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Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale.

Multivariate regression tree methodology is developed and illustrated in a study predicting the abundance of several cooccurring plant species in Missouri Ozark forests. The technique is a variation of the approach of Segal (1992) for longitudinal data. It has the potential to be applied to many different types of problems in which analysts want to predict the simultaneous cooccurrence of several dependent variables. Multivariate regression trees can also be used as an alternative to cluster analysis in situations where clusters are defined by a set of independent variables and the researcher wants clusters as homogeneous as possible with respect to a group of dependent variables.

Vegetation phenology plays a key role in terrestrial ecosystem nutrient and carbon cycles and is sensitive to global climate change. Compared with spring phenology, which has been well studied, autumn phenology is still poorly understood. In this study, we estimated the date of the end of the growing season (EOS) across the Greater Khingan Mountains, China, from 1982 to 2015 based on the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index third-generation (NDVI3g) dataset. The temporal correlations between EOS and climatic factors (e.g., preseason temperature, preseason precipitation), as well as the correlation between autumn and spring phenology, were investigated using partial correlation analysis. Results showed that more than 94% of the pixels in the Greater Khingan Mountains exhibited a delayed EOS trend, with an average rate of 0.23 days/y. Increased preseason temperature resulted in earlier EOS in most of our study area, except for the semi-arid grassland region in the south, where preseason warming generally delayed EOS. Similarly, EOS in most of the mountain deciduous coniferous forest, forest grassland, and mountain grassland forest regions was earlier associated with increased preseason precipitation, but for the semi-arid grassland region, increased precipitation during the preseason mainly led to delayed EOS. However, the effect of preseason precipitation on EOS in most of the Greater Khingan Mountains was stronger than that of preseason temperature. In addition to the climatic effects on EOS, we also found an influence of spring phenology on EOS. An earlier SOS led to a delayed EOS in most of the study area, while in the southern of mountain deciduous coniferous forest region and northern of semi-arid grassland region, an earlier SOS was often followed by an earlier EOS. These findings suggest that both climatic factors and spring phenology should be incorporated into autumn phenology models in order to improve prediction accuracy under present and future climate change scenarios.

Securing desirable regeneration is essential to sustainable forest management, yet failures are common. Detailed seedling measurements from a forest inventory across 24 northern US states were examined for plausible regeneration outcomes following overstory removal. The examination included two fundamental regeneration objectives: 1) stand replacement- securing future forest and 2) species maintenance- securing upper canopy species. Almost half the plots lacked adequate seedlings to regenerate a stand after canopy removal and over half risked compositional shifts. Based on those advance reproduction demographics, regeneration difficulties could occur on two-thirds of the plots examined. The remaining one-third were regeneration-ready. However, compared to historical norms, increased small-tree mortality rates reduces that proportion. Not all forest types rely on advance reproduction and results varied among the forest types examined. Some variability was associated with browsing intensity, as areas of high deer browsing had a lower proportion of regeneration-ready plots.

We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in old-growth hardwood forests in Missouri, Illinois, and Indiana found that 8-11% of snags had at least one visible cavity (as visually detected from the ground; smallest opening ≥2 cm diameter), about twice the percentage for live trees. Five percent of live trees and snags had cavities on mature (≥110 years) second-growth plots on timberland in Missouri. Because snags accounted for typically no more than 10% of standing trees on any of these sites, 80-85% of cavity trees are living trees. Within the subset of mature and old-growth forests, the presence of cavities was strongly related to tree diameter. Classification and regression tree models indicated that 30 cm diameter at breast height (DBH) was a threshold size useful in distinguishing cavity trees from noncavity trees in the old-growth sample. There were two diameter thresholds in the mature second-growth sample: 18 and 44 cm DBH. Cavity tree probability differed by species group and increased with increasing decay class.

There is interest in restoring shortleaf pine ( Pinus echinata ) in pine–oak woodlands where it once was abundant. Because of its shade intolerance and slow initial growth rate, shortleaf pine restoration has remained a challenge because competition from hardwoods exhibits greater initial growth following canopy removal but greater shade tolerance with canopy retention. The study objective was to examine the survival and growth of underplanted shortleaf pine seedlings relative to competing hardwoods as a function of initial seedling size, overstory density, and understory competition. In the Ozark Highlands of southeastern Missouri, USA, 48, 0.4-ha experimental units were each harvested from below to a uniform stocking level from 0 to 90 % and 30, 1–0 improved shortleaf pine seedlings were planted on a 3.7 × 7.3 m spacing. Linear or logistic regression was used to determine how shortleaf pine seedling (1) survival, (2) basal diameter growth, and (3) shoot growth were related to initial seedling size, overstory stocking, and understory competitor height during the first 5 years after underplanting. After five growing seasons, the survival rate of shortleaf pine seedlings was 50 % and was positively related to the initial basal diameter but was not related to overstory stocking or competitor height. Increasing overstory stocking decreased the basal diameter and height growth of shortleaf pine seedlings, explaining >51 % of the variation in basal diameter and 54 % of the variation in seedling height. Although competing hardwood seedlings were consistently taller than the shortleaf pine seedlings throughout the study, shortleaf pine seedlings maintained similar growth rates as competitors from the second to the fifth growing season. The eventual release of shortleaf pine is essential for recruitment, but releases can be delayed for several years after underplanting.

Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural processes on a landscape while accounting for the variability in a process by utilizing environmental and spatial random effects. A hierarchical Bayesian framework has allowed the simultaneous integration of these effects. This framework naturally assumes variables to be random and the posterior distribution of the model provides probabilistic information about the process. Two species in the genus Desmodium were used as examples to illustrate the utility of the model in Southeast Missouri, USA. In addition, two validation techniques were applied to evaluate the qualitative and quantitative characteristics of the predictions.

Predicting the effects of silvicultural choices on regeneration has been difficult with the tools available to foresters. In an effort to improve this, we developed a collection of reproduction establishment models based on stand development hypotheses and parameterized with empirical data for several species in the Missouri Ozarks. These models estimate third-year abundance parameters for established reproduction that originated from either small advance reproduction or new germination. The influence of predisturbance stand conditions was summarized by a simple presence/absence inventory of advance reproduction for each species. The influence of postdisturbance stand conditions was summarized by user-provided estimates of residual overstory density and presence/absence of a residual seed source for each species. The estimated abundance parameters can be used deterministically or with stochastic number generators to simulate regeneration after a variety of harvest-based silvicultural manipulations. This approach has the potential to increase the efficacy of regeneration modeling by reducing the inventory effort typically required and increasing compatibly for species not strongly reliant on advance reproduction.

Regenerating oaks (Quercus spp.) has remained a widespread and persistent problem throughout their natural range. Research shows that abundant oak advance reproduction is crucial for success. Although it is recognized that oak advance reproduction accumulation is inversely related to site quality, there has been little effort to model oak advance reproduction density as a function of measured levels of light, water, or nutrient supply. The objective of this study was to determine whether oak advance reproduction could be modeled and mapped with site variables. The study was conducted on the Sinkin Experimental Forest in southeastern Missouri in 20 5-ha experimental units. Vegetation and site data were collected in 120 0.5-ha circular plots with nested subplots for the inventory of the midstory (0.01 ha) and reproduction (0.004 ha). Site variables included soil available water capacity, pH, photosynthetically active radiation in the understory, forest stocking, terrain shape, and slope-aspect. Oak advance reproduction abundance was related to soil acidity and available water capacity and to other site information such as slope-aspect. Models for the red oak group species generally exhibited better fit than those for the white oaks. There also was evidence that estimates of soil acidity and available water capacity can be obtained from the SSURGO database and used in these oak advance reproduction models along with other site information to generate maps of estimated oak reproduction densities. These maps could be used for planning silvicultural interventions to increase the abundance and size of oak advance reproduction before forest regeneration.

Two challenges confronting forest landscape models (FLMs) are how to simulate fine, stand-scale processes while making large-scale (i.e., >10 7 ha) simulation possible, and how to take advantage of extensive forest inventory data such as U.S. Forest Inventory and Analysis (FIA) data to initialize and constrain model parameters. We present the LANDIS PRO model that addresses these needs. LANDIS PRO adds density and size mechanisms of resource competition. This is achieved through incorporating number of trees and DBH by species age cohort within each raster cell. Forest change is determined by the interactions of species-, stand-, and landscape-scale processes. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes include density and size-related resource competition that regulates self-thinning and seedling establishment. Landscape-scale processes include seed dispersal, as well as natural and anthropogenic disturbances. LANDIS PRO is designed to be straightforwardly comparable with forest inventory data, and thus the extensive FIA data can be directly utilized to initialize and constrain model parameters before predicting future forest change. We initialized a large landscape (∼10 7 ha) from historical FIA data (1978) and the predicted forest structure and composition following 30 years of simulation were statistically calibrated against a prior time-series of sequential FIA data (1978 to 2008). The results showed that the initialized conditions realistically represented the historical forest composition and structure at 1978, and the constrained model parameters predicted reasonable outcomes at both landscape and land type scales. The subsequent evaluation of model predictions showed that the predicted forest composition and structure were comparable with old-growth oak forests; predicted forest successional trajectories were consistent with the expected successional patterns in oak-dominated forests in the study region; and the predicted stand development patterns were in agreement with the established theories of forest stand development. This study demonstrated a framework for forest landscape modeling including model initialization, calibration, and evaluation of predictions.