Transferability of ecological forecasting models to novel biotic conditions in a long-term experimental study.

Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability.

Individual canopy tree species maps for the National Ecological Observatory Network.

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance.

Coordination of copper within a crystalline carbon nitride and its catalytic reduction of CO.

Inherently disordered structures of carbon nitrides have hindered an atomic level tunability and understanding of their catalytic reactivity. Starting from a crystalline carbon nitride, poly(triazine imide) or PTI/LiCl, the coordination of copper cations to its intralayer -triazine groups was investigated using molten salt reactions. The reaction of PTI/LiCl within CuCl or eutectic KCl/CuCl molten salt mixtures at 280 to 450 °C could be used to yield three partially disordered and ordered structures, wherein the Cu cations are found to coordinate within the intralayer cavities.

Data science competition for cross-site individual tree species identification from airborne remote sensing data.

Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites.

Zebrafish cutaneous injury models reveal that Langerhans cells engulf axonal debris in adult epidermis.

Somatosensory neurons extend enormous peripheral axons to the skin, where they detect diverse environmental stimuli. Somatosensory peripheral axons are easily damaged due to their small caliber and superficial location. Axonal damage results in Wallerian degeneration, creating vast quantities of cellular debris that phagocytes must remove to maintain organ homeostasis.

Evaluating the Virology and Evolution of Seasonal Human Coronaviruses Associated with the Common Cold in the COVID-19 Era.

Approximately 15-30% of all cases of the common cold are due to human coronavirus infections. More recently, the emergence of the more severe respiratory coronaviruses, SARS-CoV and MERS-CoV, have highlighted the increased pathogenic potential of emergent coronaviruses. Lastly, the current emergence of SARS-CoV-2 has demonstrated not only the potential for significant disease caused by emerging coronaviruses, but also the capacity of novel coronaviruses to promote pandemic spread.

A general deep learning model for bird detection in high-resolution airborne imagery.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting.

Evaluating probabilistic ecological forecasts.

Probabilistic near-term forecasting facilitates evaluation of model predictions against observations and is of pressing need in ecology to inform environmental decision-making and effect societal change. Despite this imperative, many ecologists are unfamiliar with the widely used tools for evaluating probabilistic forecasts developed in other fields. We address this gap by reviewing the literature on probabilistic forecast evaluation from diverse fields including climatology, economics, and epidemiology.

A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network.

Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information.

Estimating individual-level plant traits at scale.

Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals' fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual), or (2) using remote-sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals.

A simulation study of the use of temporal occupancy for identifying core and transient species.

Transient species, which do not maintain self-sustaining populations in a system where they are observed, are ubiquitous in nature and their presence often impacts the interpretation of ecological patterns and processes. Identifying transient species from temporal occupancy, the proportion of time a species is observed at a given site over a time series, is subject to classification errors as a result of imperfect detection and source-sink dynamics. We use a simulation-based approach to assess how often errors in detection or classification occur in order to validate the use of temporal occupancy as a metric for inferring whether a species is a core or transient member of a community.

Dynamically Programmable Magnetic Fields for Controlled Movement of Cells Loaded with Iron Oxide Nanoparticles.

Cell-based therapies are becoming increasingly prominent in numerous medical contexts, particularly in regenerative medicine and the treatment of cancer. However, since the efficacy of the therapy is largely dependent on the concentration of therapeutic cells at the treatment area, a major challenge associated with cell-based therapies is the ability to move and localize therapeutic cells within the body. In this article, a technique based on dynamically programmable magnetic fields is successfully demonstrated to noninvasively aggregate therapeutic cells at a desired location.

Ten Simple Rules for a successful remote postdoc.

Postdocs are a critical transition for early-career researchers. This transient period, between finishing a PhD and finding a permanent position, is when early-career researchers develop independent research programs and establish collaborative relationships that can make a successful career. Traditionally, postdocs physically relocate-sometimes multiple times-for these short-term appointments, which creates challenges that can disproportionately affect members of traditionally underrepresented groups in science, technology, engineering, and mathematics (STEM).

Automated data-intensive forecasting of plant phenology throughout the United States.

Phenology, the timing of cyclical and seasonal natural phenomena such as flowering and leaf out, is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there have been no automated species-level forecasts of plant phenology.

A data science challenge for converting airborne remote sensing data into ecological information.

Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification from remote sensing images. We ran a competition to help improve three tasks that are central to converting images into information on individual trees: (1) crown segmentation, for identifying the location and size of individual trees; (2) alignment, to match ground truthed trees with remote sensing; and (3) species classification of individual trees. Six teams (composed of 16 individual participants) submitted predictions for one or more tasks.

Developing a modern data workflow for regularly updated data.

Over the past decade, biology has undergone a data revolution in how researchers collect data and the amount of data being collected. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. Regularly updated data present unique challenges in quality assurance and control, data publication, archiving, and reproducibility.

Energetic equivalence underpins the size structure of tree and phytoplankton communities.

The size structure of autotroph communities - the relative abundance of small vs. large individuals - shapes the functioning of ecosystems. Whether common mechanisms underpin the size structure of unicellular and multicellular autotrophs is, however, unknown.

The proportion of core species in a community varies with spatial scale and environmental heterogeneity.

Ecological communities are composed of a combination of core species that maintain local viable populations and transient species that occur infrequently due to dispersal from surrounding regions. Preliminary work indicates that while core and transient species are both commonly observed in community surveys of a wide range of taxonomic groups, their relative prevalence varies substantially from one community to another depending upon the spatial scale at which the community was characterized and its environmental context. We used a geographically extensive dataset of 968 bird community time series to quantitatively describe how the proportion of core species in a community varies with spatial scale and environmental heterogeneity.

Comparison of large-scale citizen science data and long-term study data for phenology modeling.

Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and interannual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science USA National Phenology Network (USA-NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites.

BioTIME: A database of biodiversity time series for the Anthropocene.

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series.

The prevalence and impact of transient species in ecological communities.

Transient species occur infrequently in a community over time and do not maintain viable local populations. Because transient species interact differently than non-transients with their biotic and abiotic environment, it is important to characterize the prevalence of these species and how they impact our understanding of ecological systems. We quantified the prevalence and impact of transient species in communities using data on over 19,000 community time series spanning an array of ecosystems, taxonomic groups, and spatial scales.

Immunostimulatory CpG on Carbon Nanotubes Selectively Inhibits Migration of Brain Tumor Cells.

Even when treated with aggressive current therapies, patients with glioblastoma usually survive less than two years and exhibit a high rate of recurrence. CpG is an oligonucleotide that activates the innate immune system via Toll-like receptor 9 (TLR9) activation. Injection of CpG into glioblastoma tumors showed promise as an immunotherapy in mouse models but proved disappointing in human trials.

Forecasting biodiversity in breeding birds using best practices.

Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence.