Dataset documenting prevalence and counts of pine processionary moth tents on local host trees in 3 regions of France with different climatic environments.

The pine processionary moth Thaumetopoea pityocampa is a defoliating lepidopter that develops during winter. The larvae are gregarious and bear urticating setae that are harmful to humans and vertebrates. They shelter in conspicuous silk tents that are easy to detect. We here present a dataset comprising tree characterization and tent counts from 3 agglomerations in France located in regions with different climatic environments. The studied trees belong to various conifer species that are potential hosts for the caterpillars. In each site, we defined clusters as one target tree and its 10-62 nearest neighbors, and surveyed each tree within the clusters by informing: tree species, coordinates, size, number of tents. We characterized a total of 3690 trees, including 2009 trees in Orléans (grouped in 68 clusters), 359 trees in La Baule (18 clusters) and 1322 trees in Montpellier (52 clusters). We provide the raw data characterizing each individual tree, graphs showing the prevalence and mean number of tents for the tree species included in the survey, and maps allowing to locate each tree. This dataset brings information about host preference of the pine processionary moth and will be useful as a baseline to study spatio-temporal variability of host-insect relationships. It can also be informative for decision-makers and managers of urban greenings to avoid trees that are likely to be heavily infested for plantation in proximity to vulnerable people.

Prioritization of the concepts and skills in quantitative education for graduate students in biomedical science.

Substantial guidance is available on undergraduate quantitative training for biologists, including reports focused on biomedical science. Far less attention has been paid to the graduate curriculum and the particular challenges of the diversity of specialization within the life sciences. We propose an innovative approach to quantitative education that goes beyond recommendations of a course or set of courses or activities, derived from analysis of the expectations for students in particular programs. Due to the plethora of quantitative methods, it is infeasible to expect that biomedical PhD students can be exposed to more than a minority of the quantitative concepts and techniques employed in modern biology. We collected key recent papers suggested by the faculty in biomedical science programs, chosen to include important scientific contributions that the faculty consider appropriate for all students in the program to be able to read with confidence. The quantitative concepts and methods inherent in these papers were then analyzed and categorized to provide a rational basis for prioritization of those concepts to be emphasized in the education program. This novel approach to prioritization of quantitative skills and concepts provides an effective method to drive curricular focus based upon program-specific faculty input for science programs of all types. The results of our particular application to biomedical science training highlight the disconnect between typical undergraduate quantitative education for life science students, focused on continuous mathematics, and the concepts and skills in graphics, statistics, and discrete mathematics that arise from priorities established by biomedical science faculty. There was little reference in the key recent papers chosen by faculty to classic mathematical areas such as calculus which make up a large component of the formal undergraduate mathematics training of graduate students in biomedical areas.

Quantitative Biology at Community Colleges, a Network of Biology and Mathematics Faculty Focused on Improving Numerical and Quantitative Skills of Students.

Mastery of quantitative skills is increasingly critical for student success in life sciences, but few curricula adequately incorporate quantitative skills. Quantitative Biology at Community Colleges (QB@CC) is designed to address this need by building a grassroots consortium of community college faculty to 1) engage in interdisciplinary partnerships that increase participant confidence in life science, mathematics, and statistics domains; 2) generate and publish a collection of quantitative skills-focused open education resources (OER); and 3) disseminate these OER and pedagogical practices widely, in turn expanding the network. Currently in its third year, QB@CC has recruited 70 faculty into the network and created 20 modules. Modules can be accessed by interested biology and mathematics educators in high school, 2-year, and 4-year institutions. Here, we use survey responses, focus group interviews, and document analyses (principles-focused evaluation) to evaluate the progress in accomplishing these goals midway through the QB@CC program. The QB@CC network provides a model for developing and sustaining an interdisciplinary community that benefits participants and generates valuable resources for the broader community. Similar network-building programs may wish to adopt some of the effective aspects of the QB@CC network model to meet their objectives.

Determinants of emissions pathways in the coupled climate-social system.

The ambition and effectiveness of climate policies will be essential in determining greenhouse gas emissions and, as a consequence, the scale of climate change impacts1,2. However, the socio-politico-technical processes that will determine climate policy and emissions trajectories are treated as exogenous in almost all climate change modelling3,4. Here we identify relevant feedback processes documented across a range of disciplines and connect them in a stylized model of the climate-social system. An analysis of model behaviour reveals the potential for nonlinearities and tipping points that are particularly associated with connections across the individual, community, national and global scales represented. These connections can be decisive for determining policy and emissions outcomes. After partly constraining the model parameter space using observations, we simulate 100,000 possible future policy and emissions trajectories. These fall into 5 clusters with warming in 2100 ranging between 1.8 °C and 3.6 °C above the 1880-1910 average. Public perceptions of climate change, the future cost and effectiveness of mitigation technologies, and the responsiveness of political institutions emerge as important in explaining variation in emissions pathways and therefore the constraints on warming over the twenty-first century.

COVID-19 and crime: Analysis of crime dynamics amidst social distancing protocols.

