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In this Communication, we demonstrate that a deep artificial neural network based on a transformer architecture with self-attention layers can predict the long-time population dynamics of a quantum system coupled to a dissipative environment provided that the short-time population dynamics of the system is known. The transformer neural network model developed in this work predicts the long-time dynamics of spin-boson model efficiently and very accurately across different regimes, from weak system–bath coupling to strong coupling non-Markovian regimes. Our model is more accurate than classical forecasting models, such as recurrent neural networks, and is comparable to the state-of-the-art models for simulating the dynamics of quantum dissipative systems based on kernel ridge regression. [ABSTRACT FROM AUTHOR]

We derive an analytic expression of the non-equilibrium Fermi's golden rule (NE-FGR) expression for a Holstein–Tavis–Cumming Hamiltonian, a universal model for many molecules collectively coupled to the optical cavity. These NE-FGR expressions capture the full-time-dependent behavior of the rate constant for transitions from polariton states to dark states. The rate is shown to be reduced to the well-known frequency domain-based equilibrium Fermi's golden rule (E-FGR) expression in the equilibrium and collective limit and is shown to retain the same scaling with the number of sites in non-equilibrium and non-collective cases. We use these NE-FGR to perform population dynamics with a time-non-local and time-local quantum master equation and obtain accurate population dynamics from the initially occupied upper or lower polariton states. Furthermore, NE-FGR significantly improves the accuracy of the population dynamics when starting from the lower polariton compared to the E-FGR theory, highlighting the importance of the non-Markovian behavior and the short-time transient behavior in the transition rate constant. [ABSTRACT FROM AUTHOR]

We investigate the plasmon-mediated nonlinear dynamics and the optics of a laser emission of random nanoemitters (NEs) embedded in a two-dimensional (2D) lattice of conducting nanorings (NRs) enhanced by plasmon–polariton (PP) excitations. The interaction of quantum NEs with the PP field in the NRs perturbs the dynamics of the electronic populations in NEs, leading to a significant dependence of laser generation (dynamics) on the plasma frequency ωp of PP. This results in a strong coupling of NE field emission with the PP field and sharp variations of the average current in the NR lattice. The phase transition in the system was found when the macroscopic structures of PP fields are excited simultaneously in different regions of the system if ωp (control parameter) reaches critical value ωc. We have established the analytical dependence of the PP current I = I(ωp/ωc) on the plasma frequency, which is in excellent agreement with the results of numerical simulations. This effect may allow the design of new types of PP active devices with the use of conducting NRs in modern nanoelectronics. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Floquet mean-field dynamics and Floquet surface hopping approaches to study the nonadiabatic dynamics in periodically driven solid systems. We demonstrate that these two approaches can be formulated in both real and reciprocal spaces. Using the two approaches, we are able to simulate the interaction between electronic carriers and phonons under periodic drivings, such as strong light–matter interactions. Employing the Holstein and Peierls models, we show that strong light–matter interactions can effectively modulate the dynamics of electronic population and mobility. Notably, our study demonstrates the feasibility and effectiveness of modeling low-momentum carriers' interactions with phonons using a truncated reciprocal space basis, an approach impractical in real space frameworks. Moreover, we reveal that even with a significant truncation, carrier populations derived from surface hopping maintain greater accuracy compared to those obtained via mean-field dynamics. These results underscore the potential of our proposed methods in advancing the understanding of carrier–phonon interactions in various periodically driven materials. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of excited-state vibrational energy relaxation in photosynthetic pigments is crucial for elucidating the mechanisms underlying energy transfer processes in light-harvesting complexes. Utilizing advanced femtosecond broadband transient fluorescence (TF) spectroscopy, we explored the excited-state vibrational dynamics of Chlorophyll-a (Chl-a) both in solution and within the light-harvesting complex II (LHCII). We discovered a vibrational cooling (VC) process occurring over ∼6 ps in Chl-a in ethanol solution following Soret band excitation, marked by a notable ultrafast TF blueshift and spectral narrowing. This VC process, crucial for regulating the vibronic lifetimes, was further elucidated through the direct observation of the population dynamics of higher vibrational states within the Qy electronic state. Notably, Chl-a within LHCII demonstrated significantly faster VC dynamics, unfolding within a few hundred femtoseconds and aligning with the ultrafast energy transfer processes observed within the complex. Our findings shed light on the complex interaction between electronic and vibrational states in photosynthetic pigments, underscoring the pivotal role of vibrational dynamics in enabling efficient energy transfer within light-harvesting complexes. [ABSTRACT FROM AUTHOR]

The dynamics of the sub-Ohmic spin-boson model under polarized initial conditions at finite temperatures is investigated by employing both analytical tools and the numerically accurate hierarchical equations of motion-tensor train method. By analyzing the features of nonequilibrium dynamics, we discovered a bifurcation phenomenon, which separates two regimes of the dynamics. It is found that before the bifurcation time, increasing temperature slows down the population dynamics, while the opposite effect occurs after the bifurcation time. The dynamics is highly sensitive to both initial preparation of the bath and thermal effects. [ABSTRACT FROM AUTHOR]

As an emerging technology in liquid inactivation, one of the main challenges of pulsed electric fields (PEFs) inactivation lies in quantitatively describing and predicting its lethality to microorganisms. However, due to its cross-scaled complexity and the consequent numerous regulatory factors, there is currently still no unified framework to understand the PEF dose–response relationship and the population dynamics theoretically. In this study, a simple yet powerful model from multiscale coarse-graining method is proposed to simulate the bacterial inactivation in suspensions during PEF processing. The complex dose–response effects at the macroscale are successfully reconstructed from simple evolution rules and several coarse-graining parameters, while considering the damage and death of a single bacterium at the microscale. Our model uncovers the seemingly chaotic and even controversial dose–response relationship of PEF in literatures and systematically explores the regulatory effect of experimental parameters in a unified framework. One of the interesting findings is that PEF with shorter pulsed width enhances lethality and reduces the minimal inhibitory time at a constant energy output per pulse, owing to the phase transitions in three bacterial population dynamics (Bistability mode, Avalanche mode, and Hybrid mode). Our study provides a new insight for numerically modeling PEF lethality in liquid inactivation and could serve as a guide for dosage management in practical applications. [ABSTRACT FROM AUTHOR]

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range-with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

Decisions to disperse from a habitat stand out among organismal behaviours as pivotal drivers of ecosystem dynamics across scales. Encounters with other species are an important component of adaptive decision-making in dispersal, resulting in widespread behaviours like tracking resources or avoiding consumers in space. Despite this, metacommunity models often treat dispersal as a function of intraspecific density alone. We show, focusing initially on three-species network motifs, that interspecific dispersal rules generally drive a transition in metacommunities from homogeneous steady states to self-organized heterogeneous spatial patterns. However, when ecologically realistic constraints reflecting adaptive behaviours are imposed-prey tracking and predator avoidance-a pronounced homogenizing effect emerges where spatial pattern formation is suppressed. We demonstrate this effect for each motif by computing master stability functions that separate the contributions of local and spatial interactions to pattern formation. We extend this result to species-rich food webs using a random matrix approach, where we find that eventually, webs become large enough to override the homogenizing effect of adaptive dispersal behaviours, leading once again to predominately pattern-forming dynamics. Our results emphasize the critical role of interspecific dispersal rules in shaping spatial patterns across landscapes, highlighting the need to incorporate adaptive behavioural constraints in efforts to link local species interactions and metacommunity structure. This article is part of the theme issue Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics.

By employing the numerically accurate multiple Davydov Ansatz (mDA) formalism in combination with the thermo-field dynamics (TFD) representation of quantum mechanics, we systematically explore the influence of three parameters—temperature, photonic-mode detuning, and qubit–phonon coupling—on population dynamics and absorption spectra of the Holstein–Tavis–Cummings (HTC) model. It is found that elevated qubit–phonon couplings and/or temperatures have a similar impact on all dynamic observables: they suppress the amplitudes of Rabi oscillations in photonic populations as well as broaden the peaks and decrease their intensities in the absorption spectra. Our results unequivocally demonstrate that the HTC dynamics is very sensitive to the concerted variation of the three aforementioned parameters, and this finding can be used for fine-tuning polaritonic transport. The developed mDA-TFD methodology can be efficiently applied for modeling, predicting, optimizing, and comprehensively understanding dynamic and spectroscopic responses of actual molecular systems in microcavities. [ABSTRACT FROM AUTHOR]

Scaffolded molecular networks are important building blocks in biological pigment–protein complexes, and DNA nanotechnology allows analogous systems to be designed and synthesized. System–environment interactions in these systems are responsible for important processes, such as the dissipation of heat and quantum information. This study investigates the role of nanoscale molecular parameters in tuning these vibronic system–environment dynamics. Here, genetic algorithm methods are used to obtain nanoscale parameters for a DNA-scaffolded chromophore network based on comparisons between its calculated and measured optical spectra. These parameters include the positions, orientations, and energy level characteristics within the network. This information is then used to compute the dynamics, including the vibronic population dynamics and system–environment heat currents, using the hierarchical equations of motion. The dissipation of quantum information is identified by the system's transient change in entropy, which is proportional to the heat currents according to the second law of thermodynamics. These results indicate that the dissipation of quantum information is highly dependent on the particular nanoscale characteristics of the molecular network, which is a necessary first step before gleaning the systematic optimization rules. Subsequently, the I-concurrence dynamics are calculated to understand the evolution of the vibronic system's quantum entanglement, which are found to be long-lived compared to these system–bath dissipation processes. [ABSTRACT FROM AUTHOR]

Ecological and evolutionary theories have proposed that species traits should be important in mediating species responses to contemporary climate change; yet, empirical evidence has so far provided mixed evidence for the role of behavioral, life history, or ecological characteristics in facilitating or hindering species range shifts. As such, the utility of trait-based approaches to predict species redistribution under climate change has been called into question. We develop the perspective, supported by evidence, that trait variation, if used carefully can have high potential utility, but that past analyses have in many cases failed to identify an explanatory value for traits by not fully embracing the complexity of species range shifts. First, we discuss the relevant theory linking species traits to range shift processes at the leading (expansion) and trailing (contraction) edges of species distributions and highlight the need to clarify the mechanistic basis of trait-based approaches. Second, we provide a brief overview of range shift-trait studies and identify new opportunities for trait integration that consider range-specific processes and intraspecific variability. Third, we explore the circumstances under which environmental and biotic context dependencies are likely to affect our ability to identify the contribution of species traits to range shift processes. Finally, we propose that revealing the role of traits in shaping species redistribution may likely require accounting for methodological variation arising from the range shift estimation process as well as addressing existing functional, geographical, and phylogenetic biases. We provide a series of considerations for more effectively integrating traits as well as extrinsic and methodological factors into species redistribution research. Together, these analytical approaches promise stronger mechanistic and predictive understanding that can help society mitigate and adapt to the effects of climate change on biodiversity.

Stochastic chemical kinetics is a widely used formalism for studying stochasticity of chemical reactions inside single cells. Experimental studies of reaction networks are generally performed with cells that are part of a growing population, yet the population context is rarely taken into account when models are developed. Models that neglect the population context lose their validity whenever the studied system influences traits of cells that can be selected in the population, a property that naturally arises in the complex interplay between single-cell and population dynamics of cell fate decision systems. Here, we represent such systems as absorbing continuous-time Markov chains. We show that conditioning on non-absorption allows one to derive a modified master equation that tracks the time evolution of the expected population composition within a growing population. This allows us to derive consistent population dynamics models from a specification of the single-cell process. We use this approach to classify cell fate decision systems into two types that lead to different characteristic phases in emerging population dynamics. Subsequently, we deploy the gained insights to experimentally study a recurrent problem in biology: how to link plasmid copy number fluctuations and plasmid loss events inside single cells to growth of cell populations in dynamically changing environments. [ABSTRACT FROM AUTHOR]

Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve the monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding the application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (Negaprion brevirostris) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.

