Abstract Insect science is a dynamic and continually evolving field, driven equally by the methods we develop, critique and use and the philosophies that guide our investigations, interpretations and applications. Advances in technologies and techniques allow us to study insects at unprecedented spatial, temporal and molecular scales, revealing deeper insights into the mechanisms by which they shape our ecosystems, society and human health. At the same time, evolving philosophical perspectives are continuously advancing how we design our experiments, consider our animals, infer new phenomena and share our findings. Together, methods, philosophies and ethics form the critical foundations of insect science from which progress across entomology ultimately arises. Sharing and reflecting on these approaches is essential for innovation in entomology to extend beyond individual laboratories and subdisciplines to benefit the wider scientific community. These interdisciplinary advances are, however, typically scattered across journals, disciplines and informal exchanges, often struggling to find a suitable home, limiting their visibility and impact. To address this need, the Royal Entomological Society has launched a new journal dedicated to methods and philosophies across insect science. Approaches in Entomology: Methods, Philosophies, and Ethics provides a dedicated forum for the dissemination, evaluation and development of innovative approaches across the full breadth of insect science with a particular emphasis on methods, philosophies and ethical dialogues.

The impact of higher-order interactions, those involving more than two species, is increasingly appreciated as having the potential to strongly influence the dynamics of complex ecological systems. However, although the critical importance of the structure of pairwise interaction networks is well established, studies of higher-order interactions still largely assume random structures. Here, we demonstrate the strong impact of structured higher-order interactions on simulated ecological communities. We focus on effects caused by interaction modifications within food webs, where a consumer resource interaction is modified by a third species, and for which plausible structures can be hypothesised. We show how interaction modifications introduced under a range of non-random distributions may impact the overall network structure. Local stability and the size of the feasibility domain are critically dependent on the inter-relationship between trophic and non-trophic effects. Where interaction modifications are structured into mutual interference motifs (associated with consumers switching between resources) synergistic signs and topological effects have particularly consequential impacts. Furthermore, we show that previous results of the impact of higher-order interactions on diversity-stability relationships can be reversed when higher-order interactions are structured, not random. Empirical data on interaction modifications will be a key part of improving understanding the dynamics of communities, particularly the distribution of interaction modification signs across networks.

BackgroundClimate change and urban expansion pose significant challenges to controlling Aedes aegypti mosquito populations, a primary vector of arboviruses such as dengue, Zika, and chikungunya. This study aims assess how climate and anthropogenic factors will jointly shape Ae. aegypti densities in Brazil, which is crucial to forecasting transmission risks and informing public health strategies.MethodsThis study combined a biologically informed, stage-structured delay-differential equation model with climate and anthropogenic data. Climate projections from the Coupled Model Intercomparison Project Phase 6 under different Shared Socioeconomic Pathways (SSPs) were used to forecast future climate scenarios from 2024 to 2080. Boosted Regression Trees integrated anthropogenic factors like urbanisation, population growth, and urban accessibility. Model outputs were validated with entomological surveillance data, and the basic reproductive number for dengue fever was used to assess changes in disease transmission potential.FindingsOur findings predicted that Ae. aegypti mosquito density will increase nationally, but unevenly, exceeding thermal limits in North Brazil while rising substantially in the South and Southeast. Increases in density were particularly pronounced under high greenhouse gas emission scenario SSP5-8.5 (up to 92% in the Southeast). These trends were projected to elevate the transmission potential for dengue fever, with Southeast Brazil facing the biggest increases due to mosquito population growth outpacing human population expansion. Validation against historical data confirmed model robustness.InterpretationBy directly linking mosquito abundance to SSP-specific emissions trajectories, our results show that climate mitigation can markedly reduce disease risk. Shifting from SSP5-8.5 to SSP1-2.6 could cut projected mosquito density increases from 31% to 11% nationally by 2080. The model's spatial granularity and integration of local administrative boundaries support its utility for national and sub-national health planning. Addressing compounded risks in vulnerable peri-urban and rural populations will require coordinated interventions that span climate policy, vector control, and health equity.

Zika virus (ZIKV) is a mosquito-borne orthoflavivirus primarily transmitted among humans by Aedes aegypti. Over the past two decades, it has caused significant outbreaks associated with birth defects and neurological disorders. ZIKV consists of two main genotypes: the African and Asian lineages, each exhibiting distinct biological properties. African lineage strains are transmitted more efficiently by mosquitoes, but the genetic basis for this difference has been elusive. Here, we investigate this question by comparing recent African and Asian strains using chimeric viruses with swapped genome segments. Our results show that structural genes from the African strain enhance viral internalization, while non-structural genes improve genome replication and infectious particle production in mosquito cells. In vivo mosquito transmission is most significantly influenced by structural genes, although no single viral gene alone is decisive. We also develop a stochastic model of in vivo viral dynamics that reflects the observed patterns, suggesting the key difference between African and Asian strains lies in their ability to traverse mosquito salivary glands. Our findings imply the polygenic nature of ZIKV transmissibility has hindered Asian strains from achieving the same transmission efficiency as African strains, highlighting the role of lineage-specific adaptive landscapes in ZIKV evolution and emergence.

Sex ratio meiotic drive alleles bias their transmission by impairing the viability of non-drive gametes, leading to skewed population sex ratios. Despite theoretical predictions that drive alleles should reach fixation causing population extinction, meiotic drive persists at intermediate frequencies in wild populations, though the reasons for this are unclear. Here, we investigate how novel environmental stress and genotype-specific fitness costs contribute to drive frequency. Using a suppression-free X-linked meiotic drive system in Drosophila pseudoobscura, we exposed flies to varying doses of the pesticide permethrin and measured mortality and fecundity across genotypes. We found that drive-bearing males (SR) and drive-homozygous females (SRSR) exhibited heightened mortality, both in the presence and absence of pesticide, while heterozygous (SRST) females exhibited superior fecundity. Using a mathematical model parametrized with our empirical findings, we explored the long-term population dynamics of meiotic drive under different conditions. Our model predicts that drive frequency has a concave relationship with pesticide dose and is strongly modulated by genotype-specific female fecundity. These results suggest that novel environmental stressors and drive-induced fitness effects play key roles in determining meiotic drive frequencies. Our findings improve our understanding of drive frequencies in the wild and have direct implications for drive-based pest control.

Sociality is hypothesised to buffer populations against environmental stochasticity by stabilising key vital rates, yet this has rarely been tested across diverse taxa. We compiled demographic time series from 87 populations for 66 animal species spanning 12 taxonomic classes to test whether sociality promotes demographic buffering and whether this relationship depends on environmental context. Using stochastic elasticities and phylogenetically comparative analyses, we find broad support for the social buffering hypothesis: more social species exhibit lower sensitivity of population growth to vital rate variance (lower stochastic elasticity to vital rate variances, | T σ|), particularly in juvenile survival and reproduction. Contrary to expectations, juvenile vital rates, but not adult survival, showed the strongest buffering patterns in social taxa, suggesting life-stage-specific social mechanisms. Furthermore, we show that social species invest more in maximising the average performance of adult survival and reproduction (higher stochastic elasticity to vital rate means, T μ), consistent with adaptive strategies favouring canalisation of fitness-related traits. This buffering benefit is especially relevant in highly unpredictable environments: in climates with erratic precipitation, social species have greater demographic advantages, exhibiting even lower T σ values. Together, these findings provide support for the social buffering hypothesis, especially in highly stochastic environments. Social traits confer some demographic stability under moderate climatic conditions, but may become more relevant under extreme stochasticity—an insight particularly important for understanding population resilience in a rapidly changing world. ### Competing Interest Statement The authors have declared no competing interest. Natural Environment Research Council, NE/X013766/1

Strategies that rely on the mass release of males to suppress mosquito populations will exert selective pressure on natural mating systems. Here, we investigate how mass releases might affect the mating behaviors of wild target populations. We highlight gaps in our understanding of both variation in these aspects of mosquito behavior and the evolutionary forces that maintain variation within and between populations. We provide a mathematical framework for integrating mosquito mating ecology into models of population suppression. Given that these strategies are being increasingly deployed, anticipating and managing evolutionary responses of target population behavior should be a priority for research.

Cancer-inducing viruses (oncogenic viruses) are linked to over 10% of cancer cases. Although the molecular details of viral oncogenesis are well-documented, the evolutionary mechanisms by which viruses have acquired oncogenic properties remain poorly understood. Here, we investigate the evolutionary conditions affecting viral oncogenicity across both within- and between-host scales using mathematical models of oncovirus-immune system interactions, conceptualized as an extended shared enemy-victim relationship. We begin by examining how oncogenic traits impact within-host viral dynamics, focusing on the transformation rate of infected cells into pre-cancerous states and the pre-cancerous cell proliferation rate. In various scenarios reflecting different within-host conditions, we then identify the transformation and proliferation rates that maximize within- and between-host viral fitness. We find that the transformation rate maximizing the viral load depends on the viral production rate, immunogenicity, and the immune-mediated elimination rate of pre-cancerous cells. We also identify conditions under which an intermediate proliferation rate minimizes within- and between-host viral fitness: in that scenario, a lower or higher proliferation rate leads to a higher viral load, providing a possible explanation for the diversity of oncogenic viruses. The analyses presented here provide insights into the evolutionary drivers affecting viral oncogenicity and highlight the complexity of oncogenic virus-immune system interactions.

Random walks (RW) provide a useful modelling framework for the movement of animals at an individual level. If the RW is uncorrelated and unbiased such that the direction of movement is completely random, the dispersal is characterised by the statistical properties of the probability distribution of step lengths, or the dispersal kernel. Whether an individual exhibits short- or long-distance dispersal can be distinguished by the rate of asymptotic decay in the end-tail of the distribution of step-lengths. If the decay is exponential or faster, referred to as a thin-tail, then the step length variance is finite - as occurs in Brownian motion. On the other hand, inverse power-law step length distributions have a heavy end-tail with slower decay, resulting in an infinite step length variance, which is the hallmark of a Lévy walk. In theoretical studies of individual animal movement, various approaches have been employed to connect these dispersal mechanisms, yet they are often ad hoc. We provide a more robust method by ensuring that the survival probability, that is the probability of occurrence of steps longer than a certain threshold is the same for both distributions. Furthermore, the dispersal kernels are then standardised by adjusting the probability to minimise disparities between these distributions. By assuming the same survival probability for movement paths with commonly used thin- and heavy-tailed step length distributions, we form a relationship between the short- and long-distance dispersal of animals in different spatial dimensions. We also demonstrate how our findings can be applied in different ecological contexts, to relate dispersal kernels within theoretical models for boundary effects and spatio-temporal population dynamics. Moreover, we show that the relationship between these dispersal kernels can drastically affect the outcomes across various ecological scenarios.

Host-symbiont relationships can vary tremendously in the extent to which hosts depend on and control their symbionts. Obligate symbionts that provide micronutrients to their host are often compartmentalised to specialised host organs and depend on their hosts for survival, whereas facultative symbionts retain the ability to survive outside of their hosts. Few studies compare the extent to which a host controls and adjusts the density of obligate and facultative symbionts directly. We used tsetse as a model for teasing apart the relationships between a host (Glossina morsitans morsitans) and obligate (Wigglesworthia glossinidia) and facultative (Sodalis glossinidius) symbionts. We hypothesised that tsetse actively regulate the density of Wigglesworthia according to the host's requirements, depending on their current nutritional state and developmental age. In contrast, we postulated that Sodalis retains some independence from host control and that the growth of this symbiont is dependent on the conditions of the immediate environment, such as nutrient availability. Using qPCR, we examined how symbiont densities change across host age and the hunger cycle. Additionally, we investigated how host nutrition influences symbiont density, by comparing tsetse that were fed nutrient-poor or vitamin enriched diets. We found that the density of Wigglesworthia was not influenced by the nutritional status of the host but reflected long-term host nutritional needs. In contrast, the density of facultative Sodalis depended on the nutrient availability. We propose that tsetse tightly regulate Wigglesworthia but exert only partial control over Sodalis growth due to the relatively recent transition of this symbiont to host-associated living.

Density dependence describes the regulation of population growth rate by population density. This process is widely observed in insect populations, including vectors such as mosquitoes and agricultural pests that are targets of genetic biocontrol using gene drive technologies. While there continues to be rapid advancement in gene drive molecular design, most studies prioritise gene drive efficacy over ecology, and the role of density-dependent feedback on gene drives remains neglected. Furthermore, the details of density dependence experienced in these potential species of interest are usually poorly understood, creating additional constraints and challenges in evaluating the efficacy and efficiency of gene drive systems, especially those that promise local confinement after release. Here, we formulate and analyse a simple, non-species-specific mathematical model which integrates population dynamics by density dependence together with population genetics of a high-threshold two-locus underdominance system. Different models of density dependence and strengths of within-species competition are investigated alongside other genetic and ecological parameters. Our results suggest that for an underdominance gene drive system, density dependence processes, by acting on births or deaths, influence the population dynamics by leading to significantly different population-level suppression in the presence of a fitness cost. However, density dependence does not directly affect the fitness cost threshold for drive establishment. Moreover, we find that the magnitude and range of key ecological parameters (birth and death rates) could result in different outcomes depending on the type of density dependence employed. Our work highlights the importance of considering the ecological contexts in the design, development and deployment of gene drive molecular strategies.

Maternally-inherited sex ratio distorting microbes (SRDMs) are common among arthropod species. Typically, these microbes cause female-biased sex ratios in host broods, either by; killing male offspring, feminising male offspring, or inducing parthenogenesis. As a result, infected populations can experience drastic ecological and evolutionary change. The mechanism by which SRDMs operate is likely to alter their impact on host evolutionary ecology; despite this, the current literature is heavily biased towards a single mechanism of sex ratio distortion, male-killing. Furthermore, amidst the growing concerns surrounding the loss of arthropod diversity, research into the impact of SRDMs on the viability of arthropod populations is generally lacking. In this study, using a theoretical approach, we model the epidemiology of an understudied mechanism of microbially-induced sex ratio distortion-feminisation-to ask an understudied question-how do SRDMs impact extinction risk in a changing environment? We constructed an individual-based model and measured host population extinction risk under various environmental and epidemiological scenarios. We also used our model to identify the precise mechanism modulating extinction. We find that the presence of feminisers increases host population extinction risk, an effect that is exacerbated in highly variable environments. We also identified transmission rate as the dominant epidemiological trait responsible for driving extinction. Finally, our model shows that sex ratio skew is the mechanism driving extinction. We highlight feminisers and, more broadly, SRDMs as important determinants of the resilience of arthropod populations to environmental change.

ABSTRACT Ecology has been surprisingly slow to address the uncertainty and bias that results from using short-term time series to draw long-term inference. To improve our understanding of assumptions around the temporal structure of vital rates ( e.g. , survival, reproduction), we need tools that are feasible and capture longer-term, state-structured population dynamics. Here, we use inverse modelling of a set of integral projection models (IPMs) to show how demographic rates can be accurately reconstructed from state-structure fluctuations in a population time-series. We use a particle-filtering optimisation algorithm to fit vital rates from time-series of varying length, parameter combinations, priors, and life histories. We show how key life history traits such as generation time have little effect on the ability of our approach to accurately identify vital rates using state structure over time. Further, contrary to our expectations, the duration of our time-series data has relatively modest impact on the estimation of vital rates compared to the critical role of prior knowledge on vital rates. ur framework to estimate IPM vital rates highlights the potential of inverse models to extend time-series for demographic models, but also demonstrates that long-term time-series are not a perfect surrogate for detailed demographic inference. We discuss the need for more work exploring the conditions when inverse modelling is an adequate tool based on species traits.

Worldwide, coral reefs are facing risk from climate change. The Western Indian Ocean (WIO) harbours about 16% of global coral reefs with highly reef-dependent local communities. Coastal protection and food security depend on effective conservation management, which requires understanding species abundances. Here, we explore how fish group distribution and abundance across the WIO, categorized by their trophic function, are explained by oceanographic connectivity, sea surface temperature (SST), and chlorophyll a. We designed a proportional oceanographic connectivity metric describing the relative strength of connectivity between all WIO coral reefs and each survey site. We created statistical models for four trophic groups: grazers and detritivores, herbivorous excavators, corallivores, and primary piscivores across 51 sites in the WIO. We show that SST and chlorophyll a are strong predictors of all trophic fish groups and that the proportional oceanographic connectivity metric improved the model predictions significantly for grazers and detritivores and excavators. For excavators, peak abundances were predicted at medium connectivity, and for grazers and detritivores, at low and medium connectivity, suggesting that larvae dispersal predominates at a local scale. Decision making should include connectivity for efficient conservation area prioritization, for which our proportional oceanographic connectivity metric is a valid and useful parameter.

Random walks (RW) provide a useful modelling framework for the movement of animals at an individual level. If the RW is uncorrelated and unbiased such that the direction of movement is completely random, the dispersal is characterised by the statistical properties of the probability distribution of step lengths, or the dispersal kernel. Whether an individual exhibits short-or long-distance dispersal can be distinguished by the rate of asymptotic decay in the end-tail of the distribution of step-lengths. If the decay is exponential or faster, referred to as a thin-tail, then the step length variance is finite -as occurs in Brownian motion. On the other hand, inverse power-law step length distributions have a heavy end-tail with slower decay, resulting in an infinite step length variance, which is the hallmark of a Lévy walk. Although different approaches to relate these different dispersal mechanisms have been used, they are ad hoc and sub-optimal. We provide a more robust method by ensuring that the survival probability, that is the probability of occurrence of steps longer than a fixed characteristic step length is the same for both distributions. Moreover, we derive an optimal value for the survival probability by minimising the L2-distance between the dispersal kernels. By computing the optimal probability for movement paths with commonly used thin- and heavy-tailed step length distributions, we form equivalence between short-and long-distance dispersal of animals in different spatial dimensions. We also demonstrate how our findings can be applied to ecological scenarios, to more accurately relate dispersal mechanisms within a modelling framework for spatio-temporal population dynamics.

Abstract Parental care is essential to offspring survival in many species. Understanding why males of some species provide care, whereas others do not, has received substantial attention. Previous research has found that sexual selection can favor paternal care, yet we still do not fully understand why sexual selection favors male care in some species but not others. It is also unclear when paternal care versus other preferred male trait(s) will be favored by sexual selection. We hypothesize that sexual selection can interact with basic life history to influence the conditions under which paternal care and/or another preferred male trait will be favored by sexual selection. We used a mathematical approach in which males alone provide parental care and exhibit a non‐care trait that is preferred in mate choice. Using this approach, we demonstrate that life‐history characteristics (stage‐specific mortality, fertilization success, gamete numbers) can interact with sexual selection to influence the evolution of paternal care and/or a preferred non‐care trait. In particular, whether (1) adult mortality, egg mortality, and fertilization success are high versus low and (2) a tradeoff exists between paternal care and a non‐care preferred trait will influence whether selection most strongly favors additional paternal care or a non‐care preferred trait. In general, we would expect strong selection for more male care when it is preferred in mate choice. In some cases, mate preferences for paternal care can inhibit selection for a preferred non‐care trait. Mate preferences for paternal care can also broaden the life‐history conditions under which we would expect the elaboration of male care to occur.

BackgroundBy March 2023, the COVID-19 illness had caused over 6.8 million deaths globally. Countries restricted disease spread through non-pharmaceutical interventions (NPIs; e.g. social distancing). More severe “lockdowns” were also required to manage disease spread. Although lockdowns effectively reduce virus transmission, they substantially disrupt economies and individual well-being. Fortunately, the availability of vaccines provides alternative approaches to manage disease spread. Yet, vaccination programs take several months to implement fully, require further time for individuals to develop immunity following inoculation, may not have complete coverage and/or may be imperfectly efficacious against the virus. Given these aspects of a vaccination programme, it is important to understand how NPIs (such as lockdowns) can be used in conjunction with vaccination to achieve public health goals.MethodsWe use mathematical methods to, investigate optimal approaches for vaccination under varying lockdown lengths and/or severities to prevent COVID-19-related deaths exceeding critical thresholds.ResultsWe find that increases in vaccination rate cause a disproportionate decrease in the length and severity lockdowns to keep mortality levels below a critical threshold. With vaccination, severe lockdowns can further reduce infections by up to 89%. Notably, we include simple demographics, modelling three groups: vulnerable, front-line workers, and non-vulnerable. We investigate the sequence of vaccination. One counter-intuitive finding is that even though the vulnerable group is high risk, demographically, this is a small group and critically, per person, vaccination therefore occurs more slowly. Hence vaccinating this group first achieves limited gains in overall disease control.DiscussionImportantly, we conclude that improved disease control may be best achieved by vaccinating the non-vulnerable group coupled with longer and/or more severe NPIs.

Avian influenza viruses (AIVs) are a major economic burden to the poultry industry and pose serious zoonotic risks, with human infections being reported every year. To date, the vaccination of birds remains the most important method for the prevention and control of AIV outbreaks. Most national vaccination strategies against AIV infection use whole virus-inactivated vaccines, which predominantly trigger a systemic antibody-mediated immune response. There are currently no studies that have examined the antibody repertoire of birds that were infected with and/or vaccinated against AIV. To this end, we evaluate the changes in the H9N2-specific IgM and IgY repertoires in chickens subjected to vaccination(s) and/or infectious challenge. We show that a large proportion of the IgM and IgY clones were shared across multiple individuals, and these public clonal responses are dependent on both the immunisation status of the birds and the specific tissue that was examined. Furthermore, the analysis revealed specific clonal expansions that are restricted to particular H9N2 immunisation regimes. These results indicate that both the nature and number of immunisations are important drivers of the antibody responses and repertoire profiles in chickens following H9N2 antigenic stimulation. We discuss how the repertoire biology of avian B-cell responses may affect the success of AIV vaccination in chickens, in particular the implications of public versus private clonal selection.

ABSTRACTWe are still largely reliant on pesticides for the suppression of arthropod pests which threaten human health and food production, but the recent rise of evolved resistance among important pest species has reduced pesticide efficacy. Despite this, our understanding of strategies that effectively limit the evolution of resistance remains weak. Male‐killing sex ratio distorting microbes (SRDMs), such as Wolbachia and Spiroplasma, are common among arthropod species. Previous theoretical work has suggested that they could limit adaptive potential in two ways: first, because by distorting sex ratios they reduce the effective population size, and second, because infected females produce no male offspring which restricts gene flow. Here we present the results of a novel experiment in which we test the extent by which these two mechanisms limit the adaptive response of arthropods to pesticide. Using a fully factorial design, we manipulated the adult sex ratio of laboratory populations of Drosophila melanogaster, both in the presence and absence of SRDMs, and exposed these populations to six generations of pesticide poisoning. This design allows the effects of SRDMs on sex ratio and their effects on gene flow to be estimated separately. After six generations, individuals from populations with even sex ratios displayed a higher resistance to pesticide relative to individuals from female‐biased populations. By contrast, we found no effect of the presence of SRDMs in host populations on pesticide resistance independent of sex ratio. In addition, males were more susceptible to pesticide than females—this was true of flies from both naïve and previously exposed populations. These findings provide the first empirical proof of concept that sex ratio distortion arising from SRDMs can limit adaptation to pesticides, but cast doubt on the theoretical effect of male‐killers limiting adaptation by disrupting gene flow.

Vector or host competence can be defined as the ability of an individual to become infected and subsequently transmit a pathogen. Assays to measure competence play a key part in the assessment of the factors affecting mosquito-borne virus transmission and of potential pathogen-blocking control tools for these viruses. For mosquitoes, competence for arboviruses can be measured experimentally and results are usually analysed using standard statistical approaches. Here we develop a mechanistic approach to studying within-mosquito virus dynamics that occur during vector competence experiments. We begin by developing a deterministic model of virus replication in the mosquito midgut and subsequent escape and replication in the hemocoel. We then extend this to a stochastic model to capture the between-individual variation observed in vector competence experiments. We show that the dose-response of the probability of mosquito midgut infection and variation in the dissemination rate can be explained by stochastic processes generated from a small founding population of virions, caused by a relatively low rate of virion infection of susceptible cells. We also show that comparing treatments or species in competence experiments by fitting mechanistic models could provide further insight into potential differences. Generally, our work adds to the growing body of literature emphasizing the importance of intrinsic stochasticity in biological systems.