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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Savannas commonly consist of a discontinuous cover of overstory trees and a groundcover of grasses. Savanna models have previously demonstrated that vegetation feedbacks on fire frequency can limit the density of overstory trees, thereby maintaining savannas. Positive feedbacks of either savanna trees alone or trees and grasses together on fire frequency have been shown to result in a stable savanna equilibrium. Grass feedbacks on fire frequency, in contrast, have resulted in stable equilibria in either a grassland or forest state, but not in a savanna. These results, however, were derived from a system of differential equations that assumes that fire occurrence is strictly deterministic and that vegetation losses due to fire are continuous in time. We develop an alternative formulation of the grass-fire feedback model that assumes that fires are discrete and occur stochastically in time to examine the influence of these assumptions on the predicted state of the system. We show that incorporating fire as a discrete event can produce a recurring temporal refuge in which both grass and trees co-occur in a stable, bounded savanna. In our model, tree abundance is limited without invoking demographic bottlenecks in the transition from fire-sensitive to fire-resistant life history stages. An increasing strength of grass feedback on fire results in regular, predictable fires, which suggests that the system can also be modeled using a set of difference equations. We implement this discrete system using modified Leslie/Gower difference equations and demonstrate the existence of a bounded savanna state in this model framework. Our results confirm the potential for grass feedbacks to result in stable savannas, and indicate the importance of modeling fire as a discrete event rather than as a loss rate that is continuous in time. [Copyright &y& Elsevier]

This article provides a short introduction to standard considerations in the formal study of wages and illustrates the use of multiple regression and resampling simulation approaches in a case study of faculty salaries at one university. Multiple regression is especially beneficial where it provides information on strength of association, specific dollar estimates, and the option to identify outliers by gender. Resampling simulation allows for analysis at the department level and is beneficial where distributions depart substantially from normal, particularly where there are unequal error variances. Results indicate that both regression and simulation methods provided evidence of a sizable pay gap associated with gender, even after controlling for rank, academic field, and years of service. The gap occurs in fields traditionally viewed as female as well as science fields with typically lower female representation. Finally, we discuss implications for remediation based on these models. [ABSTRACT FROM AUTHOR]

Recent increases in reported outbreaks of tick-borne diseases have led to increased interest in understanding and controlling epidemics involving these transmission vectors. Mathematical disease models typically assume constant population size and spatial homogeneity. For tick-borne diseases, these assumptions are not always valid. The disease model presented here incorporates non-constant population sizes and spatial heterogeneity utilizing a system of differential equations that may be applied to a variety of spatial patches. We present analytical results for the one patch version and find parameter restrictions under which the populations and infected densities reach equilibrium. We then numerically explore disease dynamics when parameters are allowed to vary spatially and temporally and consider the effectiveness of various tick-control strategies. [ABSTRACT FROM AUTHOR]

• Recent empirical studies have found evidence of increased biomass production (‘overyielding’) in species mixtures relative to monoculture, but the interpretation of these results remains controversial, in part, because of the lack of a theoretical expectation. • Here, we examined the expected frequency and stability of overyielding species mixtures using Lotka–Volterra models of species dynamics in two- and four-species systems in conjunction with community, population, and specific rate of biomass production (SRP) definitions of overyielding. • Overyielding plant mixtures represented > 55% of potential species assemblages under community definitions and approximately 100% of species were either overyielding or underyielding under the population definition. Our species simulations approached their equilibria in 1–2 yr, supporting the relevancy of an equilibrial analysis. The range of parameter space that we explored produced realistic values of plot biomass, supporting their biological relevance. • We show that overyielding is expected to be common under community definitions and population definitions. Overyielding, under community or population definitions, does not imply an actual increase in the specific rate of biomass production. In addition, assemblages of overyielding and underyielding species under all three definitions can be stable over time with underyielding species persisting in the presence of overyielding species. [ABSTRACT FROM AUTHOR]

Global warming can potentially influence ecological communities through altered disturbance regimes in addition to increased temperatures. We investigate the response of pine savannas in the southeastern United States to global warming using a simple Lotka-Volterra competition model together with predicted changes to fire and hurricane disturbance regimes with global climate change. In the southeastern United States, decreased frequency of both fires and hurricanes with global warming will shift pine savannas toward a forested state. A CO2 fertilization effect that increases the growth rate of tree populations will also push southeastern landscapes from open savannas towards closed forests. Transient dynamics associated with climate driven changes in vegetation will last on the order of decades to a century. In our model, the sensitivity of savannas to relative changes in the frequency of fire versus hurricanes is linearly dependent on the growth rate and mortality of trees in fire and hurricane disturbances. [ABSTRACT FROM AUTHOR]

