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Most species have one or more natural enemies, e.g., predators, parasites, pathogens, and herbivores, among others. These species in turn typically attack multiple victim species. This leads to the possibility of indirect interactions among those victims, both positive and negative. The term apparent competition commonly denotes negative indirect interactions between victim species that arise because they share a natural enemy. This indirect interaction, which in principle can be reflected in many facets of the distribution and abundance of individual species and more broadly govern the structure of ecological communities in time and space, pervades many natural ecosystems. It also is a central theme in many applied ecological problems, including the control of agricultural pests, harvesting, the conservation of endangered species, and the dynamics of emerging diseases. At one end of the scale of life, apparent competition characterizes intriguing aspects of dynamics within individual organisms-for example, the immune system is akin in many ways to a predator that can induce negative indirect interactions among different pathogens. At intermediate scales of biological organization, the existence and strength of apparent competition depend upon many contingent details of individual behavior and life history, as well as the community and spatial context within which indirect interactions play out. At the broadest scale of macroecology and macroevolution, apparent competition may play a major, if poorly understood, role in the evolution of species' geographical ranges and adaptive radiations.

Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, and recently suggested as aiding climate analysis (Reichstein et al 2019 Nature 566 195-204, Schneider et al 2017 Geophys. Res. Lett. 44 12396-417). Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred. For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System model (ESM) diagnostics. Artificial intelligence (AI) can then build on discovered climate connections to provide enhanced warnings of approaching weather features, including extreme events. While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations.

Climate 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. This 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. Our 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. By 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.

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.

In the early stages of an outbreak, the term ‘pandemic’ can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of ‘pandemic’, an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters—such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions—on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term ‘pandemic’, and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.

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.

Many insects rely on intracellular bacterial symbionts to supplement their specialized diets with micronutrients. Using data from diverse and well-studied insect systems, we propose three lines of evidence suggesting that hosts have tight control over the density of their obligate, intracellular bacterial partners. First, empirical studies have demonstrated that the within-host symbiont density varies depending on the nutritional and developmental requirements of the host. Second, symbiont genomes are highly reduced and have limited capacity for self-replication or transcriptional regulation. Third, several mechanisms exist for hosts to tolerate, regulate and remove symbionts including physical compartmentalization and autophagy. We then consider whether such regulation is adaptive, by discussing the relationship between symbiont density and host fitness. We discuss current limitations of empirical studies for exploring fitness effects in host–symbiont relationships, and emphasize the potential for using mathematical models to formalize evolutionary hypotheses and to generate testable predictions for future work.

Can early warning systems be developed to predict influenza epidemics? Using Australian influenza surveillance and local internet search query data, this study investigated whether seasonal influenza epidemics in China, the US and the UK can be predicted using empirical time series analysis. Weekly national number of respiratory cases positive for influenza virus infection that were reported to the FluNet surveillance system in Australia, China, the US and the UK were obtained from World Health Organization FluNet surveillance between week 1, 2010, and week 9, 2018. We collected combined search query data for the US and the UK from Google Trends, and for China from Baidu Index. A multivariate seasonal autoregressive integrated moving average model was developed to track influenza epidemics using Australian influenza and local search data. Parameter estimates for this model were generally consistent with the observed values. The inclusion of search metrics improved the performance of the model with high correlation coefficients (China = 0.96, the US = 0.97, the UK = 0.96, p < 0.01) and low Maximum Absolute Percent Error (MAPE) values (China = 16.76, the US = 96.97, the UK = 125.42). This study demonstrates the feasibility of combining (Australia) influenza and local search query data to predict influenza epidemics a different (northern hemisphere) scales.

The evolution of parental care is beneficial if it facilitates offspring performance traits that are ultimately tied to offspring fitness. While this may seem self‐evident, the benefits of parental care have received relatively little theoretical exploration. Here, we develop a theoretical model that elucidates how parental care can affect offspring performance and which aspects of offspring performance (e.g., survival, development) are likely to be influenced by care. We begin by summarizing four general types of parental care benefits. Care can be beneficial if parents (1) increase offspring survival during the stage in which parents and offspring are associated, (2) improve offspring quality in a way that leads to increased offspring survival and/or reproduction in the future when parents are no longer associated with offspring, and/or (3) directly increase offspring reproductive success when parents and offspring remain associated into adulthood. We additionally suggest that parental control over offspring developmental rate might represent a substantial, yet underappreciated, benefit of care. We hypothesize that parents adjust the amount of time offspring spend in life‐history stages in response to expected offspring mortality, which in turn might increase overall offspring survival, and ultimately, fitness of parents and offspring. Using a theoretical evolutionary framework, we show that parental control over offspring developmental rate can represent a significant, or even the sole, benefit of care. Considering this benefit influences our general understanding of the evolution of care, as parental control over offspring developmental rate can increase the range of life‐history conditions (e.g., egg and juvenile mortalities) under which care can evolve. In this manuscript, we summarize four general types of parental care benefits. Care can be beneficial if parents (1) increase offspring survival during the stage in which parents and offspring are associated, (2) improve offspring quality in a way that leads to increased offspring survival and/or reproduction in the future when parents are no longer associated with offspring, (3) directly increase offspring reproductive success when parents and offspring remain associated into adulthood. We additionally suggest and find that parental control over offspring developmental rate might represent a substantial, yet underappreciated, benefit of care.

We 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.