Structural diversity of the longleaf pine ecosystem

Forest Ecology and Management 462 (2020) 117987

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Structural diversity of the longleaf pine ecosystem Ajay Sharma , Barbara Cory, Justin McKeithen, Jesse Frazier ⁎

T

West Florida Research and Education Center, University of Florida, 5988 U.S. 90, Building 4900, Milton, FL 32583, USA

ARTICLE INFO

ABSTRACT

Keywords: FIA Southern pine Slash pine regeneration Uneven-aged stand Silviculture Restoration

Structural diversity is an important attribute of forest ecosystems and is related to ecosystem stability, adaptability and resilience as well as biodiversity and productivity. Structural diversity in the longleaf pine (Pinus palustris Mill.) ecosystem, the most diverse ecosystem of North America, has not been well documented, especially across the longleaf pine’s range of occurrence. We utilized data from 919 Forest Inventory and Analysis (FIA) plots of longleaf pine distributed across 9 states of the southeastern United States and classified these plots on the bases of stand origin (natural or artificial), ownership (public or private), burn condition (burned or not burned in the past 5 years), site conditions (xeric, mesic, or hydric), and number of age classes (one or two). For each plot under a classification category, we calculated Shannon diversity index based on 5-cm diameter classes. The structural diversity estimates, based on Shannon diversity indices, were then analyzed for the entire range of the longleaf pine ecosystem. Our findings indicate that the structural diversity varies between 0.00 and 2.20 across the longleaf pine range, with mean and median structural diversity of 1.35 and 1.42, respectively. Stand origin, site condition, ownership, and number of age classes significantly affected mean structural diversity (α = 0.05). Plots with natural origin, mesic or hydric site conditions, public ownership, and two age classes had higher structural diversity. Using the geographic coordinates of each FIA plot and the corresponding Shannon index value, we created a structural diversity distribution map and a hot spot map of the longleaf pine ecosystem. The maps showed that the longleaf pine ecosystem exhibited variable and heterogeneous distribution of structural diversity in the southeastern United States, with southeastern Mississippi and central Alabama areas as the hotspots. Southcentral Georgia exhibited least structural diversity in longleaf pine forests in the southeastern United States.

1. Introduction Stand structural diversity is an important forest ecosystem attribute that can be indicative of overall health, biodiversity, habitat value, resilience, and functioning of forest ecosystems (Gao et al., 2014; Ali et al., 2016; Koontz et al., 2020). High structural diversity has been associated with high production efficiency of forest stands and is useful in forecasting forest growth and stand dynamics (Ali et al., 2016; Gough et al., 2019). Stands with high structural diversity are aesthetically pleasing and are perceived favorably by the public (Gundersen and Frivold, 2008, Paudyal et al., 2018). Structural diversity creates complex environmental conditions leading to diverse habitat niches in forest stands and greater occupation by birds and insects (MacArthur and MacArthur, 1961; Ehbrecht et al., 2017; Ehbrecht et al., 2019). Structurally complex forest stands also have inherent resilience to disturbances, both natural and anthropogenic (O'Hara, 1998; Guldin, 2019; Koontz et al., 2020). Additionally, a forest's structural complexity is a better predictor of carbon sequestration potential than tree species

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diversity (Gough et al., 2019). Multifunctional management of forests, thus, may be accomplished by managing for structural diversity (Sharma et al., 2016). Despite the importance of structural diversity in forest ecosystems and their functioning, this is one attribute that has received little attention, even less so in the southern pine forests. For example, the longleaf pine (Pinus palustris Mill.) ecosystem is the most species-diverse ecosystem in North America and provides numerous ecological and economic benefits to the southern United States region (Jose et al., 2006). However, most of the research on longleaf pine has been limited to regeneration ecology, growth and yield, groundcover restoration, and prescribed fire (Jose et al., 2006; Jack and Pecot, 2017, Laseter et al., 2018). Few studies have reported on the structural diversity in longleaf pine forests (Bechtold and Ruark, 1988; Varner et al., 2003; Stokes et al., 2010; Addington et al., 2014). These studies characterized structural diversity mostly at the stand level and in localized areas (Ford et al., 2010; Stokes et al., 2010). A holistic assessment of the longleaf pine ecosystem and its biodiversity would include an analysis

Corresponding author. E-mail addresses: [email protected] (A. Sharma), [email protected] (B. Cory), [email protected] (J. McKeithen), [email protected] (J. Frazier).

https://doi.org/10.1016/j.foreco.2020.117987 Received 29 October 2019; Received in revised form 8 February 2020; Accepted 10 February 2020 0378-1127/ © 2020 Elsevier B.V. All rights reserved.