Global warming can potentially influence ecological communities through altered disturbance regimes in addition to increased temperatures. We investigate the response of pine savannas in the southeastern United States to global warming using a simple Lotka‐Volterra competition model together with predicted changes to fire and hurricane disturbance regimes with global climate change. In the southeastern United States, decreased frequency of both fires and hurricanes with global warming will shift pine savannas toward a forested state. A CO2 fertilization effect that increases the growth rate of tree populations will also push southeastern landscapes from open savannas towards closed forests. Transient dynamics associated with climate driven changes in vegetation will last on the order of decades to a century. In our model, the sensitivity of savannas to relative changes in the frequency of fire versus hurricanes is linearly dependent on the growth rate and mortality of trees in fire and hurricane disturbances. [ABSTRACT FROM AUTHOR]

Interdisciplinary research that links human and natural systems is critical to addressing complex environmental and ecological problems. A growing number of interdisciplinary research teams investigate coupled natural-human systems, but the degree to which they actually examine two-way linkages between the systems is limited. We examined aspects of interdisciplinary teams that were explicitly funded to conduct research including such linkages by considering attributes of team leaders, team members, and analysis methods employed. Our objective was to investigate the degree to which interdisciplinary teams studying coupled natural-human systems publish research that displays two-way linkages between systems. Our analysis shows that team members' academic disciplines and the types of analysis methods that interdisciplinary teams apply play a crucial role in the success of the team in publishing articles that include two-way linkages. We found that the success of developing two-way linkages is enhanced when teams include leaders and/ or members from interdisciplinary academic disciplines (e.g., planning departments, sustainability, environmental economics, biological and ecological engineering, and individuals affiliated with more than one academic department from different discipline categories). Additionally, the presence of social science members increases the likelihood of two-way linkages, whereas the presence of physical science or biological/life science members decreases this likelihood. Among articles that included two-way linkages, essentially all utilized a conceptual-/literature-review approach, or included simulation model analysis. Based on these findings, we conclude that interdisciplinary teams are not a mere sum of people from different academic disciplines, but a group of people who have the ability to incorporate different disciplines conceptually and analytically. To move forward, it is important to acknowledge that becoming an interdisciplinary researcher takes deliberative work. Educational programs that train students and early career scholars with flexible thinking and analytical capacities may be the key to furthering coupled natural-human systems research. [ABSTRACT FROM AUTHOR]

Predictive capacity is needed to anticipate the consequences of global change. Along with the computational challenges inherent in accounting for uncertainly in models of ecological and physical processes related to global change, we face educational challenges related to developing the intellectual capital for thinking with uncertainty in mind. Hand in hand with promoting the kinds of research needed to advance the evolving science of ecological forecasting, we need to set an education agenda for developing and enhancing computational literacy of current and future ecologists, managers, and policymakers. Key elements of an educational agenda are (1) defining the audiences and what each needs to know and be able to do; (2) developing curricula and pedagogical strategies in which thinking skills and conceptual understanding are, by design, linked; (3) addressing training needs of faculty to teach effectively about uncertainty to these different audiences; and (4) creating and implementing assessment tools to explore the impact of programs designed to train ecologists to think with uncertainty in mind. Assessing the extent to which new training models have impacted forecasting research and the conceptual understanding of ecologists is an important topic for future scholarship in education, and a natural area for collaboration between forecasting and education researchers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

New graduate students in biology programs may lack the quantitative skills necessary for their research and professional careers. The acquisition of these skills may be impeded by teaching and mentoring experiences that decrease rather than increase students' beliefs in their ability to learn and apply quantitative approaches. In this opinion piece, we argue that revising instructional experiences to ensure that both student confidence and quantitative skills are enhanced may improve both educational outcomes and professional success. A few studies suggest that explicitly addressing productive failure in an instructional setting and ensuring effective mentoring may be the most effective routes to simultaneously increasing both quantitative self-efficacy and quantitative skills. However, there is little work that specifically addresses graduate student needs, and more research is required to reach evidence-backed conclusions. [ABSTRACT FROM AUTHOR]

In the article, the author reflects on the importance of increasing the efficiency of crops due to a continuous increase in the world population. He discusses the results of the study by crop physiologist Deng J. regarding the crop growth. He also states that the model assumptions in the Deng and others' study of optimum water and nutrient resource availability leads to several issues related to multiple limitations of plant growth.

Editorial. Discusses the importance of quantitative methods in the biological sciences. Importance of mathematics in biology; Increase in computational biology and bioinformatics job positions; Problems in biology students' comprehension of basic quantitative concepts.

Discusses the necessity of quantitative training for life-science students. Curricula requirement to satisfy quantitative training; Survey results on quantitative courses available to students; Development of guidelines for a satisfactory quantitative curriculum; Responsibility of both life-science and mathematics faculty members in the development of appropriate courses.

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. [ABSTRACT FROM AUTHOR]

The article discusses the parallelization of a spatially-based simulation framework in relevant to the use of different parallel architectures. This analyzes the data intensive applications of the model in conjunction with spatial information through a geographic information system. In particular, the study assesses the factors that aid in the relay of the message, including the operation of programming language. Initially, this reveals the high scalability performance in the employment of the model. Furthermore, the authors of the study concludes that the use of intensive multimodels in scientific application offers a guidance for appropriate parallelization approaches incorporating multiple temporal and spatial scales.

