Early warning signals have limited applicability to empirical lake data

Research aimed at identifying indicators of persistent abrupt shifts in ecological communities, a.k.a regime shifts, has led to the development of a suite of early warning signals (EWSs). As these often perform inaccurately when applied to real-world observational data, it remains unclear whether critical transitions are the dominant mechanism of regime shifts and, if so, which EWS methods can predict them. Here, using multi-trophic planktonic data on multiple lakes from around the world, we classify both lake dynamics and the reliability of classic and second generation EWSs methods to predict whole-ecosystem change. We find few instances of critical transitions, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly processing dependant, with most indicators not performing better than chance, multivariate EWSs being weakly superior to univariate, and a recent machine learning model performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions, developing methods suitable for predicting resilience loss not limited to the strict bounds of bifurcation theory.

[Resource differences in the populations of Protosalanx chinensis between Dalong Lake and Amuta Lake in Daqing, Northeast China]

The population structure of Protosalanx chinensis is affected by a variety of factors, including water environment, intraspecific differentiation, interspecific competition and commercial fishing. To investigate the growth performance and the current status of resource utilization of P. chinensis in two important fish farming grounds (Dalong Lake and Amuta Lake) in Daqing, Northeast China, we collected 1513 P. chinensis samples from April to December 2018 for measurement and recording the basic biological characteristics. We estimated the parameters including population growth, mortality and exploitation using Von Bertalanffy growth formula, Beverton-Holt model, Pauly's empirical formula and length-based Bayesian biomass estimation method. The results showed that the asymptotic body lengths of Dalong Lake (n=660) and Amuta Lake (n=853) populations were 183.75 and 169.10 mm, respectively. The growth performance indices were both 4.85, and the body mass growth equations were Wt=29.29×(1-e-2.1(t+0.07))3.2977 and Wt=28.72×(1-e-2.5(t+0.07))3.4168, respectively. The ages at inflection were 0.49 a and 0.43 a, and the condition factors were 5.30×10-4±1.38×10-4 and 4.00×10-4±1.61×10-4. The exploitation rates were 0.60 and 0.61, and the relative catch per unit of supplementation were 0.069 and 0.075, respectively. The current differences in population resources between Dalong Lake and Amuta Lake in Daqing were mainly manifested in the parameters related to growth characteristics. The main reason for such differences was changes in the survival conditions of P. chinensis populations after the period of dietary transition induced by the density differences.

Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity

Data deficiencies among rare or cryptic species preclude assessment of community-level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government-funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well-structured, design-based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single-species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species- and community-level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community-level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula-based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community-level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.

Extinction risks and mitigation for a megaherbivore, the giraffe, in a human-influenced landscape under climate change

Megaherbivores play "outsized" roles in ecosystem functioning but are vulnerable to human impacts such as overhunting, land-use changes, and climate extremes. However, such impacts-and combinations of these impacts-on population dynamics are rarely examined using empirical data. To guide effective conservation actions under increasing global-change pressures, we developed a socially structured individual-based model (IBM) using long-term demographic data from female giraffes (Giraffa camelopardalis) in a human-influenced landscape in northern Tanzania, the Tarangire Ecosystem. This unfenced system includes savanna habitats with a wide gradient of anthropogenic pressures, from national parks, a wildlife ranch and community conservation areas, to unprotected village lands. We then simulated and projected over 50 years how realistic environmental and land-use management changes might affect this metapopulation of female giraffes. Scenarios included: (1) anthropogenic land-use changes including roads and agricultural/urban expansion; (2) reduction or improvement in wildlife law enforcement measures; (3) changes in populations of natural predators and migratory alternative prey; and (4) increases in rainfall as predicted for East Africa. The factor causing the greatest risk of rapid declines in female giraffe abundance in our simulations was a reduction in law enforcement leading to more poaching. Other threats decreased abundances of giraffes, but improving law enforcement in both of the study area's protected areas mitigated these impacts: a 0.01 increase in giraffe survival probability from improved law enforcement mitigated a 25% rise in heavy rainfall events by increasing abundance 19%, and mitigated the expansion of towns and blockage of dispersal movements by increasing abundance 22%. Our IBM enabled us to further quantify fine-scale abundance changes among female giraffe social communities, revealing potential source-sink interactions within the metapopulation. This flexible methodology can be adapted to test additional ecological questions in this landscape, or to model populations of giraffes or other species in different ecosystems.

Joint species distribution modeling reveals a changing prey landscape for North Pacific right whales on the Bering shelf

The eastern North Pacific right whale (NPRW) is the most endangered population of whale and has been observed north of its core feeding ground in recent years with low sea ice extent. Sea ice and water temperature are important drivers for zooplankton dynamics within the whale's core feeding ground in the southeastern Bering Sea, seasonally forming stable fronts along the shelf that give rise to distinct zooplankton communities. A northward shift in NPRW distribution driven by changing distribution of prey resources could put this species at increased risk of entanglement and vessel strikes. We modeled the abundance of NPRW prey, Calanus glacialis, Neocalanus, and Thysanoessa species, using a dynamic biophysical food web model of nine zooplankton guilds in the Bering shelf zooplankton community during a period of warming (2006-2016). This model is unique among prior zooplankton studies from the region in that it includes density dependence, thereby allowing us to ask whether species interactions influence zooplankton dynamics. Modeling confirmed the importance of sea ice and ocean temperature to zooplankton dynamics in the region. Density-independent growth drove community dynamics, while dependent factors were comparatively minimal. Overall, Calanus responded to environment terms, with the strength and direction of response driven by copepodite stage. Neocalanus and Thysanoessa responses were weaker, likely due to their primary occurrence on the outer shelf. We also modeled the steady-state (equilibrium) abundance of Calanus in conditions with and without wind gusts to test whether advection of outer shelf species might disrupt the steady-state dynamics of Calanus abundance; the results did not support disruption. Given the annual fall sampling design, we interpret our results as follows: low-ice-extent winters induced stronger spring winds and weakened fronts on the shelf, thereby advecting some outer shelf species into the study region; increased development rates in these warm conditions influenced the proportion of C. glacialis copepodite stages over the season. Residual correlation suggests missing drivers, possibly predators, and phytoplankton bloom composition. Given the continued loss of sea ice in the region and projected continued warming, our findings suggest that C. glacialis will move northward, and thus, whales may move northward to continue targeting them.

How density dependence, genetic erosion and the extinction vortex impact evolutionary rescue

Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity and the fixation of maladaptive alleles, hindered adaptation and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.

Gridded livestock density database and spatial trends for Kazakhstan

Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSKD) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSKD database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000-2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.

