[go: up one dir, main page]
Next Article in Journal
Differences in Soil CO2 Efflux and Microbial Community Composition Among Slope Aspects in a Mountain Oak Forest
Previous Article in Journal
Determinants of Financial Viability of Forest Concession in Brazilian Amazon
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 10
10.3390/f15101809
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Investigating the Spatial Pattern of White Oak (Quercus alba L.) Mortality Using Ripley’s K Function Across the Ten States of the Eastern US

by
Saaruj Khadka
1,
Hong S. He
1,* and
Sougata Bardhan
2
1
School of Natural Resources, University of Missouri, 1111 Rollins St, Room 3 ABNR Building, Columbia, MO 65201, USA
2
College of Agriculture, Environment and Human Science, Lincoln University, 904 Chestnut St, 306A Foster Hall, Jefferson City, MO 65101, USA
*
Author to whom correspondence should be addressed.
Forests 2024, 15(10), 1809; https://doi.org/10.3390/f15101809
Submission received: 26 August 2024 / Revised: 8 October 2024 / Accepted: 11 October 2024 / Published: 16 October 2024
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
Browse Figures
Figure 1
<p>Study area mainly showing forest covers and others across ten states of the eastern US.</p> "> Figure 2
<p>Our study area showing (<b>A</b>) spatial distribution of WOM rate plots and (<b>B</b>) kernel density distribution of WOM rates across different latitudes and longitudes of the eastern United States.</p> "> Figure 3
<p>Ripley’s K function showing clustered, random, and uniform patterns across varying scales of (<b>A</b>) southern, (<b>B</b>) central, and (<b>C</b>) northern regions WOM rates in the eastern United States.</p> "> Figure 3 Cont.
<p>Ripley’s K function showing clustered, random, and uniform patterns across varying scales of (<b>A</b>) southern, (<b>B</b>) central, and (<b>C</b>) northern regions WOM rates in the eastern United States.</p> ">
Versions Notes

Abstract

:
White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of WOM rates across the eastern US and explore the underlying processes behind the observed spatial patterns. Multicycle forest inventory and analysis data were compiled to capture all white oak plots. WOM data were selected across plot systems that utilized declining basal areas between two periods. Ripley’s K function was used to study the spatial pattern of WOM rates. Results showed clustered patterns of WOM rates at local and broad scales that may indicate stand-level competition and regional variables affecting white oaks’ dynamics across southern and northern regions. Results also indicated random patterns at broad scales, suggesting variations in topographic and hydrological conditions across the south and northern regions. However, the central region indicated both clustered and random patterns at the local scale that might be associated with inter-species competition and the possibility of environmental heterogeneity, respectively. Furthermore, uniform patterns of WOM rate at a broad scale across all regions might suggest regions with spatially homogeneous environmental factors acting on the dynamics of white oaks. This research might be helpful in identifying impacted areas of white oaks at varying scales. Future research is needed to comprehensively assess biotic and abiotic factors at various spatial scales aimed at mitigating WOM.

1. Introduction

Oak forests are ecologically and economically significant in the eastern United States forest landscape. As a dominant canopy species, oaks shape stand structure and composition, and their extensive crowns create diverse microhabitats that support a variety of species, including nesting birds and arboreal mammals [1,2]. The production of acorns and leaf litter provides essential resources for wildlife, forming the foundation of complex food webs and contributing to increased biodiversity. Additionally, oak trees establish critical symbiotic relationships with mycorrhizal fungi through their deep root systems, enhancing nutrient cycling and uptake within forest ecosystems. Moreover, oaks play a pivotal role in carbon sequestration, storing substantial amounts of carbon to help mitigate greenhouse gas emissions and buffer the effects of climate change [3,4]. Their ecological function is further emphasized through their influence on forest succession and regeneration patterns, with effective acorn dispersal ensuring a steady supply of seeds for natural regeneration and persistence of oak-dominated forests across generations. However, oak populations throughout the eastern United States are experiencing significant declines due to alterations in stand dynamics and increased susceptibility to biotic and abiotic stressors, posing a severe threat to the stability and resilience of these forest ecosystems.
Oak mortality is a widely observed phenomenon in oak-dominated forests across the eastern United States [5,6]. This process typically begins with the browning of leaves, which subsequently turn black, curl, and eventually abscise, leading to complete defoliation [6,7,8]. The underlying causes of oak decline are often categorized into three groups: long-term predisposing, short-term inciting factors, and secondary contributing factors [8]. Predisposing factors are generally associated with stand age and maturity, which affect a tree’s physiological resilience, predisposing it to growth inhibition and vulnerability to other stressors. Inciting factors are typically linked to physical or biological stressors, such as defoliation by insects, hail damage, late spring frosts, or drought, which can result in crown dieback and browning of foliage, followed by the emergence of new leaves in severely stressed trees [9]. These conditions weaken the trees, making them susceptible to contributing factors, which include pathogenic fungi and wood-boring insects that further exacerbate stress and ultimately cause tree mortality. The recent surge in oak decline has been attributed to interactions between multiple stressors, including drought, late-season frost events, and the increased prevalence of saprophytic fungi due to changing climatic conditions [10]. Furthermore, oak borers’ impact on weakened trees in vulnerable sites has led to widespread mortality, significantly threatening the stability and regeneration potential of these forest ecosystems.
Typically, oak mortality has predominantly affected species within the red oak group. However, in recent years, white oak mortality (WOM) has emerged as a significant concern throughout the eastern United States. The spatial distribution of WOM is heterogeneous, with elevated mortality observed in resource-limited sites characterized by drought conditions, poor drainage, and soil nutrient deficiencies [11]. These low-resource environments often contribute to widespread regeneration failures and increased vulnerability to decline. Surprisingly, recent studies have also documented elevated WOM in higher-quality mesic sites, particularly in forests with high stand density and advanced maturity stages [12]. Mortality is often concentrated during the self-thinning phase, where high stand densities result in intense competition for limited resources, thereby predisposing trees to stress-related mortality [13]. WOM has been reported across a variety of topographical features, including low-lying areas, valley floors, and north-facing slopes that receive reduced sunlight exposure, which further exacerbates tree stress and mortality [14]. In addition, several ecological stressors such as excessive herbivory, heavy shade, and disturbances have been identified as critical factors influencing the susceptibility of white oaks in many parts of the eastern hardwood forest region [15].
White oak mortality has far-reaching impacts extending beyond individual tree loss to influence forest ecosystems and associated ecological processes. For example, in the Ozark Highlands, white oaks exhibit a mortality rate of 30%, with healthy crowns reduced to less than 4 m in width across a 516-hectare area [16]. Similarly, a 900-hectare study area at Baskett Wildlife Research and Education Center in the Ozark border of central Missouri reported 10% mortality of white oaks attributed to interactions between drought and pathogens stressors [17]. The widespread decline of white oaks disrupts key ecological functions, such as nutrient cycling, water regulation, and carbon sequestration, significantly altering forest ecosystem dynamics [18,19]. As a long-lived species, white oak can sequester substantial amounts of carbon over its lifespan, contributing to climate regulation and carbon management [20]. Consequently, the loss of mature white oaks may diminish carbon storage potential and exacerbate the impacts of climate change. Furthermore, the extensive canopy of white oaks provides critical habitat and nutritional resources for various flora and fauna, supporting and promoting complex food webs [21]. For instance, as a dominant canopy species across much of the eastern United States, white oak plays a crucial role in shaping forest structure and composition, influencing soil processes, species interactions, and wildlife habitat availability [22]. Its decline can, therefore, lead to cascading changes in forest dynamics, including altered recruitment patterns of new individuals and shifts in competitive interactions among plant species [23]. The loss of white oaks can also negatively affect numerous wildlife species that rely on its canopy for shelter and foraging, disrupting microhabitats and food. Such changes can further influence species distribution patterns and disrupt trophic interactions within the forest food web [24].
Previous research has predominantly focused on local or stand-level dynamics, often overlooking the broader spatial context in which WOM occurs [25,26]. Although these studies have offered valuable insights into site-specific mortality factors, they lack consideration of the complex spatial interactions operating at broader spatial scales. This represents a significant research gap, as WOM may be driven by spatially heterogeneous factors, including variability in soil properties, interspecific competition, and regional climate patterns [27]. Understanding the spatial distribution of WOM across large geographic areas is crucial for identifying high-risk areas and informing targeted management and conservation strategies. To address this gap, this study employs Ripley’s K function, a spatial point pattern analysis method, to examine WOM across broad spatial scales. Ripley’s K function is a robust tool in ecological research for detecting clustering or dispersion patterns over multiple spatial scales. Yet, its application to tree mortality, particularly white oak, remains underexplored. Although Ripley’s K function has been widely utilized to assess the spatial distribution of tree populations in various contexts [28], its use in investigating large-scale mortality patterns in oak species, specifically in white oaks across their native range, has not been comprehensively studied. The primary objective of this study is (1) to identify the spatial distribution pattern of WOM using Ripley’s K function across the southern, central, and northern regions of the eastern United States, and (2) to investigate the potential underlying processes behind the observed spatial patterns of WOM rate. We hypothesized that the observed spatial pattern of the WOM rate is clustered locally due to stand-scale competition and stress from pests and pathogens, while exhibiting more uniform patterns at broader scales, potentially driven by homogeneous environmental conditions, including consistent soil types and climatic factors.

