In the management of forests, the boundaries of individual units of land containing similar forest resources (e.g., stands) are delineated and used to guide the implementation of management activities. Traditionally, stand boundaries are drawn or digitized by hand; however, work recently has been conducted to automate the process using aerial imagery or airborne light detection and ranging (LiDAR) data as supporting resources. The work described here applies an object-based image analysis (OBIA) process to aerial imagery and to a landform index database. The size and shape of stands in the outcomes of these applications are then adjusted to conform to the desired product of land managers. These products are then intersected as they each contain information of value in the stand delineation process. The intersected database is then adjusted once again to conform to the desired product of land managers. Conformity of the size and shape of the resulting stand boundaries to a reference database drawn subjectively by hand was low to moderate. Specifically, the overall agreement for spatial and thematic (class names) accuracies was 43.0% and 56.8%, respectively. Nevertheless, the process of automating the stand delineation effort remains promising for achieving an efficient and non-subjective characterization of a structurally complex forested environment.

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor intensive and time consuming. To address this limitation, remote sensing technology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Shoal Creek and Talladega Ranger Districts of the Talladega National Forest by employing ALS-derived data and aerial photography. The quality of the predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species group models. This study confirms that quality level 2 LiDAR data were sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82), when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species group predictive models were of higher quality than general predictive models. Lastly, landscape level maps were created from the predictive models and these may be helpful to planners, forest managers, and landowners in their management efforts.

Canopy cover (CC) is the proportion of a forest floor covered by the vertical projection of tree crowns. Recently, it has become common to utilize LiDAR (light detection and ranging) canopy metrics to estimate CC over large areas. However, these metrics are primarily related to canopy density rather than the specific definition of CC. Here, two processes that employed individual tree segmentation (ITS) and area-based procedures based on LiDAR data are presented to estimate CC across the Talladega Division of the Talladega National Forest (93,694 ha) at the plot, stand, and landscape levels. The two analytical procedures were assessed using the results of a plot/grid method as a reference dataset, which focused on CC estimates within 255 field measurement fixed-area sample plots. The accuracy of a third process, employing an imagery-based visual CC assessment, was also compared against the two procedures and the reference dataset. The LiDAR-based analytical procedures were able to provide estimates of CC with an RMSE of approximately 15 %, which is acceptable for landscape-level assessments. Based on the results of this study, we conclude that CC maps, when created using LiDAR data, may be suitable for various operational tasks such as assessing the impact of forest disturbances and helping to determine the habitat suitability for certain wildlife species.

While many tree species occur across the Coastal Plain of the southeastern United States, longleaf pine (Pinus palustris C. Lawson) savannas and woodlands once dominated this region. To quantify longleaf pine’s past primacy and trends in the Coastal Plain, we combined seven studies consisting of 255,000 trees from land surveys, conducted between 1810 and 1860 with other descriptions of historical forests, including change to the present day. Our synthesis found support that Pinus palustris predominantly constituted 77% of historical Coastal Plain trees and upland oaks (Quercus) contributed another 8%. While Pinus still dominates these forests today (58% of all trees), most are now either planted loblolly (Pinus taeda L.) or slash (Pinus elliottii Engelm.) pines. Water oak (Quercus nigra L.), live oak (Quercus virginiana Mill.), sweetgum (Liquidambar styraciflua L.), and red maple (Acer rubrum L.) have increased their proportions compared to historical surveys; both longleaf pine and upland oaks have declined to ≤5% of all trees. Our work also supports previous estimates that longleaf pine originally dominated over 25–30 million ha of Coastal Plain forests. As late as the early 1900s, longleaf pine may still have covered 20 million ha, but declined to 7.1 million ha by 1935 and dropped to 4.9 million ha by 1955. Longleaf pine’s regression continued into the mid-1990s, reaching a low of about 1.3 million ha; since then, restoration efforts have produced a modest recovery to 2.3 million ha. Two centuries of overcutting, land clearing, turpentining for chemicals, fire exclusion followed by forest densification by fire-sensitive species, and other silvicultural influences, including widespread loblolly and slash pine plantations, have greatly diminished the Coastal Plain’s once extensive open longleaf pine forests.

Snags, or standing dead trees, are an important structural component of forest ecosystems. Many animals, including endangered species, depend on snags for foraging, protection, or raising young. Climate change, habitat loss, and modification of natural disturbance regimes contribute to changes in the availability and characteristics of snags in forests. Therefore, understanding what natural and artificial processes promote snags with the characteristics necessary for wildlife is a significant conservation concern. We examined how low-severity prescribed fire affected the density and characteristics of snags at 80 sites in the Talladega National Forest, Alabama. We sampled sites within 4 prescribed fire intervals, including 1-3 (previously thinned 4-23 years ago), > 3-8, > 8-12, and > 12 years. At each site, we measured snags across transects on 3 different slope positions, including the ridge, mid-slope, and valley, to account for slope-influenced fire behavior and stressors. The average diameter of snags increased in stands with the shortest prescribed fire interval, but snag height, decay class, and percentage of bark remaining were similar across all fire intervals. Snag density was lowest in the shortest fire interval due to fewer small-and medium-sized hardwood snags. A higher density of large snags was found in the shortest fire interval compared to the longest fire interval. Ridges had a greater density of snags compared to mid-slope and valley positions due to more small-and medium-sized pine snags. Although thinning followed by frequent, low-severity prescribed fire reduces snag density, the increase in density of large-diameter snags provides high-quality habitat for snag-dependent birds and bats. More intense fire and other stressors on ridges likely promote higher densities of snags. Our research indicates forest managers can use prescribed fire and thinning to accomplish multiple management goals while continuing to produce valuable snags for wildlife.

Accurate knowledge of ecological condition (EC) is crucial to evaluate the deviation of a given ecosystem from a reference condition, and is of interest to forest managers as it helps them prioritize management activities. The present study aimed to estimate EC classes of the Talladega National Forest (NF) using NAIP images, and airborne laser scanning (ALS) data, as well as field measurements from 255 plots. The results indicated that the EC classes could be distinguished using z q25 , i mean , and p 5th metrics from ALS, as well as Enhanced Vegetation Index. Among them, we used z q25 to generate a map for the entire study area. Accordingly, the dominant EC was class 3, suggesting that almost half of the forestland is composed of young and dense stands with woody understory. This cover type is not desirable in terms of wildfire risk and far from the historical conditions. Thus, NF managers might thin and/or prescribed burn these areas to improve EC.

Golden Eagles (Aquila chrysaetos) have a Holarctic distribution, but some details of that overall distribution are poorly understood, including parts of the range in eastern North America. Recent studies in the region suggest that Golden Eagles may be more widely distributed than previously recognized. For species specific conservation efforts to be effective, an understanding of the distribution of the species is essential. Thus, the goal of this study was to map the winter distribution of Golden Eagles in the eastern half of the USA. To accomplish this, we reviewed and compiled 11,981 Golden Eagle records from eight data sources, including literature and ornithology records, community science data, survey data, and telemetry data. We found that Golden Eagles were observed in winter in each of the 31 states that lie completely east of the 100th meridian and in 1244 of the 2045 counties (61%) in those states. The proportion of counties with records varied by physiographic province, with higher proportions in physiographic provinces with more rugged terrain and greater forest cover. Our study shows that Golden Eagles are more widely distributed during winter in eastern USA states than was previously recognized. This work provides an important foundation for future management and research at a time when threats to this species are expanding rapidly on the landscape.

This work describes the development and analysis of a spatially explicit environmental model to estimate the current, ecological, condition class of a managed forest landscape in the southern United States. The model could be extendable to other similar temperate forest landscapes, yet is characterized as a problem-specific, hierarchical, binary process model given the explicit relationships it recognizes between the management of southern United States pine-dominated natural forests and historical ecological conditions. The model is theoretical, based on informed proposals of the landscape processes that influence the ecological condition, and their relationship to perceived ecological condition. The modeling effort is based on spatial data that describe the historical forest community classes, forest plan provisions, fire history, silvicultural treatments, and current vegetation conditions, and six potential ecological condition classes (ECC) are assigned to lands. A case study was provided involving a large national forest, and validation of the outcomes of the modelling effort suggested that the overall accuracy when predicting the exact ecological condition class was about 46%, while the overall accuracy ±1 class was about 81%. For large, heterogeneous forest areas, issues remain in estimating the input variables relatively accurately, particularly the pine basal area.

