A Rapid Population Assessment Method for Wild Pigs Using Baited Cameras at 3 Study Site

Reliable and efficient population estimates are a critical need for effective management of invasive wild pigs (Sus scrofa). We evaluated the use of 10‐day camera grids for rapid population assessment (RPA) of wild pigs at 3 study sites that varied in vegetation communities and wild pig densities. Study areas included Buck Island Ranch, Florida; Tejon Ranch, California; and the Savannah River Site, South Carolina, USA, during 2016–2018. Rapid population assessments grids were composed of baited camera traps spaced approximately 500 or 750 m apart. Two RPA grids were deployed per study site and each grid was deployed twice (4–6 months apart) to assess changes in response to season or population control efforts. We assessed the ability of RPA grids to track population trends, how camera number influenced estimate precision, and how relative abundance indices related to density estimates. We detected changes in occupancy probability, detection probability, and N‐mixture estimates following removal operations and between seasons, but the ability of RPA grids to track population trends was dependent on the statistical method used and number of cameras traps. Increasing the number of cameras traps used in RPA grids increased precision, and these results can be used in determining survey design and estimate choice. We found that estimates of occupancy probability, detection probability, and N‐mixture estimates were positively correlated with spatially explicit capture-recapture density estimates. Thus, these less labor‐intensive estimates from RPA grids showed potential to index the relative abundance of wild pigs in some systems. Our evaluation of RPAs indicates that using study‐specific combinations of statistical method and number of cameras can provide a useful tool for monitoring wild pig presence, tracking population trends, and evaluating the effectiveness of management actions

Utilization of an Eilat Virus-Based Chimera for Serological Detection of Chikungunya Infection.

In December of 2013, chikungunya virus (CHIKV), an alphavirus in the family Togaviridae, was introduced to the island of Saint Martin in the Caribbean, resulting in the first autochthonous cases reported in the Americas. As of January 2015, local and imported CHIKV has been reported in 50 American countries with over 1.1 million suspected cases. CHIKV causes a severe arthralgic disease for which there are no approved vaccines or therapeutics. Furthermore, the lack of a commercially available, sensitive, and affordable diagnostic assay limits surveillance and control efforts. To address this issue, we utilized an insect-specific alphavirus, Eilat virus (EILV), to develop a diagnostic antigen that does not require biosafety containment facilities to produce. We demonstrated that EILV/CHIKV replicates to high titers in insect cells and can be applied directly in enzyme-linked immunosorbent assays without inactivation, resulting in highly sensitive detection of recent and past CHIKV infection, and outperforming traditional antigen preparations

Electronic Health Record Functionality Needed to Better Support Primary Care

Electronic health records (EHRs) must support primary care clinicians and patients, yet many clinicians remain dissatisfied with their system. This manuscript presents a consensus statement about gaps in current EHR functionality and needed enhancements to support primary care. The Institute of Medicine primary care attributes were used to define needs and Meaningful Use (MU) objectives to define EHR functionality. Current objectives remain disease- rather than whole-person focused, ignoring factors like personal risks, behaviors, family structure, and occupational and environmental influences. Primary care needs EHRs to move beyond documentation to interpreting and tracking information over time as well as patient partnering activities, support for team based care, population management tools that deliver care, and reduced documentation burden. While Stage 3 MU’s focus on outcomes is laudable, enhanced functionality is still needed including EHR modifications, expanded use of patient portals, seamless integration with external applications, and advancement of national infrastructure and policies

Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests

Functional responses describe how changing resource availability affects con- sumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro-ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and avail- ability. We linked heterogeneous movement data across different agro-ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental- scale movement data within agro-ecosystems to quantify the functional response of agricul- tural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as dis- tance to crops altered the magnitude of the functional response. There was a strong agricul- tural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non-agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found sig- nificant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depend- ing on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our under- standing of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro-ecosystems

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds

A metabolic engineering approach for iron and zinc biofortification of bread wheat (Triticum aestivum L.): impacts on plant growth, grain nutrition and food processing