Abstract: A landscape modeling system called the Across Trophic-Level System Simulation (or ATLSS) has been developed in an effort to project the consequences of proposed water regulation plans for restoration of the South Florida Everglades. The ATLSS Landscape Fish Model (ALFISH) is a component of the ATLSS package (written in C++), which is used to provide dynamic measures of the spatially-explicit food resources available to wading birds, namely fish. The original (serial) ALFISH model requires as much as 30h for 31-year simulations of specified scenarios. The model’s execution time has been successfully improved (by a factor of 4.5) by partitioning its data input and executing the model simultaneously (in parallel) on those partitions. This paper demonstrates how the model’s communications between partitioned data can be blocked to simulate compartmentalization effects on the input data. Minimal effects (below 1%) on the output of the original (serial) version are demonstrated. Regarding portability, both models (serial and parallel) have been successfully executed on two different computing environments: an SMP (Symmetric Multi-Processor) with 14 processors and a 14-processor network cluster. [Copyright &y& Elsevier]

Over 100 native herbaceous forest perennials are harvested in the United States. Management of these populations for continued survival requires information on the long-term effects of varied harvesting levels on populations of forest perennials. Allium tricoccum Ait. is a species that is harvested throughout its range for its edible bulb. We examined the recovery of A. tricoccum populations over a 5-year period following experimental harvests of different intensity to predict the number of years required to return to pre-harvest levels and to establish guidelines for sustainable harvesting. Our harvest treatments included removal of 0, 25, 50, 75, and 100% of plants within experimental plots. Treatments were repeated at three sites. Population recovery was monitored for 4 years at two sites and 7 years at one site. We found a relationship between the level of harvest and the negative impacts on populations of A. tricoccum, with limited population recovery in the 4 years following harvesting. We projected recovery times as a function of harvest level using a population projection model. Deterministic projections of recovery time ranged from 148 years for a 95% harvest to 2.5 years for a 5% harvest. Incorporating stochasticity into our projections increased the uncertainty in our estimates of recovery time. For example the recovery time from a 5% harvest ranged from 1 to 89 years (95% confidence limit). Based on our results, a 10% harvest once every 10 years would, on average, be a sustainable level of harvest for A. tricoccum in southern Appalachian forests. [Copyright &y& Elsevier]

While climate models have rapidly advanced in sophistication over recent decades, they lack dynamic representation of human behavior and social systems despite strong feedbacks between social processes and climate. The impacts of climate change alter perceptions of risk and emissions behavior that, in turn, influence the rate and magnitude of climate change. Addressing this deficiency in climate models requires a substantial interdisciplinary effort to couple models of climate and human behavior. We suggest a multi-model approach that considers a range of theories and implementations of human behavior and social systems, similar to the multi-model approach that has been used to explore the physical climate system. We describe the importance of linking social factors with climate processes and identify four priorities essential to advancing the development of coupled social-climate models. [ABSTRACT FROM AUTHOR]

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. If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts can be obtained by simply multiplying the individual prevalences. However, even simple epidemiological models show this to be untrue. This study develops new tests for interaction between pathogens that account for this surprising lack of statistical independence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Synthesis centers offer a unique amalgam of culture, infrastructure, leadership, and support that facilitates creative discovery on issues crucial to science and society. The combination of logistical support, postdoctoral or senior fellowships, complex data management, informatics and computing capability or expertise, and most of all, opportunity for group discussion and reflection lowers the "activation energy" necessary to promote creativity and the cross-fertilization of ideas. Synthesis centers are explicitly created and operated as community-oriented infrastructure, with scholarly directions driven by the ever-changing interests and needs of an open and inclusive scientific community. The last decade has seen a rise in the number of synthesis centers globally but also the end of core federal funding for several, challenging the sustainability of the infrastructure for this key research strategy. Here, we present the history and rationale for supporting synthesis centers, integrate insights arising from two decades of experience, and explore the challenges and opportunities for long-term sustainability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding how complex organisms function as integrated units that constantly interact with their environment is a long-standing challenge in biology. To address this challenge, organismal biology reveals general organizing principles of physiological systems and behavior -- in particular, in complex multicellular animals. Organismal biology also focuses on the role of individual variability in the evolutionary maintenance of diversity. To broadly advance these frontiers, cross-compatibility of experimental designs, methodological approaches, and data interpretation pipelines represents a key prerequisite. It is now possible to rapidly and systematically analyze complete genomes to elucidate genetic variation associated with traits and conditions that define individuals, populations, and species. However, genetic variation alone does not explain the varied individual physiology and behavior of complex organisms. We propose that such emergent properties of complex organisms can best be explained through a renewed emphasis on the context and life-history dependence of individual phenotypes to complement genetic data. [ABSTRACT FROM AUTHOR]