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of not only compositional elements but also regional scale characterization of structural diversity. Measuring structural diversity of forest ecosystems is a challenging task, and several measures to quantify aspects of structural complexity have been proposed and evaluated (Staudhammer and LeMay, 2001; Pommerening, 2002; Von Gadow and Hui, 2002; Picard and Gasparotto, 2016; Stiers et al., 2018). Most of these measures have generally focused on tree-based attributes, such as tree size differentiation, diversity of diameter classes, or spatial patterns of tree positions. Commonly-used diversity indices have included Shannon index, Simpson index, Gini index, and diameter evenness index, which may result in similar, or sometimes different, interpretations of data (Magurran, 1988; McElhinny et al., 2005; Picard and Gasparotto, 2016; Stiers et al., 2018). Some other structural diversity assessments have included data on tree size, foliage height, coarse woody debris, and charred wood (Varga et al., 2005). Tree size data are easily and objectively acquired by most forest inventories, which permit assessment of tree-size diversity (Staudhammer and LeMay, 2001, Sharma et al., 2014; Podlaski et al., 2019) and estimation of economic consequences for diversity targets (Buongiorno et al., 1994; Kant, 2002). While several measures of structural diversity exist, one of the most commonly accepted and simple indices based on tree size diversity is Shannon index (also called the Shannon–Weiner, or the Shannon–Weaver index) (Magurran, 1988; Staudhammer and LeMay, 2001; Sharma et al., 2016). Shannon index can be determined for stands with only tree size data available, without the need to have information on the spatial distribution of trees (Magurran, 1988; Sharma et al., 2016). Several factors affect structural diversity at the stand or landscape level. Anthropological and natural disturbances can cause widespread and sudden structural changes in forest stands. As a stand develops (ages), structural diversity changes from the stand initiation stage to the understory reinitiation stage and reaches its peak at the old-forest, steady-state conditions (Oliver and Larson, 1990; Parobekova et al., 2018, Podlaski et al., 2019). Forest fires (prescribed or natural) affect seedling density and recruitment as well as create snags leading to structural changes in the stands (Brockway and Lewis, 1997; Gilliam and Platt, 1999; Ford et al., 2010; Addington et al., 2014). Some site conditions (mesic and hydric) tend to have denser, more productive stands with higher growth than xeric site conditions, leading to possible changes in structural diversity (Noel et al., 1998; Gilliam and Platt, 1999; Peet, 2006). Whether production-oriented industrial plantation stands or naturally regenerating self-sustaining forests, the objectives of management may lead to structurally different stands. Naturally regenerating stands can be even-aged or uneven-aged with varying levels of structural complexity (Meng et al., 2016; Sharma et al., 2016; Brockway and Outcalt, 2017). In the southeastern United States, the majority of forest land (approximately 86%) is owned by private entities (industry, family, or other) (Oswalt et al., 2014). Private and public forest lands may have different stand structural diversity depending on differing management objectives or the lack of management on many private forests (Cohen et al., 1995; Maltamo et al., 1997; Ohmann et al., 2007; Schaich and Plieninger, 2013). Management effects on structural diversity vary but properly carried out forest management, especially based on natural regeneration, may positively influence forest structural diversity (Pach and Podlaski, 2015, Podlaski et al., 2019). Across its range, longleaf pine forests exist under different ownerships, occur in different site conditions, are managed with a gradient of intensity, and exist at different stages of development (Jose et al., 2006; Kirkman and Jack, 2017). These factors likely lead to a range of structural diversity values in the longleaf pine ecosystem across the southeastern United States. Understanding these factors affecting forest structure and identifying areas of low or high longleaf pine structural diversity in the southeastern United States will be useful in range-wide conservation and restoration planning for this important ecosystem. The overall objective of this study was to estimate and depict largescale patterns of structural diversity for the longleaf pine ecosystem

across its range in the southeastern United States. The specific objectives were: (i) to estimate structural diversity for the longleaf pine ecosystem and its relationship with the stand origin, ownership, burning, and site condition, and (ii) to construct maps depicting spatial distribution and hot spots of structural diversity for the longleaf pine ecosystem in the southeastern United States. For this purpose, we utilized the United States Department of Agriculture (USDA) Forest Service’s Forest Inventory and Analysis (FIA) data (FIADB version 7.0, https://www.fia.fs.fed.us) (O’Connell et al., 2016). Publicly available FIA data provides objective and scientifically credible information on the nation’s forest inventory and key forest ecosystem processes which allows analysts and users to assess status and trends of the nation’s forests on an annual basis. FIA data has been used in several scientific investigations across a range of fields that involved characterizing forest ecosystems and changes over large spatial and temporal scales in the United States (e.g., Duncanson et al., 2015; Hakkenberg et al., 2016; Belair and Ducey, 2018; Easterday et al., 2018; Krebs et al., 2019). 2. Materials and methods 2.1. FIA data acquisition and classification The contemporary FIA dataset is a national inventory program implemented by the USDA Forest Service (O’Connell et al., 2016). The FIA program uses a common plot design and common data collection procedures nationwide to assess forest inventory conditions across the United States. The data are collected annually on plots within each state of the nation and is the primary source for information about status and trends of the nation’s forest resources (Smith, 2002). Under the FIA design, all forest and other land uses are sampled with one permanent plot established for every 2428 ha of land (Bechtold and Patterson, 2005). A typical FIA plot consists of four 7.2 m fixed radius subplots, with three subplots spaced 36.6 m apart in a triangular arrangement and a subplot in the center (O’Connell et al., 2016). Our study plots consisted of FIA plots primarily containing longleaf pine. The area containing these plots was comprised of 9 states within the original native range of the longleaf pine ecosystem. These states included Alabama, Georgia, Florida, Louisiana, Mississippi, North Carolina, South Carolina, Texas, and Virginia. We downloaded and extracted all FIA database tables for these states from the Microsoft Access State database applications (USDA Forest Service FIA Datamart webpage (https://apps.fs.usda.gov/fia/datamart/datamart.html)). We then created a query in Microsoft Access to filter and select for plots designated either as “Longleaf Pine Forest” or “Longleaf Pine/Oak Forest” types using the FIA definitions (Arner et al., 2001; O’Connell et al., 2016; Costanza et al., 2018). There was a total of 919 plots that met our criteria. These plots were measured during the five-year period of 2011–2015. A five-year period was chosen because each state conducts FIA plot sampling annually with a measurement goal of 20% of FIA plots per year (O’Connell et al., 2016). Information accessed about the plots included plot location (geographic coordinates), ownership type, physiographic class code, stand origin, stand structure, and burn condition. Using this information, we classified the plots based on (1) ownership (public or private), (2) stand origin or mode of regeneration (natural or artificial/planted), (3) physiographic class codes or site conditions (xeric, mesic, or hydric), (4) burn condition (burned or unburned), and (5) structure/age classes (one or two age classes). FIA notes that the survey on burn condition of plots is generally done on new/replacement plots and is not conducted by all FIA work units (Bechtold and Patterson, 2005; O’Connell et al., 2016). Thus, most likely, the burn condition indicates only if the plot was burned in the past 5 years. No long-term burn history of the plots can easily be gleaned from FIA data. While the private ownership group was not specified for individual private entities by FIA, the public ownership group consisted of plots owned by USDA Forest Service (USFS), U.S. Fish and Wildlife Service (USFWS), Department of Defense (DoD), 2

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Table 1 Structural diversity (Shannon index) of the longleaf pine ecosystem in the southeastern United States based on stand origin, ownership, burn condition, site condition, and number of age classes of the plots. Classification criteria

Structural diversity (Shannon index)

P-value

Total #plots (n)

Range

Mean*

Median

Standard deviation

Stand origin

Natural Artificial

623 296

0.00–2.19 0.00–2.20

1.46a 1.12b

1.49 1.09

0.34 0.51

< 0.0001

Ownership

Public Private

402 508

0.00–2.14 0.00–2.0

1.44a 1.31b

1.50 1.41

0.37 0.49

< 0.0001

Burn condition

Burned Unburned

209 197

0.00–2.10 0.00–2.20

1.35a 1.42a

1.38 1.53

0.41 0.45

0.081

Site condition

Xeric Mesic Hydric

231 670 7

0.00–2.19 0.00–2.20 1.03–1.84

1.27c 1.40a,b 1.49a,b

1.34 1.50 1.53

0.43 0.44 0.32

< 0.001

Number of age classes

One Two

651 263

0.00–2.19 0.00–2.20

1.35a 1.43b

1.44 1.49

0.46 0.38

0.010

* Values with different letters are significantly different (α = 0.05) from each other within the classification criterion.

Other Federal Entities, State Government, and Local Government. Based on the Food Security Act of 1985, the publicly available FIA database contained plot location information that had been fuzzed from the true location to protect ownership privacy.

significantly altered given the cartographic generalization required to produce the small-scaled map for our study (Brewer and Buttenfield, 2010). 3. Results

2.2. Estimation of structural diversity

The 919 FIA study plots represented a wide range of forest structural characteristics. Overall, across the longleaf pine range, structural diversity ranged between 0.00 and 2.20. The mean and median values across all plots were 1.35 and 1.42, respectively. When classified on the bases of stand origin, ownership, burn condition, site condition, and age structure, the structural diversity values varied from the overall values. These results are presented below (Table 1):

Tree species and diameter-at-breast-height were extracted for every tree within the plots. Individual trees in each FIA plot were then classified into diameter classes. We used a total of 13 diameter classes with class widths of 5.08 cm, ranging from size 5.08 cm to 71.12 cm. In all cases, classes represented the midpoints of class widths. Individual tree data were used to derive individual tree and plot level basal areas as well as to calculate basal area for each diameter class. Shannon index was calculated as equal to −Σpi ln pi, where “pi” is the proportion of basal area constituted by a diameter class “i”. Use of the basal area as a suitable variable in determining proportion of diameter classes for calculating Shannon index as a measure of structural diversity has been adopted by several studies (e.g., McMinn, 1992; Harrington and Edwards, 1995, Staudhammer and LeMay, 2001, Sharma et al., 2014, 2016). A higher value of Shannon index was representative of a more structurally diverse plot with evenly distributed diameter sizes. A plot with just one diameter size class had a Shannon index value of 0.

