A model for rolling swarms of locusts

We construct an individual-based kinematic model of rolling migratory locust swarms. The model incorporates social interactions, gravity, wind, and the effect of the impenetrable boundary formed by the ground. We study the model using numerical simulations and tools from statistical mechanics, namely the notion of H-stability. For a free-space swarm (no wind and gravity), as the number of locusts increases, it approaches a crystalline lattice of fixed density if it is H-stable, and in contrast becomes ever more dense if it is catastrophic. Numerical simulations suggest that whether or not a swarm rolls depends on the statistical mechanical properties of the corresponding free-space swarm. For a swarm that is H-stable in free space, gravity causes the group to land and form a crystalline lattice. Wind, in turn, smears the swarm out along the ground until all individuals are stationary. In contrast, for a swarm that is catastrophic in free space, gravity causes the group to land and form a bubble-like shape. In the presence of wind, the swarm migrates with a rolling motion similar to natural locust swarms. The rolling structure is similar to that observed by biologists, and includes a takeoff zone, a landing zone, and a stationary zone where grounded locusts can rest and feed.Comment: 18 pages, 11 figure

The Subcomponents of Affect Scale (SAS): Validating a Widely Used Affect Scale

Objective There is a need for a brief affect scale that also encompasses different components of affect relevant for researchers interested in physiological and health outcomes. The Subcomponents of Affect Scale (SAS) meets this need. This 18-item scale has nine positive and nine negative affect items encompassing six subscales (calm, well-being, vigour, depression, anxiety, anger). Previous research using the SAS has demonstrated its predictive validity, but no work has tested its subscale structure or longitudinal validity. Design Data from the Common Cold Project in which individuals (N = 610) completed the SAS over the course of seven days were used. Results Confirmatory factor analysis demonstrated the reliability of the subscale structure of the SAS across seven days (positive affect subscale structure: CFIs ≥ 0.98; negative affect subscale structure: CFIs ≥ 0.94 with day 6 CFI = 0.91) and tests of factorial invariance showed the scale is valid to use over time. Conclusions These results confirm the psychometric validity of the subscale structure of the SAS and imply that the subscales can be used longitudinally, allowing for its use in health research as well as non-health research that can benefit from its subscale structure and longitudinal capabilities

Microclimate buffering and thermal tolerance across elevations in a tropical butterfly

Microclimatic variability in tropical forests plays a key role in shaping species distributions and their ability to cope with environmental change, especially for ectotherms. Nonetheless, currently available climatic datasets lack data from the forest interior and, furthermore, our knowledge of thermal tolerance among tropical ectotherms is limited. We therefore studied natural variation in the microclimate experienced by tropical butterflies in the genus Heliconius across their Andean range in a single year. We found that the forest strongly buffers temperature and humidity in the understory, especially in the lowlands where temperatures are more extreme. There were systematic differences between our yearly records and macroclimate databases (WorldClim2), with lower interpolated minimum temperatures and maximum temperatures higher than expected. We then assessed thermal tolerance of ten Heliconius butterfly species in the wild and showed that populations at high elevations had significantly lower heat tolerance than those at lower elevations. However, when we reared populations of the widespread H. erato from high and low elevations in a common-garden environment, the difference in heat tolerance across elevations was reduced, indicating plasticity in this trait. Microclimate buffering is not currently captured in publicly available datasets but could be crucial for enabling upland shifting of species sensitive to heat such as highland Heliconius. Plasticity in thermal tolerance may alleviate the effects of global warming on some widespread ectotherm species, but more research is needed to understand the long-term consequences of plasticity on populations and species

Herbivore regulation of plant abundance in aquatic ecosystems.

Herbivory is a fundamental process that controls primary producer abundance and regulates energy and nutrient flows to higher trophic levels. Despite the recent proliferation of small-scale studies on herbivore effects on aquatic plants, there remains limited understanding of the factors that control consumer regulation of vascular plants in aquatic ecosystems. Our current knowledge of the regulation of primary producers has hindered efforts to understand the structure and functioning of aquatic ecosystems, and to manage such ecosystems effectively. We conducted a global meta-analysis of the outcomes of plant-herbivore interactions using a data set comprised of 326 values from 163 studies, in order to test two mechanistic hypotheses: first, that greater negative changes in plant abundance would be associated with higher herbivore biomass densities; second, that the magnitude of changes in plant abundance would vary with herbivore taxonomic identity. We found evidence that plant abundance declined with increased herbivore density, with plants eliminated at high densities. Significant between-taxa differences in impact were detected, with insects associated with smaller reductions in plant abundance than all other taxa. Similarly, birds caused smaller reductions in plant abundance than echinoderms, fish, or molluscs. Furthermore, larger reductions in plant abundance were detected for fish relative to crustaceans. We found a positive relationship between herbivore species richness and change in plant abundance, with the strongest reductions in plant abundance reported for low herbivore species richness, suggesting that greater herbivore diversity may protect against large reductions in plant abundance. Finally, we found that herbivore-plant nativeness was a key factor affecting the magnitude of herbivore impacts on plant abundance across a wide range of species assemblages. Assemblages comprised of invasive herbivores and native plant assemblages were associated with greater reductions in plant abundance compared with invasive herbivores and invasive plants, native herbivores and invasive plants, native herbivores and mixed-nativeness plants, and native herbivores and native plants. By contrast, assemblages comprised of native herbivores and invasive plants were associated with lower reductions in plant abundance compared with both mixed-nativeness herbivores and native plants, and native herbivores and native plants. However, the effects of herbivore-plant nativeness on changes in plant abundance were reduced at high herbivore densities. Our mean reductions in aquatic plant abundance are greater than those reported in the literature for terrestrial plants, but lower than aquatic algae. Our findings highlight the need for a substantial shift in how biologists incorporate plant-herbivore interactions into theories of aquatic ecosystem structure and functioning. Currently, the failure to incorporate top-down effects continues to hinder our capacity to understand and manage the ecological dynamics of habitats that contain aquatic plants

Evidence for the ‘Good Genes’ Model: Association of MHC Class II DRB Alleles with Ectoparasitism and Reproductive State in the Neotropical Lesser Bulldog Bat, Noctilio albiventris

The adaptive immune system has a major impact on parasite resistance and life history strategies. Immunological defence is costly both in terms of immediate activation and long-term maintenance. The ‘good genes’ model predicts that males with genotypes that promote a good disease resistance have the ability to allocate more resources to reproductive effort which favours the transmission of good alleles into future generations. Our study shows a correlation between immune gene constitution (Major Histocompatibility Complex, MHC class II DRB), ectoparasite loads (ticks and bat flies) and the reproductive state in a neotropical bat, Noctilio albiventris. Infestation rates with ectoparasites were linked to specific Noal-DRB alleles, differed among roosts, increased with body size and co-varied with reproductive state particularly in males. Non-reproductive adult males were more infested with ectoparasites than reproductively active males, and they had more often an allele (Noal-DRB*02) associated with a higher tick infestation than reproductively active males or subadults. We conclude that the individual immune gene constitution affects ectoparasite susceptibility, and contributes to fitness relevant trade-offs in male N. albiventris as suggested by the ‘good genes’ model

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages