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Diet specialists and generalists face a common challenge: they must regulate the intake and balance of nutrients to achieve a target diet for optimum nutrition. When optimum nutrition is unattainable, organisms must cope with dietary imbalances and trade-off surplus and deficits of nutrients that ensue. Animals achieve this through compensatory rules that dictate how to cope with nutrient imbalances, known as ‘rules of compromise’. Understanding the patterns of the rules of compromise can provide invaluable insights into animal physiology and behaviour, and shed light into the evolution of diet specialisation. However, we lack an analytical method for quantitative comparisons of the rules of compromise within and between species. Here, I present a new analytical method that uses Thales’ theorem as foundation, and that enables fast comparisons of the rules of compromise within and between species. I then apply the method on three landmark datasets to show how the method enables us to gain insights into how animals with different diet specialisation cope with nutrient imbalances. The method opens new avenues of research to understand how animals cope with nutrient imbalances in comparative nutrition.

Animals often regulate their nutrient intake according to their physiological needs. There is evidence that different traits require specific nutrient blends, and that animals cannot always maximize all traits with a single diet (“nutritional trade-offs”). However, we still do not have a clear understanding of which traits might be involved in nutritional trade-offs. I compiled data from the Geometric Framework of Nutrition literature on the ratio of proteins and carbohydrates that maximize (best PC ratios) or minimize (worst PC ratios) several larval and adult traits in Drosophila melanogaster. Best and worst PC ratios clustered into three regions in the protein-carbohydrate nutrient space: (1) Low PC ratios (1:8 or higher) are best for lifespan but worst for growth or reproductive traits; (2) High PC ratios (1:1 or lower) are best for adult body mass, male reproduction, and larval developmental time but worst for lifespan; and (3) Intermediate PC ratios (<1:1 and >1:8) are best for female lifetime egg production, female reproductive rate, and larval survival. These findings support lifespan–reproduction nutritional trade-offs, highlight the potential for metamorphosis to solve nutritional trade-offs across life stages, and underscore the potential for intralocus sexual conflict to emerge over the expression of metabolic genes.

Animals regulate their nutrient consumption to maximize the expression of fitness traits with competing nutritional needs (“nutritional trade‐offs”). Nutritional trade‐offs have been studied using a response surface modeling approach known as the Geometric Framework for nutrition (GF). Current experimental design in GF studies does not explore the entire area of the nutritional space resulting in performance landscapes that may be incomplete. This hampers our ability to understand the properties of the performance landscape (e.g., peak shape) from which meaningful biological insights can be obtained. Here, I tested alternative experimental designs to explore the full range of the performance landscape in GF studies. I compared the performance of the standard GF design strategy with three alternatives: hexagonal, square, and random points grid strategies with respect to their accuracy in reconstructing baseline performance landscapes from a landmark GF dataset. I showed that standard GF design did not reconstruct the properties of baseline performance landscape appropriately particularly for traits that respond strongly to the interaction between nutrients. Moreover, the peak estimates in the reconstructed performance landscape using standard GF design were accurate in terms of the nutrient ratio but incomplete in terms of peak shape. All other grid designs provided more accurate reconstructions of the baseline performance landscape while also providing accurate estimates of nutrient ratio and peak shape. Thus, alternative experimental designs can maximize information from performance landscapes in GF studies, enabling reliable biological insights into nutritional trade‐offs and physiological limits within and across species. In behavioral ecology, we have a powerful method, known as the Geometric Framework for Nutrition (GF), to study nutritional ecology. However, we have not yet, in the three decades since it was proposed, fully investigated whether its fundamental experimental design is likewise powerful. This study investigate the original and alternative sampling designs to reconstruct GF performance landscapes.

Amino acids are the building blocks of proteins and enzymes which are essential for life. Understanding amino acid usage offers insights into protein function and molecular mechanisms underlying life histories. However, genome-wide patterns of amino acid usage across domains of life remain poorly understood. Here, we analysed the proteomes of 5590 species across four domains and found that only a few amino acids are consistently the most and least used. This differential usage results in lower amino acid usage diversity at the most and least frequent ranks, creating a ubiquitous inverted U-shape pattern of amino acid diversity and rank which we call an ‘edge effect’ across proteomes and domains of life. This effect likely stems from protein secondary structural constraints, not the evolutionary chronology of amino acid incorporation into the genetic code, highlighting the functional rather than evolutionary influences on amino acid usage. We also tested other contemporary hypotheses regarding amino acid usage in proteomes and found that amino acid usage varies across life’s domains and is only weakly influenced by growth temperature. Our findings reveal a novel and pervasive amino acid usage pattern across genomes with the potential to help us probe deep evolutionary relationships and advance synthetic biology.

