swamp Tupelo)/Red Maple Fraxinus nigra Marshall (Black Ash)/Ulmus x x x x americana L. (American Elm)/Red Maple Betula nigra L. (River Birch)/Platanus (sycamore) x x x x Populus (cottonwood) x x x Salix (willow) x x x Sycamore/ Carya illinoinensis (Wangenh.) K.Koch x x x x (Pecan)/American Elm Celtis laevigata Willdenow (Sugarberry)/Celtis x x x x occidentalis L. (Hackberry)/elm/Fraxinus pennsylvanica Marshall (Green Ash) Acer saccharinum L. (Silver Maple)/American Elm x x x Red Maple/lowland x x x Cottonwood/willow x x x x Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 56 Maple/beech/birch Sugar Maple/beech/Betula alleghaniensis Britt. x x x x (Yellow Birch) Prunus serotina Ehrh. (Black Cherry) x x x x Hard maple (mulitple spp.)/Tilia americana L. x x x x (Basswood) Red Maple/upland x x x Aspen/birch Populus (aspen) x x x x Betula populifolia Marsh. (Gray Birch) x Other hardwoods Other hardwoods x x x x Exotic hardwoods Paulownia x x Other exotic hardwoods x x x x Southeastern Naturalist 57 K.C. Randolph 2017 Vol. 16, No. 1 Appendix 2. Ecoregion sections and their intersecting states where Sassafras trees, saplings, seedlings, or the Sassafras/Eastern Persimmon forest type are found in the eastern United States. Ecoregion section Intersecting state 211F Northern Glaciated Allegheny Plateau NY, PA 211G Northern Unglaciated Allegheny Plateau NY, PA 212H Northern Lower Peninsula MI 221A Lower New England CT, ME, MA, NH, NJ, NY, PA, RI, VT 221B Hudson Valley NJ, NY, PA, VT 221D Northern Appalachian Piedmont DE, MD, NJ, NY, PA, VA 221E Southern Unglaciated Allegheny Plateau KY, OH, PA, WV 221F Western Glaciated Allegheny Plateau OH, PA, NY 221H Northern Cumberland Plateau KY, TN 221J Central Ridge and Valley GA, TN, VA 222H Central Till Plains-Beech-Maple IL, IN, OH 222I Erie and Ontario Lake Plain OH, PA, NY 222J South Central Great Lakes IL, IN, MI, OH 222K Southwestern Great Lakes Morainal IL, IN, WI 222U Lake Whittlesey Glaciolacustrine Plain IN, MI, OH 223A Ozark Highlands AR, IN, KS, MO, OK 223B Interior Low Plateau-Transition Hills IN, KY 223D Interior Low Plateau-Shawnee Hills IL, IN, KY 223E Interior Low Plateau-Highland Rim AL, KY, TN 223F Interior Low Plateau-Bluegrass IN, KY, OH 223G Central Till Plains-Oak Hickory IL, IN 231A Southern Appalachian Piedmont AL, GA, NC, SC 231B Coastal Plains-Middle AL, GA, MS, TN 231C Southern Cumberland Plateau AL, GA, TN 231D Southern Ridge and Valley AL, GA, TN 231E Mid Coastal Plains-Western AR, LA, OK, TX 231G Arkansas Valley AR, OK 231H Coastal Plains-Loess IL, KY, LA, MS, TN 231I Central Appalachian Piedmont NC, SC, VA 232A Northern Atlantic Coastal Plain DE, MD, NJ, PA, VA 232B Gulf Coastal Plains and Flatwoods AL, GA, FL, LA, MS 232C Atlantic Coastal Flatwoods GA, FL, NC, SC 232E Louisiana Coastal Prairie and Marshes LA, MS, TX 232F Coastal Plains and Flatwoods-Western Gulf LA, TX 232G Florida Coastal Lowlands-Atlantic FL 232H Middle Atlantic Coastal Plains and Flatwoods DE, MD, NC, VA 232I Northern Atlantic Coastal Flatwoods NC, VA 232J Southern Atlantic Coastal Plains and Flatwoods FL, GA, NC, SC 232K Florida Coastal Plains Central Highlands FL 232L Gulf Coastal Lowlands AL, FL, LA, MS 234A Southern Mississippi Alluvial Plain AR, LA, MS 234D White and Black River Alluvial Plains AR, IL, KY, LA, MS, MO, TN Southeastern Naturalist K.C. Randolph 2017 Vol. 16, No. 1 58 234E Arkansas Alluvial Plains AR, LA 251C Central Dissected Till Plains IL, IA, KS, MO, NE, SD 251D Central Till Plains and Grand Prairies IL, IN 251E Osage Plains KS, MO, OK 251F Flint Hills KS, OK 255A Cross Timbers and Prairie KS, OK, TX 255B Blackland Prairie TX 255C Oak Woods and Prairie TX 255D Central Gulf Prairie and Marshes TX 332F South Central and Red Bed Plains KS, OK, TX M211B New England Piedmont MA, NH, VT M211C Green-Taconic-Berkshire Mountains CT, NY, MA, VT M221A Northern Ridge and Valley MD, NJ, PA, TN, VA, WV M221B Allegheny Mountains MD, PA, VA, WV M221C Northern Cumberland Mountains KY, VA, WV M221D Blue Ridge Mountains GA, MD, NC, PA, SC, TN, VA, WV M223A Boston Mountains AR, OK M231A Ouachita Mountains AR, OK