Timing is Not Everything: Assessing the Efficacy of Pre- Versus Post-Harvest Herbicide Applications in Mitigating the Burgeoning Birch Phenomenon in Regenerating Hardwood Stands

Sweet birch (Betula lenta L.) is aggressively recruiting in temperate forest understories of the eastern United States and often dominates the post-disturbance seedling community, diminishing diversity and hindering sustainable silviculture. The type and timing of silvicultural actions affect birch recruitment via their effects on seedling recruitment, survival, and growth. Here, we examine birch regeneration under two contrasting treatment sequences: pre- versus post-shelterwood harvest herbicide application (H–S vs. S–H) in combination with white-tailed deer (Odocoileus virginianus Zimmerman) browsing (fenced vs. unfenced) at 22 sites in northwestern Pennsylvania, USA. Additionally, we examine how treatments interact with additional site factors, including potential propagule sources and site productivity (i.e., integrated moisture index). We found the S–H sequence initially reduced birch density by 71% relative to the H–S sequence; however, the magnitude of this reduction waned over five growing seasons. Furthermore, birch proliferated following the H–S sequence only where mature birch were present. Deer browsing reduced birch height by 29% relative to fenced areas protected from browsing; however, by the fifth growing season birch seedlings were over twice as tall as other hardwood species across all treatments. Finally, increasingly mesic sites enhanced birch height growth. In sum, although post-harvest herbicide (S–H) provides short-lived control over birch, land managers should also consider browse pressure, seed source, and site productivity, as these may enhance or diminish the efficacy of post-shelterwood herbicide sequence effects on birch

Effects of Seedling Quality and Family on Performance of Northern Red Oak Seedlings on a Xeric Upland Site

Cultural practices to develop larger, more robust oak seedlings have been developed, however, the potential improvement conferred by these larger seedlings has received limited testing in the Northeast. We evaluated the effect of seedling size and pedigree on the survival, growth, and competitive ability of northern red oak (Quercus rubra L.) seedlings planted on a xeric site in northeastern Pennsylvania. We planted seedlings from a state tree nursery that represented locally available seedling stock, as well as high-quality seedlings from seven half-sibling families grown following improved nursery protocol. Half-sibling families were split into three size classes based on their root collar diameter and height; large, average, and poor. Eleven years after planting, survival across seedling treatments ranged from 45 percent for locally available seedlings, to 96 percent for one half-sibling family. Two families showed superior growth, survival, and competitive ability compared with the others. Seedling size class conferred moderate height and diameter advantage in four and three of the families, respectively. Initial seedling size was an important variable in models predicting survival, diameter, and dominance (competitive ability). Over time, the relationship between initial diameter and height diminished

Machine learning-based spectral and spatial analysis of hyper- and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health, and early and accurate disease detection is essential for timely and precision forest management. The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees. In this study, Dutch elm disease (DED; caused by Ophiostoma novo-ulmi,) and American elm (Ulmus americana) was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper- and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection. Hyper- and multi-spectral images were collected from leaves of American elm genotypes with varied disease susceptibilities after mock-inoculation and inoculation with O. novo-ulmi under greenhouse conditions. Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes. Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED. Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees. In addition, spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes. Though further studies are needed to assess applications in other pathosystems, hyper- and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees

Genetic and physiological data implicating the new human gene G72 and the gene for d-amino acid oxidase in schizophrenia

A map of 191 single-nucleotide polymorphism (SNPs) was built across a 5-Mb segment from chromosome 13q34 that has been genetically linked to schizophrenia. DNA from 213 schizophrenic patients and 241 normal individuals from Canada were genotyped with this marker set. Two 1,400- and 65-kb regions contained markers associated with the disease. Two markers from the 65-kb region were also found to be associated to schizophrenia in a Russian sample. Two overlapping genes G72 and G30 transcribed in brain were experimentally annotated in this 65-kb region. Transfection experiments point to the existence of a 153-aa protein coded by the G72 gene. This protein is rapidly evolving in primates, is localized to endoplasmic reticulum/Golgi in transfected cells, is able to form multimers and specifically binds to carbohydrates. Yeast two-hybrid experiments with the G72 protein identified the enzyme d-amino acid oxidase (DAAO) as an interacting partner. DAAO is expressed in human brain where it oxidizes d-serine, a potent activator of N-methyl-D-aspartate type glutamate receptor. The interaction between G72 and DAAO was confirmed in vitro and resulted in activation of DAAO. Four SNP markers from DAAO were found to be associated with schizophrenia in the Canadian samples. Logistic regression revealed genetic interaction between associated SNPs in vicinity of two genes. The association of both DAAO and a new gene G72 from 13q34 with schizophrenia together with activation of DAAO activity by a G72 protein product points to the involvement of this N-methyl-d-aspartate receptor regulation pathway in schizophrenia