In response to the pandemic in early 2020, cities implemented states of emergency and stay at home orders to reduce virus spread. Changes in social dynamics due to local restrictions impacted human behavior and led to a shift in crime dynamics. We analyze shifts in crime types by comparing crimes before the implementation of stay at home orders and the time period shortly after these orders were put in place across three cities. We find consistent changes across Chicago, Baltimore, and Baton Rouge with significant declines in total crimes during the time period immediately following stay at home orders. The starkest differences occurred in Chicago, but in all three cities the crime types contributing to these declines were related to property crime and statutory crime rather than interpersonal crimes.

Grassland-to-crop conversion in agricultural landscapes has lasting impact on the trait diversity of bees.

CONTEXT: Global pollinator decline has motivated much research to understand the underlying mechanisms. Among the multiple pressures threatening pollinators, habitat loss has been suggested as a key-contributing factor. While habitat destruction is often associated with immediate negative impacts, pollinators can also exhibit delayed responses over time. OBJECTIVES: We used a trait-based approach to investigate how past and current land use at both local and landscape levels impact plant and wild bee communities in grasslands through a functional lens. METHODS: We measured flower and bee morphological traits that mediate plant-bee trophic linkage in 66 grasslands. Using an extensive database of 20 years of land-use records, we tested the legacy effects of the landscape-level conversion of grassland to crop on flower and bee trait diversity. RESULTS: Land-use history was a strong driver of flower and bee trait diversity in grasslands. Particularly, bee trait diversity was lower in landscapes where much of the land was converted from grassland to crop long ago. Bee trait diversity was also strongly driven by plant trait diversity computed with flower traits. However, this relationship was not observed in landscapes with a long history of grassland-to-crop conversion. The effects of land-use history on bee communities were as strong as those of current land use, such as grassland or mass-flowering crop cover in the landscape. CONCLUSIONS: Habitat loss that occurred long ago in agricultural landscapes alters the relationship between plants and bees over time. The retention of permanent grassland sanctuaries within intensive agricultural landscapes can offset bee decline.

Changing the Nature of Quantitative Biology Education: Data Science as a Driver.

We live in a data-rich world with rapidly growing databases with zettabytes of data. Innovation, computation, and technological advances have now tremendously accelerated the pace of discovery, providing driverless cars, robotic devices, expert healthcare systems, precision medicine, and automated discovery to mention a few. Even though the definition of the term data science continues to evolve, the sweeping impact it has already produced on society is undeniable. We are at a point when new discoveries through data science have enormous potential to advance progress but also to be used maliciously, with harmful ethical and social consequences. Perhaps nowhere is this more clearly exemplified than in the biological and medical sciences. The confluence of (1) machine learning, (2) mathematical modeling, (3) computation/simulation, and (4) big data have moved us from the sequencing of genomes to gene editing and individualized medicine; yet, unsettled policies regarding data privacy and ethical norms could potentially open doors for serious negative repercussions. The data science revolution has amplified the urgent need for a paradigm shift in undergraduate biology education. It has reaffirmed that data science education interacts and enhances mathematical education in advancing quantitative conceptual and skill development for the new generation of biologists. These connections encourage us to strive to cultivate a broadly skilled workforce of technologically savvy problem-solvers, skilled at handling the unique challenges pertaining to biological data, and capable of collaborating across various disciplines in the sciences, the humanities, and the social sciences. To accomplish this, we suggest development of open curricula that extend beyond the job certification rhetoric and combine data acumen with modeling, experimental, and computational methods through engaging projects, while also providing awareness and deep exploration of their societal implications. This process would benefit from embracing the pedagogy of experiential learning and involve students in open-ended explorations derived from authentic inquiries and ongoing research. On this foundation, we encourage development of flexible data science initiatives for the education of life science undergraduates within and across existing models.

Development of the BioCalculus Assessment (BCA).

We describe the development and initial validity assessment of the 20-item BioCalculus Assessment (BCA), with the objective of comparing undergraduate life science students' understanding of calculus concepts in different courses with alternative emphases (with and without focus on biological applications). The development process of the BCA included obtaining input from a large network of scientists and educators as well as students in calculus and biocalculus courses to accumulate evidential support of the instrument's content validity and response processes of test takers. We used the Rasch model to examine the internal structure of scores from students who have experienced calculus instruction in the two methods. The analysis involved three populations (Calculus 1, Calculus 2, and Biocalculus) for which the Calc 1 and Calc 2 students were not exposed to calculus concepts in a life science setting, while the Biocalculus students were presented concepts explicitly with a life science emphasis. Overall, our findings indicate that the BCA has reasonable validity properties, providing a diagnostic tool to assess the relative learning success and calculus comprehension of undergraduate biology majors from alternative methods of instruction that do or do not emphasize life science examples.

Coinfections by noninteracting pathogens are not independent and require new tests of interaction.

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of coinfected hosts is expected to be higher than multiplication would suggest. By modelling the dynamics of multiple noninteracting pathogens causing chronic infections, we develop a pair of novel tests of interaction that properly account for nonindependence between pathogens causing lifelong infection. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models.

Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap.

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya.

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Using Active Learning to Teach Concepts and Methods in Quantitative Biology.

This article provides a summary of the ideas discussed at the 2015 Annual Meeting of the Society for Integrative and Comparative Biology society-wide symposium on Leading Students and Faculty to Quantitative Biology through Active Learning. It also includes a brief review of the recent advancements in incorporating active learning approaches into quantitative biology classrooms. We begin with an overview of recent literature that shows that active learning can improve students' outcomes in Science, Technology, Engineering and Math Education disciplines. We then discuss how this approach can be particularly useful when teaching topics in quantitative biology. Next, we describe some of the recent initiatives to develop hands-on activities in quantitative biology at both the graduate and the undergraduate levels. Throughout the article we provide resources for educators who wish to integrate active learning and technology into their classrooms.

Optimal control in individual-based models: implications from aggregated methods.

Use of individual-based models (IBMs) has been expanding in both theoretical and applied ecology. IBMs include details at the level of individuals that may lead to different conclusions from aggregated modeling methods. There has been essentially no guidance available on how to most effectively manage populations when the underlying dynamics are best modeled through IBMs. Using a simple resource-consumer IBM, we investigate whether optimal control theory applied to an aggregated model (AM) can effectively control a harmful species modeled by an IBM or whether interactions between individuals, their spatial distribution, and/or landscape heterogeneities limit the effectiveness of a control derived for the AM. If optimal policies derived from an AM are determined to be generally effective in managing a population modeled with considerably greater complexity, this provides evidence that optimal management strategies may be relatively insensitive to the details of individual behavior and the associated effects on population response. We investigate these issues and find that if there is weak spatial heterogeneity in the resource, the optimal control derived from the AM can be used effectively to control the harmful species in the IBM. The approach is more limited in the case of very strong spatial heterogeneity in the resource. This suggests investigation of a mixture of simplified models in conjunction with detailed simulation models when individual differences affect population processes.

Order of events matter: comparing discrete models for optimal control of species augmentation.

We investigate optimal timing of augmentation of an endangered/threatened species population in a target region by moving individuals from a reserve or captive population. This is formulated as a discrete-time optimal control problem in which augmentation occurs once per time period over a fixed number of time periods. The population model assumes the Allee effect growth functions in both target and reserve populations and the control objective is to maximize the target and reserve population sizes over the time horizon while accounting for costs of augmentation. Two possible orders of events are considered for different life histories of the species relative to augmentation time: move individuals either before or after population growth occurs. The control variable is the proportion of the reserve population to be moved to the target population. We develop solutions and illustrate numerical results which indicate circumstances for which optimal augmentation strategies depend upon the order of events.

Optimal control of a rabies epidemic model with a birth pulse.

A system of ordinary differential equations describes the population dynamics of a rabies epidemic in raccoons. The model accounts for the dynamics of a vaccine, including loss of vaccine due to animal consumption and loss from factors other than racoon uptake. A control method to reduce the spread of disease is introduced through temporal distribution of vaccine packets. This work incorporates the effect of the seasonal birth pulse in the racoon population and the attendant increase in new-borns which are susceptible to the diseases, analysing the impact of the timing and length of this pulse on the optimal distribution of vaccine packets. The optimization criterion is to minimize the number of infected raccoons while minimizing the cost of distributing the vaccine. Using an optimal control setting, numerical results illustrate strategies for distributing the vaccine depending on the timing of the infection outbreak with respect to the birth pulse.

Vegetation, fire, and feedbacks: a disturbance-mediated model of savannas.

Savanna models that are based on recurrent disturbances such as fire result in nonequilibrium savannas, but these models rarely incorporate vegetation feedbacks on fire frequency or include more than two states (grasses and trees). We develop a disturbance model that includes vegetation-fire feedbacks, using a system of differential equations to represent three main components of savannas: grasses, fire-tolerant savanna trees, and fire-intolerant forest trees. We investigate the stability of savannas in the presence of positive feedbacks of fire frequency with (1) grasses, (2) savanna trees, and (3) grasses and savanna trees together while also allowing for negative feedbacks of forest trees on fire frequency. We find that positive feedbacks between fire frequency and savanna trees, alone or together with grasses, can stabilize savannas, blocking the conversion of savannas to forests. Negative feedbacks of forest trees on fire frequency shift the range of parameter space that supports savannas, but they do not generally alter our results. We propose that pyrogenic trees that modify characteristics of fire regimes are ecosystem engineers that facilitate the persistence of savannas, generating both threshold fire frequencies with rapid changes in community composition when these thresholds are crossed and hystereses with bistable community states.

Optimal control of vaccine distribution in a rabies metapopulation model.

We consider an SIR metapopulation model for the spread of rabies in raccoons. This system of ordinary differential equations considers subpopulations connected by movement. Vaccine for raccoons is distributed through food baits. We apply optimal control theory to find the best timing for distribution of vaccine in each of the linked subpopulations across the landscape. This strategy is chosen to limit the disease optimally by making the number of infections as small as possible while accounting for the cost of vaccination.