3.1. Structural diversity Vs. stand origin Of all 919 plots, 623 plots (67.8%) were of natural origin. These naturally regenerated longleaf pine plots had significantly higher structural diversity (mean = 1.46) than artificially regenerated plots (mean = 1.12) (p < 0.0001). Fifty percent of the naturally regenerated plots had structural diversity higher than 1.49 as compared to the median structural diversity of 1.09 for artificially regenerated longleaf pine (Table 1). Also evident from Fig. 1 is how naturally regenerated plots had a higher proportion of longleaf/oak forests (19.4% of all plots) than those in planted plots, which had only 10.1% longleaf/oak forests of all plots. Also, a considerably higher proportion (82%) of publicly-owned forests were naturally regenerated than those owned by the private entities (56%).

2.3. Data analyses and structural diversity mapping For each classification category, we calculated ranges, means, medians and standard deviations of Shannon index values. Z-tests (when comparing two means) or analysis of variance (ANOVA, when comparing more than two means) were carried out and Tukey’s HSD (Honestly Significant Difference) test was performed at α = 0.05 to test for significant differences in the means for all categories. Geographic coordinates (latitude-longitude) and Shannon indices of all plots were imported into ArcGIS program to create a structural diversity spatial distribution map. Hotspot analysis (Getis-Ord Gi) using Hotspot Analysis Tool of ArcGIS was conducted on Shannon index values of the plots (Mitchel, 2005; Getis and Ord, 2010). This tool identified statistically significant spatial clusters of high values (hot spots) and low values (cold spots) for Shannon index at confidence levels of 90, 95, and 99%. A total of 7 cluster categories covering a range of scenarios were represented in the hot spot map: one category of nonsignificance along with three hot spot and three cold spot categories, each reflecting a different configuration of spatial significance (Mitchel, 2005). It may be noted that geographic coordinates in the public FIA database are fuzzed (O’Connell et al., 2016). However, it is unlikely that visual interpretation of spatial distribution for structural diversity was

3.2. Structural diversity Vs. ownership Approximately 44.2% and 55.8% of the study plots were under public and private ownership, respectively (Table 1). Publicly-owned longleaf pine plots had higher structural diversity (mean = 1.44, median = 1.50) than privately-owned plots (mean = 1.31, median = 1.41) (p = < 0.0001). As can be seen in Fig. 2, “longleaf forest” plots were approximately equally owned by private and public entities. However, “longleaf/oak” forests were primarily privately owned. 3.3. Structural diversity Vs. burn condition Important to note is that only 44% of all study plots indicated whether they were burned or not. The other 56% made no indication as to their burn condition. Nevertheless, of all plots with information on 3

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Natural

80 longleaf forest longleaf/ oak forest

Frequency (# of plots)

60

Natural

USFS USFWS DoD Other Fed. State gov't Local gov't Unspecified private

40 20 0

0.0

80

0.5

1.0

1.5

X Data

2.0

0.0

2.0

0.0

Planted

longleaf forest longleaf/ oak forest

60

1.0 USFS0.5 USFWS X Data DoD State gov't Local gov't Unspecified Private

1.5

2.0 Planted

40 20 0

0.0

0.5

1.0

1.5

0.5

1.0

1.5

2.0

Structural diversity (Shannon Index) Fig. 1. Structural diversity of (top) naturally regenerated and (below) artificially regenerated/planted longleaf pine plots in the southeastern United States. Naturally regenerated stands have higher structural diversity than artificially regenerated stands. The public ownership group consisted of plots owned by USDA Forest Service (USFS), U.S. Fish and Wildlife Service (USFWS), Department of Defense (DoD), Other Federal Entities, State Government, and Local Government.

longleaf forest longleaf/ oak forest

60

Frequency (# of plots)

USFS USFWS DoD Other Fed. State gov't Local gov't Unspecified private

Public

80

Longleaf forest

40 20 0 80

Private 0.5

0.0

1.0

1.5

2.0

0.0

1.5

2.0

0.0

X Data

longleaf forest longleaf/ oak forest

60

1.0 USFS0.5 USFWS X Data DoD State gov't Local gov't Unspecified private

1.5

2.0

1.5

2.0

Longleaf/ Oak forest

40 20 0

0.0

0.5

1.0

0.5

1.0

Structural diversity (Shannon Index) Fig. 2. Structural diversity of publicly-owned and privately-owned longleaf pine plots in the southeastern United States. The public ownership group consisted of plots owned by USDA Forest Service (USFS), U.S. Fish and Wildlife Service (USFWS), Department of Defense (DoD), Other Federal Entities, State Government, and Local Government.

