Community composition of black flies during and after the 2020 vesicular stomatitis virus outbreak in Southern New Mexico, USA

BACKGROUND

Vesicular stomatitis virus (VSV), a vector-borne pathogen of livestock, emerges periodically in the western US. In New Mexico (NM), US, most cases occur close to the Rio Grande River, implicating black flies (Simulium spp.) as a possible vector. In 2020, VS cases were reported in NM from April to May, although total black fly abundance remained high until September. We investigated the hypothesis that transience of local VSV transmission results from transient abundance of key, competent black fly species. Additionally, we investigated whether irrigation canals in southern NM support a different community of black flies than the main river. Lastly, to gain insight into the source of local black flies, in 2023 we collected black fly larvae prior to the release of water into the Rio Grande River channel.

METHODS

We randomly sub-sampled adult black flies collected along the Rio Grande during and after the 2020 VSV outbreak. We also collected black fly adults along the river in 2021 and 2022 and at southern NM farms and irrigation canals in 2022. Black fly larvae were collected from dams in the area in 2023. All collections were counted, and individual specimens were subjected to molecular barcoding for species identification.

RESULTS

DNA barcoding of adult black flies detected four species in 2020: Simulium meridionale (N = 158), S. mediovittatum (N = 83), S. robynae (N = 26) and S. griseum/notatum (N = 1). Simulium robynae was only detected during the VSV outbreak period, S. meridionale showed higher relative abundance, but lower absolute abundance, during the outbreak than post-outbreak period, and S. mediovittatum was rare during the outbreak period but predominated later in the summer. In 2022, relative abundance of black fly species did not differ significantly between the Rio Grande sites and farm and irrigation canals. Intriguingly, 63 larval black flies comprised 56% Simulium vittatum, 43% S. argus and 1% S. encisoi species that were either extremely rare or not detected in previous adult collections.

CONCLUSIONS

Our results suggest that S. robynae and S. meridionale could be shaping patterns of VSV transmission in southern NM. Thus, field studies of the source of these species as well as vector competence studies are warranted.

Uniting Experiments and Big Data to advance ecology and conservation

Many ecologists increasingly advocate for research frameworks centered on the use of 'big data' to address anthropogenic impacts on ecosystems. Yet, experiments are often considered essential for identifying mechanisms and informing conservation interventions. We highlight the complementarity of these research frameworks and expose largely untapped opportunities for combining them to speed advancements in ecology and conservation. With nascent but increasing application of model integration, we argue that there is an urgent need to unite experimental and big data frameworks throughout the scientific process. Such an integrated framework offers potential for capitalizing on the benefits of both frameworks to gain rapid and reliable answers to ecological challenges.

A big data-model integration approach for predicting epizootics and population recovery in a keystone species

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.

Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model

With the implementation of national strategies such as sports power and national fitness, the sports economy has become an important element of high-quality national development, and the demand for sports economy and management talents is greatly increased. Particularly in the new area with big data as the typical feature, the teaching content, teaching method, and teaching mode of sports economics and management majors have put forward new requirements. The continuous progress of storage and network technology has prompted the generation of massive multisource spatiotemporal data in various fields. The advantage of association analysis algorithms is that they are easy to code and implement. The relationships found by association analysis can take two forms: frequent itemsets or association rules. We use correlation analysis methods to perform correlation learning between sports economy and related big data and thus improve the development of sports economy. Mining and analyzing the relevant big data can precisely reveal the problems of sports economic development and can realize the fine management of sports, thus contributing to the healthy development of sports. Mastering the skills of acquiring, analyzing, and applying big data is the core content of sports economic analysis. The sports economy has refined and intelligent management means, and its adoption of virtual reality reflects the current situation and development trend of the sports business, which further highlights the status and role of multisource big data in the sports economy. Based on these, this paper proposed a sports economy mining algorithm in view of the correlation analysis and big data model. Then, we verified the effectiveness of the model through experiments, which laid the foundation for the development of the sports economy.

Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions

Vesicular stomatitis (VS) is the most common vesicular livestock disease in North America. Transmitted by direct contact and by several biting insect species, this disease results in quarantines and animal movement restrictions in horses, cattle and swine. As changes in climate drive shifts in geographic distributions of vectors and the viruses they transmit, there is considerable need to improve understanding of relationships among environmental drivers and patterns of disease occurrence. Multidisciplinary approaches integrating pathology, ecology, climatology, and biogeophysics are increasingly relied upon to disentangle complex relationships governing disease. We used a big data model integration approach combined with machine learning to estimate the potential geographic range of VS across the continental United States (CONUS) under long-term mean climate conditions over the past 30 years. The current extent of VS is confined to the western portion of the US and is related to summer and winter precipitation, winter maximum temperature, elevation, fall vegetation biomass, horse density, and proximity to water. Comparison with a climate-only model illustrates the importance of current processes-based parameters and identifies regions where uncertainty is likely to be greatest if mechanistic processes change. We then forecast shifts in the range of VS using climate change projections selected from CMIP5 climate models that most realistically simulate seasonal temperature and precipitation. Climate change scenarios that altered climatic conditions resulted in greater changes to potential range of VS, generally had non-uniform impacts in core areas of the current potential range of VS and expanded the range north and east. We expect that the heterogeneous impacts of climate change across the CONUS will be exacerbated with additional changes in land use and land cover affecting biodiversity and hydrological cycles that are connected to the ecology of insect vectors involved in VS transmission.

