Scales Of Biological Organization Research Articles

Gaming for the Education of Biology in High Schools

Game-based learning refers to an educational approach where games (digital or analogue) are used in order to engage students in interactive and immersive experiences designed to teach specific concepts, skills or subjects. Gamification refers to the application of game design elements, such as point systems, rewards, narratives, and competition, to non-game contexts. Game elements, mechanics and structures, when incorporated into the learning process, can enhance student understanding and increase engagement, motivation and retention of educational content. Teaching Biology can present challenges mainly due to the complexity of the subject matter, the different scales of biological organisation, and because it often includes challenging and counterintuitive concepts that may contradict students’ preconceived notions. Integrating gaming into the high school Biology curriculum not only tackles the challenges of teaching complex concepts but can also promote student engagement. Customising gaming experiences to Biology intricacies enhances critical thinking and creates a dynamic learning environment tailored to the demands of high school biological education. This entry explores the integration of gaming and gamification in high school Biology education to overcome challenges in sustaining student interest. Additionally, the article highlights the diverse applications of games in education, showcasing their versatility in enriching the educational process. Future research should evaluate specific games, explore design principles, and consider challenges associated with implementation. In conclusion, using games in Biology education promises to enhance engagement, promote active learning, and deepen understanding, contributing to narrowing the gap in biological literacy.

Modeling in molecular genetics allows students to make connections between biological scales.

Students often see college courses as the presentation of disconnected facts, especially in the life sciences. Student-created Structure Mechanism/Relationship Function (SMRF) models were analyzed to understand students' abilities to make connections between genotype, phenotype, and evolution. Students were divided into two sections; one section received instructions that included a specific gene as an example related to larger issues like human disease or the environment. The other section was only given generic examples, like gene X and phenotype Y. Coding of exam models and a comprehensive (extensive) model reveled students were able to make links and work within and between biological scales of organization. Modeling provided a way to show and allow students to practice and demonstrate the ability to build step-by-step causal relationships that link ideas together. We also observed a small differing with students receiving the specific prompt performing better than students receiving generic prompt at the point in the semester where linking across many biological scales was required to be successful.

The effects of climate change on the ecology of fishes

Ocean warming and acidification are set to reshuffle life on Earth and alter ecological processes that underpin the biodiversity, health, productivity, and resilience of ecosystems. Fishes contribute significantly to marine, estuarine, and freshwater species diversity and the functioning of marine ecosystems, and are not immune to climate change impacts. Whilst considerable effort has been placed on studying the effects of climate change on fishes, much emphasis has been placed on their (eco)physiology and at the organismal level. Fishes are affected by climate change through impacts at various levels of biological organisation and through a large variety of traits, making it difficult to make generalisations regarding fish responses to climate change. Here, we briefly review the current state of knowledge of climate change effects on fishes across a wide range of subfields of fish ecology and evaluate these effects at various scales of biological organisation (from genes to ecosystems). We argue that a more holistic synthesis of the various interconnected subfields of fish ecology and integration of responses at different levels of biological organisation are needed for a better understanding of how fishes and their populations and communities might respond or adapt to the multi-stressor effects of climate change. We postulate that studies using natural analogues of climate change, meta-analyses, advanced integrative modelling approaches, and lessons learned from past extreme climate events could help reveal some general patterns of climate change impacts on fishes that are valuable for management and conservation approaches. Whilst these might not reveal many of the underlying mechanisms responsible for observed biodiversity and community change, their insights are useful to help create better climate adaptation strategies for their preservation in a rapidly changing ocean.

Transformation of coral communities subjected to an unprecedented heatwave is modulated by local disturbance.