ACC neural ensemble dynamics are structured by strategy prevalence

Medial frontal cortical areas are thought to play a critical role in the brain's ability to flexibly deploy strategies that are effective in complex settings, yet the underlying circuit computations remain unclear. Here, by examining neural ensemble activity in male rats that sample different strategies in a self-guided search for latent task structure, we observe robust tracking during strategy execution of a summary statistic for that strategy in recent behavioral history by the anterior cingulate cortex (ACC), especially by an area homologous to primate area 32D. Using the simplest summary statistic - strategy prevalence in the last 20 choices - we find that its encoding in the ACC during strategy execution is wide-scale, independent of reward delivery, and persists through a substantial ensemble reorganization that accompanies changes in global context. We further demonstrate that the tracking of reward by the ACC ensemble is also strategy-specific, but that reward prevalence is insufficient to explain the observed activity modulation during strategy execution. Our findings argue that ACC ensemble dynamics is structured by a summary statistic of recent behavioral choices, raising the possibility that ACC plays a role in estimating - through statistical learning - which actions promote the occurrence of events in the environment.

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution

During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.

Applications of Fourier Transform-Infrared spectroscopy in microbial cell biology and environmental microbiology: advances, challenges, and future perspectives

Microorganisms play pivotal roles in shaping ecosystems and biogeochemical cycles. Their intricate interactions involve complex biochemical processes. Fourier Transform-Infrared (FT-IR) spectroscopy is a powerful tool for monitoring these interactions, revealing microorganism composition and responses to the environment. This review explores the diversity of applications of FT-IR spectroscopy within the field of microbiology, highlighting its specific utility in microbial cell biology and environmental microbiology. It emphasizes key applications such as microbial identification, process monitoring, cell wall analysis, biofilm examination, stress response assessment, and environmental interaction investigation, showcasing the crucial role of FT-IR in advancing our understanding of microbial systems. Furthermore, we address challenges including sample complexity, data interpretation nuances, and the need for integration with complementary techniques. Future prospects for FT-IR in environmental microbiology include a wide range of transformative applications and advancements. These include the development of comprehensive and standardized FT-IR libraries for precise microbial identification, the integration of advanced analytical techniques, the adoption of high-throughput and single-cell analysis, real-time environmental monitoring using portable FT-IR systems and the incorporation of FT-IR data into ecological modeling for predictive insights into microbial responses to environmental changes. These innovative avenues promise to significantly advance our understanding of microorganisms and their complex interactions within various ecosystems.

Fifty+ years of primate research illustrates complex drivers of abundance and increasing primate numbers

Many primate populations are threatened by human actions and a central tool used for their protection is establishing protected areas. However, even if populations in such areas are protected from hunting and deforestation, they still may be threatened by factors such as climate change and its cascading impacts on habitat quality and disease dynamics. Here we provide a long-term and geographically wide-spread population assessment of the five common diurnal primates of Kibale National Park, Uganda. Over 7 year-long or longer census efforts that spanned 52 years, our team walked 1466 km, and recorded 480 monkey groups. Populations were generally relatively stable with a few exceptions, for which no apparent causative factors could be identified. This stability is unexpected as many ecological changes documented over the last 34+ years (e.g., decreasing food abundance and quality) were predicted to have negative impacts. Populations of some species declined at some sites but increased at others. This highlights the need for large, protected areas so that declines in particular areas are countered by gains in others. Kibale has large areas of regenerating forest and this most recent survey revealed that after 20+ years, forest regeneration in many of these areas appears sufficient to sustain sizeable primate populations, except for blue monkeys that have not colonized these areas. Indeed, the average primate abundance in the regenerating forest was only 8.1% lower than in neighboring old-growth forest. Thus, park-wide primate abundance has likely increased, despite many pressures on the park having risen; however, some areas in the park remain to be assessed. Our study suggests that the restoration, patrolling, and community outreach efforts of the Uganda Wildlife Authority and their partners have contributed significantly to protecting the park and its animals.

City-level population projection for China under different pathways from 2010 to 2100

Cities play a fundamental role in policy decision-making processes, necessitating the availability of city-level population projections to better understand future population dynamics and facilitate research across various domains, including urban planning, shrinking cities, GHG emission projections, GDP projections, disaster risk mitigation, and public health risk assessment. However, the current absence of city-level population projections for China is a significant gap in knowledge. Moreover, aggregating grid-level projections to the city level introduces substantial errors of approximately 30%, leading to discrepancies with actual population trends. The unique circumstances of China, characterized by comprehensive poverty reduction, compulsory education policies, and carbon neutrality goals, render scenarios like SSP4(Shared Socioeconomic Pathways) and SSP5 less applicable. To address the aforementioned limitations, this study made three key enhancements, which significantly refines and augments our previous investigation. Firstly, we refined the model, incorporating granular demographic data at the city level. Secondly, we redesigned the migration module to consider both regional and city-level population attractiveness. Lastly, we explored diverse fertility and migration scenarios.

A new set of metrics and framework to assess the colonization potential of riverscapes by wind-dispersed plant species

Quantifying the potential of a braided riverscape to be colonized by a plant species is essential for assessing the ecological state of the river and provides an important basis for nature conservation planning and the implementation of restoration measures. Common connectivity indices are largely unsuitable for describing the situation for the mostly wind-dispersed plant species. Our approach provides a set of comparable metrics that allows the quantification of the colonization potential of riverscapes at the patch and riverscape level. We propose a set of cell-based, spatially explicit measures that can easily be implemented. We demonstrate their application using two typical plant species and three riverscapes with different habitat configurations as examples. Our metrics consider shape, size and the spatial configuration of habitat patches, along with the dispersal characteristics of the respective species. The metrics provide a linear, balanced, and realistic representation of the colonization potential at the cell, patch, and riverscape levels. The results are comparable between different riverscapes and species, can be easily extended and used for further modeling. The metrics provide a valuable tool for the planning and evaluation of conservation, restoration, and reintroduction measures and close the gap between habitat availability analyses and large-scale terrestrial connectivity indices.

Assessing multiple free-roaming dog control strategies in a flexible agent-based model

Management of free-roaming dog populations is required to mitigate the threat of pathogens like rabies, minimize conflict with people, wildlife, and livestock and improve dog welfare however there are multiple strategies currently employed including sterilization, vaccination, and lethal removal. This work describes an agent-based stochastic model, 'StreetDogSim' that can be used as a planning tool to investigate the predicted impact of different strategies with variable implementation approaches and adjustable parameters to match local conditions. Here, we explore the effects of different management strategies with additional variation in their duration, intensity, and vaccine quality on important population metrics such as overall size, demographics, vaccination coverage, time until effective population suppression, and duration of suppression. Under most model parameterizations, a strategy that targets females for sterilization with vaccination outperforms all other options with respect to population control and demographic changes.