The coexistence of obligate mutualists is often precariously close to tipping points where small environmental changes can drive catastrophic shifts in species composition. For example, microbial ecosystems can collapse by the decline of a strain that provides an essential resource on which other strains cross-feed. Here, we show that tipping points, ecosystem collapse, bistability and hysteresis arise even with very weak (non-obligate) mutualism provided the population is spatially structured. Based on numeric solutions of a metacommunity model and mean-field analyses, we demonstrate that weak mutualism lowers the minimal dispersal rate necessary to avoid stochastic extinction, while species need to overcome a mean threshold density to survive in this low dispersal rate regime. Our results allow us to make numerous predictions for mutualistic metacommunities regarding tipping points, hysteresis effects, and recovery from external perturbations, and let us draw general conclusions for ecosystems even with random, not necessarily mutualistic, interactions and systems with density-dependent dispersal rather than direct mutualistic interactions.

Avian population sizes fluctuate and change over vast spatial scales, but the mechanistic underpinnings remain poorly understood. A key question is whether spatial and annual variation in avian population dynamics is driven primarily by variation in breeding season recruitment or by variation in overwinter survival. We present a method using large‐scale volunteer‐collected data from project eBird to develop species‐specific indices of net population change as proxies for survival and recruitment, based on twice‐annual, rangewide snapshots of relative abundance in spring and fall. We demonstrate the use of these indices by examining spatially explicit annual variation in survival and recruitment in two well‐surveyed nonmigratory North American species, Carolina wren Thryothorus ludovicianus and northern cardinal Cardinalis cardinalis. We show that, while interannual variation in both survival and recruitment is slight for northern cardinal, eBird abundance data reveal strong and geographically coherent signals of interannual variation in the overwinter survival of Carolina wren. As predicted, variation in wintertime survival dominates overall interannual population fluctuations of wrens and is correlated with winter temperature and snowfall in the northeastern United States, but not the southern United States. This study demonstrates the potential of participatory science (also known as citizen science) datasets like eBird for inferring variation in demographic rates and introduces a new complementary approach towards illuminating the macrodemography of North American birds at comprehensive continental extents. [ABSTRACT FROM AUTHOR]

Populations of forest trees exhibit large temporal fluctuations, but little is known about the synchrony of these fluctuations across space, including their sign, magnitude, causes and characteristic scales. These have important implications for metapopulation persistence and theoretical community ecology. Using data from permanent forest plots spanning local, regional and global spatial scales, we measured spatial synchrony in tree population growth rates over sub-decadal and decadal timescales and explored the relationship of synchrony to geographical distance. Synchrony was high at local scales of less than 1 km, with estimated Pearson correlations of approximately 0.6–0.8 between species' population growth rates across pairs of quadrats. Synchrony decayed by approximately 17–44% with each order of magnitude increase in distance but was still detectably positive at distances of 100 km and beyond. Dispersal cannot explain observed large-scale synchrony because typical seed dispersal distances (<100 m) are far too short to couple the dynamics of distant forests on decadal timescales. We attribute the observed synchrony in forest dynamics primarily to the effect of spatially synchronous environmental drivers (the Moran effect), in particular climate, although pests, pathogens and anthropogenic drivers may play a role for some species. [ABSTRACT FROM AUTHOR]