Corals are imminently threatened by climate change-amplified marine heatwaves. However, how to conserve coral reefs remains unclear, since those without local anthropogenic disturbances often seem equally or more susceptible to thermal stress as impacted ones. We disentangle this apparent paradox, revealing that the relationship between reef disturbance and heatwave impacts depends upon the scale of biological organization. We show that a tropical heatwave of globally unprecedented duration (~1 year) culminated in an 89% loss of hard coral cover. At the community level, losses depended on pre-heatwave community structure, with undisturbed sites, which were dominated by competitive corals, undergoing the greatest losses. In contrast, at the species level, survivorship of individual corals typically declined as local disturbance intensified. Our study reveals both that prolonged heatwaves projected under climate change will still have winners and losers and that local disturbance can impair survival of coral species even under such extreme conditions.

Building a knowledge graph to enable precision medicine

Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. PrimeKG contains an abundance of ‘indications’, ‘contradictions’, and ‘off-label use’ drug-disease edges that lack in other knowledge graphs and can support AI analyses of how drugs affect disease-associated networks. We supplement PrimeKG’s graph structure with language descriptions of clinical guidelines to enable multimodal analyses and provide instructions for continual updates of PrimeKG as new data become available.

Viral infection in the ocean-A journey across scales.

Viruses are the most abundant biological entity in the ocean and infect a wide range of microbial life across bacteria, archaea, and eukaryotes. In this essay, we take a journey across several orders of magnitude in the scales of biological organization, time, and space of host-virus interactions in the ocean, aiming to shed light on their ecological relevance. We start from viruses infecting microbial host cells by delivering their genetic material in seconds across nanometer-size membranes, which highjack their host's metabolism in a few minutes to hours, leading to a profound transcriptomic and metabolic rewiring. The outcome of lytic infection leads to a release of virions and signaling molecules that can reach neighboring cells a few millimeters away, resulting in a population whose heterogeneous infection level impacts the surrounding community for days. These population dynamics can leave unique metabolic and biogeochemical fingerprints across scales of kilometers and over several decades. One of the biggest challenges in marine microbiology is to assess the impact of viruses across these scales, from the single cell to the ecosystem level. Here, we argue that the advent of new methodologies and conceptual frameworks represents an exciting time to pursue these efforts and propose a set of important challenges for the field. A better understanding of host-virus interactions across scales will inform models of global ocean ecosystem function in different climate change scenarios.

Population variability in thermal performance of pre-spawning adult Chinook salmon.

Climate change is causing large declines in many Pacific salmon populations. In particular, warm rivers are associated with high levels of premature mortality in migrating adults. The Fraser River watershed in British Columbia, Canada, supports some of the largest Chinook salmon (Oncorhynchus tshawytscha) runs in the world. However, the Fraser River is warming at a rate that threatens these populations at critical freshwater life stages. A growing body of literature suggests salmonids are locally adapted to their thermal migratory experience, and thus, population-specific thermal performance information can aid in management decisions. We compared the thermal performance of pre-spawning adult Chinook salmon from two populations, a coastal fall-run from the Chilliwack River (125km cooler migration) and an interior summer-run from the Shuswap River (565km warmer migration). We acutely exposed fish to temperatures reflecting current (12°C, 18°C) and future projected temperatures (21°C, 24°C) in the Fraser River and assessed survival, aerobic capacity (resting and maximum metabolic rates, absolute aerobic scope (AAS), muscle and ventricle citrate synthase), anaerobic capacity (muscle and ventricle lactate dehydrogenase) and recovery capacity (post-exercise metabolism, blood physiology, tissue lactate). Chilliwack Chinook salmon performed worse at high temperatures, indicated by elevated mortality, reduced breadth in AAS, enhanced plasma lactate and potassium levels and elevated tissue lactate concentrations compared with Shuswap Chinook salmon. At water temperatures exceeding the upper pejus temperatures (Tpejus, defined here as 80% of maximum AAS) of Chilliwack (18.7°C) and Shuswap (20.2°C) Chinook salmon populations, physiological performance will decline and affect migration and survival to spawn. Our results reveal population differences in pre-spawning Chinook salmon performance across scales of biological organization at ecologically relevant temperatures. Given the rapid warming of rivers, we show that it is critical to consider the intra-specific variation in thermal physiology to assist in the conservation and management of Pacific salmon.