Twenty-nine years of continuous monthly capture-mark-recapture data of multimammate mice (Mastomys natalensis) in Morogoro, Tanzania

The multimammate mice (Mastomys natalensis) is the most-studied rodent species in sub-Saharan Africa, where it is an important pest species in agriculture and carrier of zoonotic diseases (e.g. Lassa virus). Here, we provide a unique dataset that consists of twenty-nine years of continuous monthly capture-mark-recapture entries on one 3 ha mosaic field (MOSA) in Morogoro, Tanzania. It is one of the most accurate and long-running capture-recapture time series on a small mammal species worldwide and unique to Africa. The database can be used by ecologists to test hypotheses on the population dynamics of small mammals (e.g. to test the effect of climate change), or to validate new algorithms on real long-term field data (e.g. new survival analyses techniques). It is also useful for both scientists and decision-makers who want to optimize rodent control strategies and predict outbreaks of multimammate mice.

Increasing densities of Pacific crown-of-thorns starfish (Acanthaster cf. solaris) at Lizard Island, northern Great Barrier Reef, resolved using a novel survey method

Recurrent population irruptions of Pacific crown-of-thorns starfish (CoTS, Acanthaster cf. solaris) are among the foremost causes of coral mortality on Australia's Great Barrier Reef (GBR). Early intervention during the initiation of new population irruptions represents the best opportunity to effectively manage this threat. However, current survey methods are not sufficiently sensitive to detect changes in CoTS densities during the early onset of population irruptions. Using scooter-assisted large area diver-based (SALAD) surveys, this study revealed increasing densities of CoTS at Lizard Island from 2019 to 2022. Inferred densities of adult CoTS (which account for distinct sets of observed feeding scars where starfish were not detected) increased from 4.90 ha-1 (± 0.85 SE) in 2019 to 17.71 ha-1 (± 2.3 SE) in 2022. A wide range of size classes were recorded suggesting that recruitment over several years is contributing to increasing densities. Importantly, the sustained density increases reported here denote that renewed CoTS population irruptions may soon become fully established at Lizard Island and more broadly in the northern GBR, especially without early intervention through effective population management.

Disrupting cellular memory to overcome drug resistance

Gene expression states persist for varying lengths of time at the single-cell level, a phenomenon known as gene expression memory. When cells switch states, losing memory of their prior state, this transition can occur in the absence of genetic changes. However, we lack robust methods to find regulators of memory or track state switching. Here, we develop a lineage tracing-based technique to quantify memory and identify cells that switch states. Applied to melanoma cells without therapy, we quantify long-lived fluctuations in gene expression that are predictive of later resistance to targeted therapy. We also identify the PI3K and TGF-β pathways as state switching modulators. We propose a pretreatment model, first applying a PI3K inhibitor to modulate gene expression states, then applying targeted therapy, which leads to less resistance than targeted therapy alone. Together, we present a method for finding modulators of gene expression memory and their associated cell fates.

Whole-lake acoustic telemetry to evaluate survival of stocked juvenile fish

Estimates of juvenile survival are critical for informing population dynamics and the ecology of fish, yet these demographic parameters are difficult to measure. Here, we demonstrate that advances in animal tracking technology provide opportunities to evaluate survival of juvenile tagged fish. We implemented a whole-lake telemetry array in conjunction with small acoustic tags (including tags < 1.0 g) to track the fate of stocked juvenile cisco (Coregonus artedi) as part of a native species restoration effort in the Finger Lakes region of New York, USA. We used time-to-event modeling to characterize the survival function of stocked fish, where we infer mortality as the cessation of tag detections. Survival estimates revealed distinct stages of juvenile cisco mortality including high immediate post-release mortality, followed by a period of elevated mortality during an acclimation period. By characterizing mortality over time, the whole-lake biotelemetry effort provided information useful for adapting stocking practices that may improve survival of stocked fish, and ultimately the success of the species reintroduction effort. The combination of acoustic technology and time-to-event modeling to inform fish survival may have wide applicability across waterbodies where receiver arrays can be deployed at scale and where basic assumptions about population closure can be satisfied.

Population dynamics of Listeria spp., Salmonella spp., and Escherichia coli on fresh produce: A scoping review

Collation of the current scope of literature related to population dynamics (i.e., growth, die-off, survival) of foodborne pathogens on fresh produce can aid in informing future research directions and help stakeholders identify relevant research literature. A scoping review was conducted to gather and synthesize literature that investigates population dynamics of pathogenic and non-pathogenic Listeria spp., Salmonella spp., and Escherichia coli on whole unprocessed fresh produce (defined as produce not having undergone chopping, cutting, homogenization, irradiation, or pasteurization). Literature sources were identified using an exhaustive search of research and industry reports published prior to September 23, 2021, followed by screening for relevance based on strict, a priori eligibility criteria. A total of 277 studies that met all eligibility criteria were subjected to an in-depth qualitative review of various factors (e.g., produce commodities, study settings, inoculation methodologies) that affect population dynamics. Included studies represent investigations of population dynamics on produce before (i.e., pre-harvest; n = 143) and after (i.e., post-harvest; n = 144) harvest. Several knowledge gaps were identified, including the limited representation of (i) pre-harvest studies that investigated population dynamics of Listeria spp. on produce (n = 13, 9% of pre-harvest studies), (ii) pre-harvest studies that were carried out on non-sprouts produce types grown using hydroponic cultivation practices (n = 7, 5% of pre-harvest studies), and (iii) post-harvest studies that reported the relative humidity conditions under which experiments were carried out (n = 56, 39% of post-harvest studies). These and other knowledge gaps summarized in this scoping review represent areas of research that can be investigated in future studies.

Motility mediates satellite formation in confined biofilms

Bacteria have spectacular survival capabilities and can spread in many, vastly different environments. For instance, when pathogenic bacteria infect a host, they expand by proliferation and squeezing through narrow pores and elastic matrices. However, the exact role of surface structures-important for biofilm formation and motility-and matrix density in colony expansion and morphogenesis is still largely unknown. Using confocal laser-scanning microscopy, we show how satellite colonies emerge around Escherichia coli colonies embedded in semi-dense hydrogel in controlled in vitro assays. Using knock-out mutants, we tested how extra-cellular structures, (e.g., exo-polysaccharides, flagella, and fimbria) control this morphology. Moreover, we identify the extra-cellular matrix' density, where this morphology is possible. When paralleled with mathematical modelling, our results suggest that satellite formation allows bacterial communities to spread faster. We anticipate that this strategy is important to speed up expansion in various environments, while retaining the close interactions and protection provided by the community.