Myotis austroriparius (Southeastern Myotis) traditionally inhabit bottomland hardwood forests along the Atlantic coastal plain and lower Mississippi River Valley. This insectivorous bat is a species of conservation concern in Alabama and Georgia and was thought to be restricted to the Southern Coastal Plain and Southeastern Plains ecoregions. Based primarily on cave, transportation-structure, and mist-netting surveys, we documented occurrences of Southeastern Myotis in 14 Alabama and 20 Georgia counties outside and along the border of the accepted range, including in the Piedmont, Ridge and Valley, Interior Plateau, and Southwestern Appalachians ecoregions. We reviewed observations of bats year-round in both states, including pregnant and lactating females and winter hibernacula. Roosting sites included caves, culverts, bridges, and tree cavities. Combined, these observations provide strong evidence that the range of Southeastern Myotis should consist of all Alabama and Georgia except for the Blue Ridge ecoregion. This increase in distribution may result from increased surveying efforts and/or range expansion due to climate change, as observed in other southeastern bat species.

This study focused on the rare and threatened plant species eastern turkeybeard (Xerophyllum asphodeloides (L.) Nutt.) and its presence or absence in the Talladega National Forest in Alabama, USA. An ensemble suitable habitat map was developed using four different modeling methods (MaxEnt, Generalized Linear Model, Generalized Additive Model, and Random Forest). AUC evaluation scores for each model were 0.99, 0.96, 0.98, and 0.99, respectively. Biserial correlation scores for models ranged from 0.71 (GLM) to 0.94 (RF). The four different models agreed suitable habitat was found to cover 159.57 ha of the land. The ground slope variable was the most contributive variable in the MaxEnt and RF models and was also significant in the GLM and GAM models. The knowledge gained from this research can be used to establish and implement habitat suitability strategies across the Talladega National Forest and similar ecosystems in the southern United States.

Xerophyllum asphodeloides (Xerophyllaceae), known as eastern turkeybeard, is an herbaceous perennial found in eastern North America. Due to decline and destruction of its habitat, several states rank X. asphodeloides as “Imperiled” to “Critically Imperiled”. Protocols for seed cryopreservation, in vitro germination, sustainable shoot micropropagation, shoot establishment in soil, and seed germination are presented. Seeds from two tested sources were viable after 20 months of cryopreservation. Germination of isolated embryos in vitro was necessary to overcome strong seed dormancy. Shoot multiplication and elongation occurred on ½ MS medium without PGRs. Shoots rooted in vitro without PGRs or with 0.5 mg/L NAA or after NAA rooting powder treatment and placement in potting mix. When planted in wet, peaty soil mixes, shoots grew for two months and then declined. When planted in a drier planting mix containing aged bark, most plants continued growth. In the field, plant survival was 73% after three growing seasons. Safeguarding this species both ex situ and in situ is possible and offers a successful approach to conservation. Whole seeds germinated after double dormancy was overcome by incubation under warm moist conditions for 12 weeks followed by 12 weeks cold at 4 °C and then warm.

ABSTRACT Conservation biologists need to effectively monitor species given resource limitations and the inherent challenges of assessing long‐term demographic processes. We assessed gopher tortoise ( Gopherus polyphemus ) abundance at a landscape scale and at the scale of 3 local populations within the Conecuh National Forest (CNF), Alabama, USA, between 1991 and 2017. We collected landscape‐level data from line transect distance sampling arranged uniformly across the CNF during a single season (2011); we obtained data for local populations from long‐term mark‐recapture of individuals at 3 sites selected based on prior knowledge of high density at each. At a landscape scale, we estimated 5,242 (95% CI = 3,538–7,768) tortoises occurred across the approximately 34,000‐ha forest, yielding a density of 0.14–0.32 tortoises/ha. These low densities across the landscape suggest that, on average, management activities across the property have not allowed tortoise populations to retain the social structure needed for long‐term persistence. The 3 local populations, however, contained 25–60 individuals and densities of 1.9–6.9 tortoises/ha. Over the study period, populations at 2 sites were stable and the third experienced significant population growth. Mean annual survival of individuals was 0.89 and invariant across size classes. Overall, line transect distance sampling is important for assessing landscape‐scale abundance of tortoises but may fail to detect local clusters of high‐density sites important for population persistence. Our mark‐recapture efforts at the local scale revealed that small populations on these high‐density sites can exhibit long‐term stability or growth even though they do not meet current established criteria for viability. Improved models that incorporate immigration and emigration and better reflect the dynamics of peripheral populations would assist in determining how such populations best contribute to species recovery and regional conservation targets. © 2020 The Wildlife Society.