Protein–protein complexes can undermine ultrasensitivity-dependent biological adaptation

Robust perfect adaptation (RPA) is a ubiquitously observed signalling response across all scales of biological organization. A major class of network architectures that drive RPA in complex networks is the Opposer module-a feedback-regulated network into which specialized integral-computing 'opposer node(s)' are embedded. Although ultrasensitivity-generating chemical reactions have long been considered a possible mechanism for such adaptation-conferring opposer nodes, this hypothesis has relied on simplified Michaelian models, which neglect the presence of protein-protein complexes. Here we develop complex-complete models of interlinked covalent-modification cycles with embedded ultrasensitivity, explicitly capturing all molecular interactions and protein complexes. Strikingly, we demonstrate that the presence of protein-protein complexes thwarts the network's capacity for RPA in any 'free' active protein form, conferring RPA capacity instead on the concentration of a larger protein pool consisting of two distinct forms of a single protein. We further show that the presence of enzyme-substrate complexes, even at comparatively low concentrations, play a crucial and previously unrecognized role in controlling the RPA response-significantly reducing the range of network inputs for which RPA can obtain, and imposing greater parametric requirements on the RPA response. These surprising results raise fundamental new questions as to the biochemical requirements for adaptation-conferring Opposer modules within complex cellular networks.

Towards a genetic theory of island biogeography: Inferring processes from multidimensional community‐scale data

AbstractBackgroundMacArthur and Wilson's theory of island biogeography has been a foundation for obtaining testable predictions from models of community assembly and for developing models that integrate across scales and disciplines. Historically, however, these developments have focused on integration across ecological and macroevolutionary scales and on predicting patterns of species richness, abundance distributions, trait data and/or phylogenies. The distribution of genetic variation across species within a community is an emerging pattern that contains signatures of past population histories, which might provide an historical lens for the study of contemporary communities. As intraspecific genetic diversity data become increasingly available at the scale of entire communities, there is an opportunity to integrate microevolutionary processes into our models, moving towards development of a genetic theory of island biogeography.Motivation/goalWe aim to promote the development of process‐based biodiversity models that predict community genetic diversity patterns together with other community‐scale patterns. To this end, we review models of ecological, microevolutionary and macroevolutionary processes that are best suited to the creation of unified models, and the patterns that these predict. We then discuss ongoing and potential future efforts to unify models operating at different organizational levels, with the goal of predicting multidimensional community‐scale data including a genetic component.Main conclusionsOur review of the literature shows that despite recent efforts, further methodological developments are needed, not only to incorporate the genetic component into existing island biogeography models, but also to unify processes across scales of biological organization. To catalyse these developments, we outline two potential ways forward, adopting either a top‐down or a bottom‐up approach. Finally, we highlight key ecological and evolutionary questions that might be addressed by unified models including a genetic component and establish hypotheses about how processes across scales might impact patterns of community genetic diversity.

What common‐garden experiments tell us about climate responses in plants

Abstract Common garden experiments are indoor or outdoor plantings of species or populations collected from multiple distinct geographic locations, grown together under shared conditions. These experiments examine a range of questions for theory and application using a variety of methods for analysis. The eight papers of this special feature comprise a cross section of contemporary approaches, summarized and synthesized here by what they tell us about the relationships between climate‐related trait spectra and fitness optima. Four of the eight papers are based on field experiments in prairie, desert, Mediterranean and boreal biomes. Representative of many common garden experiments, these experiments reveal consistent evidence of traits varying with population climate provenance, but evidence of a tradeoff between growth and tolerance traits or of consistent fitness optimization at home is scant, in contrast to trait theory. Two synthesis papers highlight dominant patterns of trait divergence, including for an exotic invasive species. One theoretical paper warned that unknown kinship relationships between populations can result in the misidentification of adaptive trait divergence. A third synthesis paper formulated novel and ambitious goals for common‐garden studies through including measurement of response variables at multiple levels of biological organization. The featured papers discuss multiple avenues for improving common garden studies. Genomic analysis, together with the quantification of kinship relationships, will continue to reveal the influence of environmental drivers on gene selection. Measuring a more complete set of fitness traits, especially for traits related to regeneration, will permit the development of projection models to explicitly link trait spectra, climate patterns and fitness consequences. More standardized data reporting will additionally improve abilities to synthesize findings across experiments. Testing population performance in competition with other species will produce more robust fitness comparisons between genotypes, especially for slower‐growing genotypes in higher‐resource environments. Adding gardens in and beyond climatic edge locations will furthermore strengthen the understanding of population failure and species exclusion. Finally, there is unrealized potential in adding ecosystem‐level observations to common‐garden studies that will enhance integrative analysis across scales of biological organization and scientific domains. Synthesis. With novel, creative designs, data integration and synthesis, common garden experiments will continue to advance the understanding of trait ensembles interacting with climate across scales of biological organization, provide pivotal data for global change models and guide ecological applications such as restoration of habitats for rare and climate sensitive species.

Hyperbolic Geometry in Biological Systems

Across different scales of biological organization, biological networks often exhibit hierarchical tree‐like organization. For networks with such structure, hyperbolic geometry provides a nnatural metric because of its exponentially expanding resolution. In this talk, I will describe how the use of hyperbolic geometry can be helpful for visualizing and analyzing high‐dimensional datasets. Our examples will include data from plant and animal volatiles that serve as input for the sence of smell (Zhou et al Science Advances 2018) and patterns of gene expression in diverse mammalian cell types (Zhou and Sharpe, iScience 2021). We find that local noise causes data to exhibit Euclidean geometry on small scales, but that at broader scales hyperbolic geometry becomes visible and pronounced. The hyperbolic maps are typically larger for datasets of more diverse and differentiated cells, e.g. with a range of ages. We find that adding a constraint on large distances according to hyperbolic geometry improves the performance of t‐SNE algorithm to a large degree causing it to outperform other leading methods, such as UMAP and standard t‐SNE. I will conclude with a discussion of new opportunities for identifying age‐ and disease‐related factors afforded by hyperbolic maps.

Riverine barrier hypothesis explains the structure of dung beetle communities in Brazilian Coastal Sand-dune forests

The Riverine Barrier Hypothesis (RBH) predicts that tropical rivers can be effective barriers to species dispersion, affecting different scales of biological organization, from genes to assemblages. We disentangled the latitudinal from the river presence effect to test the hypothesis that the Doce river mouth acted as a geographical, historical barrier for insects through North and South directions of the Brazilian coast, affecting the current community structure. We sampled dung beetle communities. In 16 Coastal Sand-dune Forest (“Restinga”) patches, across the Doce River Basin in the Brazilian coastline, nine through the South and seven through the North direction of the Doce River's mouth. We analyzed the relationship between the community composition and richness relative to the river (North and South regions) and latitude using linear modeling over a distance matrix (DistLM) approach. The latitude explained 17.74% of the total variation in dung beetle species composition, and the regional position relative to Doce River's mouth exhibited a significant effect (Pseudo F = 2.8479; p = 0.005) only after removing the latitudinal effect, here representing the habitat filtering. Both variables explained together with a total of 32.52% of the variability in dung beetles composition at coastal sand dune forests. The geographical position alone explained 48% of the richness difference between the North and South regions of the Doce River's mouth (Pseudo F = 16.534; p < 0.01). Meanwhile, the latitudinal position of the sites exhibited no significant relationship with species richness. Our findings highlight the role of Doce River as a biogeographical filter in the Doce river basin, while it represents the primary determinant of geographical segregation of the two key-species central to the dung beetle community structure.

The geometry of decision-making in individuals and collectives

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process, we reveal the occurrence of spontaneous and abrupt "critical" transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one among, the remaining options. This bifurcation process repeats until only one option-the one ultimately selected-remains. Thus, we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space-time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological contexts, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

Temperature-dependence of metabolism and fuel selection from cells to whole organisms.

Temperature affects nearly every aspect of how organisms interact with and are constrained by their environment. Measures of organismal energetics, such as metabolic rate, are highly temperature-dependent and governed through temperature effects on rates of biochemical reactions. Characterizing the relationships among levels of biological organization can lend insight into how temperature affects whole-organism function. We tested the temperature dependence of cellular oxygen consumption and its relationship to whole-animal metabolic rate in garter snakes (Thamnophis elegans). Additionally, we tested whether thermal responses were linked to shifts in the fuel source oxidized to support metabolism with the use of carbon stable isotopes. Our results demonstrate temperature dependence of metabolic rates across levels of biological organization. Cellular (basal, adenosine triphosphate-linked) and whole-animal rates of respiration increased with temperature but were not correlated within or among individuals, suggesting that variation in whole-animal metabolic rates is not due simply to variation at the cellular level, but rather other interacting factors across scales of biological organization. Counter to trends observed during fasting, elevated temperature did not alter fuel selection (i.e., natural-abundance stable carbon isotope composition in breath, δ13 Cbreath ). This consistency suggests the maintenance and oxidation of a single fuel source supporting metabolism across a broad range of metabolic demands.

On the needs for combining physiological principles and mathematics to improve crop models

• Basic concepts of crop modelling founders are fading into oblivion since the mid-1990s. • The multi-model ensemble approach does not facilitate the models as a heuristic tool. • A bottom-up approach that assembles all biological elements is not conducive to simulating the crop. • An intermediate approach combining physiology and mathematics is recommended. • Leaf area index and radiation use efficiency are used as showcases. • Outstanding areas for further model development and caveats are outlined. Major crop models were developed before the 1990s and many of their algorithms are (semi-)empirical. In the recent two decades, the number of models has grown rapidly, but the increase in their quality does not match the growth in number. Often, an ensemble of multiple models is required to make a useful prediction. This ensemble approach does not facilitate the improvement of models as a tool to understand crop physiological mechanisms. On the other hand, following a bottom-up approach that tries to assemble all elements along different scales of biological organisation may result in numerically clumsy models that are not necessarily more robust than existing models in simulating phenotypes at the crop scale. We argue that to model complex crop phenotypes in a simple yet accurate manner, crop modellers should be inspired by experiences in some fundamental sciences. For example, physicists used sound theories and solid mathematics in thought experiments, and came up with seemingly simple equations to explain the behaviour of very diverse systems, from sub-atomic particles to the largest clusters of galaxies. We review examples, where biological insights and mathematics are combined to derive simple equations that apply to different processes of crop growth. The essence of this modelling approach is a combination of simplicity , elegance and robustness . Models integrating those equations can predict crop phenotypes as well as generate hypotheses or emerging properties to assist in knowing the unknowns. Thus, crop models should not only provide practical predictions, but should especially be considered as a research tool for data interpretation, system design, and heuristic understanding.

Trade-offs Among Resilience, Robustness, Stability, and Performance and How We Might Study Them.

Biological systems are likely to be constrained by trade-offs among robustness, resilience, and performance. A better understanding of these trade-offs is important for basic biology, as well as applications where biological systems can be designed for different goals. We focus on redundancy and plasticity as mechanisms governing some types of trade-offs, but mention others as well. Whether trade-offs are due to resource constraints or "design" constraints (i.e., structure of nodes and links within a network) will also affect the types of trade-offs that are important. Identifying common themes across scales of biological organization will require that researchers use similar approaches to quantifying robustness, resilience, and performance, using units that can be compared across systems.

Larger cells have relatively smaller nuclei across the Tree of Life.

Larger cells have larger nuclei, but the precise relationship between cell size and nucleus size remains unclear, and the evolutionary forces that shape this relationship are debated. We compiled data for almost 900 species – from yeast to mammals – at three scales of biological organisation: among‐species, within‐species, and among‐lineages of a species that was artificially selected for cell size. At all scales, we showed that the ratio of nucleus size to cell size (the ‘N: C’ ratio) decreased systematically in larger cells. Size evolution appears more constrained in nuclei than cells: cell size spans across six orders of magnitude, whereas nucleus size varies by only three. The next important challenge is to determine the drivers of this apparently ubiquitous relationship in N:C ratios across such a diverse array of organisms.