2. Materials and Methods

2.1. Study Area

This study was carried out in the eastern United States, a region predominantly characterized by extensive oak-dominated forests [29]. The study area spans ten states, covering approximately 1.27 × 107 km2 of land (Figure 1). To account for regional variability in environmental conditions and white oak mortality patterns, the study area was stratified into three distinct regions based on latitudinal gradients: the southern region (low latitude: 30-to-36-degree N latitude), the central region (medium latitude: 36-to-37.5-degree N latitude), and the northern region (high latitude: 37.5-to-42.5-degree N latitude). The southern, central, and northern regions contained 678, 747, and 797 white oak plots, respectively. This regional subdivision was implemented to capture the spatial heterogeneity of factors influencing WOM across the latitudinal gradient, facilitating a more nuanced analysis of mortality patterns across the study area.
The southern region has a warm temperate to subtropical climate, with long growing seasons [30]. In this region, yearly rainfall varies between 1000 and 1500 mm. The temperature ranges from 16 to 21 °C. Soils, primarily Ultisols and Alfisols, tend to be more weathered and less fertile, limiting white oak growth potential. This region supports diverse forests where white oak faces competition from faster-growing species like loblolly pine and other hardwoods [31]. Additionally, the prevalence of pests and pathogens in warmer climates heightens mortality risk.
In contrast, the central region experiences a more temperate climate with moderate rainfall, fertile Alfisols and Inceptisols, and oak-hickory forests [32]. The temperature ranges from 4 to 18 °C. The annual precipitation ranges from 50 to 1600 mm. Despite abundant rainfall in this region and altered fire conditions [33], this has impacted historical disturbance regimes of oak forests, increasing stand density and competition, and climate variability, including droughts and storms, creating distinct mortality pressures [34].
The northern region, with its cooler temperate climate, shorter growing seasons, and mesic forest, is dominated by Spodosols and Alfisols [30]. Precipitation ranges from 600 to 1100 mm per year. The mean annual temperatures range from 2 to 11 °C. In this region, white oak contends with competition from cold-adapted species (e.g., red maple, beech, paper birch, etc.) and is more susceptible to cold stress and unique disturbance events, such as frost and windthrow [35].

2.2. Data Acquisition and Processing

2.2.1. Forest Inventory and Analysis Program

The FIA program has systematically monitored national forest resources across all ownership categories throughout the eastern United States [36]. We utilized forest inventory data collected and processed within a relational database framework structured in multiple phases. This relational database structure enables seamless integration of diverse data sources, including historical records, satellite imagery, and remote sensing, thereby enhancing the depth and comprehensiveness of the forest inventory dataset. Such a structure supports efficient data querying, which is essential for generating reports, conducting analysis, and summarizing statistics [37]. The relational organization of the data ensures accessibility, consistency, and effective management of extensive and interdependent datasets, critical for evidence-based decision-making and long-term forest management planning. For instance, stratified estimations were employed to calculate population parameters for various variables, depending on the required scale and level of information [38]. We selected this inventory data due to its advantage over other databases, as FIA plots are systematically distributed with minimal geospatial bias. In our study region (eastern United States), each FIA plot is recorded with at least one forested condition and remeasured every five years for annual inventory. Each plot captures critical attributes for all tree species, including plot identification, survey years, and species-specific metrics [37]. Our analysis utilized phase 2 and phase 3 ground plots, where each plot was measured using a fixed radius sampling design.
We incorporated condition-level information from FIA-sampled data, with each condition defined by distinct land-use changes or vegetation within a plot. A condition was characterized by at least ten percent crown or canopy cover of live tally trees within the forested condition. Furthermore, condition-level data were categorized using discrete variables such as forest type, stand size, species composition, stand structure, stand origin, ownership group, and disturbance history to comprehensively represent stand characteristics.
The FIA program employs periodic and annual inventories, conducted approximately every ten and five years, respectively. However, discrepancies in plot design, sampling, and measurement protocols between periodic and annual inventories prevent the integration of both datasets. Thus, our study exclusively used annualized survey data to ensure consistency in assessing white oak mortality. For instance, the earliest annual inventory data within our study area date back to 1998 for Virginia, representing the oldest inventory among the ten states included in the analysis. The majority of white oak forest plots were inventoried and updated until 2019. Consequently, we defined our temporal scope from 1998 to 2019 to investigate spatial patterns of white oak mortality across the different regions of the eastern United States.

2.2.2. Variables Selection for Live and Declining White Oaks

Our plot data were obtained from the USDA Forest Service DataMart, where each plot had been pre-assigned to a specific stratum as part of the national forest inventory program implemented across all states. A stratum is defined as a collection of plots that share similar classifications derived from remotely sensed imagery and other geospatial data sources. Within each defined estimation unit, the weighing of the stratum is determined based on its proportional representation with the total sampled area [36]. Within the same plot dataset, geographic coordinates (latitudes and longitudes) were recorded for each pot, delineating a 0.40 ha (1 acre) sample area. However, confidentiality concerns did not allow precise spatial coordinates for all individual trees. To address this, we utilized FIA plot data where the spatial location of each plot was recorded within a one-mile radius of its original location. This adjustment is made to ensure landowners’ privacy, as public disclosure of exact plot locations could pose security risks [39].
We processed FIA data focusing exclusively on live white oaks (Quercus alba L.). This selection was made due to the limited research on the regional-scale pattern of white oak mortality, often overshadowed by studies on other oak species. Previous research indicates that white oaks are particularly vulnerable to a distinct set of biotic and abiotic stressors compared to other oak species [34]. By concentrating on white oaks, we aim to gain a deeper understanding of these species-specific stressors, enabling the development of targeted management strategies to mitigate their decline. Data processing began with the earliest available survey year (1998) and extended through 2019, covering ten states in the eastern United States. The white oak data were extracted from the USDA Forest Service database, using attributes such as inventory year (INVYR) as surveyed year; county code (COUNTYCD) refers to a specific county number within each state and unique plot number (PLOT) assigned to individual white oak trees. Additional variables included species code (SPCD) representing particular tree species and tree status code (STATUSCD), where a code of 1 represents alive trees as recorded by the FIA program. Furthermore, trees per acre unadjusted (TPA_UNADJ) were used to indicate the theoretical density of trees per acre. The dataset also included cycle numbers (CYCLE), assigned to distinguish sets of plots measured over specific time intervals, and current diameter (DIA) as stem diameter measured at breast height at 1.37 m (4.5 feet) above the ground level. This detailed dataset provided a robust foundation for analyzing the spatial distribution and mortality patterns of white oak across the study region.

2.3. Data Processing and Analysis

2.3.1. Selection of Live and Declining White Oak Plots from FIA

Annualized data spanning from 1998 to 2019 were processed to locate white oak plots (Table 1). Following the refinement of our variables, i.e., plot, forest condition, and tree, we selected a plot to narrow our criteria, i.e., locating white oak plots across accessible forest land. For instance, in Missouri, plot information was organized by unique plot numbers associated with each county and measurement cycle, along with corresponding geographic coordinates (latitudes and longitudes) and survey years. We focused on sampled plots that exhibited at least one condition of accessible forest land. Notably, the condition table included attributes that were broadly consistent across plots, except for location data, which facilitated the association of specific plots. Throughout this data processing, we further refined our condition and tree datasets by applying criteria emphasizing accessible forest land. Essential attributes from tree data such as unique plot number, county code, tree species code, diameter, trees per acre, and cycle were utilized to effectively identify all white oak trees.
We selected white oak trees identified by the species code (e.g., 802) from the tree data. Utilizing this dataset, we calculated the basal area of white oak using the formula: Basal area of white oak = 0.005454 × (Current Diameter)2 × trees per acre unadjusted. Subsequently, we combined forest condition data with tree-level data based on unique plot numbers to extract specific information about white oak basal areas. Given our focus on annual inventory data, our analysis was confined to the timeframe specified for each state. For example, in Missouri, the data for cycle five encompassed survey years from 1999 to 2003, while subsequent cycles were defined as follows: cycle 6 included 2004 to 2008, cycle seven covered 2009 to 2013, and cycle 8 extended from 2014 to 2019. We applied a similar grouping strategy to other states, ensuring that we identified standard white oak plots among all cycles. To establish a connection between different cycles for a given timeframe, we developed a formula that integrated county and plot numbers i.e., county code + (plot)/100,000. This methodology effectively linked each cycle to its corresponding plot data. This comprehensive process allowed us to identify all declining white oak plots based on changes in basal area. We repeated these steps across other states in our study area to identify declining white oak plots. Ultimately, our analysis yielded 2220 declining white oak plots from a total of 7405 live white oak plots across ten states. All data processing was conducted using ArcMap version 10.8.1, facilitating the spatial analysis required for this study.

2.3.2. White Oak Mortality Rate Analysis

Following the compilation of 2220 declining white oak plots, we identified a declining plot across each state, which included cycles (e.g., cycles 5, 6, 7, and 8 for Missouri) along with their respective basal areas. To quantify changes in the basal area, we utilized the Raster Calculator tool in ArcGIS to subtract the basal area of each consecutive cycle. For instance, we computed the following differences: cycle 6 basal area minus cycle 5 basal area, cycle 7 basal area minus cycle 6 basal area, and cycle 8 basal area minus cycle 7 basal area. This subtraction method enabled us to capture all negative basal areas (changes) indicative of white oak decline, also referred to as white oak mortality (WOM). To ensure the accuracy of our analysis, we omitted any repeated plots but included the sum of their basal areas for assessing white oak decline within each state’s plot system. For instance, basal area change was calculated as follows:
B a s a l   a r e a   c h a n g e   m 2 ha − 1 = B a s a l   a r e a   o f   c y c l e   6 − B a s a l   a r e a   o f   c y c l e   5
Subsequently, for the WOM rate, we compiled cycles containing declining white oak plots that exhibited negative basal area changes along with their corresponding survey years. This approach allowed us to assess basal area changes from an earlier cycle (e.g., cycle 5; the survey year 1999) to a more recent cycle (e.g., cycle 8, the survey year 2019). We then calculated the WOM rate by dividing the basal area change by the difference in survey years, i.e., the survey year 2019—the survey year 1999. We did a similar process with other remaining states and compiled as WOM rate. We calculated the mortality rate as follows:
M o r t a l i t y   r a t e   m 2 ha − 1   y − 1 = R e d u c e d   b a s a l   a r e a   R e c e n t   c y c l e   s u r v e y   y e a r − O l d   c y c l e   s u r v e y   y e a r
We assumed oak forests in this region have mostly gone through the self-thinning stage [40,41,42], and thus, the decline of the white oak basal area can be attributed to WOM. For WOM rates, we categorized them based on the natural breaks as very low, low, medium, high, and very high.
Additionally, we created a kernel density map using point data for WOM rates to gain insights into the density of mortality points across our study area. This density map enabled us to identify areas with higher and lower concentrations of WOM rates. All density analyses were conducted using ArcMap version 10.8.1, facilitating a comprehensive evaluation of spatial patterns in WOM.