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30

Unburned

longleaf forest longleaf/ oak forest

25 20

Frequency (# of plots)

Unburned

USFS USFWS State gov't Local gov't Unspecified private

15 10 5 0

0.0

25

0.5

1.0

1.5

0.5

1.0

1.5

longleaf forest X Data longleaf/ oak forest

2.0 Burned

0.0

20

1.0 USFS0.5 USFWS X Data DoD State gov't Unspecified private

1.5

2.0

Burned

15 10 5 0

0.0

2.0

0.0

0.5

1.0

1.5

2.0

Structural diversity (Shannon Index) Fig. 3. Structural diversity of (top) unburned and (below) burned longleaf pine plots in the southeastern United States. The public ownership group consisted of plots owned by USDA Forest Service (USFS), U.S. Fish and Wildlife Service (USFWS), Department of Defense (DoD), Other Federal Entities, State Government, and Local Government.

burn condition, 51.5% of the plots were reported burned (Table 1, Fig. 3). These burned plots had lower mean (1.35) and median (1.38) structural diversity values than those of unburned plots (mean = 1.42, median = 1.53), but the differences were not significant (p = 0.081). Not surprisingly, a majority of the plots (55%) that were reported burned were “longleaf forest” compared to “longleaf/oak forests” (Fig. 3). Notably and interestingly, a majority of unburned plots (80%) were privately owned, while 67% of burned plots were owned by the public.

greater proportion by public entities compared to plots with one age class which were more proportionately distributed among public and private ownerships. 3.6. Spatial distribution of structural diversity Fig. 6A shows the distribution of structural diversity of longleaf pine across its range of occurrence in the southeastern United States. The study plots had a range of structural diversity values distributed across the longleaf pine range, with some areas containing clusters of plots with low or high structural diversity. Fig. 6(B1-B6) shows the corresponding distribution of longleaf pine plots based on stand origin, ownership, burn condition, site condition, number of age classes, and forest type. Longleaf pine regions with high structural diversity (hot spots with 95–99% confidence) were found in southeastern Mississippi and central and southwestern Alabama (Fig. 7), in addition to a small area (hot spot with 90% confidence) in coastal South Carolina. Southcentral Georgia, exhibited the least structural diversity (cold spots with 95–99% confidence), bordered by area of cold spot with 90% confidence. Parts of north and southcentral Florida also had low structural diversity (cold spot with 95% confidence). The remaining longleaf pine range showed no statistical significance.

3.4. Structural diversity Vs. site condition The site condition (xeric, mesic, or hydric) of plots significantly affected their structural diversity (p < 0.001) (Table 1, Fig. 4). Less than 1% of all plots occurred on hydric sites; however, these hydric plots had significantly higher structural diversity (mean = 1.49, median = 1.53) (p = 0.001) than the xeric plots. A majority of the longleaf pine plots (73.8%) were located in mesic site condition and they were also characterized by high structural diversity (mean = 1.40, median = 1.50), which was statistically similar to the hydric site. Xeric site condition constituted 25.4% of all plots and exhibited the lowest structural diversity (mean = 1.27, median = 1.34). 3.5. Structural diversity Vs. number of age classes

4. Discussion

Interestingly, the majority of the longleaf pine plots (71.2%) consisted of one age class. The two age class stands constituted the remaining longleaf pine plots. The FIA database did not have longleaf pine plots that were designated as uneven-aged or multi-aged stands. As expected, however, plots with one-age class had lower structural diversity (mean = 1.35, median = 1.44) than plots with two age classes (mean = 1.43, median = 1.49) (p = 0.010) (Table 1). As suggested by Fig. 5, plots with two age classes were owned in

Forest structural diversity is an important attribute of forest ecosystems that can help explain relationships between forest management, stand structure and development, forest environment, biodiversity, and ecosystem functioning. In this study, we have characterized structural diversity of the longleaf pine ecosystem based on stand origin, ownership, burn condition, site condition, and age structure across its range of occurrence in the southeastern United States. 5

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Xeric

longleaf forest longleaf/ oak forest

80 60

Frequency (# of plots)

Xeric

USFS USFWS DoD Other Fed. State gov't Local gov't Unspecified private

40 20 0 80

0.0

0.5

1.0

1.5

2.0

1.5

2.0

longleaf forestX Data longleaf/ oak forest

Mesic

0.0

60 40

0.5 1.0 USFS USFWS X Data DoD Other Fed. State gov't Local gov't Unspecified private

Mesic

1.5

2.0

1.5

2.0

20 0

0.0

0.5

1.0

0.0

0.5

1.0

Structural diversity (Shannon Index) Fig. 4. Structural diversity of longleaf pine plots occurring in (top) xeric and (bottom) mesic site conditions in the southeastern United States. Hydric site condition was represented by only 7 plots and is not shown in the figure. The public ownership group consisted of plots owned by USDA Forest Service (USFS), U.S. Fish and Wildlife Service (USFWS), Department of Defense (DoD), Other Federal Entities, State Government, and Local Government.

One-aged forests

80

USFS USFWS DoD Other Fed. State gov't Local gov't Unspecified private

longleaf forest longleaf/ oak forest

Frequency (# of plots)

60

One-aged forests

40 20 0 80

0.0

0.5

1.0

1.5

2.0

0.0

1.5

2.0

0.0

Two-aged forests

forestX Data

longleaf longleaf/ oak forest

60

1.0 1.5 USFS0.5 Two-aged USFWS X Data DoD Other Fed. State gov't Local gov't Unspecified Private

2.0

forests

40 20 0

0.0

0.5

1.0

0.5

1.0

1.5

2.0

Structural diversity (Shannon Index) Fig. 5. Structural diversity of longleaf pine in plots with (top) one age class, and (below) two age classes in the southeastern United States. The public ownership group consisted of plots owned by USDA Forest Service (USFS), U.S. Fish and Wildlife Service (USFWS), Department of Defense (DoD), Other Federal Entities, State Government, and Local Government.