© 2019 Jesse Taylor BeasleyHuman iron (Fe) and zinc (Zn) deficiencies are among the most prevalent nutritional disorders in the world and manifest as a range of health issues including fatigue, impaired cognitive development and increased mortality. Micronutrient supplements and fortificants are frequently used to increase human Fe and Zn intakes yet these strategies require continuous investment and frequently miss rural populations; improving the density and/or bioavailability of Fe and Zn in staple crops (a process known as biofortification) represents a powerful alternative. Bread wheat (Triticum aestivum L.) is cultivated on more land than any other crop and is processed into a range of food products to supply ~20% of the daily calories consumed by humans. The wheat grain is mostly comprised of starch and protein, with micronutrients such as Fe and Zn concentrated in the outer aleurone layer of the grain. In the aleurone layer, Fe and Zn are complexed to compounds that inhibit their absorption (bioavailability) in the human gut and the aleurone layer is removed during grain milling to produce white flour. White flour (representing the inner wheat endosperm) contains low concentrations of dietary Fe and Zn, and high consumption of either wholemeal flour or white wheat flour coincides with high prevalences of human Fe and Zn deficiencies. Generating Fe and Zn biofortified wheat through conventional breeding in modern wheat cultivars is hindered by inherently low grain Fe and Zn concentrations and a lack of genetic variation for these traits. Nicotianamine (NA) is a low-molecular weight metal chelator present in all higher plants with high affinity for Fe2+, Zn2+, and other divalent metal cations. In graminaceous plant species such as wheat, NA serves as the biosynthetic precursor to 2’-deoxymugineic acid (DMA), a root-secreted mugineic acid family phytosiderophore that chelates ferric iron (Fe3+) in the rhizosphere for subsequent uptake by the plant. Both NA and/or DMA are the major chelators of Fe within white wheat flour, and NA is known to enhance Fe bioavailability in cereal grain. For these reasons, increasing the biosynthesis of NA/DMA through upregulation of nicotianamine synthase (NAS) genes has emerged as a popular strategy for Fe and Zn biofortification of cereal crops. In this study we employed constitutive expression (CE) of the rice (Oryza sativa L.) nicotianamine synthase 2 (OsNAS2) gene in bread wheat to upregulate the biosynthesis of NA and DMA, and evaluate plant growth, grain nutrition and food processing properties of CE-OsNAS2 wheat. Our lead CE-OsNAS2 wheat transgenic event (CE-1) demonstrated higher concentrations of Fe, Zn, NA and DMA in wholemeal flour, white flour and white bread, altered distribution of Fe in the grain, and higher white flour Fe bioavailability relative to null segregant (NS) control. Protein composition, dough rheology and breadmaking properties were similar between CE-1 and NS white flour, and a chicken (Gallus gallus) feeding study over a period of six weeks demonstrated that chickens consuming CE-1 white flour had improved Fe status, intestinal morphology and microbial populations relative to chickens that consumed NS white flour. Multi-location confined field trial (CFT) evaluation over three field seasons demonstrated no differences between CE-1 and NS agronomic performance apart from plant height. Throughout all CFTs, grain yield was negatively correlated with grain Fe, Zn, and protein concentrations yet not correlated with grain NA and DMA concentrations. White flour Fe bioavailability was positively correlated with white flour NA concentrations, and we determined NA to be the strongest enhancer of in vitro Fe bioavailability identified to date. Together these results suggest the proportion of Fe that is chelated to enhancers of bioavailability (such as NA and DMA) should be prioritized in future crop biofortification efforts and highlight new strategies for developing Fe and Zn biofortified wheat as a more nutritious staple food

Annotation and Molecular Characterisation of the TaIRO3 and TaHRZ Iron Homeostasis Genes in Bread Wheat (Triticum aestivum L.)

Effective maintenance of plant iron (Fe) homoeostasis relies on a network of transcription factors (TFs) that respond to environmental conditions and regulate Fe uptake, translocation, and storage. The iron-related transcription factor 3 (IRO3), as well as haemerythrin motif-containing really interesting new gene (RING) protein and zinc finger protein (HRZ), are major regulators of Fe homeostasis in diploid species like Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa L.), but remain uncharacterised in hexaploid bread wheat (Triticum aestivum L.). In this study, we have identified, annotated, and characterised three TaIRO3 homoeologs and six TaHRZ1 and TaHRZ2 homoeologs in the bread wheat genome. Protein analysis revealed that TaIRO3 and TaHRZ proteins contain functionally conserved domains for DNA-binding, dimerisation, Fe binding, or polyubiquitination, and phylogenetic analysis revealed clustering of TaIRO3 and TaHRZ proteins with other monocot IRO3 and HRZ proteins, respectively. Quantitative reverse-transcription PCR analysis revealed that all TaIRO3 and TaHRZ homoeologs have unique tissue expression profiles and are upregulated in shoot tissues in response to Fe deficiency. After 24 h of Fe deficiency, the expression of TaHRZ homoeologs was upregulated, while the expression of TaIRO3 homoeologs was unchanged, suggesting that TaHRZ functions upstream of TaIRO3 in the wheat Fe homeostasis TF network

Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests

Functional responses describe how changing resource availability affects con- sumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro-ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and avail- ability. We linked heterogeneous movement data across different agro-ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental- scale movement data within agro-ecosystems to quantify the functional response of agricul- tural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as dis- tance to crops altered the magnitude of the functional response. There was a strong agricul- tural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non-agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found sig- nificant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depend- ing on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our under- standing of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro-ecosystems