2.3.3. Spatial Distribution Analysis of WOM Rates by Univariate Ripley’s K Function

We employed Ripley’s K function for spatial pattern analysis, given that our dataset for white oak mortality was structured around specific points or events, facilitating a plot-level analysis. Ripley’s K function is well-known for its ability to compare observed spatial patterns against those expected under complete spatial randomness (CSR), providing insights into clustering, random, and uniform spatial patterns [43]. This function effectively detects deviations from randomness (whether clustering or regularity) across a range of distances, thus offering a comprehensive view of spatial structure associated with WOM. In contrast, alternative spatial analysis techniques, such as kernel density estimations assess spatial autocorrelation at a single, fixed scale, making it less flexible for complex patterns and unable to substantiate specific distributions like CSR. Furthermore, methods such as Moran’s I primarily focus on the overall tendencies within, which may overlook localized patterns or varying relationships across different scales, a nuance that Ripley’s K function adeptly addresses. Additionally, edge effects are also typically disregarded by other methods, potentially resulting in biased outcomes, whereas Ripley’s K function accounts for these effects [44].
We utilized multi-distance spatial cluster analysis Ripley’s K function [45], which employs nearest neighbor distance to analyze spatial distribution patterns. The analysis was conducted using ArcMap version 10.8.1, which determines whether our spatial pattern exhibits clustering, randomness, or uniformity across each scale analysis [46]. Our dataset comprised all 2220 WOM points from 1998 to 2019, including their geographical coordinates useful for Ripley’s K function. In applying Ripley’s K function, we set the initial distance to 50 km, with an increment distance of 10 km. Edge correction was performed using Ripley’s edge correction formula [44], which systematically counts and measures points within the study area. To evaluate our null hypothesis of CSR, we established upper and lower confidence envelopes at the 99.9% level, generated from randomizations of 999 plot points using the default random generator in ArcGIS. We assessed whether our expected curve (ExpectedK) deviated from CSR. If the observed curve (ObservedK) is above the expected curve and upper envelope, there is spatial clustering. If it is below the expected curve and lower envelope, then WOM points follow spatial dispersion (uniformity). There is also a greater chance that both observed and expected curves could denote complete spatial randomness. For graphical interpretation, we employed the transformed K function (L(d)) to represent Ripley’s K function analysis results.

3. Results

3.1. White Oak Mortality Rate Spatial Distribution Patterns

The spatial distribution of white oak mortality (WOM) rates across the eastern United States exhibited significant variation in both magnitude and density. Overall, WOM rates were widespread, yet displayed distinct regional differences in spatial patterns across the southern, central, and northern regions. In the southern region, mortality was predominantly characterized by very low to low WOM rates (e.g., parts of Alabama, Arkansas, and Tennessee), covering large areas across different latitudes and longitudes (Figure 2A). However, a few plots of high as well as very high mortality rates were also found in this region (e.g., parts of Arkansas and Tennessee). Despite the larger area of a low and very low plot density of WOM rates, some locations across the southern region depicted a high-plot density distribution of WOM rates (e.g., parts of Arkansas and Tennessee; Figure 2B).
The central region exhibited a heterogeneous spatial distribution of WOM rates. Much of the central region showed very low and low WOM rates; however, scattered pockets of medium to very high mortality were evident, particularly in parts of Missouri, Arkansas, and Virginia (Figure 2A). This region has the most diversified forest landscape that consists of very high to high plot density distribution (e.g., parts of Missouri, Arkansas, Tennessee, Kentucky, and Virginia) of WOM rate compared to southern and northern regions. Most of this region depicted a very low to low and medium plot-density distribution of WOM rates, which were greater in area than very high and high plot-density distribution (Figure 2B).
Most of the northern region exhibited very few plots of WOM rate distributed across our study area (e.g., Illinois, Indiana, and Ohio). Though this region had very few plots of high to very high WOM rates (e.g., Missouri, west Virginia, and Virginia), the overall plot distribution to WOM rates was higher as compared to other regions (Figure 2A). Similarly, the plot density distribution of WOM rate mostly was very low and consisted of a few areas of medium, high, and very high plot-density distribution to certain latitudes and longitudes. Despite the larger area of land mass, this region represented a very low plot-density distribution of WOM rates (Figure 2B).
These spatial patterns of WOM rates across the southern, central, and northern regions suggest a complex interaction of biotic and abiotic factors. In the southern region, the dominance of very low mortality reveals favorable climatic conditions and lower pest pressure. In contrast, localized high mortality hotspots may result from increased competition, nutrient deficiencies, or isolated pest outbreaks. In the central region, diverse stand structures and species composition indicate a spatial heterogeneity, with mature and high-density stands more vulnerable to drought and resource competition. In contrast, the northern region’s sparse, low-density patterns suggest reduced susceptibility to widespread mortality, likely due to cooler temperatures, higher precipitation, and less environmental stress, with localized mortality driven by site-level stressors.

3.2. Spatial Distribution Patterns of WOM Rate at Southern, Central, and Northern Regions Using Ripley’s K Function

The spatial pattern of the WOM rate across the southern region indicated a clustering pattern until about 360 km (Figure 3A). This is because the observed K function exceeds the upper envelope of the 99.9% confidence interval, indicating statistically significant clustering at these scales, and we accept our null hypothesis. This means points of WOM rate in these scales are more closely spaced than would be expected under spatial randomness. However, the intensity of the clustering pattern, which is not constant, peaks at 200 km. Most of the southern region depicted clustering patterns of WOM rate at the local scale. At distances between 360 km and 395 km, the observed K function falls within the 99.9% confidence interval defined by the upper and lower envelopes, suggesting no significant deviation from randomness. This random pattern of the WOM rate is across a broad scale but covers a low range of distances. The random pattern shows that the point of WOM rate has an equal chance of occurring anywhere in the southern region. At distances beyond 40 km, the observed K function lies outside the lower envelope of a 99.9% confidence interval, suggesting a uniform pattern at broad scales, and we accept our null hypothesis. This tells us that the points of the WOM rates were more regularly spaced than expected under spatial randomness. The central and northern regions of WOM rate also depicted similar kinds of uniform patterns across a broad scale.
The central region WOM rate spatial patterns depicted a slightly lower intensity of observed K function at the local scale in comparison with spatial patterns of southern and central region WOM rates (Figure 3B). We found the clustering pattern until 200 km across the central region where the intensity of clustering is much smaller, and we accept our null hypothesis. This clustering is because the observed K function, which is statistically significant, exceeds the upper envelope of the 99.9% confidence interval. Unlike other regions, the observed K function is much closer to the random line at the local scale. That is why there is a random pattern from 230 km to 430 km as the observed K function, being statistically significant, falls within the 99.9% confidence interval. This indicates that the likelihood of WOM rate occurrences is more evenly distributed at medium scales compared to the local scale. However, there is a uniform pattern at a broad scale in which the observed K function lies completely outside the lower envelope at a given distance of 430 km and beyond, and we accept our null hypothesis. This uniform pattern is much farther from the random line and represents a slightly lower intensity of observed K function in comparison to the spatial patterns of the southern and northern region WOM rate. Overall, the pattern of the WOM rate is depicted as more random at the local scale but at a broad scale, it is less uniform, which is similar to the southern region of the WOM rate.
The spatial patterns across the northern region of WOM rate presented more clustering patterns than a random and uniform pattern (Figure 3C). Results showed a complete clustering pattern across local, medium, and parts of broad scales in which the observed K function exceeds the upper envelope of the 99.9% confidence interval, being statistically significant, at a distance of 480 km. The clustering pattern at the local scale slowly starts with low intensity, which is also much closer to the random line and slightly at the peak when it reaches around 20 km. This indicates that there is a tendency for WOM rate points to be aggregated at these scales. However, the clustering pattern slightly decreases as it reaches medium scale and lasts until 470 km of broad scale, too. Unlike the southern and central WOM rates, patterns presented by the observed K function are varied across each scale. This is because random and uniform patterns are presented only across broad scales and a few distance ranges. Results show that the observed K function lies completely within the 99.9% confidence interval defined by the upper and lower envelopes, suggesting no significant deviation from randomness between 480 km and 520 km. This tells us that the points are distributed in a manner consistent with complete spatial randomness (CSR). Beyond 520 km, there is a uniform pattern at a broad scale that covers few distances in this region. The uniform pattern is because the observed K function lies outside the lower envelope of the 99.9% confidence interval at a given distance, which is statistically significant, and we accept our null hypothesis. This tells us that points are more evenly distributed and there is a tendency for points to avoid each other across these scales.
The clustering, randomness, and uniformity of WOM rates at varying scales suggest that spatial patterns are shaped by a combination of biotic and abiotic stressors. In the southern region, local scale clustering reveals the influence of competition, soil nutrient deficiencies, or pest outbreaks, while broader scale clustering highlights the role of climatic factors. Similarly, random patterns at a broad scale in the southern region may result from factors such as landscape heterogeneity. The uniform patterns at broad scales likely reflect stand-level interventions or environmental gradients. In the central region, local scale clustering may be driven by environmental stressors such as site-scale processes, whereas random patterns at local scales suggest the occurrence of environmental heterogeneity such as floods or droughts, and variation in soil types. Furthermore, broad-scale uniformity indicates consistent biotic stress or land management interventions in the region. In the northern region, clustering across all scales suggests a strong aggregation from variations in altitude and soil moisture gradients. However, a limited random pattern at broad scales suggests a stochastic process such as sporadic pest outbreaks, and weather extremes. Finally, broad-scale uniformity of WOM rate might suggest the influence of homogeneous environmental factors such as consistent soil moisture, land use patterns, and management practices.