The Use and Underuse of Model Systems in Infectious Disease Ecology and Evolutionary Biology.

AbstractEver since biologists began studying the ecology and evolution of infectious diseases (EEID), laboratory-based model systems have been important for developing and testing theory. Yet what EEID researchers mean by the term "model systems" and what they want from them is unclear. This uncertainty hinders our ability to maximally exploit these systems, identify knowledge gaps, and establish effective new model systems. Here, we borrow a definition of model systems from the biomolecular sciences to assess how EEID researchers are (and are not) using 10 key model systems. According to this definition, model systems in EEID are not being used to their fullest and, in fact, cannot even be considered model systems. Research using these systems consistently addresses only two of the three fundamental processes that underlie disease dynamics-transmission and disease, but not recovery. Furthermore, studies tend to focus on only a few scales of biological organization that matter for disease ecology and evolution. Moreover, the field lacks an infrastructure to perform comparative analyses. We aim to begin a discussion of what we want from model systems, which would further progress toward a thorough, holistic understanding of EEID.

From the leaf to the community: Distinct dimensions of phytochemical diversity shape insect–plant interactions within and among individual plants

Abstract Plant secondary chemistry is known to be an important driver of plant–insect community structure across ecological scales. Recently, the concept of phytochemical diversity (PD) has been introduced to help describe variation in plant secondary chemistry and explain how this variation affects community structure. Previous studies show that PD among individuals and species results in phytochemical mosaics, known as the phytochemical landscape. However, plant traits can vary at finer scales, such as within individuals, and even a single host plant may be perceived as an entire phytochemical landscape by an interacting insect. Using the neotropical shrub Piper amalago, we tested and compared how herbivory, caterpillar biodiversity and plant–herbivore network structure are affected by the compositional (number and concentration of compounds) and structural (diversity of distinct chemical structures) dimensions of PD. We analysed variation among individual plants and among‐plant height strata within individual plants. This allowed us to decompose PD within and among plant individuals and analyse how variation at both scales affects the plant–herbivore network. We found that both within and among plants greater structural diversity decreased herbivore feeding damage. Furthermore, each dimension of PD has different effects on herbivore biodiversity and network structure depending on the scale of biological organization. Within plants, the compositional dimension, specifically low concentrations of compounds tentatively identified as Piper amides, increased herbivore biodiversity. This dimension also increased the capacity of strata within plants to mediate ecological cascades through direct and indirect effects on herbivore abundance in the plant–herbivore network. In contrast, a greater structural diversity among plants decreased herbivore biodiversity and the capacity of plants to affect all other herbivores and plants directly and indirectly in the network. Synthesis. Based on our results we expand the concept of the phytochemical landscape to multiple scales of biological organization and provide evidence that PD may be maintained by how its multiple dimensions have distinct roles across scales of biological organization.

Linking biodiversity into national economic accounting

Biodiversity underpins the supply of ecosystem services essential for well-being and economic development, yet biodiversity loss continues at a substantial rate. Linking biodiversity indicators with national economic accounts provides a means of mainstreaming biodiversity into economic planning and monitoring processes. Here we examine the various strategies for biodiversity indicators to be linked into national economic accounts, specifically the System of Environmental-Economic Accounts Experimental Ecosystem Accounting (SEEA EEA) framework. We present what has been achieved in practice, using various case studies from across the world. These case studies demonstrate the potential of economic accounting as an integrating, mainstreaming framework that explicitly considers biodiversity. With the right indicators for the different components of biodiversity and scales of biological organisation, this can directly support more holistic economic planning approaches. This will be a significant step forward from relying on the traditional indicators of national economic accounts to guide national planning. It is also essential if society’s objectives for biodiversity and sustainable development are to be met.