Forest Inventory And Analysis Research Articles

Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data

ABSTRACTEffective modelling of forest susceptibility to defoliating insect outbreaks requires a better understanding of outbreak dynamics, which includes detailed knowledge of the pre- and post-outbreak forest status as well as subsequent feedback mechanisms. In this paper, we strive to fill the forest status need by combining archived Landsat sensor data (pre- and post-outbreak) with different formats and dates of the U.S. Forest Service’s Forest Inventory and Analysis (FIA) data (periodic [1970s, 1990s] and annual [2003–2006]). Specifically, we explore the utility of these FIA ground data for calibrating models of forest species and type abundance for mapping past forest composition in the Border Lakes Ecoregion (BLE) of Upper Midwest of the US. Model calibration results between Landsat reflectance and FIA ground data for both total forest basal area and balsam fir (Abies balsamea) relative basal area, a preferred host of the spruce budworm (SBW, Choristoneura fumiferana), were poor to moderate (R2adj 0.39 and 0.48, respectively). Results for aspen (Populous tremuloides) and spruce (Picea glauca and P. mariana) abundance yielded substantially better accuracies (R2adj 0.64 and 0.78; RMSE 15.56 and 10.65 m2 ha−1, respectively). Groupings of tree species into broadleaved and conifers substantially improved model calibration result (R2adj range: 0.72–0.91), except for the SBW host group (A. balsamea, P. glauca, and P. mariana). Periodic FIA ground data from the early 1990s generated stronger models compared to other FIA-Landsat date combinations tested. A paired t-test of abundance differences between undisturbed forest from the older 1977 and 1990 periodic inventories was significant (p-value < 0.0001), suggesting possible effects of variable FIA sampling protocol or ground plot positional accuracy through time. However, a similar paired t-test of abundance difference between periodic FIA (1990) and annual FIA (2003–2006) was not significant (p-value = 0.249). We posit four potential factors that may have contributed to weak Landsat-FIA calibration results for species abundance: 1) variation in FIA subplot arrangement and sampling protocols through time, 2) variability in species abundance and heterogeneity among FIA sampling across adjacent Landsat orbital paths, 3) understory species (balsam fir) that are largely hidden from remote detection, and 4) cloud cover and orbital phase mismatches preventing capture of key forest phenology aids. While past and present FIA sampling protocols were not specifically designed for integration with 30-meter satellite sensor data, careful pairing of FIA ground data (past or present) with Landsat sensor data can facilitate reasonable estimates, of forest abundance for generalized forest types, and possibly forest species when heterogeneity is low. Nevertheless, we recommend that FIA subplot sampling protocols be augmented to include measurements of forest conditions that are more amenable to integration with 30-meter Landsat sensor data.

Supporting National Forest System Planning with Forest Inventory and Analysis Data

AbstractIn 2012, the US Forest Service promulgated new regulations for land-management planning that emphasize the importance of scientifically credible assessment and monitoring strategies for adaptive forest planning and the maintenance or restoration of ecological integrity. However, in an era of declining budgets, the implementation of robust assessment and monitoring strategies represents a significant challenge for fulfilling the intent of the new planning rule. In this article, we explore opportunities for using data and products produced by the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program to support the implementation of the 2012 Planning Rule. FIA maintains a nationally consistent statistical sample of field plots that covers most national forests with hundreds of plots. We suggest that leveraging FIA data and products can generate efficiencies for assessment, planning, and monitoring requirements detailed in the 2012 Planning Rule, and help fulfill the adaptive intent of the new planning rule. However, strong national leadership and investment in regional-level analytical capacity, FIA liaisons, and decision-support tools are essential for systematically realizing the benefits of FIA data for forest planning across the National Forest System.

The ecological and economic determinants of eastern redcedar (Juniperus virginiana) encroachment in grassland and forested ecosystems: A case study from Oklahoma

Frequent fires were used as a management tool to maintain prairies, savannas, and woodlands in the southern Great Plains of the United States. However, fire exclusion beginning in the mid-1900s allowed for the establishment and growth of fire-intolerant species such as eastern redcedar (Juniperus virginiana: ERC) beyond their naturally occurring habitats. Apart from the reduction in burning, wide soil and climate adaptability, and seed dispersal by birds have facilitated the expansion of ERC in the southern Great Plains. The encroachment of ERC has caused heavy ecological and economic losses to Oklahoma and thus has been a major management concern for the past few decades. This study utilized count data modeling to analyze USDA Forest Service's (USFS) Forest Inventory and Analysis (FIA) data to investigate the relationship between available ecological and economic factors and the abundance of ERC in grassland and forested ecosystems of Oklahoma. The results suggested that low site productivity, high basal area, dense canopy, and silt loam soil texture significantly increase the abundance of ERC on a given site. The results also indicated that the rate of ERC encroachment is 3.3% higher in the softwood and 2.0% higher in the miscellaneous forests, compared to the hardwood forests. However, the economic variables of the study such as ownership type, adoption of active management, and proximity to a metropolitan area did not show a significant relationship to the abundance of ERC.

Mapping black ash dominated stands using geospatial and forest inventory data in northern Minnesota, USA

Emerald ash borer (EAB; Agrilus planipennis Fairmaire, 1888) has been a persistent disturbance for ash forests in the United States since 2002. Of particular concern is the impact that EAB will have on the ecosystem functioning of wetlands dominated by black ash (Fraxinus nigra Marsh.). In preparation, forest managers need reliable and complete maps of black ash dominated stands. Traditionally, forest survey data from the United States Forest Inventory and Analysis (FIA) Program have provided rigorous measures of tree species at large spatial extents but are limited when providing estimates for smaller management units (e.g., stands). Fortunately, geospatial data can extend forest survey information by generating predictions of forest attributes at scales finer than those of the FIA sampling grid. In this study, geospatial data were integrated with FIA data in a randomForest model to estimate and map black ash dominated stands in northern Minnesota in the United States. The model produced low error rates (overall error = 14.5%; area under the curve (AUC) = 0.92) and was strongly informed by predictors from soil saturation and phenology. These results improve upon FIA-based spatial estimates at national extents by providing forest managers with accurate, fine-scale maps (30 m spatial resolution) of black ash stand dominance that could ultimately support landscape-level EAB risk and vulnerability assessments.

Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. Spatial, statistical and machine learning algorithms were used to relate United States Forest Service Forest Inventory and Analysis (FIA) field plot data to relatively normalized Landsat 8 imagery based texture. Modeling algorithms employed include softmax neural networks and multiple hurdle models that combine softmax neural network predictions with linear regression models to estimate key forest characteristics across 2.3 million ha in Georgia, USA. Forest metrics include forest type, basal area and stand density. Results show strong relationships between Landsat 8 imagery based texture and field data (map accuracy &gt; 0.80; square root basal area per ha residual standard errors &lt; 1; natural log transformed trees per ha &lt; 1.081). Model estimates depicting spatially explicit, fine resolution raster surfaces of forest characteristics for multiple coniferous and deciduous species across the study area were created and made available to the public in an online raster database. These products can be integrated with existing tabular, vector and raster databases already being used to guide longleaf pine conservation and restoration in the region.

Predicting understory vegetation structure in selected western forests of the United States using FIA inventory data

Understory vegetation structure and its relationship with forest canopies and site conditions are important determinants of carbon stocks, wildlife habitat, and fuel loading for wildland fire assessments. Comprehensive studies are needed to assess these relationships through the use of consistently collected field-based data. One approach to achieve this is to make use of preexisting forest inventory data to estimate understory vegetation height and cover from site and overstory attributes. In this study, overstory, understory, and abiotic data describing site conditions were obtained from over 6700 Forest Inventory and Analysis (FIA) fixed radius plots collected between 2000 and 2012 to assess how understory vegetation cover and height vary with overstory attributes and site characteristics. The focus was restricted to four common forest types including lodgepole pine (Pinus contorta var. latifolia), Douglas-fir (Pseudotsuga menziesii), ponderosa pine (Pinus ponderosa), and grand fir (Abies grandis) found on approximately 43 million hectares in the western United States. Random Forest regression classification trees were developed for cover and height of shrub and herb understories as a function of field-measured predictor variables. Separate analyses were undertaken for the Pacific Northwest (PNW) and the Interior West (IW) Forest Inventory and Analysis (FIA) regions. Models developed from the IW data generally performed better and the OOB (out-of-bag) percent variance explained varied from 8.08% for forb height to 39.24% for shrub height. For the PNW data, percent variance explained ranged from 13.82% for forb height to 27.4% for shrub height. Percent variance explained values were higher in all corresponding models for the IW than PNW, except for forb and grass height. Differences in model performance were smallest in the case of forb cover (27.17% vs. 26.15%) and greatest in the case of percent shrub cover (30.92% vs. 15.53%) for IW and PNW models, respectively. Cover models within each dataset performed better, on average, than their associated height models. The most influential variables for predicting understory cover and height were ones representing overstory conditions and conform to ecological expectation corroborated by many studies examining the influence of forest overstories on understory vegetation dynamics. Several variables, including aspect, slope, and stand disturbance and treatment, were not important and contrary to expectation. Predicting understory vegetation attributes to aid assessments of carbon, fuel, and wildlife habitat may be more generalizable across forests of the western U.S. using standardized national inventory data in conjunction with improved measurements.

The tortoise and the hare: A race between native tree species and the invasive Chinese tallow

Species-specific growth rate and its response to interspecific competition can determine the winners and losers in forest stand development following disturbance. In the southeastern US, Chinese tallow [Triadica sebifera (L.) Small], a non-native, fast-growing, invasive tree readily displaces native species. However, its rapid early height growth may not compensate for its shorter ultimate stature and earlier senescence when competing with fast growing native tree species of larger stature and longer lifespans. In this study, we compared the growth and competitiveness of Chinese tallow to two native species, slash pine (Pinus elliottii Englem.) and sweetgum (Liquidambar styraciflua L.), using two datasets representing different spatial scales. Plot data from Parris Island, South Carolina obtained by conducting stem analyses, were used to determine height and diameter growth patterns in relation to age and competition. Landscape-scale data from the U.S. Forest Inventory and Analysis (FIA) program were used to determine the relationship of relative importance value (rIV) and periodic annual DBH increment along a competition gradient. We found that Chinese tallow displayed faster diameter and height growth initially, but slowed down considerably after eight years, compared to slash pine. Slash pine was the least tolerant of competition among the three species, and competition had less effect on the growth of Chinese tallow and sweetgum. Our findings suggest that stand-replacing disturbance favors the rapid growth of Chinese tallow for the first decade, even under intense competition. Further, the establishment of native tree species would require effective control of Chinese tallow immediately following disturbance. Efforts to manage Chinese tallow while promoting the growth of native trees may include site preparation to reduce initial abundance of this invasive species, artificial regeneration of native species to provide them ‘head-start’ during the first few years of growth, and release treatments early in stand development to reduce competition for native species.

Assessment on latitudinal tree species richness using environmental factors in the southeastern United States.

The southeastern region of the United States exhibits an unusual trend of decreasing tree species richness (TSR) from higher to lower latitudes over the Florida peninsula. This trend contradicts the widely marked latitudinal diversity gradient where species richness is highest in tropical zones and decreases towards extratropical regions. This study aims to assess the environmental factors that prompt this atypical inverse latitudinal gradient seen in TSR using the USDA Forest Service’s Forest Inventory and Analysis (FIA) database. Fifteen variables under four categories of forested area, groundwater, soil properties, and climate groups were examined to model TSR in the region. Generalized linear models (GLMs) with Poisson distributions first assessed individual variables to test explanatory power then the LASSO regularization method was utilized to extract two subsets of the most influential variables to predict TSR. Forest area and four climate variables (mean annual temperature, precipitation seasonality, mean temperature of coldest quarter, and mean precipitation of driest quarter) were the top five variables during the initial GLM assessment implying their potential individual influence in regulating TSR. Two subsets of LASSO models contained seven and three predictor variables, respectively. Frist subset includes seven predictors, presented in highest to low standardized coefficient, mean temperature of coldest quarter, forested area, precipitation seasonality, mean precipitation of driest quarter, water table depth, spodosol, and available water storage. The other subset further excluded four lowest influential variables from the first set, leaving the top three variables from the first subset. The first subset of the LASSO model predicted TSR with 63.4% explained deviance while the second subset reproduced 60.2% of deviance explained. With only three variables used, the second model outperformed the first model evaluated by the AIC value. We conclude that forest patch area, mean temperature of coldest quarter, and precipitation seasonality are the highly influential variables of TSR among environmental factors in the southeastern region of U.S., but evolutionary or historic cause should be further incorporated to fully understand tree species diversity pattern in this region.

Important Insect and Disease Threats to United States Tree Species and Geographic Patterns of Their Potential Impacts

Diseases and insects, particularly those that are non-native and invasive, arguably pose the most destructive threat to North American forests. Currently, both exotic and native insects and diseases are producing extensive ecological damage and economic impacts. As part of an effort to identify United States tree species and forests most vulnerable to these epidemics, we compiled a list of the most serious insect and disease threats for 419 native tree species and assigned a severity rating for each of the 1378 combinations between mature tree hosts and 339 distinct insect and disease agents. We then joined this list with data from a spatially unbiased and nationally consistent forest inventory to assess the potential ecological impacts of insect and disease infestations. Specifically, potential host species mortality for each host/agent combination was used to weight species importance values on approximately 132,000 Forest Inventory and Analysis (FIA) plots across the conterminous 48 United States. When summed on each plot, these weighted importance values represent an estimate of the proportion of the plot’s existing importance value at risk of being lost. These plot estimates were then used to identify statistically significant geographic hotspots and coldspots and of potential forest impacts associated with insects and diseases in total, and for different agent types. In general, the potential impacts of insects and diseases were greater in the West, where there are both fewer agents and less diverse forests. The impact of non-native invasive agents, however, was potentially greater in the East. Indeed, the impacts of current exotic pests could be greatly magnified across much of the Eastern United States if these agents are able to reach the entirety of their hosts’ ranges. Both the list of agent/host severities and the spatially explicit results can inform species-level vulnerability assessments and broad-scale forest sustainability reporting efforts, and should provide valuable information for decision-makers who need to determine which tree species and locations to target for monitoring efforts and pro-active management activities.

Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery

Sustainable forest management is hugely dependent on high-quality estimates of forest site productivity, but it is challenging to generate productivity maps over large areas. We present a method for generating site index (a measure of such forest productivity) maps for plantation loblolly pine (Pinus taeda L.) forests over large areas in the southeastern United States by combining airborne laser scanning (ALS) data from disparate acquisitions and Landsat-based estimates of forest age. For predicting canopy heights, a linear regression model was developed using ALS data and field measurements from the Forest Inventory and Analysis (FIA) program of the US Forest Service (n = 211 plots). The model was strong (R2 = 0.84, RMSE = 1.85 m), and applicable over a large area (~208,000 sq. km). To estimate the site index, we combined the ALS estimated heights with Landsat-derived maps of stand age and planted pine area. The estimated bias was low (−0.28 m) and the RMSE (3.8 m, relative RMSE: 19.7%, base age 25 years) was consistent with other similar approaches. Due to Landsat-related constraints, our methodology is valid only for relatively young pine plantations established after 1984. We generated 30 m resolution site index maps over a large area (~832 sq. km). The site index distribution had a median value of 19.4 m, the 5th percentile value of 13.0 m and the 95th percentile value of 23.3 m. Further, using a watershed level analysis, we ranked these regions by their estimated productivity. These results demonstrate the potential and value of remote sensing based large-area site index maps.

Investigation of the Peninsula Effect Using the Latitudinal Abundance Pattern for Tree Species in Florida

The peninsula effect is a biological diversity pattern found in peninsulas in which the number of species decreases toward the tip of the peninsula. The geometry hypothesis, as one proposed cause of the peninsula effect, attempts to predict this pattern by examining the peculiarities of peninsular geometry. As peninsulas are characterized by their isolated positions, it has been suggested that a decreased immigration-to-extinction rate is the cause of the decrease in species diversity from the base to the tip of a peninsula. We aimed to test the geometry hypothesis on tree species in the Florida peninsula by modeling the latitudinal abundance pattern using sample-based tree inventory data. We postulated that the current abundance distribution of a species is a ramification of past immigration–extinction dynamics in a peninsula, as well as an important indicator of such dynamics in the future. The latitudinal abundance patterns of 113 tree species in Florida in the southeastern United States were simulated with the Huisman–Olff–Fresco (HOF) model using the USDA Forest Service’s Forest Inventory and Analysis (FIA) database. Evidence species for the geometry hypothesis were then selected if the simulated latitudinal abundance pattern was asymmetric with its abundance maxima occurring within the Florida peninsula (i.e., approximately 31.5° latitude or lower). Our HOF model results found that most species (87% of 113 species) did not experience any steep abundance decline along the Florida peninsula when compared with their general trend across the range, suggesting that the observed diversity pattern of tree species in Florida could merely be a continuation of latitudinal diversity gradients in the southeastern United States, independent of peninsular geometry.

Analyzing the impacts of forest disturbance on individual tree diameter increment across the US Lake States.

Disturbances play a critical role in forest ecosystem dynamics. Disturbances cause changes in forest structure which in turn influence the species composition of the site and alter landscape patterns over time. The impacts of disturbance are seen over a broad spectrum of spatial scales and varying intensities, ranging from biotic agents such as insect and leaf disease outbreaks to abiotic agents such as a windstorm (a stand-replacing disturbance). This study utilized Forest Inventory and Analysis (FIA) data collected between 1999 and 2014 in the US Lake States (Michigan, Minnesota, and Wisconsin) to examine the impacts that disturbances have on the growth of residual trees using species-specific diameter increment equations. Results showed that animal and weather damage were the most common disturbance agents and fires were the least common in the region. Results also indicated that while the diameter increment equations performed well on average (overprediction of 0.08 ± 1.98cm/10years in non-disturbed stands), when the data were analyzed by species and disturbance agent, the model equation was rarely validated using equivalence tests (underprediction of 0.30 ± 2.24cm/10years in non-disturbed stands). This study highlights the importance of monitoring forest disturbances for their impacts on forest growth and yield.

Contiguous United States wildland fire emission estimates during 2003–2015

Abstract. Wildfires are a major source of air pollutants in the United States. Wildfire smoke can trigger severe pollution episodes with substantial impacts on public health. In addition to acute episodes, wildfires can have a marginal effect on air quality at significant distances from the source, presenting significant challenges to air regulators' efforts to meet National Ambient Air Quality Standards. Improved emission estimates are needed to quantify the contribution of wildfires to air pollution and thereby inform decision-making activities related to the control and regulation of anthropogenic air pollution sources. To address the need of air regulators and land managers for improved wildfire emission estimates, we developed the Missoula Fire Lab Emission Inventory (MFLEI), a retrospective, daily wildfire emission inventory for the contiguous United States (CONUS). MFLEI was produced using multiple datasets of fire activity and burned area, a newly developed wildland fuels map and an updated emission factor database. Daily burned area is based on a combination of Monitoring Trends in Burn Severity (MTBS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) burned area and active fire detection products, incident fire perimeters, and a spatial wildfire occurrence database. The fuel type classification map is a merger of a national forest type map, produced by the USDA Forest Service (USFS) Forest Inventory and Analysis (FIA) program and the Geospatial Technology and Applications Center (GTAC), with a shrub and grassland vegetation map developed by the USFS Missoula Forestry Sciences Laboratory. Forest fuel loading is from a fuel classification developed from a large set (&gt; 26 000 sites) of FIA surface fuel measurements. Herbaceous fuel loading is estimated using site-specific parameters with the Normalized Difference Vegetation Index from MODIS. Shrub fuel loading is quantified by applying numerous allometric equations linking stand structure and composition to biomass and fuels, with the structure and composition data derived from geospatial data layers of the LANDFIRE project. MFLEI provides estimates of CONUS daily wildfire burned area, fuel consumption, and pollutant emissions at a 250 m × 250 m resolution for 2003–2015. A spatially aggregated emission product (10 km × 10 km, 1 day) with uncertainty estimates is included to provide a representation of emission uncertainties at a spatial scale pertinent to air quality modeling. MFLEI will be updated, with recent years, as the MTBS burned area product becomes available. The data associated with this article can be found at https://doi.org/10.2737/RDS-2017-0039 (Urbanski et al., 2017).

Sampling bias overestimates climate change impacts on forest growth in the southwestern United States

Climate−tree growth relationships recorded in annual growth rings have recently been the basis for projecting climate change impacts on forests. However, most trees and sample sites represented in the International Tree-Ring Data Bank (ITRDB) were chosen to maximize climate signal and are characterized by marginal growing conditions not representative of the larger forest ecosystem. We evaluate the magnitude of this potential bias using a spatially unbiased tree-ring network collected by the USFS Forest Inventory and Analysis (FIA) program. We show that U.S. Southwest ITRDB samples overestimate regional forest climate sensitivity by 41–59%, because ITRDB trees were sampled at warmer and drier locations, both at the macro- and micro-site scale, and are systematically older compared to the FIA collection. Although there are uncertainties associated with our statistical approach, projection based on representative FIA samples suggests 29% less of a climate change-induced growth decrease compared to projection based on climate-sensitive ITRDB samples.

Shifts in forest composition in the eastern United States

Forest ecosystems in the United States (U.S.) are facing major challenges such as climate change, exotic species invasions, and landscape fragmentation. It is widely believed that forest composition in the eastern U.S. is transitioning from shade-intolerant, fire-tolerant species to shade-tolerant, fire-intolerant species, but most evidence is anecdotal or localized. No comprehensive studies exist to quantify the shifts in forest composition across multiple genera at a regional scale. Here, we examined the genus-level compositional changes in eastern U.S. forests to: (1) quantify the extent and magnitude of this transition, and (2) assess the influence of shade and fire tolerance traits on abundance change. Genus-level data were compiled from the Forest Inventory and Analysis (FIA) database across 37 states in the eastern U.S. for the last three decades. We analyzed shifts in forest composition with three metrics—stem density, basal area, and importance value—for 10 of the most abundant genera (Acer, Betula, Carya, Fraxinus, Nyssa, Pinus, Populus, Prunus, Quercus, and Ulmus). In addition, we estimated density-weighted fire and shade tolerances for each genus using species-level published data, assessed the shifts in spatial patterns of these traits, and analyzed the associations between these traits and county-level abundance changes. In general, Acer, Fraxinus, Pinus, and Prunus increased in abundance during the study period. Acer experienced the largest increase in abundance across the study area. In contrast, Carya, Nyssa, Quercus and Ulmus decreased in abundance in the majority of the study region, with Quercus having the largest and most extensive decline. Although density-weighted shade and fire tolerances were correlated at the genus level, shade tolerance was a better predictor of genus-level abundance change than fire tolerance. Traits of fire and shade tolerance are not always interchangeable when used to predict the dynamics of a genus, and management decision making based on traits should focus at the species level when possible. Our analyses provide evidence that forest composition has shifted in the last three decades in the eastern United States across multiple genera, and the shifts are more closely related to species’ shade tolerance than fire tolerance.

Applications of the United States Forest Inventory and Analysis dataset: a review and future directions

The United States Forest Inventory and Analysis (FIA) program has been monitoring national forest resources in the United States for over 80 years; presented here is a synthesis of research applications for FIA data. A review of over 180 publications that directly utilize FIA data is broken down into broad categories of application and further organized by methodologies and niche research areas. The FIA program provides the most comprehensive forest database currently available, with permanent plots distributed across all forested lands and ownerships in the United States and plot histories dating back to the early 1930s. While the data can be incredibly powerful, users need to understand the spatial resolution of ground-based plots and the nature of the FIA plot coordinate system must be applied correctly. As the need for accurate assessments of national forest resources continues to be a global priority, particularly related to carbon dynamics and climate impacts, such national forest inventories will continue to be an important source of information on the status of and trends in these ecosystems. The advantages and limitations of FIA’s national forest inventory data are highlighted, and suggestions for further expansion of the FIA program are provided.

Topography and soils-based mapping reveals fine-scale compositional shifts over two centuries within a central Appalachian landscape

When public lands were surveyed in the U.S., “witness trees” were often recorded to facilitate the relocation of property boundaries, and these records provide a snapshot of forest conditions prior to Euro-American settlement and land clearing. This study utilizes witness trees and present-day plot data to explore long-term vegetation changes at a regional scale. Landscape classes for a 5000 km2 study area in Appalachian Ohio were defined by slope, aspect, topographic position, soil pH, and available water capacity. Cluster analysis and ordination revealed topo-edaphic patterns in the presettlement (c. 1800) and present-day forests, based on 5765 witness trees and 3249 contemporary trees occurring on 547 Forest Inventory and Analysis (FIA) subplots. Mesophication is evident, as the oak-hickory presettlement forest is now dominated by maple-poplar. Size-class analysis suggests the presettlement forest also experienced mesophication following xeric conditions of the preceding centuries. In both presettlement and contemporary forests, ridges form distinctive communities compared to slopes and valleys, although topographic distinctiveness is now more prevalent. Several taxa revealed changes in site affinities over the past two centuries; shifts in their realized niches suggest mesophication acts through diverse individualistic responses to a multiple set of interacting drivers. Specifically, regionally documented changes in land use, drought, N deposition, and fire at the time of the original surveys lead to altered competitive relationships.

Metrics for comparing stand structure and dynamics between Ecological Reserves and managed forest of Maine, USA.

A data set of common forest metrics was prepared using inventory data from Ecological Reserves in Maine, northeastern USA. An Ecological Reserve is generally defined as an area where timber harvesting does not occur and natural disturbance events are allowed to proceed without significant human influence. Beginning in the early 2000s, permanent, long term monitoring plots were established in Reserves across Maine. To date, 50 Reserves occupying approximately 70,820 ha with a total of 1,103 monitoring plots comprise Maine's Ecological Reserve System. A goal of the Ecological Reserve Monitoring program is to remeasure plots every 10years and about half of the plots have been remeasured since the initial inventory. Stand metrics were calculated for both monitoring rounds and include: live tree basal area, live tree density, large (diameter at breast height, dbh ≥40cm) and very large (dbh ≥51cm) live tree density, standing dead tree density, large (dbh ≥40cm) and very large (dbh ≥51cm) standing dead tree density, total and large (diameter at transect intersect ≥40cm) downed coarse woody debris volume, as well as various stand dynamic metrics. For comparison, the same metrics were computed for managed forests in Maine using permanent plot data from the US Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program. Information on Ecological Reserve monitoring plots includes Ecological Reserve name, forest-type group, geographic location, elevation, slope, aspect, and harvest history. This data should prove invaluable for assessing and evaluating long-term changes in Ecological Reserves across the broad ecological/climate zones that are present in Maine. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper. These data are freely available for non-commercial scientific use.

Recent Trends in Large Hardwoods in the Pacific Northwest, USA

Forest densification, wildfires, and disease can reduce the growth and survival of hardwood trees that are important for biological and cultural diversity within the Pacific Northwest of USA. Large, full-crowned hardwoods that produce fruit and that form large cavities used by wildlife were sustained by frequent, low-severity fires prior to Euro-American colonization. Shifts in fire regimes and other threats could be causing declines in, large hardwood trees. To better understand whether and where such declines might be occurring, we evaluated recent trends in Forest Inventory and Analysis (FIA) data from 1991–2016 in California and southern Oregon. We included plots that lay within areas of frequent fire regimes during pre-colonial times and potential forest habitats for fisher, a rare mammal that depends on large live hardwoods. We analyzed changes in basal area for eight hardwood species, both overall and within size classes, over three time periods within ecoregions, and in public and private land ownerships. We found the basal area to generally be stable or increasing for these species. However, data for California black oak suggested a slight decline in basal area overall, and among both very large trees and understory trees; that decline was associated with fire mortality on national forest lands. In addition, mature trees with full crowns appeared to sharply decline across all species. Many trends were not statistically significant due to high variation, especially since more precise data from remeasured trees were only available for the two most recent time periods. Continued analysis of these indicators using remeasured trees will help to evaluate whether conservation efforts are sustaining large, full-crowned trees and their associated benefits.

Lagged mortality among tree species four years after an exceptional drought in east Texas

AbstractIn 2011, east Texas experienced the worst drought on record causing extensive tree mortality. Initial mortality estimates for 2012 varied among tree genera. A rapid damage assessment (RDA) estimated that 65.5 (± 7.3) million trees died as a result of the drought in this region one year post‐drought. However, this estimate was untested against established monitoring networks. Moreover, pests and physiological damage can elevate tree mortality multiple years beyond a drought event. Since the RDA was unable to quantify multi‐year trends, it remained unclear whether these drivers caused increased tree mortality in east Texas beyond one year post‐drought and how different species responded over time. To address these questions, we compared total 2012 standing dead tree (SDT) estimates (i.e., drought‐killed plus all other SDT excluding harvested or salvaged trees) derived from the RDA and U.S. Forest Service Forest Inventory and Analysis (FIA) data for east Texas. Total SDT estimates did not significantly differ between the RDA (120.5 ± 8.5 million) and FIA (108.4 ± 8.7 million). Furthermore, total SDT estimates for the four most common genera (Pinus, Quercus, Liquidambar, Ulmus), which comprised over 80% of all species, did not significantly differ between the RDA and FIA. Additionally, we used logistic regression and FIA data from east Texas for 2011 through four years post‐drought (2012–2015) to examine temporal trends in plot‐level drought‐ and pest‐driven tree mortality of seven key species (Pinus taeda, Pinus echinata, Quercus nigra, Quercus stellata, Quercus falcata, Liquidambar styraciflua, Ulmus alata) from the four most common genera. At the plot‐level, drought‐driven mortality was immediate for the three Quercus species (notably Q. falcata) and L. styraciflua which significantly increased in 2012 while P. taeda mortality was delayed, not increasing significantly until 2013. Pest‐driven mortality increased from 2013 to 2015 for all species, with the highest mortality observed in Q. falcata and lowest in P. taeda and U. alata. This study affirms the validity and value of independent sampling efforts to quantify mortality immediately following major disturbance and also demonstrates the need for longer‐term species‐level assessments beyond the initial year post‐drought to account for differential impacts from drought and pests.