Shifts in competitive structures can drive variation in species' phenology

For many species, a well documented response to anthropogenic climate change is a shift in various aspects of its life history, including its timing or phenology. Often, these phenological shifts are associated with changes in abiotic factors used as proxies for resource availability or other suitable conditions. Resource availability, however, can also be impacted by competition, but the impact of competition on phenology is less studied than abiotic drivers. We fit generalized additive models (GAMs) to a long-term experimental dataset on small mammals monitored in the southwestern United States and show that altered competitive landscapes can drive shifts in breeding timing and prevalence, and that, relative to a dominant competitor, other species exhibit less specific responses to environmental factors. These results suggest that plasticity of phenological responses, which is often described in the context of annual variation in abiotic factors, can occur in response to biotic context as well. Variation in phenological responses under different biotic conditions shown here further demonstrates that a more nuanced understanding of shifting biotic interactions is useful to better understand and predict biodiversity patterns in a changing world.

mSphere of Influence: If virulence is energetically costly, how can it be maintained?

Kimberly Davis works in the field of bacterial pathogenesis and studies heterogeneity in bacterial populations within host tissues. In this mSphere of Influence article, she reflects on how the paper "Stabilization of cooperative virulence by the expression of an avirulent phenotype" by M. Diard et al. (M. Diard et al., Nature 494:353-6, 2013, DOI: 10.1038/nature11913) impacted the way she thinks about bacterial population dynamics and the costs and benefits of producing virulence factors during infection.

Limited congruence in phylogeographic patterns observed for riverine predacious beetles sharing distribution along the mountain rivers

Riverine predacious beetles (RPB) (Carabidae, Staphylinidae) are highly diverse and numerous elements of riverine ecosystems. Their historical and contemporary distribution and diversity are highly dependent on natural flow regimes and topography of watercourses. Despite broad knowledge of their ecology, data on population genetic diversity and connectivity are lacking. This study aimed to fill this gap in order to solve two principal hypotheses assuming (i) congruence of phylogeographic patterns observed for RPB indicating that they share a common history and the ecological adaptations to the dynamic environment, (ii) genetic structuration of populations according to river basins. The Carpathian populations of four ground beetles and three rove beetles were examined using cytochrome oxidase and arginine kinase sequencing. There are substantial differences in RPB demographic history and current genetic diversity. Star-like phylogeny of Bembidion and complex haplotype networks of Paederus/Paederidus, with some haplotypes being drainage-specific and others found in distant populations, indicate a general lack of isolation by distance. Signs of recent demographic expansion were detected for most RPB with the latest population collapse for some rove beetles. To some extent, migration of examined species has to be limited by watersheds. Observed phylogeographic patterns are essential for correctly understanding RPB meta-population functioning.

Differences in determinants of active aging between older Brazilian and English adults: ELSI-Brazil and ELSA

This study aimed to investigate differences in determinants of active aging between older Brazilian and English adults and to verify the association of behavioral, personal, and social determinants with physical health. This cross-sectional study was based on the ELSI-Brazil (2015-2016) and ELSA (2016-2017) cohorts. Active aging determinants included behavior (smoking, sedentary lifestyle, and poor sleep quality), personal (cognitive function and life satisfaction), and social determinants (education, loneliness, and volunteering), according to the World Health Organization. Physical health included activities limitation and multimorbidity. We estimated age- and sex-adjusted prevalence for each indicator and mean score, and used the negative binomial regression for statistical analysis. We included 16,642 participants, 9,409 from Brazil and 7,233 from England. Overall, all active aging determinants were worse in Brazil than in England, except for life satisfaction (no difference). The most remarkable difference was found for social determinants score in Brazil (mean difference of 0.18; p < 0.05), mainly due to a significantly lower education level in Brazil (70.6%; 95% confidence interval - 95%CI: 69.7-71.5) than England (37.1%; 95%CI: 35.1-39.1). All determinants (behavioral, personal, and social) were associated with health in Brazil and in England. However, the behavioral domain was stronger associated with health in England (coefficient = 2.76; 95%CI: 2.46-3.10) than in Brazil (coefficient = 1.38; 95%CI: 1.26-1.50; p < 0.001). Older English adults beneficiate more from healthier behaviors than Brazilians, which depend more on social policies.

Sampling Lasioderma serricorne (Coleoptera: Anobiidae) in 2 coffee bean warehouses in New Jersey

Cigarette beetle, Lasioderma serricorne (F.), is one of the most common stored-product pests. We monitored their population dynamics and distribution in two coffee bean warehouses in New Jersey, USA, using pheromone traps and sticky traps during September 2018-October 2020, and light traps in 2020. The two warehouses only implemented treatment procedures for controlling Indian meal moth (Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae)) during the study period. The first L. serricorne adult appeared on pheromone traps from late May to early June when temperature reached 21-22 °C, and the last L. serricorne adult appeared on pheromone traps from late October to mid-November when temperature dropped to 10-14 °C. The majority of L. serricorne was caught during July-October. Light traps caught 5.5- and 2.2-times more L. serricorne per trap than pheromone traps in Warehouse 1 and 2, respectively. Warehouse 1 had a significantly higher density of L. serricorne than Warehouse 2. The L. serricorne activity peaks were not always clear and varied between year and the two warehouses. Zero to 3 hot spots, where had the largest numbers of L. serricorne, were identified from July to October in each warehouse based on pheromone traps, and their locations were similar through the months both in 2019 and 2020. The L. serricorne counts from pheromone traps placed inside warehouse were at least 2.3-times more than those placed outside. Also, the L. serricorne active period outside of the warehouses was shorter than that from inside of the warehouses.

Understanding and leveraging phenotypic plasticity during metastasis formation

Cancer metastasis is the process of detrimental systemic spread and the primary cause of cancer-related fatalities. Successful metastasis formation requires tumor cells to be proliferative and invasive; however, cells cannot be effective at both tasks simultaneously. Tumor cells compensate for this trade-off by changing their phenotype during metastasis formation through phenotypic plasticity. Given the changing selection pressures and competitive interactions that tumor cells face, it is poorly understood how plasticity shapes the process of metastasis formation. Here, we develop an ecology-inspired mathematical model with phenotypic plasticity and resource competition between phenotypes to address this knowledge gap. We find that phenotypically plastic tumor cell populations attain a stable phenotype equilibrium that maintains tumor cell heterogeneity. Considering treatment types inspired by chemo- and immunotherapy, we highlight that plasticity can protect tumors against interventions. Turning this strength into a weakness, we corroborate current clinical practices to use plasticity as a target for adjuvant therapy. We present a parsimonious view of tumor plasticity-driven metastasis that is quantitative and experimentally testable, and thus potentially improving the mechanistic understanding of metastasis at the cell population level, and its treatment consequences.

Climate warming accelerates somatic growth of an Arctic fish species in high-latitude lakes

High-latitude aquatic ecosystems are responding to rapid climate warming. A longer ice-free season with higher water temperatures may accelerate somatic growth in lake ectotherms, leading to widespread ecological implications. In fish, rising temperatures are expected to boost rates of food intake and conversion, and predictions based on empirical relationships between temperature and growth suggest a substantial increase in fish growth rates during the last decades. Fish abundance negatively affects growth by limiting food availability. This field study addresses the effects of climate warming on growth of a subarctic population of Arctic charr (Salvelinus alpinus (L.) over nearly 40 years. Juvenile growth of 680 individuals of Arctic charr, was reconstructed by sclerochronological analysis using sagittal otoliths sampled annually from the early 1980s to 2016. Statistical modelling revealed a positive effect of water temperature, and a negative effect of abundance on somatic growth in juvenile individuals. Temperature dependence in growth was significant for average and fast-growing individuals across all investigated age classes. These findings suggest that, as temperatures rise, somatic growth of Arctic charr will increase in high latitude lakes. Climate warming will thus influence cold water fish life history and size-structured interactions, with important consequences for their populations and ecosystems.

The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance

Approximately 10% of antimicrobials used by humans in low- and middle-income countries are estimated to be substandard or falsified. In addition to their negative impact on morbidity and mortality, they may also be important drivers of antimicrobial resistance. Despite such concerns, our understanding of this relationship remains rudimentary. Substandard and falsified medicines have the potential to either increase or decrease levels of resistance, and here we discuss a range of mechanisms that could drive these changes. Understanding these effects and their relative importance will require an improved understanding of how different drug exposures affect the emergence and spread of resistance and of how the percentage of active pharmaceutical ingredients in substandard and falsified medicines is temporally and spatially distributed.

Sperm-dependent asexual species and their role in ecology and evolution

Sexual reproduction is the primary mode of reproduction in eukaryotes, but some organisms have evolved deviations from classical sex and switched to asexuality. These asexual lineages have sometimes been viewed as evolutionary dead ends, but recent research has revealed their importance in many areas of general biology. Our review explores the understudied, yet important mechanisms by which sperm-dependent asexuals that produce non-recombined gametes but rely on their fertilization, can have a significant impact on the evolution of coexisting sexual species and ecosystems. These impacts are concentrated around three major fields. Firstly, sperm-dependent asexuals can potentially impact the gene pool of coexisting sexual species by either restricting their population sizes or by providing bridges for interspecific gene flow whose type and consequences substantially differ from gene flow mechanisms expected under sexual reproduction. Secondly, they may impact on sexuals' diversification rates either directly, by serving as stepping-stones in speciation, or indirectly, by promoting the formation of pre- and postzygotic reproduction barriers among nascent species. Thirdly, they can potentially impact on spatial distribution of species, via direct or indirect (apparent) types of competition and Allee effects. For each such mechanism, we provide empirical examples of how natural sperm-dependent asexuals impact the evolution of their sexual counterparts. In particular, we highlight that these broad effects may last beyond the tenure of the individual asexual lineages causing them, which challenges the traditional perception that asexual lineages are short-lived evolutionary dead ends and minor sideshows. Our review also proposes new research directions to incorporate the aforementioned impacts of sperm-dependent asexuals. These research directions will ultimately enhance our understanding of the evolution of genomes and biological interactions in general.

Population Regulation and Density-Dependent Demography in the Trinidadian Guppy

AbstractClassic theory for density-dependent selection for delayed maturation requires that a population be regulated through some combination of adult fecundity and/or juvenile survival. We tested whether those demographic conditions were met in four experimental populations of Trinidadian guppies in which delayed maturation of males evolved when the densities of those populations became high. We used monthly mark-recapture data to examine population dynamics and demography in these populations. Three of the four populations displayed clear evidence of regulation. In all four populations, monthly adult survival rates were independent of biomass density or actually increased with increased biomass density. Juvenile recruitment, which is a combination of adult fecundity and juvenile survival, decreased as biomass density increased in all four populations. Demography showed marked seasonality, with greater survival and higher recruitment in the dry season than the wet season. Population regulation via juvenile recruitment supports the hypothesis that density-dependent selection was responsible for the evolution of delayed maturity in males. This body of work represents one of the few complete tests of density-dependent selection theory.

Making waves: Comparative analysis of gene drive spread characteristics in a continuous space model

With their ability to rapidly increase in frequency, gene drives can be used to modify or suppress target populations after an initial release of drive individuals. Recent advances have revealed many possibilities for different types of drives, and several of these have been realized in experiments. These drives have advantages and disadvantages related to their ease of construction, confinement and capacity to be used for modification or suppression. Though characteristics of these drives have been explored in modelling studies, assessment in continuous space environments has been limited, often focusing on outcomes rather than fundamental properties. Here, we conduct a comparative analysis of many different gene drive types that have the capacity to form a wave of advance in continuous space using individual-based simulations in continuous space. We evaluate the drive wave speed as a function of drive performance and ecological parameters, which reveals substantial differences between drive performance in panmictic versus spatial environments. In particular, we find that suppression drive waves are uniquely vulnerable to fitness costs and undesired CRISPR cleavage activity in embryos by maternal deposition. Some drives, however, retain robust performance even with widely varying efficiency parameters. To gain a better understanding of drive waves, we compare their panmictic performance and find that the rate of wild-type allele removal is correlated with drive wave speed, though this is also affected by other factors. Overall, our results provide a useful resource for understanding the performance of drives in spatially continuous environments, which may be most representative of potential drive deployment in many relevant scenarios.

Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity

Microbial life in low-energy ecosystems relies on individual energy conservation, optimizing energy use in response to interspecific competition and mutualistic interspecific syntrophy. Our study proposes a novel community-level strategy for increasing energy use efficiency. By utilizing an oxidation-reduction (redox) reaction network model that represents microbial redox metabolic interactions, we investigated multiple species-level competition and cooperation within the network. Our results suggest that microbial functional diversity allows for metabolic handoffs, which in turn leads to increased energy use efficiency. Furthermore, the mutualistic division of labour and the resulting complexity of redox pathways actively drive material cycling, further promoting energy exploitation. Our findings reveal the potential of self-organized ecological interactions to develop efficient energy utilization strategies, with important implications for microbial ecosystem functioning and the co-evolution of life and Earth.