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

Sciurus niger L. (Eastern Fox Squirrel) is associated with montane Pinus palustris Mill. (Longleaf Pine) forests in the Piedmont and Ridge and Valley, but little is known about the species' distribution and abundance within this region. We conducted an occupancy study of Eastern Fox Squirrels in montane Longleaf Pine forests of the Talladega National Forest, AL. We surveyed 73 camera trap sites for Eastern Fox Squirrels and measured surrounding vegetation and landscape features. Eastern Fox Squirrels were patchily distributed across the study area and only observed at 11% of sites. Occupancy modeling indicated Eastern Fox Squirrels had a relatively high probability of detection (0.680) but a low probability of occupancy (0.111). Eastern Fox Squirrel occupancy was negatively associated with slope steepness. This result is possibly because prescribed fire and other restoration efforts of open-pine conditions associated with Eastern Fox Squirrel habitat in Talladega National Forest are focused on logistically accessible ridges and more moderately sloped areas. Steep slopes also likely decrease accessibility and dispersion ability for Eastern Fox Squirrels. The overall low occupancy of Eastern Fox Squirrels in the Shoal Creek Ranger District of Talladega National Forest may be linked to the highly fragmented montane Longleaf Pine habitat caused by topography and past fire suppression.

Our objective was to interpret the presence and magnitude of landscape modification by Native Americans on Georgia's southern coastal plain. Specifically, we aimed to understand how the Native American presence influenced the distribution of fire-tolerant, mast-bearing and fruit-bearing tree species in the fire-dominated landscape of south-west Georgia.Our study area was comprised of sixteen contiguous counties in the south-west region of Georgia, in southeast USA bordering the Atlantic, investigating the taxon Angiosperms and Gymnosperms native to the early landscape of this region.We used witness tree data collected during the early 1820s across sixteen modern-day counties to reconstruct pre-settlement forest composition, particularly pyrophillic trees that are well-adapted to tolerate fire, and mast- and fruit-bearing species. We then used geographic distribution models (Boosted Regression Tree) to interpret the presence and magnitude of landscape modification by Native Americans on Georgia's forested south-west plain.The pre-settlement distribution of pyrophillic and mast-bearing trees within our study area were best explained by a combination of environmental (topographic relief, proximity to riparian zones, and soil depth) and Native American factors (AUC = 0.64 and 0.66, respectively).However, the addition of Native American presence as predictors greatly increased the explanatory power of soft mast (fruit)-bearing models (AUC = +0.17).Our results demonstrate that Native American activities had a measurable influence on pre-settlement plant communities in south-western Georgia. However, the effects of these activities on vegetative composition were most notable in the distributions of fruit-bearing trees. In contrast, distributions of fire-tolerant and mast-bearing taxa were found to be largely explained by a combination of environmental and anthropogenic factors.

Fire is an essential ecological process and management tool for many forested landscapes, particularly the pine (Pinus spp.) forests of the southern USA. Within the Talladega National Forest in Alabama, where restoration and maintenance of pine ecosystems is a priority, fire frequency (both wild and prescribed) was assessed using a geographical process applied to a fire history database. Two methods for assessing fire frequency were employed: (1) a simple method that utilised the entire range of years acknowledged in the database and (2) a conservative method that was applied only the date of the first and last fires recorded at each location. Analyses were further separated by (a) method of mean fire return interval calculation (weighted by area or Weibull) and (b) fire season interval with analyses conducted on growing season and dormant season fires. Analyses of fire frequency for national forest planning purposes may help determine whether a prescribed fire program mimics ecological and historical fire frequencies and meets intended objectives. The estimated fire return interval was between ~5 and 6.5 years using common, straightforward (simple) methods. About one-third of the forest receives no fire management and about half of the balance has sufficiently managed fuels.

Conservation and restoration of biodiversity and ecosystems is increasingly important due to negative impacts caused by expanding human populations, changing land use, and climate change. Understanding drivers of system processes supports efficient restoration and successful conservation of biodiversity and ecosystems. We describe a long-term forest monitoring program established as a component of a long-term ecological monitoring approach and used in conjunction with adaptive management to actively restore a longleaf pine (Pinus palustris) system. Eight panels of 108 monitoring points were selected within our 11,740 ha study area near Newton, GA, USA using a Random Tessellation Stratified design with hierarchical randomization. Each year two panels were sampled, resulting in a complete survey of all points every four years. To date, data have been collected over four sampling intervals (2002-2017). Collected data included basic forestry measurements and quantification of understory conditions. For an initial analysis of the data, we calculated estimates and error of average volume, change in volume, mortality, diameter distribution, and ingrowth. Additionally, we compared effects of management practices, which varied between plots, on these estimates. The tree volume on the study area is comprised primarily of longleaf pine with similar to 80% of this volume from trees >= 34 cm DBH. Pine tree volume increased by 13.29 (+/- 2.03) m(3)/ha between Intervals 1 and 4. Pine mortality during each sample interval was relatively stable over the study period, 2.98 (+/- 0.39) -4.56% (+/- 0.58), and primarily attributed to timber harvest. Management activities resulted in increased longleaf pine ingrowth and reduced hardwood volume. Our analyses indicate that through our management actions we have successfully made progress towards achieving our overall restoration goals for the site. Data collected from the long-term monitoring program were also used to provide information for scientific studies regarding water conservation in longleaf pine systems and wildlifehabitat relationships. As monitoring progresses, collected data will be used to assess progress and guide further restoration using adaptive management and dynamic reference models.

Gopher tortoises (Gopherus polyphemus) are candidates for range-wide listing as threatened under the U.S. Endangered Species Act. Reliable population estimates are important to inform policy and management for recovery of the species. Line transect distance sampling has been adopted as the preferred method to estimate population size. However, when tortoise density is low, it can be challenging to obtain enough tortoise observations to reliably estimate the probability of detection, a vital component of the method. We suggest a modification to the method based on counting usable tortoise burrows (more abundant than tortoises) and separately accounting for the proportion of burrows occupied by tortoises. The increased sample size of burrows can outweigh the additional uncertainty induced by the need to account for the proportion of burrows occupied. We demonstrate the method using surveys conducted within a 13,118-ha portion of the Gopher Tortoise Habitat Management Unit at Fort Gordon Army Installation, Georgia. We used a systematic random design to obtain more precise estimates, using a newly developed systematic variance estimator. Individual transects had a spatially efficient design (pseudocircuits), which greatly improved sampling efficiency on this large site. Estimated burrow density was 0.091 +/- 0.011 burrows/ha (CV = 12.6%, 95% CI = 0.071-0.116), with 25% of burrows occupied by a tortoise (CV = 14.4%), yielding a tortoise density of 0.023 +/- 0.004 tortoise/ha (CV = 19.0%, 95% CI = 0.016-0.033) and a population estimate of 297 tortoises (95% CI = 210-433). These techniques are applicable to other studies and species. Surveying burrows or nests, rather than animals, can produce more reliable estimates when it leads to a significantly larger sample of detections and when the occupancy status can reliably be ascertained. Systematic line transect survey designs give better precision and are practical to implement and analyze.

Status and trends of gopher tortoise (Gopherus polyphemus) populations are a critical information need for natural resource managers, researchers, and policy makers. Many tortoise populations are small and isolated, which can present challenges for deriving population estimates. Our objective was to compare abundance and density estimates for a small tortoise population derived using a total burrow count versus estimates obtained with line transect distance sampling (LTDS) using repeated surveys. We also compared results of the 2 survey methods using standard burrow-to-tortoise correction factors versus assessing occupancy of all burrows with a camera scope. In addition, we compared LTDS data obtained using a compass and measuring tape to define transects to those obtained using a Global Positioning System (GPS) and Personal Data Assistant (PDA) field computer to navigate transects. Line transect distance sampling with repeated surveys (both with a measuring tape and compass and with a GPS-PDA) yielded sufficient observations of tortoises to calculate population estimates. From 18% to 31% of burrows were occupied by tortoises as determined with the burrow camera. We found 25 burrows during the LTDS survey that we did not find in the total count survey, which demonstrated that the assumption of 100% detection for the total count was not met; hence, density or abundance measurements derived with this method were underestimates. We recommend using GPS-PDA technology, scoping all burrows detected, and using LTDS with repeated surveys to estimate abundance and density for small gopher tortoise populations.

We describe 12 polymorphic tetranucleotide and pentanucleotide loci in the red-cockaded woodpecker (Picoides borealis). An average of 6.25 alleles per locus was identified based on a screening of 21 individuals from the Joseph W. Jones Ecological Center in southwestern Georgia. Observed heterozygosity ranged from 0.048 to 0.952. These markers could be used for both population level studies and individual identification.