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Fig. 6. Map showing the distribution of (A) structural diversity (Shannon index) and (B1) stand origin, (B2) ownership, (B3) burn condition, (B4) site condition, (B5) number of age classes, and (B6) forest type of longleaf pine plots in the southeastern United States. In Figure B2, the USFS (USDA Forest Service), USFWS (U.S. Fish and Wildlife Service), DoD, (Department of Defense), Other federal (Other Federal Entities), State gov’t (State Government), and Local gov’t (Local Government) constitute publicly-owned plots. Due to the small scale of the map, some plots overlap and are not distinguishable in the figure.

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Fig. 7. Hot and cold spots of structural diversity (Shannon index) of the longleaf pine ecosystem in the southeastern United States. Due to the small scale of the map, some plots overlap and are not distinguishable in the figure.

4.1. Structural diversity as a function of stand origin, ownership, burning, site condition, and age structure

structural diversity of burned and unburned plots in our study, the FIA data were not robust to address this question. As mentioned before, the FIA generally reports plot burn conditions on new or replacement plots only (thus effectively restricting burning information to past 5 or fewer years). Clearly, the effects of burn frequency and long-term burn history cannot be appropriately evaluated using FIA plot data. Also, only 44% of our study plots had any burn condition information available. Interestingly, directly or indirectly, ownership can have considerable impacts on forest structure (Cohen et al., 1995; Maltamo et al., 1997; Ohmann et al., 2007). In our study, 44.2% and 55.8% of the study plots were under public and private ownership, respectively, and publicly-owned forests were structurally more diverse. Longleaf pine forests are managed for different objectives (Kirkman et al., 2017). For example, industrial longleaf plantations, though not as common as loblolly and slash pines, are primarily managed for timber. Stands managed for timber usually have uniform stand structures with low structural diversity (Sharma et al., 2016). On the contrary, publicly owned longleaf pine forests are primarily managed and restored for ecosystem benefits such as habitat, groundcover diversity, and water quality and quantity, and increasingly rely on natural regeneration for transitioning to uneven-aged stands (Sharma et al., 2019). A naturally regenerated stand for habitat restoration using uneven-aged silviculture tends to have high complexity (Brockway and Outcalt, 2017). Some private landowners, particularly family forest landowners, also value and maintain their stands for multiple purposes, including production, recreation, and aesthetics (Kirkman et al., 2017). Over the past few decades, massive longleaf pine plantations have been raised, both on private and public lands, and they still represent even-aged, low structural diversity stands (McIntyre et al., 2018). As these young stands develop, many of them will be converted to uneven-aged stands (Sharma et al., 2012, 2014), leading to an increase in structural diversity with time. Focus on the practices of conservation or close-tonature (uneven-aged) silviculture for multiple-use management has likely led to higher structural diversity for publicly owned forests

Structural diversity of a forest is affected by a multitude of factors. In our study, structural diversity was higher in longleaf pine plots that were publicly owned, naturally originated, occurring in mesic or hydric sites, or had more than one age class. Structural diversity did not differ significantly whether the plots were burned or not in the past 5 years. Naturally originated stands tend to have higher structural diversity than planted stands (Uuttera and Maltamo, 1995). This is mainly due to the increased time needed for establishment of stands using natural regeneration methods. New seedlings germinate in different years following regeneration harvesting or other disturbances. As these seedlings grow and survive, their number decreases with time. As a certain proportion of seedlings survive each year, the number of stems increases. Because of these processes, the naturally regenerated stand develops a relatively wide heterogeneity in age and diameter distribution (Uuttera and Maltamo, 1995). In contrast, artificial regeneration is typically completed within the short span of few days with trees growing fairly uniformly until the stand is harvested at a later time. Compared to artificial regeneration, natural regeneration also generates a denser stand, which tends to increase tree mortality and stratify tree or vegetation layers during further development of the stand (Uuttera and Maltamo, 1995). Fire is an integral component of the longleaf pine ecosystem, that affects its understory community, natural regeneration and recruitment, hardwoods abundance and thereby stand structure (Brockway and Lewis, 1997; Gilliam and Platt, 1999; Varner et al., 2003; Ford et al., 2010; Addington et al., 2014). However, burn studies evaluating seasonality and frequency of fire have shown that prescribed burns must occur at short intervals in longleaf pine stands to create conditions significantly different than unburned stands (Brockway and Lewis, 1997, Gilliam and Platt, 1999, Haywood et al., 2001, Glitzenstein et al., 2003, Ford et al., 2010). While there was no statistical difference in 8

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compared to privately-owned forests. As can be seen (Figs. 6-B2 and 7), structural diversity hot spots were mostly located in publicly-owned lands, though all public lands were not hot spots. Ecological site condition heavily influences structure and composition of the longleaf pine community (Jose et al., 2006; Peet, 2006). While more than 135 vegetation associations can be distinguished among the three broad site conditions (hydric, medic, xeric), xeric sites are generally less productive than mesic or hydric longleaf sites and consist of scattered overstory longleaf pine with low structural and compositional diversity (Peet, 2006). Natural regeneration and recruitment of longleaf pine are also affected by ecological site conditions (Gilliam and Platt, 1999). Despite longleaf pine’s adaptability to xeric conditions, xeric sites, when subjected to prolonged drought conditions, can suffer from lack of recruitment and death of mature trees, leading to further decrease in structural diversity (Jose et al., 2006). We also found that natural regeneration of longleaf pine was more common on public lands than private lands. Similarly, burning was also practiced more on public lands. Public lands also had a greater proportion of forests with two age classes. This is possible because management of longleaf pine on public lands is increasingly focusing on the provision of multiple benefits while creating and/or maintaining diverse and multi-aged longleaf pine forests. Natural disturbance-based management promotes natural regeneration, multiple age classes and frequent burning of these ecosystems (Jose et al., 2006; Kirkman and Jack, 2017). Frequent burning leads to reduction of hardwoods (such as oaks, etc.) in longleaf forests. This is possibly why a greater proportion of longleaf-oak forests as compared to longleaf forests were found on private lands because they were burned less than the public forests. While structural diversity is an important forest characteristic, it is not clear if there is an ideal range of complexity that should be targeted in the management of longleaf pine forests. Structurally complex, uneven-aged longleaf pine stands are recommended for improving wildlife habitat (James et al., 2001; McConnell, 2002); however, no single level of structural diversity would be optimum for all species that depend on these ecosystems. Optimum levels for different species or management goals (such as production, groundcover diversity, recreation, aesthetics, etc.) would not necessarily require the maximum possible structural diversity that could be achieved in each stand. Red-cockaded woodpecker (Picoides borealis), for example, a US federally listed endangered species, requires habitat characterized by open stand conditions consisting of large old pines (for cavity) and low density of small pines with little or no midstory and abundant native bunchgrass and forb groundcover (US Fish and Wildlife Service, 2003). Historically, the original longleaf pine forests had an open and simple structure and pine densities were consistent with open and closed woodlands of largediameter trees (Hanberry et al., 2018). However, due to fire exclusion, the original longleaf forests have been converted to a variety of firesusceptible species mixes or loblolly and slash pine, composed of dense, small-diameter trees (Hanberry et al., 2018). Across the longleaf pine range, it can be argued that the optimal achievement of multiple benefits may require maintaining an assortment of a low, medium, and high levels of structural diversity interspersed throughout the landscape.

better option if the tree size diversity is low (Lexerod and Eid, 2006; O'Hara, 2014). Some studies have formulated tree-level harvest optimization for structure-based forest management (Bettinger and Tang, 2015). Our study provides important spatial distribution information, presenting a beneficial contribution to action plans seeking to conserve and restore characteristics of healthy longleaf pine ecosystems in the southeastern United States. Information on structural diversity could also be useful for agencies such as The Longleaf Alliance, which aims to return the southeastern United States to its native landscape/native ecosystem. Further, this information can be used as a baseline in many other studies comparing longleaf pine with other pine or forest species, or monitoring structural dynamics of the longleaf pine ecosystem over time. Implications of the findings of this study merit consideration for at least three aspects of forest management: (1) understanding the effects of ownership, origin, site condition, and age classes on structural diversity, (2) identifying the hot spots and cold spots of structural diversity that can be considered in regional conservation and restoration planning, and (3) developing practices, or formulating policies, that could be implemented to increase or regulate structural diversity of the longleaf pine ecosystem. To manage structural diversity in the longleaf pine ecosystem, coordinating and refining ownership-specific incentive schemes may be helpful. On public lands, uneven-aged management approaches should be complemented with goals aimed at promoting open and old-growth forest conditions and historical attributes while allowing site-specific variations in management practices. For private lands, suitable incentives and remuneration schemes for the provisioning of structural-diversity-regulated ecosystem services should be considered. Moreover, consulting and dissemination of information on the importance of structural diversity restoration, conservation and sustainable forest management of longleaf pine are recommended. Public land ownership in the southeastern United States is only about 13% (Oswalt et al., 2012, 2014), while these public lands contain 44.2% of FIA longleaf pine plots. Public lands also have higher than average longleaf pine volume per hectare, while private lands have lower than average volumes (Oswalt et al., 2012). This underscores the significance of public lands to longleaf pine restoration and conservation in the southeastern United States. 5. Conclusions This study assessed structural diversity of the longleaf pine ecosystem across its range in the southeastern United States. Longleaf pine was structurally more diverse on plots that were publicly-owned, naturally regenerated, occurred in mesic or hydric sites, or had more than one age class. We did not find any statistical difference in burned and unburned plots; however, the FIA data on burn conditions were not considered robust enough to address effects of burning on structure of longleaf pine ecosystems. Across its range, the longleaf pine ecosystem exhibited a variable structural diversity distribution that created heterogeneous structural conditions, with distinct hot and cold spots. Notably, hot spots of structural diversity were located where publiclyowned FIA plots were concentrated. This emphasizes the importance and relevance of public lands to the restoration, conservation and sustainable management of the longleaf pine ecosystem. Selection of the structural diversity measure (Shannon index for diversity in tree diameters in a stand) for this study was based on common and accepted use in literature, and results provided useful insight into the longleaf pine ecosystem. However, structural diversity, in its most comprehensive sense, will also include snags, litter accumulation, coarse woody debris, and the spatial distribution of individual trees or age classes across the landscape, which all together create a complex structure (McElhinny et al., 2005; O'Hara, 2014). We suggest that these additional attributes of structural diversity and their dynamics should be examined in future studies. This study suggested some additional interesting patterns in the data. For example, the unburned plots had