Academia is far from a meritocratic distribution of opportunities. This leads to inequalities, lack of diversity, and unfairness. The objective of this conceptual paper is to propose an integrative framework to help the academic community address its pervasive but persistent inequalities of opportunities. The framework emerges from the intersections of Bourdieu, Bronfenbrenner, and Rawls frameworks and propose the use of ethical artificial intelligence (AI) to contextualise merit and recreate true equality of opportunities. More specifically, I argue that academia has structures and doxa that may be inaccessible to individuals from different social origins, and are perpetuated by privileged individuals who achieve positions of power within academia. The privileged individuals inherit and are exposed to opportunities to acquire capital from early life, resulting in the continuation of status quo practices and alienation of minorities that do not share—or do not have the ability to acquire—capital. I argue that this process occurs as a result of the social origins of the individual and, as Bronfenbrennian framework suggests, disadvantaged individuals lack both the (inherited) capital, but also lack the ability and opportunities to acquire capital relative to privileged counterparts. I argue that the only way to mitigate this inequitable system is to retrieve the Rawlsian original position of ignorance (veil of ignorance) in the allocation of academic capital based on merit, which can only be objectively quantified relative to social origins of individuals. As opposed to current subjective assessments (e.g., peer-review) or lottery systems, I propose the use of Big Data and ethical AI to reconstruct the position of ignorance and contextualise merit based on the expected merit given individuals’ social origins. I also discuss the concept of ‘years post-PhD’ as it is used to introduce fairness in allocation of academic capital and propose a different and less relativistic landmark that accounts for the years post-first authorship publication. This is a novel conceptual framework which can stimulate further research into the ecology of social justice.

Urban areas are growing, often at the expense of native ecosystems. As a result, indigenous lands (ILs) have become critical refuges for biodiversity, essential for sustainability and sit at the intersection of cultural, economic, and environmental interests. ILs play a double role in this context: they protect native biodiversity but are often framed as barriers to economic growth. In Brazil, nearly 14% of the territory is demarcated as ILs. This has led to conflicts with Brazil’s agricultural sector, particularly in the southernmost states, where agribusiness drives the economy. We hypothesize that this conflict leads to agricultural encroachment of ILs, which might become extension of farms, compromising their sustainability. We analyzed two decades of public data on soy coverage within ILs in Brazil’s southernmost states (Paraná, Santa Catarina, and Rio Grande do Sul) and found that soy cultivation in ILs increased by over 116% in the last two decades, peaking in 2019 at 177% above the 2001 baseline. We argue that ILs urgently need a framework that enables the communities therein to benefit from income originating from land lease, while ensuring that encroachment is limited and does not pose threats to native biodiversity. This can be challenging due to growing political pressure to weaken socioenvironmental protection and ILs’ demarcation but is nevertheless essential for the sustainable coexistence of urban areas, farms, and ILs.

The technological revolution of past decades has led teaching and learning of evolutionary biology to move away from its naturalist origins. As a result, students’ learning experiences and training on the science of natural history—which entails careful observations and meticulous data curation to generate insight—have been compromised compared with the times of the pioneers in the field. But will technology cause the extinction of natural history in its traditional form? In this essay, we provide a visionary—albeit not yet possible—perspective of the future of natural history in the technological era. We review the main concepts and applications of key state-state-of-the-art technologies to the teaching and learning of Biology including Virtual and Mixed Reality (VMR). Next, we review the current knowledge in artificial life, and describe our visionary model for the future of natural history voyages—the BioVR—which is an immersive world where students can experience evolution in action, and also shape how evolution can occur in virtual worlds. We finish the essay with a cautionary tale as to the known negative sides of using VMR technologies, and why future applications should be designed with care to protect the intended learning outcomes and students’ experience. Our aim is to stimulate debates on how new technologies can revolutionise teaching and learning across scenarios, which can be useful for improving learning outcomes of biological concepts in face-to-face, blended, and distance learning programmes.

The developmental environment can potentially alter the adult social environment and influence traits targeted by sexual selection such as body size. In this study, we manipulated larval density in male and female Drosophila melanogaster, which results in distinct adult size phenotypes-high (low) densities for small (large) adults-and measured sexual selection in experimental groups consisting of adult males and females from high, low, or a mixture of low and high larval densities. Overall, large adult females (those reared at low larval density) had more matings, more mates and produced more offspring than small females (those reared at high larval density). The number of offspring produced by females was positively associated with their number of mates (i.e. there was a positive female Bateman gradient) in social groups where female size was experimentally varied, likely due to the covariance between female productivity and mating rate. For males, we found evidence that the larval environment affected the relative importance of sexual selection via mate number (Bateman gradients), mate productivity, paternity share, and their covariances. Mate number and mate productivity were significantly reduced for small males in social environments where males were of mixed sizes, versus social environments where all males were small, suggesting that social heterogeneity altered selection on this subset of males. Males are commonly assumed to benefit from mating with large females, but in contrast to expectations we found that in groups where both the male and female size varied, males did not gain more offspring per mating with large females. Collectively, our results indicate sex-specific effects of the developmental environment on the operation of sexual selection, via both the phenotype of individuals, and the phenotype of their competitors and mates.

Population density modulates a wide range of eco-evolutionary processes including inter- and intra-specific competition, fitness and population dynamics. In holometabolous insects, the larval stage is particularly susceptible to density-dependent effects because the larva is the resource-acquiring stage. Larval density-dependent effects can modulate the expression of life-history traits not only in the larval and adult stages but also downstream for population dynamics and evolution. Better understanding the scope and generality of density-dependent effects on life-history traits of current and future generations can provide useful knowledge for both theory and experiments in developmental ecology. Here, we review the literature on larval density-dependent effects on fitness of non-social holometabolous insects. First, we provide a functional definition of density to navigate the terminology in the literature. We then classify the biological levels upon which larval density-dependent effects can be observed followed by a review of the literature produced over the past decades across major non-social holometabolous groups. Next, we argue that host-microbe interactions are yet an overlooked biological level susceptible to density-dependent effects and propose a conceptual model to explain how density-dependent effects on host-microbe interactions can modulate density-dependent fitness curves. In summary, this review provides an integrative framework of density-dependent effects across biological levels which can be used to guide future research in the field of ecology and evolution.