The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States

We synthesize insights from current understanding of drought impacts at stand‐to‐biogeographic scales, including management options, and we identify challenges to be addressed with new research. Large stand‐level shifts underway in western forests already are showing the importance of interactions involving drought, insects, and fire. Diebacks, changes in composition and structure, and shifting range limits are widely observed. In the eastern US, the effects of increasing drought are becoming better understood at the level of individual trees, but this knowledge cannot yet be confidently translated to predictions of changing structure and diversity of forest stands. While eastern forests have not experienced the types of changes seen in western forests in recent decades, they too are vulnerable to drought and could experience significant changes with increased severity, frequency, or duration in drought. Throughout the continental United States, the combination of projected large climate‐induced shifts in suitable habitat from modeling studies and limited potential for the rapid migration of tree populations suggests that changing tree and forest biogeography could substantially lag habitat shifts already underway. Forest management practices can partially ameliorate drought impacts through reductions in stand density, selection of drought‐tolerant species and genotypes, artificial regeneration, and the development of multistructured stands. However, silvicultural treatments also could exacerbate drought impacts unless implemented with careful attention to site and stand characteristics. Gaps in our understanding should motivate new research on the effects of interactions involving climate and other species at the stand scale and how interactions and multiple responses are represented in models. This assessment indicates that, without a stronger empirical basis for drought impacts at the stand scale, more complex models may provide limited guidance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134257/1/gcb13160_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134257/2/gcb13160.pd

Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations

Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice

Economic Competition and Racial/Ethnic Disparities in Sentencing: A Test of Economic Threat Perspective

Economic threat arguments within the broader racial/ethnic threat theory suggest that economic competition between minorities and Whites encourages the majority group to apply formal social controls on minorities to maintain their advantaged positions. Prior sentencing research has given limited attention to economic threat and has only done so using cross-sectional measures, which does not capture changing economic circumstances (a key element of racial/ethnic threat). The goal of this study is to provide a test of economic threat—and racial/ethnic threat more broadly—utilizing time variant measures. To achieve this goal, we use case-level data from the Minnesota Sentencing Guidelines Commission (N = 122,666) and county-level data from the United States Census Bureau. Multilevel regression models reveal partial but limited support for economic threat. Specifically, counties with a growing portion of minorities living above the poverty line between 2000 and 2010 had larger minority disadvantages (in comparison to Whites) at incarceration. However, economic threat measures do not significantly contextualize minority–White sentence length differences, while the broader racial/ethnic threat measures do not significantly influence minority–White outcomes at the incarceration or sentencing length decision. The results suggest that economic threat may explain a small but limited portion of the racial disparities identified

Land Use Changes, Disturbances, and Their Interactions on Future Forest Aboveground Biomass Dynamics in the Northern US

Land use change (LUC), disturbances, and their interactions play an important role in regional forest carbon (C) dynamics. Here we quantified how these activities and events may influence future aboveground biomass (AGB) dynamics in forests using national forest inventory (NFI) and Landsat time series data in the Northern United States (US). Total forest AGB predictions were based on simulations of diameter growth, mortality, and recruitment using matrix growth models under varying levels of LUC and disturbance severity (low (L), medium (M), and high (H)) every five years from 2018 to 2098. Land use change included the integrated effects of deforestation and reforestation/afforestation (forest [F]&rarr;agriculture [A], settlements [S, urbanization/other], and A&amp;S&rarr;F), specifically, conversion from F&rarr;A, F&rarr;S, F&rarr;A&amp;S, A&rarr;F, S&rarr;F, and A&amp;S&rarr;F. Disturbances included natural and anthropogenic disturbances such as wildfire, weather, insects and disease, and forest harvesting. Results revealed that, when simultaneously considering both medium LUC and disturbances, total forest AGB predictions of LUC + fire, LUC + weather, LUC + insect &amp; disease, and LUC + harvest indicated substantial increases in regional C stocks (&plusmn; standard deviation) from 1.88 (&plusmn;0.13) to 3.29 (&plusmn;0.28), 3.10 (&plusmn;0.24), 2.91 (&plusmn;0.19), and 2.68 (&plusmn;0.17) Pg C, respectively, from 2018 to 2098. An uncertainty analysis with fuzzy sets suggested that medium LUC under disturbances would lead to greater forest AGB C uptake than undisturbed forest C uptake with high certainty, except for LUC + harvest. The matrix models in this study were parameterized using NFI and Landsat data from the past few decades. Thus, our results imply that if recent trends persist, LUC will remain an important driver of forest C uptake, while disturbances may result in C emissions rather than undisturbed forest C uptake by 2098. The combined effects of LUC and disturbances may serve as an important driver of C uptake and emissions in the Northern US well into the 21st century

Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data

The ability to harmonize data sources with varying temporal, spatial, and ecosystem measurements (e.g. forest structure to soil organic carbon) for creation of terrestrial carbon baselines is paramount to refining the monitoring of terrestrial carbon stocks and stock changes. In this study, we developed and examined the short- (5 years) and long-term (30 years) performance of matrix models for incorporating light detection and ranging (LiDAR) strip samples and time-series Landsat surface reflectance high-level data products, with field inventory measurements to predict aboveground biomass (AGB) dynamics for study sites across the eastern USA—Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC). The rows and columns of the matrix were stand density (i.e. number of trees per unit area) sorted by inventory plot and by species group and diameter class. Through model comparisons in the short-term, we found that average stand basal area ( B ) predicted by three matrix models all fell within the 95% confidence interval of observed values. The three matrix models were based on (i) only field inventory variables (inventory), (ii) LiDAR and Landsat-derived metrics combined with field inventory variables (LiDAR + Landsat + inventory), and (iii) only Landsat-derived metrics combined with field inventory variables (Landsat + inventory), respectively. In the long term, predicted AGB using LiDAR + Landsat + inventory and Landsat + inventory variables had similar AGB patterns (differences within 7.2 Mg ha ^−1 ) to those predicted by matrix models with only inventory variables from 2015–2045. When considering uncertainty derived from fuzzy sets all three matrix models had similar AGBs (differences within 7.6 Mg ha ^−1 ) by the year 2045. Therefore, the use of matrix models enabled various combinations of LiDAR, Landsat, and field data, especially Landsat data, to estimate large-scale AGB dynamics (i.e. central component of carbon stock monitoring) without loss of accuracy from only using variables from forest inventories. These findings suggest that the use of Landsat data alone incorporating elevation ( E ), plot slope ( S ) and aspect ( A ), and site productivity ( C ) could produce suitable estimation of AGB dynamics (ranging from 67.1–105.5 Mg ha ^−1 in 2045) to actual AGB dynamics using matrix models. Such a framework may afford refined monitoring and estimation of terrestrial carbon stocks and stock changes from spatially explicit to spatially explicit and spatially continuous estimates and also provide temporal flexibility and continuity with the Landsat time series

Influence of transect length and downed woody debris abundance on precision of the line-intersect sampling method

Abstract Background Accurate downed woody debris (DWD) volume or mass estimates are needed for numerous applications such as fuel loading, forest carbon, and biodiversity/habitat assessments. The line-intersect sampling (LIS) method of inventorying DWD is widely used in forest inventories and ecological studies because it is time-efficient and unbiased. Despite its widespread use, the appropriate transect length needed to achieve a desired precision at a particular location has received relatively little attention. Methods We conducted intensive LIS sampling at 33 locations representing eight mature or old-growth forest types in northeastern USA, providing a range of forest conditions and DWD volumes (from 17 to 323 m3∙ha− 1). We used these empirical field data to test, through simulations, the effect of increasing transect length (up to 340 m at each location) on precision of associated LIS volume estimates. Importantly, we used a novel application of copula models to account for within-transect spatial autocorrelation of DWD volumes during our simulations, thereby properly addressing variance estimates. Results As expected, precision consistently improved with increasing cumulative transect length, and locations with lower DWD volumes required longer transects to achieve a given level of precision. We developed models relating precision, transect length, and DWD volume that allows us to gauge a suitable LIS transect length for desired precision levels. Conclusions LIS provides an attractive method for estimating DWD volume for a given localized area of interest. For the forest types sampled here, and for the particular copula model framework employed, transect lengths of ca. 120 m provide a reasonable level of precision, ranging from 18% to 60% coefficients of variation

Defining and assessing urban forests to inform management and policy

The context in which trees and forests grow in cities is highly variable and influences the provision of ecological, social, and economic benefits. Understanding the spatial extent, structure, and composition of forests is necessary to guide urban forest policy and management, yet current forest assessment methodologies vary widely in scale, sampling intensity, and focus. Current definitions of the urban forest include all trees growing in the urban environment, and have been translated to the design of urban forest assessments. However, such broad assessments may aggregate types of urban forest that differ significantly in usage and management needs. For example, street trees occur in highly developed environments, and are planted and cared for on an individual basis, whereas forested natural areas often occur in parkland, are managed at the stand level, and are primarily sustained by natural processes such as regeneration. We use multiple datasets for New York City to compare the outcomes from assessments of the entire urban forest, street trees, and forested natural areas. We find that non-stratified assessments of the entire urban forest are biased towards abundant canopy types in cities (e.g. street trees) and underestimate the condition of forested natural areas due to their uneven spatial arrangement. These natural areas account for one quarter of the city’s tree canopy, but represent the majority of trees both numerically and in terms of biomass. Non-stratified assessments of urban forest canopy should be modified to accurately represent the true composition of different urban forest types to inform effective policy and management

A chemical screen in zebrafish embryonic cells establishes that Akt activation is required for neural crest development

The neural crest is a dynamic progenitor cell population that arises at the border of neural and non-neural ectoderm. The inductive roles of FGF, Wnt, and BMP at the neural plate border are well established, but the signals required for subsequent neural crest development remain poorly characterized. Here, we conducted a screen in primary zebrafish embryo cultures for chemicals that disrupt neural crest development, as read out by crestin:EGFP expression. We found that the natural product caffeic acid phenethyl ester (CAPE) disrupts neural crest gene expression, migration, and melanocytic differentiation by reducing Sox10 activity. CAPE inhibits FGF-stimulated PI3K/Akt signaling, and neural crest defects in CAPE-treated embryos are suppressed by constitutively active Akt1. Inhibition of Akt activity by constitutively active PTEN similarly decreases crestin expression and Sox10 activity. Our study has identified Akt as a novel intracellular pathway required for neural crest differentiation