4. Discussion

4.1. Spatial Patterns of WOM Rate at Various Scales and Their Ecological Significance

The spatial pattern analysis of the WOM rate in the southern region revealed a predominantly clustered pattern at the local scale. Clustering of WOM at local scales often indicates localized stressors or specific ecological interactions between biotic and abiotic factors. Stressors such as stand competition, pest pathogen outbreaks, or localized drought conditions are acting in the concentrated area. Research has shown that stand-level competition is more often during stem exclusion stage [47]. White oaks, during the stem exclusion stage, that are deprived of sunlight, moisture, and space may eventually die off [48]. Studies suggest that localized clustering often indicates species-specific pathogens or interactions among tree species that exacerbate stress in certain areas [49]. For instance, the proximity of white oaks to one another can facilitate the rapid spread of pests or diseases, leading to high mortality rates in small clusters. Likewise, there is a complex oak decline, such as pest and pathogen pressures leading to spatially clustered oak mortality; once a tree is weakened by environmental stress (e.g., frostbite), it becomes more susceptible to pests like Ambrosia beetles or fungal pathogens like Armillaria root rot [50]. Clustering of WOM rate at the local scale also indicates localized drought in combination with high temperature impacting white oaks. For instance, research found that severe drought events, particularly combined with high temperatures, resulted in the clustering of tree mortality across the oak-dominated forest of the southern United States [51]. Sometimes, poor soil conditions significantly affect the localized clustering of WOM rate. It is because soils with compacted, nutrient-poor, or shallow soil exhibit higher mortality rates in cluster forms if the same type of soil is concentrated at the local scale [29]. However, broad-scale clustering could reflect broader ecological processes, such as disturbances (e.g., climate change, fire, wind events) and anthropogenic factors. A cluster pattern at a broad scale suggests larger landscape processes, such as habitat fragmentation, which reduce genetic diversity and resilience to environmental stressors [52]. Laurance et al. [53] documented that fragmented forests are more vulnerable to environmental stressors because edge effects (e.g., exposure to wind, sunlight, and invasive species) weaken tree resilience. Broad-scale clustering may also indicate impacts of human development leading to isolated forest patches, making white oaks in these areas more susceptible to stressors [54]. In addition, decades of fire suppression altered oak forest structure, leading to denser forests with more competition, which may lead to clustering mortality at a broad scale [55].
Also, the spatial pattern of the WOM rate in the southern region indicated a random pattern across a few distances of broad scale. This random pattern indicates WOM occurring at regional sites including poor and good resource sites and various topographic and hydrological conditions [45,56]. For instance, studies found that regions with poor drainage experienced a greater mortality rate during extended wet periods, while drier upland sites mostly suffered during droughts. Such spatial heterogeneity across broad landscapes often indicates impacts from both favorable and unfavorable environmental conditions [57]. A random mortality pattern at a broad scale also indicates differing soil and resource conditions across this region. For instance, research on oak mortality has documented that mortality across southern oak forests was found not only at low-quality sites but also across the better-resource sites, where competition from faster-growing species or environmental stressors such as drought impacted white oaks [58]. This random pattern at a broad scale tells us that even white oaks in favorable conditions could succumb to stress under the right circumstances. Research by Haavik et al. [59] supports the idea that the random pattern of WOM at a broad scale tied to biotic factors like pest and pathogen outbreaks and oak decline phenomena could affect some areas where these pressures are less intense or variable. Likewise, the random distribution pattern of WOM rate across a large scale may also indicate this, especially in regions where pests are sporadic, and environmental stress is patchy. Random patterns are mostly possible in those areas where extended and uneven drought events occur. For instance, studies across the Ozark highlands of Arkansas and Missouri reported that extreme drought events increased from 6% during 1999–2005 to 15% during 2006–2010 with irregular patterns [60]. Others found that the random pattern of WOM rate indicates severe weather events such as storms and hurricanes in the broader regions of Arkansas and Illinois [61]. Kim et al. [62] documented that a random pattern of WOM rate reflects tropical winds followed by hurricanes in the region where hardwood tree species, including oaks, were highly impacted.
Like the central and northern regions, our results indicated that most of the southern regions had a significant uniform pattern of WOM rate at a broad scale. This uniform pattern at a broad scale indicates mortality in the mature stands of white oaks, which are not replacing themselves and dying in a consistent pattern across these scales [63]. Other reasons leading to a uniform pattern of WOM may suggest that some areas might be affected by prolonged drought and flooding events across the low fertility sites of the oak forest. For instance, Greenberg and Collins [64] reported that the southern oak-hickory forest had been greatly impacted by drought stress as a major concerning factor in oak decline, where mortality was found across the water deficit areas in a consistent manner. However, in managed forests, uniform mortality could also be a result of human interventions such as clearcutting and selective logging. This managed forest might be affected by heightened stress from increased sun and wind exposure, which can result in consistent die-offs resulting in the decrease in the net primary productivity of tree species [65].
The observed clustered pattern of WOM rate in the central region raises intriguing queries concerning the underlying mechanisms associated with the dynamics of WOM. The observed clustered patterns at the local scale suggest site scale processes such as self-thinning could be acting in the region. This is because studies have found that self-thinning, a natural process, accumulates over time and can cause mortality in clusters due to intraspecific competition in the forest stand [66]. For instance, studies on oak forests across the central region (e.g., Illinois Ozark hills) showed massive competition with shade-tolerant species such as sugar maple, red maple, and American beech competing for sunlight, space, and nutrients in this region [67,68]. In this region, the spatial clustering reflects the possibility that these site-scale processes were responsible for white oak decline locally.
Our results also depicted a random pattern across the local scale of the central region. This random pattern of WOM may indicate environmental heterogeneity in the core area, which might result in a variety of microhabitats that have an impact on tree mortality rates. For instance, research by Prasad [69] found that environmental heterogeneity such as disparities in soil types, moisture content, and other ecological parameters often contribute to random patterns of tree mortality in this region. Others found that random patterns of WOM at the local scale suggest disease outbreaks combined with drought events leading to irregular death patterns [70,71]. There is also a greater chance that random patterns at this scale suggest ice storms and other localized variables such as random floods and landslides from heavy rainfall can eventually impact white oaks in a random manner [60]. The observed randomness at the local scale could represent temporal dynamics, in which short-term variations in environmental variables impact mortality events [72,73]. Over time, these variations can result in inconsistent geographic clustering. Likewise, the random patterns may suggest the edge effects in which the edges of white oaks stand in this region might have faced distinct ecological conditions compared to the interior. This is because studies have found that adjacent forest canopies restrict light availability and mitigated tree growth on the outer margins of even-aged forests and gaps may result in random mortality patterns [74,75].
A uniform pattern in WOM rates at a broad scale indicates that ecological factors are driving the dynamics of WOM. This uniform distribution of WOM rates at larger scales could be facilitated by consistency in soil moisture content, and drought–pathogen interactions. For instance, studies have found that white oak stands throughout the large territory of the central region demonstrated mortality events that occurred at the affected sites of climatic events such as consistent drought, storms, frosts, etc., in the concentrated area [76]. Similarly, uniform patterns of mortality may indicate effects from human-induced variables like land use patterns such as private landownership across most of the states (e.g., Kentucky, Tennessee, Virginia, etc.). There might be a possibility for uniform patterns of WOM where land fragmentation and change in land covers prevail in a consistent and timely manner [39,77]. Moreover, random patterns of mortality in the central region may suggest consistent areas with poor soil conditions, particularly in nutrient-deficient upland areas creating resource limitations for a weaker tree [29].
Our results from spatial patterns at both local and broad scale clustering in the northern region WOM rate indicate various aspects of their ecological significance. Local and broad-scale clustering of WOM in this region may suggest areas with soil compaction, low moisture levels, and specific species interactions leading to tree mortality [78,79]. For instance, studies on WOM at the local scale have documented that exotic insect pests were invading the tree trunk and causing defoliation resulting in low abundance and distribution of white oaks in the affected areas [80]. In addition, local scale clustering may reveal that site scale processes such as self-thinning in white oak stands accumulate throughout the region, which may have resulted in region-wide WOM in clusters [81,82]. In addition, elevation and distance from water indicate local and broad scale spatial clustering. For instance, research by Hanberry et al. [83] reported that environmental variability, such as elevation changes or proximity to bodies of water limits tree survival in the core area. In this situation, white oaks may be dying in a cluster due to stress from cold and changes in moisture levels at a high altitude. Likewise, there is also a greater chance that broad-scale clustering may indicate regional variables such as climate change (e.g., regional drought), and regional pest outbreaks affecting the dynamics of WOM rates at larger spatial scales should be considered [84].
We also found broad-scale random patterns across the northern region that may be associated with various environmental stochastic processes. For instance, the northern region of our study area is subjected to intermittent weather extremes, such as localized severe storms or extreme precipitation [85,86]. Our results from the random patterns in this region may indicate several stochastic processes are acting in the dynamics of the white oak forests. Evidence from previous research found that random patterns on a broad scale could also be an indication of sporadic frost events, along with variable moisture conditions, that usually create spatially random tree stress [87]. Stress could be facilitated by uneven drought events and winter cold extremes affecting white oaks across these scales, leading to mortality in both low and high-quality sites. There is also heterogeneity in soil quality and nutrient availability across the northern region that may have contributed to random patterns at a broad scale. Research by Fei et al. [5] reported that site variability leads to inconsistent stress levels across white oak populations, resulting in random mortality rather than clustered patterns. Similarly, random patterns at a broad scale may reflect sporadic pest outbreaks in the region, particularly when these biotic factors do not uniformly affect the white oak forest canopy. For instance, a study by Lovett et al. [80] on gypsy moths and other pests reported that outbreaks may not create consistent clustering but rather impact white oaks in random locations, depending on pest populations and forest composition.
A uniform pattern of WOM rate across a broad scale of the northern region suggests there might be spatially homogeneous environmental factors affecting white oaks. Factors such as constant soil types, climate, and land use methods that involve private landownership, consistent logging, or land management programs could strongly exhibit uniform mortality across larger scales. For instance, there might be poor soil quality and moisture retention issues at upland sites of a broader scale, resulting in water stress, especially during dry periods. The consistent scarcity of fertile soil and water in this region may have contributed to uniform mortality from the stress created across white oak forests [5]. Additionally, uniform patterns at a broad scale in the northern region may indicate probable thinning and logging activities that may have exposed residual white oak trees to environmental stressors, such as increased wind and sun exposure, leading to consistent mortality in harvested areas [88]. However, our results also indicated random and uniform patterns across a broad scale. These spatial patterns in larger landscapes suggest edge effects and landscape characteristics impacting white oak stands. The reason may include locations near forest boundaries or regions with distinct topographical features that may provide randomness within specific broad-scale ranges while contributing to homogeneity in neighboring places [89,90].