A tool for detecting giant kelp canopy biomass decline in the Californias

Kelp species provide many ecosystem services associated with their three-dimensional structures. Among these, fast-growth, canopy-forming species, like giant kelp Macrocystis pyrifera, are the foundation of kelp forests across many temperate reefs. Giant kelp populations have experienced regional declines in different parts of the world. Giant kelp canopy is very dynamic and can take years to recover from disturbance, challenging comparisons of standing biomass with historical baselines. The Santa Barbara Coastal LTER (SBC LTER), curates a time series of Landsat sensed surface cover and biomass for giant kelp in the west coast of North America. In the last decade, this resource has been fundamental to understanding the species' population dynamics and drivers. However, simple ready-to-use summary statistics aimed at classifying regional kelp decline or recovery are not readily available to stakeholders and coastal managers. To this end, we describe here two simple metrics made available through the R package kelpdecline. First, the proportion of Landsat pixels in decline (PPD), in which current biomass is compared with a historical baseline, and second, a pixel occupancy trend (POT), in which current year pixel occupancy is compared to the time-series long probability of occupancy. The package produces raster maps and output tables summarizing kelp decline and trends over a 0.25 × 0.25° scale. Using kelpdecline, we show how sensitivity analysis on PPD parameter variation can increase the confidence of kelp decline estimates.

Using epigenetic clocks to investigate changes in the age structure of critically endangered Māui dolphins

The age of an individual is an essential demographic parameter but is difficult to estimate without long-term monitoring or invasive sampling. Epigenetic approaches are increasingly used to age organisms, including nonmodel organisms such as cetaceans. Māui dolphins (Cephalorhynchus hectori maui) are a critically endangered subspecies endemic to Aotearoa New Zealand, and the age structure of this population is important for informing conservation. Here we present an epigenetic clock for aging Māui and Hector's dolphins (C. h. hectori) developed from methylation data using DNA from tooth aged individuals (n = 48). Based on this training data set, the optimal model required only eight methylation sites, provided an age correlation of .95, and had a median absolute age error of 1.54 years. A leave-one-out cross-validation analysis with the same parameters resulted in an age correlation of .87 and median absolute age error of 2.09 years. To improve age estimation, we included previously published beluga whale (Delphinapterus leucas) data to develop a joint beluga/dolphin clock, resulting in a clock with comparable performance and improved estimation of older individuals. Application of the models to DNA from skin biopsy samples of living Māui dolphins revealed a shift from a median age of 8-9 years to a younger population aged 7-8 years 10 years later. These models could be applied to other dolphin species and demonstrate the ability to construct a clock even when the number of known age samples is limited, removing this impediment to estimating demographic parameters vital to the conservation of critically endangered species.

YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images

Develoment of image recognition AI algorithms for flower-visiting arthropods has the potential to revolutionize the way we monitor pollinators. Ecologists need light-weight models that can be deployed in a field setting and can classify with high accuracy. We tested the performance of three deep learning light-weight models, YOLOv5nano, YOLOv5small, and YOLOv7tiny, at object recognition and classification in real time on eight groups of flower-visiting arthropods using open-source image data. These eight groups contained four orders of insects that are known to perform the majority of pollination services in Europe (Hymenoptera, Diptera, Coleoptera, Lepidoptera) as well as other arthropod groups that can be seen on flowers but are not typically considered pollinators (e.g., spiders-Araneae). All three models had high accuracy, ranging from 93 to 97%. Intersection over union (IoU) depended on the relative area of the bounding box, and the models performed best when a single arthropod comprised a large portion of the image and worst when multiple small arthropods were together in a single image. The model could accurately distinguish flies in the family Syrphidae from the Hymenoptera that they are known to mimic. These results reveal the capability of existing YOLO models to contribute to pollination monitoring.

Allee effects in an invasive social wasp: an experimental study in colonies of Vespula germanica

Allee effects rely on the existence of mutually beneficial intraspecific interactions that increase individual fitness and per capita growth rate, as the number of individuals in a population or group increases. When the number of individuals falls below a given number, the success of a group or population drops. Social insects heavily rely on cooperation between individuals for various tasks such as foraging and breeding. In this study, we experimentally explored component Allee effects and the underlying mechanisms in colonies of the social wasp Vespula germanica. After the removal of workers, we counted the number of gynes produced, estimated the body mass index as a proxy of their quality, and registered the protein-food collected by foragers in colonies. Our research revealed a correlation between the decrease in worker population and a subsequent decrease in the production of gynes. However, the removal of workers did not impact the quality of the produced gynes or the quantity of protein-food collected by the colonies. These findings highlight the crucial role of the worker caste in the success of eusocial species and suggested an ability of workers to perform multiple tasks that enable colonies to respond to disturbances. Additionally, our study provides the first evidence of Allee effects at the colony level of V. germanica, with potential practical implications for managing this invasive species.

Research on the relationship between population distribution pattern and urban industrial facility agglomeration in China

Investigating the impact of industrial facility agglomeration on population distribution provides valuable insights for advancing urban and regional development, as well as aiding in planning, forecasting, and achieving regional equilibrium. However, there remains a notable gap in understanding the influence and mechanisms of industrial facility agglomeration on population distribution, particularly when considering different industry types and diverse regions comprehensively. Additionally, conventional panel data used to assess industrial facility agglomeration are constrained by limitations in coverage and timeliness. In contrast, Point of Interest (POI) data offers a superior solution with its real-time, fine-grained, and innovative advantages. This study utilizes real-time and fine-grained POI data in conjunction with the LandScan population raster dataset to precisely assess industrial facility agglomeration in 352 administrative units at the prefecture level and above in China. The key findings of this research can be summarized as follows: (1) factors influencing urban population growth rates have evolved, with increased significance attributed to Government Agencies and Social Groups, alongside a consistent impact from Science, Education, and Cultural Services. (2) The correlation between industrial facility agglomerations and population growth rates displayed linear relationships in 2015 and 2021, with varying strengths and directional shifts. (3) Regional disparities in industrial facility agglomeration patterns underscore the necessity for customized strategies to optimize industrial structures, foster innovation-driven sectors, and promote sustainable population growth.