4.2. Implications for management This study has provided compiled, comprehensive information on longleaf pine structural diversity across its range, which has several potential applications for forest management planning. Structural diversity cold spots have been identified that must be targeted for the restoration of structural attributes of these forests, as well as to achieve heterogeneity at the landscape scale, to enhance ecosystem resiliency and meet diversity goals of longleaf pine restoration. Structural diversity can also be used to make silvicultural or operational decisions. For example, it is suggested that selective cuttings are most profitable in stands where tree size diversity is high whereas clearcuttings might be a 9

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higher, albeit not significantly, structural diversity, the privately-owned plots had lower diversity but the majority of unburned plots were privately owned. FIA data may not adequately or appropriately be used to examine these relationships, but these patterns may deserve additional investigation. Relationships between species diversity and structural diversity is another area which should be further investigated in future studies.

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CRediT authorship contribution statement Ajay Sharma: Conceptualization, Methodology, Investigation, Writing - original draft, Supervision, Project administration, Funding acquisition. Barbara Cory: Conceptualization, Writing - review & editing. Justin McKeithen: Investigation, Methodology, Data curation. Jesse Frazier: Formal analysis. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The research was funded by the USDA NIFA McIntire Stennis, project #1014653. The authors also express their sincere gratitude to Drs. Wes Wood, Red Baker and Jarek Nowak for their support. Comments of two anonymous reviewers helped significantly improve the manuscript. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2020.117987. References Addington, R.N., Greene, T.A., Harrison, W.C., Sorrell, G.G., Elmore, M.L., Hermann, S.M., 2014. Restoring longleaf pine: effects of seasonal prescribed fire and overstory density on vegetation structure of a young longleaf pine plantation. For. Sci. 61, 135–143. Ali, A., Yan, E., Chen, H.Y., Chang, S.X., Zhao, Y., Yang, X., Xu, M., 2016. Stand structural diversity rather than species diversity enhances aboveground carbon storage in secondary subtropical forests in Eastern China. Biogeosciences 13, 4627–4635. Arner, S.L., Woudenberg, S., Waters, S., Vissage, J., MacLean, C., Thompson, M., Hansen, M., 2001. National algorithms for determining stocking class, stand size class, and forest type for Forest Inventory and Analysis plots. Internal Rep. US Department of Agriculture, Forest Service, Northeastern Research Station, Newtown Square, PA 10p. Bechtold, W.A., Patterson, P.L., 2005. The enhanced forest inventory and analysis program-national sampling design and estimation procedures. Gen.Tech.Rep.SRS-80. US Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC p.80. Bechtold, W.A., Ruark, G.A., 1988. Structure of pine stands in the Southeast. In: Res.Pap. SE-274, vol. 80. US Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, Asheville, NC, p.274. Belair, E.P., Ducey, M.J., 2018. Patterns in forest harvesting in New England and New York: Using FIA data to evaluate silvicultural outcomes. J. Forestry 116 (3), 273–282. Bettinger, P., Tang, M., 2015. Tree-level harvest optimization for structure-based forest management based on the species mingling index. Forests 6, 1121–1144. Brewer, C.A., Buttenfield, B.P., 2010. Mastering map scale: balancing workloads using display and geometry change in multi-scale mapping. Geoinformatica 14 (2), 221–239. Brockway, D.G., Lewis, C.E., 1997. Long-term effects of dormant-season prescribed fire on plant community diversity, structure and productivity in a longleaf pine wiregrass ecosystem. For. Ecol. Manage. 96, 167–183. Brockway, D.G., Outcalt, K.W., 2017. Influence of reproduction cutting methods on structure, growth and regeneration of longleaf pine forests in flatwoods and uplands. For. Ecol. Manage. 389, 249–259. Buongiorno, J., Dahir, S., Lu, H.C., Lin, C.R., 1994. Tree size diversity and economic returns in uneven-aged forest stands. For. Sci. 40, 83–103. Cohen, W.B., Spies, T.A., Fiorella, M., 1995. Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, USA. Int. J. Remote Sens. 16, 721–746. Costanza, J.K., Faber-Langendoen, D., Coulston, J.W., Wear, D.N., 2018. Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches. Forest Ecosyst. 5, 8.

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