4.2. Management Implications for WOM Rate at Varying Scales

Forest managers should focus on localized interventions to reduce mortality hotspots in southern, central, and northern regions where WOM is clustered. For instance, our study indicated that clustering in WOM rates is primarily driven by factors like insect pest infestations and competition [91]. Effective management may involve long-term pest and pathogen monitoring combined with the use of GIS spatial mapping to track pest occurrences in high-WOM zones. For this, utilizing tree health assessment tools, such as dendrochronology, can help evaluate the impacts of pest outbreaks before and after the infestation, allowing for timely intervention at multiple scales [92]. Additionally, competition from mesophytic species can be managed through targeted thinning and prescribed burns to maintain white oak dominance and reduce the spread of fast-growing species like maple. Furthermore, forest managers can monitor changes in survival and growth rates of white oaks before and after the interventions, using both field surveys and remote sensing to track canopy closure and undergrowth competition in the affected areas [93].
Our results from the random patterns at local scales suggest small-scale, site-specific factors like variation in soil quality, microclimatic conditions, and sporadic pest outbreaks. This can be managed by developing monitoring programs to detect signs of stress or mortality and employ soil amendment strategies to enhance resilience. Similarly, forest managers can utilize targeted thinning to reduce stand-level competition, particularly in areas prone to water scarcity or poor nutrient availability [94]. At the broad scale of southern and northern regions, regional management approaches can be implemented in the affected areas of WOM. For instance, forest managers can enhance the overall resilience of white oak forests by promoting species diversity and structural heterogeneity to buffer against widespread environmental stressors. Implementing policies across the southern, central, and northern regions that promote mixed-species reforestation and consider climate-adaptive management frameworks could help mitigate the impact of large-scale random mortality patterns.
Our results from uniform patterns at broad scales across all regions suggest widespread stress such as poor management practices (e.g., fire suppression) and soil nutrient deficiencies. To address this situation, forest managers can reintroduce prescribed burns at regular intervals, which can improve stand vigor and reduce susceptibility to pests and disease, and competition from fast-growing species. The uniform patterns of WOM rate also suggest soil-related factors such as nutrient or moisture deficiencies prevailing in this region. To mitigate this, researchers can implement the application of soil amendments and the promotion of mycorrhizal fungi associations, which could enhance soil health and improve white oak resilience. For instance, methods incorporating soil texture, nutrient levels, and organic matter content can be analyzed by pairing this with tree ring studies to identify correlations between periods of nutrient depletion and increased mortality of white oaks. In addition, management interventions such as selective thinning to create variable stand structures, promoting underrepresented species, and introducing age-class diversity can reduce the risk of uniform mortality in white oaks.
Further investigation on climate variability, particularly focusing on drought and temperature extremes, could aid in mitigating the effects of various spatial pattern formation. This can be achieved by conducting drought simulation experiments where different white oak stands are subjected to varying degrees of controlled water stress. Through pairing this with climate model data to predict regions that may face the highest future drought risk, combining physiological studies of white oak tree water use with geographic data on drought events can be implemented to reduce mortality. Finally, future research should include more advanced machine learning modeling techniques such as random forest or spatial autoregressive models to effectively predict WOM based on multiple predictors (e.g., climate data, pest outbreaks, and soil composition), which can improve prediction accuracy by accounting for the complex interplay between biotic and abiotic stressors.

4.3. Limitations of the Study

Our research consists of several limitations, such as FIA data across ten states were not consistently reported by the FIA program from 1998 to 2019 in which the data may not have captured the full extent of white oak mortality, only if declining basal areas were not promptly recorded for our study area. Similarly, previous plot information across the ten states of our study area mainly was missing and did not bring significant results while linking with newer plots. As a result, the declining plots between sampling periods might not be reflected in the dataset, potentially skewing the assessment of WOM over survey years. While Ripley’s K function is a powerful tool for analyzing spatial patterns, it has its constraints. The analysis is sensitive to the choice of scale and may not adequately capture complex interactions or spatial structures at different scales.
The study may not fully account for all biotic and abiotic factors influencing white oak mortality. For example, interactions between different species, soil conditions, or climatic variations could confound the observed relationships, making it challenging to isolate the specific factors contributing to mortality. Findings from the study may not be universally applicable, however they provide insights on WOM rates across varying scales, across all regions within the eastern USA. Local ecological, climatic, and human-influenced conditions could lead to different mortality dynamics, limiting the generalizability of results to broader contexts.

5. Conclusions

Our study revealed a diverse range of spatial patterns in WOM rates across the eastern United States, including clustered, random, and uniform, with varying scales across the southern, central, and northern regions. A prominent finding was the consistent presence of clustered patterns at local scales across each region, underscoring the complex interactions between white oak populations and site-specific environmental conditions. The localized clustering of WOM rates indicates that stand-level competition and topographical and edaphic factors are key determinants in shaping these spatial patterns. This supports the notion that self-thinning processes, driven by site scale factors, are crucial in understanding white oak mortality dynamics, highlighting the intricacies of mechanisms affecting white oak stands across finer scales. Such fine-scale environmental elements are critical in mortality studies and suggest site-specific management approaches accounting for local variations in topography and soil conditions. Additionally, our findings demonstrate that the clustered patterns extend to broader scales in southern and northern regions, suggesting that macro-scale factors such as climate variability elevation gradients and proximity to water bodies significantly influence WOM. These broad-scale clusters imply that regional climatic and topographical drivers exert a strong influence on the distribution of WOM. Consequently, forest managers should adopt localized strategies to mitigate mortality impacts, such as targeted thinning operations to reduce stand competition, pests, and disease management in high-mortality areas and prescribed burns to maintain white oak dominance while limiting the encroachment of more competitive species.
Our investigation also identified distinct random patterns at broad scales in both southern and northern regions, suggesting that stochastic factors or broader environmental drivers such as climatic anomalies and geological variability may play a significant role in determining WOM events. The presence of random patterns also implies that broader scale processes, such as irregular drought events and floods in the core area, are likely contributing factors to observed random WOM. Managing these random patterns requires early detection of stress indicators and adaptive forest management strategies to bolster forest resilience, such as enhancing species diversity to reduce vulnerability and identifying key biotic and abiotic stressors for targeted interventions.
Conversely, uniform patterns of WOM were observed across all three regions i.e., southern, central, and northern, highlighting the influence of spatially homogeneous environmental conditions on mortality distribution at broad scales. These uniform patterns may suggest the presence of regions with consistent soil properties, stable climatic conditions, and land use practices, such as private land ownership and uniform forest management regimes. The stability of these tree growth conditions and land use practices likely contribute to the uniformity in WOM rates, providing valuable insights for conservation and forest management in these areas. Addressing broad-scale uniform mortality may require interventions such as reintroducing prescribed fire, optimizing soil management, and focusing on regenerating white oak saplings to restore forest health in affected areas.
The results could be useful for forest managers, landowners, and policymakers to investigate the specific mechanism underlying these relationships and assess the long-term impacts on white oak forest ecosystems. Forest managers should identify WOM hotspots and implement targeted management in highly affected areas. Finally, evaluating the interplay between biotic and abiotic stressors (e.g., insect pests, drought, etc.) and refining forest management practices would provide a more comprehensive understanding of the spatial distribution of white oaks and enhance management effectiveness across the species range.

Author Contributions

Conceptualization, S.K.; methodology, S.K.; validation, S.K. and H.S.H.; writing—original draft preparation, S.K.; writing—review and editing, S.K., H.S.H. and S.B.; supervision, H.S.H.; project administration, H.S.H. and S.B.; funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.

Funding

United States Department of Agriculture/National Institute of Food and Agriculture 1890 Capacity Building Grant, Award number 2021-38821-34704.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