The Maize Caterpillar Mythimna (= Leucania) loreyi (Duponchel, 1827) (Lepidoptera: Noctuidae): Identification, Distribution, Population Density and Damage in Tunisia

Surveys were conducted during 2020 and 2021 to study the emerging lepidopteran pests inflicting cereals in Tunisia, with specific emphasis on maize and sorghum crops. A species was collected from traps placed in the Jendouba, Bizerte, Nabeul and Gabes regions. Thus, this study carried out first report on its identification, distribution, population density and damage. Results showed that M. loreyi was abundant in all prospected areas, with total adult captures reaching 4779 and 9499 moths on sorghum and maize, respectively, during 2020. Moreover, the mean infestation percentage reached its maximum during August at 31.05% and 20.69% for the Jendouba and Bizerte regions, respectively, while the highest infestations were observed in the Gabes and Nabeul regions during July, with respective mean values of 13.54% and 21.35%. In addition, results revealed that the highest pest incidence occurred in the Gabes region, with values of 11.1 ± 0.47 and 5.7 ± 0.48 during 2020 and 2021, respectively. Additionally, results pointed out that M. loreyi achieved two summer generations in the different localities of Tunisia. Overall, this study provides basic insights into the ecology and population biology of M. loreyi, which are required to establish an effective pest control program.

The Sex Ratio Indicates the Conclusion and Onset of Population Cycles in the Beet Webworm Loxostege sticticalis L. (Lepidoptera: Pyralidae)

Beet webworms, Loxostege sticticalis L. (Lepidoptera: Pyralidae), are one of the most destructive pest insects in northern China, and their populations outbreak periodically. Developing an indicator that defines the ending and beginning of the occurrence period cycle is urgent for the population forecast and theoretical study. The sex ratio can be a primary pathway through which species regulate population size. We measured the maximum mating potential of both females and males and the population net reproductive rate under different sex ratios (e.g., 3:1, 2:1, 1:1, 1:2, 1:3). The maximum mating frequency of males was 2.91 times that of females. The progeny contribution per mating decreased with increased mating times in males. The variation in population net reproductive rate affected by the sex ratio fits the parabolic curve analysis and peaked at 1.82 for females vs. males. Our results illustrate the quantitative connection phenomenon shown by the historical data: population outbreaks occur at a sex ratio of two or more and collapse at a sex rate lower than one. Simultaneously, the sex ratio may be utilized as a definite indicator for the beginning and end of the future occurrence cycle in the beet webworm.

Environmental Stability and Its Importance for the Emergence of Darwinian Evolution

The emergence of Darwinian evolution represents a central point in the history of life as we know it. However, it is generally assumed that the environments in which life appeared were hydrothermal environments, with highly variable conditions in terms of pH, temperature or redox levels. Are evolutionary processes favored to appear in such settings, where the target of biological adaptation changes over time? How would the first evolving populations compete with non-evolving populations? Using a numerical model, we explore the effect of environmental variation on the outcome of the competition between evolving and non-evolving populations of protocells. Our study found that, while evolving protocells consistently outcompete non-evolving populations in stable environments, they are outcompeted in variable environments when environmental variations occur on a timescale similar to the average duration of a generation. This is due to the energetic burden represented by adaptation to the wrong environmental conditions. Since the timescale of temperature variation in natural hydrothermal settings overlaps with the average prokaryote generation time, the current work indicates that a solution must have been found by early life to overcome this threshold.

A simulation-based option to assess data-limited fisheries off West African waters

Most sophisticated stock assessment models often need a large amount of data to assess fish stocks, yet this data is often lacking for most fisheries worldwide, resulting in the increasing demand for data-limited stock assessment methods. To estimate fish stock status, one class of these data-limited methods uses simply catch time series data and, in other instances, life history information or fishery characteristics. These catch-only methods (COMs) built differently are known to make assumptions about changes in fishing effort and may perform differently under various fishing scenarios. As a case study, this paper used European anchovy (Engraulis encrasicolus) caught in the northwest African waters, though very economically and ecologically important, but still unassessed. Our study investigated the performance of five COMs under different fishing scenarios using as a reference the life-history information of the European anchovy captured in this region of the Atlantic. Hence, the present study developed a simulation approach to evaluate the performance of the five COMs in inferring the stock biomass status (B/BMSY) with consideration of different fishing scenarios under prior information true to anchovy. All five COMs mostly underestimated B/BMSY throughout the simulation period, especially under constant fishing mortality, and in the last five years of the simulation during all fishing scenarios. Overall, these COMs were generally poor classifiers of stock status, however, the state-space COM (SSCOM) generally performed better than the other COMs as it showed possibilities of recovering an overfished stock. When these methods were explored using actual anchovy catch data collected in the northwest African waters, SSCOM yielded results that were deferred from the other COMs. This study being the first to assess this species' stock in this area using a suite of COMs, presents more insights into the species stock status, and what needs to be considered before scientifically putting in place management measures of the stock in the area.

Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, human stool samples are available approximately once per day, while commensal population doubling times are likely on the order of minutes-to-hours. Despite this mismatch in timescales, much of the prior work on human gut microbiome time series modeling has assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a stationary system, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent a steady-state endpoint of these dynamics. We formalize this idea as stochastic logistic growth. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors.

Extreme events, trophic chain reactions, and shifts in phenotypic selection

Demographic consequences of rapid environmental change and extreme climatic events (ECEs) can cascade across trophic levels with evolutionary implications that have rarely been explored. Here, we show how an ECE in high Arctic Svalbard triggered a trophic chain reaction, directly or indirectly affecting the demography of both overwintering and migratory vertebrates, ultimately inducing a shift in density-dependent phenotypic selection in migratory geese. A record-breaking rain-on-snow event and ice-locked pastures led to reindeer mass starvation and a population crash, followed by a period of low mortality and population recovery. This caused lagged, long-lasting reductions in reindeer carrion numbers and resultant low abundances of Arctic foxes, a scavenger on reindeer and predator of migratory birds. The associated decrease in Arctic fox predation of goose offspring allowed for a rapid increase in barnacle goose densities. As expected according to r- and K-selection theory, the goose body condition (affecting reproduction and post-fledging survival) maximising Malthusian fitness increased with this shift in population density. Thus, the winter ECE acting on reindeer and their scavenger, the Arctic fox, indirectly selected for higher body condition in migratory geese. This high Arctic study provides rare empirical evidence of links between ECEs, community dynamics and evolution, with implications for our understanding of indirect eco-evolutionary impacts of global change.