Provoking a Cultural Shift in Data Quality

Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.

Toward a Generalizable Framework of Disturbance Ecology Through Crowdsourced Science

Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas.

Ecological prediction at macroscales using big data: Does sampling design matter?

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8-million-km² area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

The Complexity of Urban Eco-evolutionary Dynamics

Urbanization is changing Earth's ecosystems by altering the interactions and feedbacks between the fundamental ecological and evolutionary processes that maintain life. Humans in cities alter the eco-evolutionary play by simultaneously changing both the actors and the stage on which the eco-evolutionary play takes place. Urbanization modifies land surfaces, microclimates, habitat connectivity, ecological networks, food webs, species diversity, and species composition. These environmental changes can lead to changes in phenotypic, genetic, and cultural makeup of wild populations that have important consequences for ecosystem function and the essential services that nature provides to human society, such as nutrient cycling, pollination, seed dispersal, food production, and water and air purification. Understanding and monitoring urbanization-induced evolutionary changes is important to inform strategies to achieve sustainability. In the present article, we propose that understanding these dynamics requires rigorous characterization of urbanizing regions as rapidly evolving, tightly coupled human–natural systems. We explore how the emergent properties of urbanization affect eco-evolutionary dynamics across space and time. We identify five key urban drivers of change—habitat modification, connectivity, heterogeneity, novel disturbances, and biotic interactions—and highlight the direct consequences of urbanization-driven eco-evolutionary change for nature's contributions to people. Then, we explore five emerging complexities—landscape complexity, urban discontinuities, socio-ecological heterogeneity, cross-scale interactions, legacies and time lags—that need to be tackled in future research. We propose that the evolving metacommunity concept provides a powerful framework to study urban eco-evolutionary dynamics.

Management Strategies for Reducing the Risk of Equines Contracting Vesicular Stomatitis Virus (VSV) in the Western United States

Vesicular stomatitis viruses (VSVs) cause a condition known as vesicular stomatitis (VS), which results in painful lesions in equines, cattle, swine, and camelids, and when transmitted to humans, can cause flu-like symptoms. When animal premises are affected by VS, they are subject to a quarantine. The equine industry more broadly may incur economic losses due to interruptions of animal trade and transportation to shows, competitions, and other events. Equine owners, barn managers, and veterinarians can take proactive measures to reduce the risk of equines contracting VS. To identify appropriate risk management strategies, it helps to understand which biting insects are capable of transmitting the virus to animals, and to identify these insect vectors' preferred habitats and behaviors. We make this area of science more accessible to equine owners, barn managers, and veterinarians, by (1) translating the most relevant scientific information about biting insect vectors of VSV and (2) identifying practical management strategies that might reduce the risk of equines contracting VSV from infectious biting insects or from other equines already infected with VSV. We address transmission risk at four different spatial scales-the animal, the barn/shelter, the barnyard/premises, and the surrounding environment/neighborhood-noting that a multiscale and spatially collaborative strategy may be needed to reduce the risk of VS.

Forecasting semi-arid biome shifts in the Anthropocene

Shrub encroachment, forest decline and wildfires have caused large-scale changes in semi-arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi-arid ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi-arid biome shifts. Moving from small to large scales, first, tree-level models that simulate the carbon costs of drought-induced plant hydraulic failure are improving predictions of delayed-mortality responses to drought. Second, tracer-informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small-scale water movement processes at large scales. Fourth, remotely-sensed measurements of plant traits such as relative canopy moisture are providing early-warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi-arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high-risk areas for management.

Long-Term Ecological Research and Evolving Frameworks of Disturbance Ecology

Detecting and understanding disturbance is a challenge in ecology that has grown more critical with global environmental change and the emergence of research on social–ecological systems. We identify three areas of research need: developing a flexible framework that incorporates feedback loops between social and ecological systems, anticipating whether a disturbance will change vulnerability to other environmental drivers, and incorporating changes in system sensitivity to disturbance in the face of global changes in environmental drivers. In the present article, we review how discoveries from the US Long Term Ecological Research (LTER) Network have influenced theoretical paradigms in disturbance ecology, and we refine a framework for describing social–ecological disturbance that addresses these three challenges. By operationalizing this framework for seven LTER sites spanning distinct biomes, we show how disturbance can maintain or alter ecosystem state, drive spatial patterns at landscape scales, influence social–ecological interactions, and cause divergent outcomes depending on other environmental changes.

Optimizing the use of biologgers for movement ecology research

The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.