We acknowledge the University of Missouri School of Natural Resources for its facilities and support.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Newell, F.L.; Rodewald, A.D. Role of Topography, Canopy Structure, and Floristics in Nest-Site Selection and Nesting Success of Canopy Songbirds. For. Ecol. Manag. 2011, 262, 739–749. [Google Scholar] [CrossRef]
  2. Wohlleben, P.; Billinghurst, J.; Wohlleben, P. The Hidden Life of Trees: The Illustrated Edition; Black Inc.: Melbourne, VIC, Australia, 2018; ISBN 9781771642484. [Google Scholar]
  3. Littlefield, C.E.; D’Amato, A.W. Identifying Trade-Offs and Opportunities for Forest Carbon and Wildlife Using a Climate Change Adaptation Lens. Conserv. Sci. Pract. 2022, 4, e12631. [Google Scholar] [CrossRef]
  4. Luo, M.; Anees, S.A.; Huang, Q.; Qin, X.; Qin, Z.; Fan, J.; Han, G.; Zhang, L.; Shafri, H.Z.M. Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models. Forests 2024, 15, 975. [Google Scholar] [CrossRef]
  5. Fei, S.; Kong, N.; Steiner, K.C.; Moser, W.K.; Steiner, E.B. Change in Oak Abundance in the Eastern United States from 1980 to 2008. For. Ecol. Manag. 2011, 262, 1370–1377. [Google Scholar] [CrossRef]
  6. Shifley, S.R.; Fan, Z.; Kabrick, J.M.; Jensen, R.G. Oak Mortality Risk Factors and Mortality Estimation. For. Ecol. Manag. 2006, 229, 16–26. [Google Scholar] [CrossRef]
  7. Greenberg, C.H. Long-Term Recovery Dynamics Following Hurricane-Related Wind Disturbance in a Southern Appalachian Forest. For. Ecol. Manag. 2021, 502, 119704. [Google Scholar] [CrossRef]
  8. Reed, S.E.; English, J.T.; Muzika, R.M. Phytophthora Species Detected in Two Ozark Forests with Unusual Patterns of White Oak Mortality. Plant Dis. 2019, 103, 102–109. [Google Scholar] [CrossRef]
  9. Nagle, A.M.; Long, R.P.; Madden, L.V.; Bonello, P. Association of Phytophthora cinnamomi with White Oak Decline in Southern Ohio. Plant Dis. 2010, 94, 1026–1034. [Google Scholar] [CrossRef] [PubMed]
  10. Wang, C.; He, H.S.; Kabrick, J.M. A Remote Sensing-Assisted Risk Rating Study to Predict Oak Decline and Recovery in the Missouri Ozark Highlands, USA. GISci. Remote Sens. 2008, 45, 406–425. [Google Scholar] [CrossRef]
  11. McConnell, M.E.; Balci, Y. Phytophthora cinnamomi as a Contributor to White Oak Decline in Mid-Atlantic United States Forests. Plant Dis. 2014, 98, 319–327. [Google Scholar] [CrossRef]
  12. Dey, D.C. Sustaining Oak Forests in Eastern North America: Regeneration and Recruitment, the Pillars of Sustainability. For. Sci. 2014, 60, 926–942. [Google Scholar] [CrossRef]
  13. Greenberg, C.H.; Keyser, T.L.; Speer, J.H. Temporal Patterns of Oak Mortality in a Southern Appalachian Forest (1991–2006). Nat. Areas J. 2011, 31, 131–137. [Google Scholar] [CrossRef]
  14. Balci, Y.; Long, R.P.; Mansfield, M.; Balser, D.; MacDonald, W.L. Involvement of Phytophthora Species in White Oak (Quercus alba) Decline in Southern Ohio. For. Pathol. 2010, 40, 430–442. [Google Scholar] [CrossRef]
  15. Radcliffe, D.C.; Hix, D.M.; Matthews, S.N. Predisposing Factors’ Effects on Mortality of Oak (Quercus) and Hickory (Carya) Species in Mature Forests Undergoing Mesophication in Appalachian Ohio. For. Ecosyst. 2021, 8, 7. [Google Scholar] [CrossRef]
  16. Fan, Z.; Kabrick, J.M.; Spetich, M.A.; Shifley, S.R.; Jensen, R.G. Oak Mortality Associated with Crown Dieback and Oak Borer Attack in the Ozark Highlands. For. Ecol. Manag. 2008, 255, 2297–2305. [Google Scholar] [CrossRef]
  17. Wood, J.D.; Knapp, B.O.; Muzika, R.M.; Stambaugh, M.C.; Gu, L. The Importance of Drought-Pathogen Interactions in Driving Oak Mortality Events in the Ozark Border Region. Environ. Res. Lett. 2018, 13, 015004. [Google Scholar] [CrossRef]
  18. Peltzer, D.A.; Allen, R.B.; Lovett, G.M.; Whitehead, D.; Wardle, D.A. Effects of Biological Invasions on Forest Carbon Sequestration. Glob. Chang. Biol. 2010, 16, 732–746. [Google Scholar] [CrossRef]
  19. Schlesinger, W.H.; Dietze, M.C.; Jackson, R.B.; Phillips, R.P.; Rhoades, C.C.; Rustad, L.E.; Vose, J.M. Forest Biogeochemistry in Response to Drought. Glob. Chang. Biol. 2016, 22, 2318–2328. [Google Scholar] [CrossRef]
  20. Hui, D.; Deng, Q.; Tian, H.; Luo, Y. Climate Change and Carbon Sequestration in Forest Ecosystems. In Handbook of Climate Change Mitigation and Adaptation; Springer: New York, NY, USA, 2015; pp. 1–40. [Google Scholar]
  21. Webster, C.R.; Dickinson, Y.L.; Burton, J.I.; Frelich, L.E.; Jenkins, M.A.; Kern, C.C.; Raymond, P.; Saunders, M.R.; Walters, M.B.; Willis, J.L. Promoting and Maintaining Diversity in Contemporary Hardwood Forests: Confronting Contemporary Drivers of Change and the Loss of Ecological Memory. For. Ecol. Manag. 2018, 421, 98–108. [Google Scholar] [CrossRef]
  22. Allen, D.; Dick, C.W.; Strayer, E.; Perfecto, I.; Vandermeer, J. Scale and Strength of Oak–Mesophyte Interactions in a Transitional Oak–Hickory Forest. Can. J. For. Res. 2018, 48, 1366–1372. [Google Scholar] [CrossRef]
  23. McDowell, N.G.; Allen, C.D.; Anderson-Teixeira, K.; Aukema, B.H.; Bond-Lamberty, B.; Chini, L.; Clark, J.S.; Dietze, M.; Grossiord, C.; Hanbury-Brown, A.; et al. Pervasive Shifts in Forest Dynamics in a Changing World. Science 2020, 368, eaaz9463. [Google Scholar] [CrossRef] [PubMed]
  24. Muhly, T.B.; Hebblewhite, M.; Paton, D.; Pitt, J.A.; Boyce, M.S.; Musiani, M. Humans Strengthen Bottom-Up Effects and Weaken Trophic Cascades in a Terrestrial Food Web. PLoS ONE 2013, 8, e64311. [Google Scholar] [CrossRef] [PubMed]
  25. Larrieu, L.; Courbaud, B.; Drénou, C.; Goulard, M.; Bütler, R.; Kozák, D.; Kraus, D.; Krumm, F.; Lachat, T.; Müller, J.; et al. Key Factors Determining the Presence of Tree-Related Microhabitats: A Synthesis of Potential Factors at Site, Stand and Tree Scales, with Perspectives for Further Research. For. Ecol. Manag. 2022, 515, 120235. [Google Scholar] [CrossRef]
  26. Clark, S.L.; Schweitzer, C.J. Stand Dynamics of an Oak Woodland Forest and Effects of a Restoration Treatment on Forest Health. For. Ecol. Manag. 2016, 381, 258–267. [Google Scholar] [CrossRef]
  27. LeBlanc, D.C.; Terrell, M.A. Comparison of Growth-Climate Relationships between Northern Red Oak and White Oak across Eastern North America. Can. J. For. Res. 2011, 41, 1936–1947. [Google Scholar] [CrossRef]
  28. Sadahiro, Y.; Matsumoto, H. Analysis of a Spatial Point Pattern in Relation to a Reference Point. J. Geogr. Syst. 2024, 26, 351–373. [Google Scholar] [CrossRef]
  29. McEwan, R.W.; Dyer, J.M.; Pederson, N. Multiple Interacting Ecosystem Drivers: Toward an Encompassing Hypothesis of Oak Forest Dynamics across Eastern North America. Ecography 2011, 34, 244–256. [Google Scholar] [CrossRef]
  30. Osman, K.T. Forest Soils: Properties and Management; Springer International Publishing: New York, NY, USA, 2013; Volume 9783319025414, ISBN 9783319025414. [Google Scholar]
  31. Guldin, J.M. Silvicultural Options in Forests of the Southern United States under Changing Climatic Conditions. New For. 2019, 50, 71–87. [Google Scholar] [CrossRef]
  32. Hanberry, B.B.; Nowacki, G.J. Oaks Were the Historical Foundation Genus of the East-Central United States. Quat. Sci. Rev. 2016, 145, 94–103. [Google Scholar] [CrossRef]
  33. Mehmood, K.; Anees, S.A.; Luo, M.; Akram, M.; Zubair, M.; Khan, K.A.; Khan, W.R. Assessing Chilgoza Pine (Pinus gerardiana) Forest Fire Severity: Remote Sensing Analysis, Correlations, and Predictive Modeling for Enhanced Management Strategies. Trees For. People 2024, 16, 100521. [Google Scholar] [CrossRef]
  34. Hanberry, B.B.; Abrams, M.D.; Arthur, M.A.; Varner, J.M. Reviewing Fire, Climate, Deer, and Foundation Species as Drivers of Historically Open Oak and Pine Forests and Transition to Closed Forests. Front. For. Glob. Chang. 2020, 3, 56. [Google Scholar] [CrossRef]
  35. Rogers, B.M.; Jantz, P.; Goetz, S.J. Vulnerability of Eastern US Tree Species to Climate Change. Glob. Chang. Biol. 2017, 23, 3302–3320. [Google Scholar] [CrossRef] [PubMed]
  36. Tinkham, W.T.; Mahoney, P.R.; Hudak, A.T.; Domke, G.M.; Falkowski, M.J.; Woodall, C.W.; Smith, A.M.S. Applications of the United States Forest Inventory and Analysis Dataset: A Review and Future Directions. Can. J. For. Res. 2018, 48, 1251–1268. [Google Scholar] [CrossRef]
  37. Gray, A.; Brandeis, T.; Shaw, J.; McWilliams, W.; Miles, P. Forest Inventory and Analysis Database of the United States of America (FIA). Biodivers. Ecol. 2012, 4, 225–231. [Google Scholar] [CrossRef]
  38. Anees, S.A.; Mehmood, K.; Khan, W.R.; Sajjad, M.; Alahmadi, T.A.; Alharbi, S.A.; Luo, M. Integration of Machine Learning and Remote Sensing for above Ground Biomass Estimation through Landsat-9 and Field Data in Temperate Forests of the Himalayan Region. Ecol. Inform. 2024, 82, 102732. [Google Scholar] [CrossRef]
  39. Khadka, S.; Gyawali, B.R.; Shrestha, T.B.; Cristan, R.; Antonious, G.; Poudel, H.P. Exploring Relationships among Landownership, Landscape Diversity, and Ecological Productivity in Kentucky. Land Use Policy 2021, 111, 105723. [Google Scholar] [CrossRef]
  40. Garnas, J.R.; Ayres, M.P.; Liebhold, A.M.; Evans, C. Subcontinental Impacts of an Invasive Tree Disease on Forest Structure and Dynamics. J. Ecol. 2011, 99, 532–541. [Google Scholar] [CrossRef]
  41. Iverson, L.R.; Hutchinson, T.F.; Prasad, A.M.; Peters, M.P. Thinning, Fire, and Oak Regeneration across a Heterogeneous Landscape in the Eastern U.S.: 7-Year Results. For. Ecol. Manag. 2008, 255, 3035–3050. [Google Scholar] [CrossRef]
  42. Yang, S.I.; Brandeis, T.J. Estimating Maximum Stand Density for Mixed-Hardwood Forests among Various Physiographic Zones in the Eastern US. For. Ecol. Manag. 2022, 521, 120420. [Google Scholar] [CrossRef]
  43. Robeson, S.M.; Li, A.; Huang, C. Point-Pattern Analysis on the Sphere. Spat. Stat. 2014, 10, 76–86. [Google Scholar] [CrossRef]
  44. Zhang, G.; Huang, Q.; Zhu, A.X.; Keel, J.H. Enabling Point Pattern Analysis on Spatial Big Data Using Cloud Computing: Optimizing and Accelerating Ripley’s K Function. Int. J. Geogr. Inf. Sci. 2016, 30, 2230–2252. [Google Scholar] [CrossRef]
  45. Miron, A.C.; Bezerra, T.G.; Nascimento, R.G.M.; Emmert, F.; Pereira, R.S.; Higuchi, N. Spatial Distribution of Six Managed Tree Species Is Influenced by Topography Conditions in the Central Amazon. J. Environ. Manag. 2021, 281, 111835. [Google Scholar] [CrossRef] [PubMed]
  46. Wehenkel, C.; Brazão-Protázio, J.M.; Carrillo-Parra, A.; Martínez-Guerrero, J.H.; Crecente-Campo, F. Spatial Distribution Patterns in the Very Rare and Species-Rich Picea chihuahuana Tree Community (Mexico). PLoS ONE 2015, 10, e0140442. [Google Scholar] [CrossRef]
  47. Ngo Bieng, M.A.; Perot, T.; de Coligny, F.; Goreaud, F. Spatial Pattern of Trees Influences Species Productivity in a Mature Oak-Pine Mixed Forest. Eur. J. For. Res. 2013, 132, 841–850. [Google Scholar] [CrossRef]
  48. Bose, A.K.; Weiskittel, A.; Kuehne, C.; Wagner, R.G.; Turnblom, E.; Burkhart, H.E. Tree-Level Growth and Survival Following Commercial Thinning of Four Major Softwood Species in North America. For. Ecol. Manag. 2018, 427, 355–364. [Google Scholar] [CrossRef]
  49. Contreras-Cornejo, H.A.; Larsen, J.; Fernández-Pavía, S.P.; Oyama, K. Climate Change, a Booster of Disease Outbreaks by the Plant Pathogen Phytophthora in Oak Forests. Rhizosphere 2023, 27, 100719. [Google Scholar] [CrossRef]
  50. Rabiey, M.; Hailey, L.E.; Roy, S.R.; Grenz, K.; Al-Zadjali, M.A.S.; Barrett, G.A.; Jackson, R.W. Endophytes vs Tree Pathogens and Pests: Can They Be Used as Biological Control Agents to Improve Tree Health? Eur. J. Plant. Pathol. 2019, 155, 711–729. [Google Scholar] [CrossRef]
  51. Crouchet, S.E.; Jensen, J.; Schwartz, B.F.; Schwinning, S. Tree Mortality After a Hot Drought: Distinguishing Density-Dependent and -Independent Drivers and Why It Matters. Front. For. Glob. Chang. 2019, 2, 21. [Google Scholar] [CrossRef]
  52. Zambrano, J.; Garzon-Lopez, C.X.; Yeager, L.; Fortunel, C.; Cordeiro, N.J.; Beckman, N.G. The Effects of Habitat Loss and Fragmentation on Plant Functional Traits and Functional Diversity: What Do We Know so Far? Oecologia 2019, 191, 505–518. [Google Scholar] [CrossRef]
  53. Laurance, W.F.; Curran, T.J. Impacts of Wind Disturbance on Fragmented Tropical Forests: A Review and Synthesis. Austral. Ecol. 2008, 33, 399–408. [Google Scholar] [CrossRef]
  54. Harper, K.A.; Macdonald, S.E.; Burton, P.J.; Chen, J.; Brosofske, K.D.; Saunders, S.C.; Euskirchen, E.S.; Roberts, D.; Jaiteh, M.S.; Esseen, P.A. Edge Influence on Forest Structure and Composition in Fragmented Landscapes. Conserv. Biol. 2005, 19, 768–782. [Google Scholar] [CrossRef]
  55. Matlack, G.R. Reassessment of the Use of Fire as a Management Tool in Deciduous Forests of Eastern North America. Conserv. Biol. 2013, 27, 916–926. [Google Scholar] [CrossRef]
  56. Petroselli, A.; Vessella, F.; Cavagnuolo, L.; Piovesan, G.; Schirone, B. Ecological Behavior of Quercus Suber and Quercus Ilex Inferred by Topographic Wetness Index (TWI). Trees-Struct. Funct. 2013, 27, 1201–1215. [Google Scholar] [CrossRef]
  57. Pawlikowski, N.C.; Coppoletta, M.; Knapp, E.; Taylor, A.H. Spatial Dynamics of Tree Group and Gap Structure in an Old-Growth Ponderosa Pine-California Black Oak Forest Burned by Repeated Wildfires. For. Ecol. Manag. 2019, 434, 289–302. [Google Scholar] [CrossRef]
  58. Kabrick, J.M.; Dey, D.C.; Jensen, R.G.; Wallendorf, M. The Role of Environmental Factors in Oak Decline and Mortality in the Ozark Highlands. For. Ecol. Manag. 2008, 255, 1409–1417. [Google Scholar] [CrossRef]
  59. Haavik, L.J.; Billings, S.A.; Guldin, J.M.; Stephen, F.M. Emergent Insects, Pathogens and Drought Shape Changing Patterns in Oak Decline in North America and Europe. For. Ecol. Manag. 2015, 354, 190–205. [Google Scholar] [CrossRef]
  60. Fan, Z.; Fan, X.; Crosby, M.K.; Moser, W.K.; He, H.; Spetich, M.A.; Shifley, S.R. Spatio-Temporal Trends of Oak Decline and Mortality under Periodic Regional Drought in the Ozark Highlands of Arkansas and Missouri. Forests 2012, 3, 614–631. [Google Scholar] [CrossRef]
  61. Muller, J.J.; Nagel, L.M.; Palik, B.J. Forest Adaptation Strategies Aimed at Climate Change: Assessing the Performance of Future Climate-Adapted Tree Species in a Northern Minnesota Pine Ecosystem. For. Ecol. Manag. 2019, 451, 117539. [Google Scholar] [CrossRef]
  62. Kim, D.; Millington, A.C.; Lafon, C.W. Biotic and Spatial Factors Potentially Explain the Susceptibility of Forests to Direct Hurricane Damage. J. Ecol. Environ. 2019, 43, 37. [Google Scholar] [CrossRef]
  63. Aldrich, P.R.; Parker, G.R.; Ward, J.S.; Michler, C.H. Spatial Dispersion of Trees in an Old-Growth Temperate Hardwood Forest over 60 Years of Succession. For. Ecol. Manag. 2003, 180, 475–491. [Google Scholar] [CrossRef]
  64. Greenberg, C.H.; Collins, B.S. Managing Forest Ecosystems Natural Disturbances and Historic Range of Variation Type, Frequency, Severity, and Post-Disturbance Structure in Central Hardwood Forests USA; Springer: Berlin/Heidelberg, Germany, 2016; Volume 32. [Google Scholar] [CrossRef]
  65. Mehmood, K.; Anees, S.A.; Rehman, A.; Rehman, N.U.; Muhammad, S.; Shahzad, F.; Liu, Q.; Alharbi, S.A.; Alfarraj, S.; Ansari, M.J.; et al. Assessment of Climatic Influences on Net Primary Productivity along Elevation Gradients in Temperate Ecoregions. Trees For. People 2024, 18, 100657. [Google Scholar] [CrossRef]
  66. Rozas, V. Structural Heterogeneity and Tree Spatial Patterns in an Old-Growth Deciduous Lowland Forest in Cantabria, Northern Spain. Plant Ecol. 2006, 185, 57–72. [Google Scholar] [CrossRef]
  67. Chapman, R.A.; Heitzman, E.; Shelton, M.G. Long-Term Changes in Forest Structure and Species Composition of an Upland Oak Forest in Arkansas. For. Ecol. Manag. 2006, 236, 85–92. [Google Scholar] [CrossRef]
  68. Hoover, N.M. Succession of an Upland Oak/Hickory Forest in the Central Hardwood Region; Southern Illinois University at Carbondale: Carbondale, IL, USA, 2018. [Google Scholar]
  69. Prasad, A.M. Macroscale Intraspecific Variation and Environmental Heterogeneity: Analysis of Cold and Warm Zone Abundance, Mortality, and Regeneration Distributions of Four Eastern US Tree Species. Ecol. Evol. 2015, 5, 5033–5048. [Google Scholar] [CrossRef]
  70. Bréda, N.; Badeau, V. Forest Tree Responses to Extreme Drought and Some Biotic Events: Towards a Selection According to Hazard Tolerance? C. R. Geosci. 2008, 340, 651–662. [Google Scholar] [CrossRef]
  71. Turner, M.G.; Calder, W.J.; Cumming, G.S.; Hughes, T.P.; Jentsch, A.; LaDeau, S.L.; Lenton, T.M.; Shuman, B.N.; Turetsky, M.R.; Ratajczak, Z.; et al. Climate Change, Ecosystems and Abrupt Change: Science Priorities. Philos. Trans. R. Soc. B Biol. Sci. 2020, 375, 20190105. [Google Scholar] [CrossRef]
  72. Petritan, A.M.; Petritan, I.C.; Hevia, A.; Walentowski, H.; Bouriaud, O.; Sánchez-Salguero, R. Climate Warming Predispose Sessile Oak Forests to Drought-Induced Tree Mortality Regardless of Management Legacies. For. Ecol. Manag. 2021, 491, 119097. [Google Scholar] [CrossRef]