A mathematical investigation of polyaneuploid cancer cell memory and cross-resistance in state-structured cancer populations

The polyaneuploid cancer cell (PACC) state promotes cancer lethality by contributing to survival in extreme conditions and metastasis. Recent experimental evidence suggests that post-therapy PACC-derived recurrent populations display cross-resistance to classes of therapies with independent mechanisms of action. We hypothesize that this can occur through PACC memory, whereby cancer cells that have undergone a polyaneuploid transition (PAT) reenter the PACC state more quickly or have higher levels of innate resistance. In this paper, we build on our prior mathematical models of the eco-evolutionary dynamics of cells in the 2N+ and PACC states to investigate these two hypotheses. We show that although an increase in innate resistance is more effective at promoting cross-resistance, this trend can also be produced via PACC memory. We also find that resensitization of cells that acquire increased innate resistance through the PAT have a considerable impact on eco-evolutionary dynamics and extinction probabilities. This study, though theoretical in nature, can help inspire future experimentation to tease apart hypotheses surrounding how cross-resistance in structured cancer populations arises.

Population dynamics of Nyssomyia whitmani (Diptera: Psychodidae) in domestic and peridomestic environments in Northeast Argentina, a tegumentary leishmaniasis outbreak area

In the present study, the temporal dynamics of the main vector of Leishmania braziliensis, Nyssomyia whitmani, was measured by monthly captures of phlebotominae sandflies during 5 consecutive years (from 2011 to 2016) in the Paranaense region of South America. The captures were performed in environments where the human-vector contact risk is high: domiciliary and peridomiciliary environments in a rural area endemic of tegumentary leishmaniasis. Nyssomyia whitmani was recorded as the dominant species of the phlebotominae ensemble in all domiciliary and peridomiciliary environments (House, Chicken Shed, Pigsty, and Forest Edge). Using generalized additive models, intra- and interannual fluctuations were observed, modulated by meteorological variables such as the minimum temperature and the accumulated precipitation 1 wk prior to capture. The installation of a pigsty by the farmer during the study period allowed us to observe and describe the so-called "pigsty effect" where the Ny. whitmani population was spatially redistributed, turning the pigsty as the environment that obtained the highest phlebotominae record counts, thus maintaining the farm overall abundance, supporting the idea that the environmental management of the peridomicile could have an impact on the reduction of epidemiological risk by altering the spatial distribution of the phlebotominae ensemble in the environments.

Population genomic analysis of an emerging pathogen Lonsdalea quercina affecting various species of oaks in western North America

Understanding processes leading to disease emergence is important for effective disease management and prevention of future epidemics. Utilizing whole genome sequencing, we studied the phylogenetic relationship and diversity of two populations of the bacterial oak pathogen Lonsdalea quercina from western North America (Colorado and California) and compared these populations to other Lonsdalea species found worldwide. Phylogenetic analysis separated Colorado and California populations into two Lonsdalea clades, with genetic divergence near species boundaries, suggesting long isolation and populations that differ in genetic structure and distribution and possibly their polyphyletic origin. Genotypes collected from different host species and habitats were randomly distributed within the California cluster. Most Colorado isolates from introduced planted trees, however, were distinct from three isolates collected from a natural stand of Colorado native Quercus gambelii, indicating cryptic population structure. The California identical core genotypes distribution varied, while Colorado identical core genotypes were always collected from neighboring trees. Despite its recent emergence, the Colorado population had higher nucleotide diversity, possibly due to its long presence in Colorado or due to migrants moving with nursery stock. Overall, results suggest independent pathogen emergence in two states likely driven by changes in host-microbe interactions due to ecosystems changes. Further studies are warranted to understand evolutionary relationships among L. quercina from different areas, including the red oak native habitat in northeastern USA.

Lytic bacteriophages affect the population dynamics of multi-strain microbial communities

Background: Lytic bacteriophages infect and lyse bacteria and, as a by-product, may affect diversity in microbial communities through selective predation on abundant bacterial strains. We used a complex dairy starter named Ur to investigate population dynamics of Lactococcus lactis, Lactococcus cremoris and Leuconostoc mesenteroides strains in terms of constant-diversity and periodic selection models. Methods: To mimic the starter Ur, we designed blends of 24 strains representing all eight previously identified genetic lineages in the starter culture. The blends were propagated by daily transfers in milk for over 500 generations in the presence or absence of a cocktail of lytic bacteriophages. The relative abundance of genetic lineages of L. lactis, L. cremoris and Lc. mesenteroides strains present in the complex blend, as well as phage presence, were monitored. Results: Control blends without phage predation showed decreased strain diversity, leading to a stable state due to the domination of the fittest strain(s) of a particular lineage according to periodic selection dynamics. However, in phage-challenged blends, predation caused a large shift in the microbial composition by killing the fittest and sensitive strains. Conclusion: It was demonstrated that phage-challenged blends maintained their diversity at the level of genetic lineages, thus providing experimental support for the constant-diversity dynamics model in a complex microbial community.

Density-dependent predatory impacts of an invasive beetle across a subantarctic archipelago

Biological invasions represent a major threat to biodiversity, especially in cold insular environments characterized by high levels of endemism and low species diversity which are heavily impacted by global warming. Terrestrial invertebrates are very responsive to environmental changes, and native terrestrial invertebrates from cold islands tend to be naive to novel predators. Therefore, understanding the relationships between predators and prey in the context of global changes is essential for the management of these areas, particularly in the case of non-native predators. Merizodus soledadinus (Guérin-Méneville, 1830) is an invasive non-native insect species present on two subantarctic archipelagos, where it has extensive distribution and increasing impacts. While the biology of M. soledadinus has recently received attention, its trophic interactions have been less examined. We investigated how characteristics of M. soledadinus, its density, as well as prey density influence its predation rate on the Kerguelen Islands where the temporal evolution of its geographic distribution is precisely known. Our results show that M. soledadinus can have high ecological impacts on insect communities when present in high densities regardless of its residence time, consistent with the observed decline of the native fauna of the Kerguelen Islands in other studies. Special attention should be paid to limiting factors enhancing its dispersal and improving biosecurity for invasive insect species.

Contributions to Dynamic Analysis of Differential Evolution Algorithms

The Differential Evolution (DE) algorithm is one of the most successful evolutionary computation techniques. However, its structure is not trivially translatable in terms of mathematical transformations that describe its population dynamics. In this work, analytical expressions are developed for the probability of enhancement of individuals after each application of a mutation operator followed by a crossover operation, assuming a population distributed radially around the optimum for the sphere objective function, considering the DE/rand/1/bin and the DE/rand/1/exp algorithm versions. These expressions are validated by numerical experiments. Considering quadratic functions given by f(x)=xTDTDx and populations distributed according to the linear transformation D-1 of a radially distributed population, it is also shown that the expressions still hold in the cases when f(x) is separable (D is diagonal) and when D is any nonsingular matrix and the crossover rate is Cr=1.0. The expressions are employed for the analysis of DE population dynamics. The analysis is extended to more complex situations, reaching rather precise predictions of the effect of problem dimension and of the choice of algorithm parameters.