Shifts in dominant tree mycorrhizal associations in response to anthropogenic impacts

Plant-fungal symbioses play critical roles in vegetation dynamics and nutrient cycling, modulating the impacts of global changes on ecosystem functioning. Here, we used forest inventory data consisting of more than 3 million trees to develop a spatially resolved “mycorrhizal tree map” of the contiguous United States. We show that abundances of the two dominant mycorrhizal tree groups—arbuscular mycorrhizal (AM) and ectomycorrhizal trees—are associated primarily with climate. Further, we show that anthropogenic influences, primarily nitrogen (N) deposition and fire suppression, in concert with climate change, have increased AM tree dominance during the past three decades in the eastern United States. Given that most AM-dominated forests in this region are underlain by soils with high N availability, our results suggest that the increasing abundance of AM trees has the potential to induce nutrient acceleration, with critical consequences for forest productivity, ecosystem carbon and nutrient retention, and feedbacks to climate change

Refined forest land use classification with implications for United States national carbon accounting

The United States provides annual estimates of carbon sources and sinks as part of its National Green-house Gas Inventory (NGHGI). Within this effort, carbon stocks and fluxes are reported for six land use categories that are relevant to economic sectors and land use policy. The goal of this study is to develop methodologies that will allow the US to align with an internationally agreed upon forest land use definition which requires forest to be able to reach 5 m in height at maturity. Models to assess height potential are available for a majority of US forests except for woodland ecosystems. We develop a set of models to assess height potential in these systems. Our results suggest that ∼13.5 million ha of forests are unlikely to meet the international definition of forests due to environmental limitations to maximum attainable height. The incorporation of this height criteria in the NGHGI results in a carbon stock transfer of ∼848 Tg from the forest land use to woodland land use (a sub-category of grasslands) with minimal effect on sequestration rates. The development of a forest land use definition sensitive to climatic factors in this study enables a land use classification system that can be responsive to climate change effects on land uses themselves while being more consistent across a host of international and domestic carbon reporting efforts

Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass

Inferences for forest-related spatial problems can be enhanced using remote sensing-based maps constructed with nearest neighbours techniques. The non-parametric k-nearest neighbours (k-NN) technique calculates predictions as linear combinations of observations for sample units that are nearest in a space of auxiliary variables to population units for which predictions are desired. Implementations of k-NN require four choices: a distance or similarity metric, the specific auxiliary variables to be used with the metric, the number of nearest neighbours, and a scheme for weighting the nearest neighbours. The study objective was to compare optimized k-NN configurations with respect to confidence intervals for airborne laser scanning-assisted estimates of mean volume or biomass per unit area for study areas in Norway, Italy, and the USA. Novel features of the study include a new neighbour weighting scheme, a statistically rigorous method for selecting feature variables, simultaneous optimization with respect to all four k-NN implementation choices and comparisons based on confidence intervals for population means. The primary conclusions were that optimization greatly increased the precision of estimates and that the results of optimization were similar for the k-NN configurations considered. Together, these two conclusions suggest that optimization itself is more important than the particular k-NN configuration that is optimized

Quantifying understorey vegetation in the US Lake States: a proposed framework to inform regional forest carbon stocks

The contribution of understorey vegetation (UVEG) to forest ecosystem biomass and carbon (C) across diverse forest types has, to date, eluded quantification at regional and national scales. Efforts to quantify UVEG C have been limited to field-intensive studies or broad-scale modelling approaches lacking field measurements. Although large-scale inventories of UVEG C are not common, species-and community-level inventories of vegetation structure are available and may prove useful in quantifying UVEG C stocks. This analysis developed a general framework for estimating UVEG C stocks by employing per cent cover estimates of UVEG from a region-wide forest inventory coupled with an estimate of maximum UVEG C across the US Lake States (i.e. Michigan, Minnesota and Wisconsin). Estimates of UVEG C stocks from this approach reasonably align with expected C stocks in the study region, ranging from 0.86+0.06 Mg ha 21 in red pine-dominated to 1.59+0.06 Mg ha 21 for aspen/birch-dominated forest types. Although the data employed here were originally collected to assess broad-scale forest structure and diversity, this study proposes a framework for using UVEG inventories as a foundation for estimating C stocks in an often overlooked, yet important ecosystem C pool

Accounting for density reduction and structural loss in standing dead trees: Implications for forest biomass and carbon stock estimates in the United States

<p>Abstract</p> <p>Background</p> <p>Standing dead trees are one component of forest ecosystem dead wood carbon (C) pools, whose national stock is estimated by the U.S. as required by the United Nations Framework Convention on Climate Change. Historically, standing dead tree C has been estimated as a function of live tree growing stock volume in the U.S.'s National Greenhouse Gas Inventory. Initiated in 1998, the USDA Forest Service's Forest Inventory and Analysis program (responsible for compiling the Nation's forest C estimates) began consistent nationwide sampling of standing dead trees, which may now supplant previous purely model-based approaches to standing dead biomass and C stock estimation. A substantial hurdle to estimating standing dead tree biomass and C attributes is that traditional estimation procedures are based on merchantability paradigms that may not reflect density reductions or structural loss due to decomposition common in standing dead trees. The goal of this study was to incorporate standing dead tree adjustments into the current estimation procedures and assess how biomass and C stocks change at multiple spatial scales.</p> <p>Results</p> <p>Accounting for decay and structural loss in standing dead trees significantly decreased tree- and plot-level C stock estimates (and subsequent C stocks) by decay class and tree component. At a regional scale, incorporating adjustment factors decreased standing dead quaking aspen biomass estimates by almost 50 percent in the Lake States and Douglas-fir estimates by more than 36 percent in the Pacific Northwest.</p> <p>Conclusions</p> <p>Substantial overestimates of standing dead tree biomass and C stocks occur when one does not account for density reductions or structural loss. Forest inventory estimation procedures that are descended from merchantability standards may need to be revised toward a more holistic approach to determining standing dead tree biomass and C attributes (i.e., attributes of tree biomass outside of sawlog portions). Incorporating density reductions and structural loss adjustments reduces uncertainty associated with standing dead tree biomass and C while improving consistency with field methods and documentation.</p

Interactions between white-tailed deer density and the composition of forest understories in the northern United States

Forest understories across the northern United States (US) are a complex of tree seedlings, endemic forbs, herbs, shrubs, and introduced plant species within a forest structure defined by tree and forest floor attributes. The substantial increase in white-tailed deer (Odocoileus virginianus Zimmerman) populations over the past decades has resulted in heavy browse pressure in many of these forests. To gain an objective assessment of the role of deer in forested ecosystems, a region-wide forest inventory across the northern US was examined in concert with white-tailed deer density information compiled at broad scales. Results indicate that deer density may be an additional driver of tree seedling abundance when analyzed along with stand attributes such as above ground biomass, relative density, and stand age. Tree seedling abundance generally decreased as deer density increased above 5.8 deer km2 for all forest type groups with the exception of oak-dominated forests. Findings indicate that introduced plant species, of which 393 were recorded in this study, increased in areas with higher deer density. The abundance of white-tailed deer is just as important as forest stand and site attributes in the development of forest understories. Given the complexity of forest and land use dynamics across the northern US, this study provides directions for future research as more data linking forest-dependent wildlife and forest dynamics at regional and national scales become available

Refined forest land use classification with implications for United States national carbon accounting

The United States provides annual estimates of carbon sources and sinks as part of its National Green-house Gas Inventory (NGHGI). Within this effort, carbon stocks and fluxes are reported for six land use categories that are relevant to economic sectors and land use policy. The goal of this study is to develop methodologies that will allow the US to align with an internationally agreed upon forest land use definition which requires forest to be able to reach 5 m in height at maturity. Models to assess height potential are available for a majority of US forests except for woodland ecosystems. We develop a set of models to assess height potential in these systems. Our results suggest that ∼13.5 million ha of forests are unlikely to meet the international definition of forests due to environmental limitations to maximum attainable height. The incorporation of this height criteria in the NGHGI results in a carbon stock transfer of ∼848 Tg from the forest land use to woodland land use (a sub-category of grasslands) with minimal effect on sequestration rates. The development of a forest land use definition sensitive to climatic factors in this study enables a land use classification system that can be responsive to climate change effects on land uses themselves while being more consistent across a host of international and domestic carbon reporting efforts