  73. Kalyuzhny, M.; Kadmon, R.; Shnerb, N.M. A Neutral Theory with Environmental Stochasticity Explains Static and Dynamic Properties of Ecological Communities. Ecol. Lett. 2015, 18, 572–580. [Google Scholar] [CrossRef]
  74. Van Couwenberghe, R.; Collet, C.; Lacombe, E.; Pierrat, J.C.; Gégout, J.C. Gap Partitioning among Temperate Tree Species across a Regional Soil Gradient in Windstorm-Disturbed Forests. For. Ecol. Manag. 2010, 260, 146–154. [Google Scholar] [CrossRef]
  75. Lhotka, J.M.; Stringer, J.W. Forest Edge Effects on Quercus Reproduction within Naturally Regenerated Mixed Broadleaf Stands. Can. J. For. Res. 2013, 43, 911–918. [Google Scholar] [CrossRef]
  76. Hanberry, B.B.; Dey, D.C.; He, H.S. Regime Shifts and Weakened Environmental Gradients in Open Oak and Pine Ecosystems. PLoS ONE 2012, 7, e41337. [Google Scholar] [CrossRef] [PubMed]
  77. Yang, J.; He, H.S.; Shifley, S.R. Spatial Controls of Occurrence and Spread of Wildfires in the Missouri Ozark Highlands. Ecol. Appl. 2008, 18, 1212–1225. [Google Scholar] [CrossRef] [PubMed]
  78. Annighöfer, P.; Beckschäfer, P.; Vor, T.; Ammer, C. Regeneration Patterns of European Oak Species (Quercus petraea (Matt.) Liebl., Quercus. robur. L.) in Dependence of Environment and Neighborhood. PLoS ONE 2015, 10, e0134935. [Google Scholar] [CrossRef]
  79. Copes-Gerbitz, K.; Arabas, K.; Larson, E.; Gildehaus, S. A Multi-Proxy Environmental Narrative of Oregon White Oak (Quercus garryana) Habitat in the Willamette Valley, Oregon. Northwest Sci. 2017, 91, 160–185. [Google Scholar] [CrossRef]
  80. Lovett, G.M.; Weiss, M.; Liebhold, A.M.; Holmes, T.P.; Leung, B.; Lambert, K.F.; Orwig, D.A.; Campbell, F.T.; Rosenthal, J.; McCullough, D.G.; et al. Nonnative Forest Insects and Pathogens in the United States: Impacts and Policy Options. Ecol. Appl. 2016, 26, 1437–1455. [Google Scholar] [CrossRef] [PubMed]
  81. Southgate, E.W.B.R.; Thompson, J.E. Secondary Forest Succession in a Post-Agricultural Landscape in the Hudson Valley, New York. Northeast. Nat. 2014, 21, 1. [Google Scholar] [CrossRef]
  82. Wang, J.; Maduako, I.N. Spatio-Temporal Urban Growth Dynamics of Lagos Metropolitan Region of Nigeria Based on Hybrid Methods for LULC Modeling and Prediction. Eur. J. Remote Sens. 2018, 51, 251–265. [Google Scholar] [CrossRef]
  83. Hanberry, B.B.; Kabrick, J.M.; He, H.S. Densification and State Transition Across the Missouri Ozarks Landscape. Ecosystems 2014, 17, 66–81. [Google Scholar] [CrossRef]
  84. Mehmood, K.; Anees, S.A.; Rehman, A.; Pan, S.; Tariq, A.; Zubair, M.; Liu, Q.; Rabbi, F.; Khan, K.A.; Luo, M. Exploring Spatiotemporal Dynamics of NDVI and Climate-Driven Responses in Ecosystems: Insights for Sustainable Management and Climate Resilience. Ecol. Inform. 2024, 80, 102532. [Google Scholar] [CrossRef]
  85. Barlow, M.; Gutowski, W.J.; Gyakum, J.R.; Katz, R.W.; Lim, Y.K.; Schumacher, R.S.; Wehner, M.F.; Agel, L.; Bosilovich, M.; Collow, A.; et al. North American Extreme Precipitation Events and Related Large-Scale Meteorological Patterns: A Review of Statistical Methods, Dynamics, Modeling, and Trends; Springer: Berlin/Heidelberg, Germany, 2019; Volume 53, ISBN 0123456789. [Google Scholar]
  86. Walsh, C.R.; Patterson, R.T. Attribution of Observed Periodicity in Extreme Weather Events in Eastern North America. Earth Space Sci. 2022, 9, e2022EA002359. [Google Scholar] [CrossRef]
  87. Pederson, N.; Dyer, J.M.; Mcewan, R.W.; Hessl, A.E.; Mock, C.J.; Orwig, D.A.; Rieder, H.E.; Cook, B.I. The Legacy of Episodic Climatic Events in Shaping Temperate, Broadleaf Forests. Ecol. Monogr. 2014, 84, 599–620. [Google Scholar] [CrossRef]
  88. Titus, B.D.; Brown, K.; Helmisaari, H.S.; Vanguelova, E.; Stupak, I.; Evans, A.; Clarke, N.; Guidi, C.; Bruckman, V.J.; Varnagiryte-Kabasinskiene, I.; et al. Sustainable Forest Biomass: A Review of Current Residue Harvesting Guidelines. Energy Sustain. Soc. 2021, 11, 10. [Google Scholar] [CrossRef]
  89. Santos, B.A.; Arroyo-Rodríguez, V.; Moreno, C.E.; Tabarelli, M. Edge-Related Loss of Tree Phylogenetic Diversity in the Severely Fragmented Brazilian Atlantic Forest. PLoS ONE 2010, 5, e12625. [Google Scholar] [CrossRef] [PubMed]
  90. Collins, R.J.; Carson, W.P. The Effects of Environment and Life Stage on Quercus Abundance in the Eastern Deciduous Forest, USA: Are Sapling Densities Most Responsive to Environmental Gradients? For. Ecol. Manag. 2004, 201, 241–258. [Google Scholar] [CrossRef]
  91. Sun, G.; Caldwell, P.V.; Mcnulty, S.G. Modelling the Potential Role of Forest Thinning in Maintaining Water Supplies under a Changing Climate across the Conterminous United States. Hydrol. Process 2015, 29, 5016–5030. [Google Scholar] [CrossRef]
  92. Hudgins, E.J.; Leung, B.; MacQuarrie, C.J.K.; McCullough, D.G.; Francis, A.; Lovett, G.M.; Guo, Q.; Potter, K.M.; Cullingham, C.I.; Koch, F.H.; et al. Five Organizing Themes for Invasive Forest Insect and Disease Management in Canada and the United States. For. Ecol. Manag. 2024, 566, 122046. [Google Scholar] [CrossRef]
  93. Guignabert, A.; Jonard, M.; Messier, C.; André, F.; de Coligny, F.; Doyon, F.; Ponette, Q. Adaptive Forest Management Improves Stand-Level Resilience of Temperate Forests under Multiple Stressors. Sci. Total Environ. 2024, 948, 174168. [Google Scholar] [CrossRef]
  94. Frelich, L.E.; Reich, P.B.; Peterson, D.W. Fire in Upper Midwestern Oak Forest Ecosystems: An Oak Forest Restoration and Management Handbook; Gen. Tech. Rep. PNW-GTR-914; US Department of Agriculture, Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 2015; Volume 914, p. 64. [Google Scholar]
Forests 15 01809 g001
Figure 1. Study area mainly showing forest covers and others across ten states of the eastern US.
Figure 1. Study area mainly showing forest covers and others across ten states of the eastern US.
Forests 15 01809 g001
Forests 15 01809 g002
Figure 2. Our study area showing (A) spatial distribution of WOM rate plots and (B) kernel density distribution of WOM rates across different latitudes and longitudes of the eastern United States.
Figure 2. Our study area showing (A) spatial distribution of WOM rate plots and (B) kernel density distribution of WOM rates across different latitudes and longitudes of the eastern United States.
Forests 15 01809 g002
Forests 15 01809 g003aForests 15 01809 g003b
Figure 3. Ripley’s K function showing clustered, random, and uniform patterns across varying scales of (A) southern, (B) central, and (C) northern regions WOM rates in the eastern United States.
Figure 3. Ripley’s K function showing clustered, random, and uniform patterns across varying scales of (A) southern, (B) central, and (C) northern regions WOM rates in the eastern United States.
Forests 15 01809 g003aForests 15 01809 g003b
Table 1. Ten states with inventory years, cycles, number of live white oak (WO) plots, and declining WO plots.
Table 1. Ten states with inventory years, cycles, number of live white oak (WO) plots, and declining WO plots.
StateInventory YearsCyclesLive WO PlotsDeclining WO Plots
Alabama2001–20198, 9, and 10716166
Arkansas2000–20198, 9, 10, and 111158400
Illinois2001–20195, 6, 7, and 824463
Indiana1999–20195, 6, 7, and 820643
Kentucky2000–20175, 6, 7, and 8775207
Missouri1999–20195, 6, 7, and 81544527
Ohio2001–20195, 6, 7, and 833767
Tennessee2000–20177, 8, 9, and 10909305
Virginia1998–20197, 8, 9, 10, and 11855314
West Virginia2004–20196, 7, and 8661128
Total 74052220
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Khadka, S.; He, H.S.; Bardhan, S. Investigating the Spatial Pattern of White Oak (Quercus alba L.) Mortality Using Ripley’s K Function Across the Ten States of the Eastern US. Forests 2024, 15, 1809. https://doi.org/10.3390/f15101809

AMA Style

Khadka S, He HS, Bardhan S. Investigating the Spatial Pattern of White Oak (Quercus alba L.) Mortality Using Ripley’s K Function Across the Ten States of the Eastern US. Forests. 2024; 15(10):1809. https://doi.org/10.3390/f15101809

Chicago/Turabian Style

Khadka, Saaruj, Hong S. He, and Sougata Bardhan. 2024. "Investigating the Spatial Pattern of White Oak (Quercus alba L.) Mortality Using Ripley’s K Function Across the Ten States of the Eastern US" Forests 15, no. 10: 1809. https://doi.org/10.3390/f15101809

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop