Interaction of Seed Dispersal and Environmental Filtering Affects Woody Encroachment Patterns in Coastal Grassland

Encroachment of woody plants into grasslands has occurred worldwide and includes coastal ecosystems. This conversion process is mediated by seed dispersal patterns, environmental filtering, and biotic interactions. As spatiotemporally heterogeneous, harsh environments, barrier islands present a unique set of challenges for dispersal and establishment. Environmental conditions act as filters on dispersed seeds, thereby influencing encroachment and distribution patterns. Seldom have patterns of propagule dispersal been considered in the context of woody encroachment. We quantified dispersal and post‐dispersal processes of an encroaching woody population of Morella cerifera relative to directional rate of encroachment and observed distribution patterns on an Atlantic coastal barrier island with strong environmental filtering. We analyzed historic foredune elevation as a proxy for reduced interior environmental stress. The dispersal kernel was leptokurtic, a common characteristic of expanding populations, but rate of encroachment has slowed since 2005. Expansion pattern was related to foredune elevation, which limits encroachment below a threshold elevation. This difference between dispersal kernel behavior and encroachment rate is due to limited availability of suitable habitat for Morella and temporal variability in chlorides during the time of germination. Our results demonstrate that processes mediating seeds and seedling success must be accounted for to better understand establishment patterns of encroaching woody plants

Evaluating and Mapping Internet Connectivity in the United States

We evaluated Internet connectivity in the United States, drawn from different definitions of connectivity and different methods of analysis. Using DNS cache manipulation, traceroutes, and a crowdsourced “site ping” method we identify patterns in connectivity that correspond to higher population or coastal regions of the US. We analyze the data for quality strengths and shortcomings, establish connectivity heatmaps, state rankings, and statistical measures of the data. We give comparative analyses of the three methods and present suggestions for future work building off this report

Indications of a positive feedback between coastal development and beach nourishment

Beach nourishment, a method for mitigating coastal storm damage or chronic erosion by deliberately replacing sand on an eroded beach, has been the leading form of coastal protection in the United States for four decades. However, investment in hazard protection can have the unintended consequence of encouraging development in places especially vulnerable to damage. In a comprehensive, parcel-scale analysis of all shorefront single-family homes in the state of Florida, we find that houses in nourishing zones are significantly larger and more numerous than in non-nourishing zones. The predominance of larger homes in nourishing zones suggests a positive feedback between nourishment and development that is compounding coastal risk in zones already characterized by high vulnerability

Anthropogenic controls on overwash deposition: Evidence and consequences

Accelerated sea level rise and the potential for an increase in frequency of the most intense hurricanes due to climate change threaten the vitality and habitability of barrier islands by lowering their relative elevation and altering frequency of overwash. High-density development may further increase island vulnerability by restricting delivery of overwash to the subaerial island. We analyzed pre-Hurricane Sandy and post-Hurricane Sandy (2012) lidar surveys of the New Jersey coast to assess human influence on barrier overwash, comparing natural environments to two developed environments (commercial and residential) using shore-perpendicular topographic profiles. The volumes of overwash delivered to residential and commercial environments are reduced by 40% and 90%, respectively, of that delivered to natural environments. We use this analysis and an exploratory barrier island evolution model to assess long-term impacts of anthropogenic structures. Simulations suggest that natural barrier islands may persist under a range of likely future sea level rise scenarios (7-13mm/yr), whereas developed barrier islands will have a long-term tendency toward drowning

Indications of a positive feedback between coastal development and beach nourishment: COASTAL DEVELOPMENT BEACH NOURISHMENT

Beach nourishment, a method for mitigating coastal storm damage or chronic erosion by deliberately replacing sand on an eroded beach, has been the leading form of coastal protection in the United States for four decades. However, investment in hazard protection can have the unintended consequence of encouraging development in places especially vulnerable to damage. In a comprehensive, parcel‐scale analysis of all shorefront single‐family homes in the state of Florida, we find that houses in nourishing zones are significantly larger and more numerous than in non‐nourishing zones. The predominance of larger homes in nourishing zones suggests a positive feedback between nourishment and development that is compounding coastal risk in zones already characterized by high vulnerability

Exploring Marine and Aeolian Controls on Coastal Foredune Growth Using a Coupled Numerical Model

Coastal landscape change represents aggregated sediment transport gradients from spatially and temporally variable marine and aeolian forces. Numerous tools exist that independently simulate subaqueous and subaerial coastal profile change in response to these physical forces on a range of time scales. In this capacity, coastal foredunes have been treated primarily as wind-driven features. However, there are several marine controls on coastal foredune growth, such as sediment supply and moisture effects on aeolian processes. To improve understanding of interactions across the land-sea interface, here the development of the new Windsurf-coupled numerical modeling framework is presented. Windsurf couples standalone subaqueous and subaerial coastal change models to simulate the co-evolution of the coastal zone in response to both marine and aeolian processes. Windsurf is applied to a progradational, dissipative coastal system in Washington, USA, demonstrating the ability of the model framework to simulate sediment exchanges between the nearshore, beach, and dune for a one-year period. Windsurf simulations generally reproduce observed cycles of seasonal beach progradation and retreat, as well as dune growth, with reasonable skill. Exploratory model simulations are used to further explore the implications of environmental forcing variability on annual-scale coastal profile evolution. The findings of this work support the hypothesis that there are both direct and indirect oceanographic and meteorological controls on coastal foredune progradation, with this new modeling tool providing a new means of exploring complex morphodynamic feedback mechanisms

Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)

Development and validation of a Surgical Prioritization and Ranking Tool and Navigation Aid for Head and Neck Cancer (SPARTAN‐HN) in a scarce resource setting: Response to the COVID‐19 pandemic

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163412/2/cncr33114_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163412/1/cncr33114.pd

Copy Number Variation of CCL3-like Genes Affects Rate of Progression to Simian-AIDS in Rhesus Macaques (Macaca mulatta)

Variation in genes underlying host immunity can lead to marked differences in susceptibility to HIV infection among humans. Despite heavy reliance on non-human primates as models for HIV/AIDS, little is known about which host factors are shared and which are unique to a given primate lineage. Here, we investigate whether copy number variation (CNV) at CCL3-like genes (CCL3L), a key genetic host factor for HIV/AIDS susceptibility and cell-mediated immune response in humans, is also a determinant of time until onset of simian-AIDS in rhesus macaques. Using a retrospective study of 57 rhesus macaques experimentally infected with SIVmac, we find that CCL3L CNV explains approximately 18% of the variance in time to simian-AIDS (p<0.001) with lower CCL3L copy number associating with more rapid disease course. We also find that CCL3L copy number varies significantly (p<10−6) among rhesus subpopulations, with Indian-origin macaques having, on average, half as many CCL3L gene copies as Chinese-origin macaques. Lastly, we confirm that CCL3L shows variable copy number in humans and chimpanzees and report on CCL3L CNV within and among three additional primate species. On the basis of our findings we suggest that (1) the difference in population level copy number may explain previously reported observations of longer post-infection survivorship of Chinese-origin rhesus macaques, (2) stratification by CCL3L copy number in rhesus SIV vaccine trials will increase power and reduce noise due to non-vaccine-related differences in survival, and (3) CCL3L CNV is an ancestral component of the primate immune response and, therefore, copy number variation has not been driven by HIV or SIV per se

De Novo Mutations in SLC1A2 and CACNA1A Are Important Causes of Epileptic Encephalopathies

Epileptic encephalopathies (EEs) are the most clinically important group of severe early-onset epilepsies. Next-generation sequencing has highlighted the crucial contribution of de novo mutations to the genetic architecture of EEs as well as to their underlying genetic heterogeneity. Our previous whole-exome sequencing study of 264 parent-child trios revealed more than 290 candidate genes in which only a single individual had a de novo variant. We sought to identify additional pathogenic variants in a subset (n = 27) of these genes via targeted sequencing in an unsolved cohort of 531 individuals with a diverse range of EEs. We report 17 individuals with pathogenic variants in seven of the 27 genes, defining a genetic etiology in 3.2% of this unsolved cohort. Our results provide definitive evidence that de novo mutations in SLC1A2 and CACNA1A cause specific EEs and expand the compendium of clinically relevant genotypes for GABRB3. We also identified EEs caused by genetic variants in ALG13, DNM1, and GNAO1 and report a mutation in IQSEC2. Notably, recurrent mutations accounted for 7/17 of the pathogenic variants identified. As a result of high-depth coverage, parental mosaicism was identified in two out of 14 cases tested with mutant allelic fractions of 5%–6% in the unaffected parents, carrying significant reproductive counseling implications. These results confirm that dysregulation in diverse cellular neuronal pathways causes EEs, and they will inform the diagnosis and management of individuals with these devastating disorders