This study presents a stochastic model of seed dispersal based on a branching random walk (BRW) framework, incorporating both homogeneous and non-homogeneous Poisson point processes (PPP). Building on the model introduced by Coletti et al. (2023), we examine the effects of habitat reduction on seed dispersal dynamics. We analyze the phase transition behavior of the BRW model under varying conditions of habitat fragmentation, focusing on how these conditions influence the critical dispersal rate. Specifically, we study a BRW on the real line with a non-homogeneous PPP driven by a log-normal density, constrained between spatial barriers. Our simulations localize the critical dispersal rate with respect to barrier positions and compare this dependence between homogeneous and non-homogeneous models. [ABSTRACT FROM AUTHOR]

ABSTRACT Bigeye tuna (Thunnus obesus; BET) and yellowfin tuna (Thunnus albacares; YFT) are commercially and ecologically important Atlantic Ocean species. Numerous studies have examined interacting species with clearly synchronous or asynchronous dynamics, but few have investigated interactions among tuna species. This study investigated the effects of climate indices on the standardized catch per unit effort (CPUE) and habitat preferences of BET and YFT in the Atlantic Ocean. The indicators for both tuna species were found to be influenced by the Atlantic Multidecadal Oscillation (AMO) and Tropical North Atlantic index. The AMO had the strongest effect on standardized CPUE for the two species, and habitat suitability also reflected AMO trends. We compared CPUE trends in overlapping suitable habitat areas and estimated variations in primary prey abundance for between BET and YFT to evaluate their species' competition for limited prey and habitat area resources. The standardized CPUE, habitat suitability index (HSI), and primary prey levels (squid and crustaceans) of BET all increased following the change to the positive AMO phase after the 1990s. Although the HSI value for YFT also increased in an area of habitat overlap, the corresponding standardized CPUE decreased. We suggest that this pattern of a decreasing‐standardized CPUE for YFT may have been caused by competition for limited prey and habitat area resources in the overlap area. [ABSTRACT FROM AUTHOR]

While there is a global trend in kelp forests decline, sub-Antarctic Beagle Channel kelp ecosystems are among the most stable and extensive in the world, acting as a climatic refuge, indicating its high conservation value. Nevertheless, these coastal ecosystems are increasingly exposed to anthropogenic drivers that can affect kelp strategies and promote their replacement by turf. This study evaluated the urban impact of a coastal city in the Beagle Channel (Ushuaia, 54° S 68° W) on giant kelp (Macrocystis pyrifera) population dynamics. Seasonal samplings were conducted between 2020 and 2023 in two contrasting sites: an urban site directly exposed to Ushuaia pressures and a non-urban site. Our results indicate a reduction in both kelp adult density and kelp recruitment at the urban site. Here, high sediment inputs were the main factor affecting kelp recruitment, but higher density of potential grazers, and changes in the understory macroalgae assemblage were further observed. Morpho-functional composition and sediment trapped within the macroalgae suggest the prevalence of turf-forming algae dominating the urban site's understory assemblage. If neglected, the decline in kelp recruitment rates and the replacement of the forest by turf could eventually lead to a local loss of this urban-influenced sub-Antarctic kelp forest. [ABSTRACT FROM AUTHOR]

This paper is concerned with existence, non-existence and uniqueness of positive (coexistence) steady states to a predator-prey system with prey-dependent dispersal. To overcome the analytical obstacle caused by the cross-diffusion structure embedded in the prey-dependent dispersal, we use a variable transformation to convert the problem into an elliptic system without cross-diffusion structure. The transformed system and pre-transformed system are equivalent in terms of the existence or non-existence of positive solutions. Then we employ the index theory alongside the method of the principle eigenvalue to give a nearly complete classification for the existence and non-existence of positive solutions. Furthermore we show the uniqueness of positive solutions and characterize the asymptotic profile of solutions for small or large diffusion rates of species. Our results pinpoint the positive role of prey-dependent dispersal on the population dynamics for the first time by showing that the prey-dependent dispersal in the predator-prey system is a beneficial strategy increasing the chance of predator's survival and hence promoting the coexistence of species. [ABSTRACT FROM AUTHOR]

Common carp, Cyprinus carpio, is a global invasive species illegally introduced into the Groenvlei system, South Africa, in the 1990s for recreational angling. To manage this population effectively, basic information is needed on its biology including growth and reproduction. This study examined the growth trends of C. carpio using otolith growth zone deposition rates which were validated using a chemical mark–recapture experiment (n = 16). From a total of 140 C. carpio, length-at-age was best described using the Von Bertalanffy growth model for males and females. The average lengths-at-50% maturity were estimated at 314 mm FL (males) and 262 mm FL (females). Compared with other South African C. carpio populations, the Groenvlei population is relatively fast growing and long lived, with the oldest female aged 20 years and the oldest male aged 18 years, likely facilitated by relatively favourable environmental conditions at Groenvlei. Data from this study provide a baseline at the commencement of C. carpio management interventions at Groenvlei and an opportunity to monitor changes in population dynamics facilitated by these efforts. [ABSTRACT FROM AUTHOR]

Accurately estimating the effective reproduction number (Rt) of a circulating pathogen is a fundamental challenge in the study of infectious disease. The fields of epidemiology and pathogen phylodynamics both share this goal, but to date, methodologies and data employed by each remain largely distinct. Here we present EpiFusion: a joint approach that can be used to harness the complementary strengths of each field to improve estimation of outbreak dynamics for large and poorly sampled epidemics, such as arboviral or respiratory virus outbreaks, and validate it for retrospective analysis. We propose a model of Rt that estimates outbreak trajectories conditional upon both phylodynamic (time-scaled trees estimated from genetic sequences) and epidemiological (case incidence) data. We simulate stochastic outbreak trajectories that are weighted according to epidemiological and phylodynamic observation models and fit using particle Markov Chain Monte Carlo. To assess performance, we test EpiFusion on simulated outbreaks in which transmission and/or surveillance rapidly changes and find that using EpiFusion to combine epidemiological and phylodynamic data maintains accuracy and increases certainty in trajectory and Rt estimates, compared to when each data type is used alone. We benchmark EpiFusion's performance against existing methods to estimate Rt and demonstrate advances in speed and accuracy. Importantly, our approach scales efficiently with dataset size. Finally, we apply our model to estimate Rt during the 2014 Ebola outbreak in Sierra Leone. EpiFusion is designed to accommodate future extensions that will improve its utility, such as explicitly modelling population structure, accommodations for phylogenetic uncertainty, and the ability to weight the contributions of genomic or case incidence to the inference. Author summary: Understanding infectious disease spread is fundamental to protecting public health, but can be challenging as disease spread is a phenomenon that cannot be directly observed. So, epidemiologists use data in conjunction with mathematical models to estimate disease dynamics. Often, combinations of different models and data can be used to answer the same questions–for example 'traditional' epidemiology commonly uses case incidence data (the number of people who have tested positive for a disease during a certain time period) whereas phylodynamic models use pathogen genomic sequence data and our knowledge of the way their genomes evolve to model disease population dynamics. Each of these approaches have strengths and limitations, and data of each type can be sparse or biased, particularly during rapidly developing outbreaks or in countries with poor pathogen surveillance. An increasing number of approaches attempt to fix this problem by incorporating diverse concepts and data types together in their models. We aim to contribute to this movement by introducing EpiFusion, a modelling framework that improves the efficiency and precision at which we can monitor important changes in pathogen transmission (specifically, in the effective reproduction number). EpiFusion uses particle filtering to simulate epidemic trajectories over time and weight their likelihood according to both case incidence data and a phylogenetic tree using separate observation models, resulting in the inference of trajectories in agreement with both sets of data. Improvements in our ability to accurately and confidently model pathogen spread help us to respond to infectious disease outbreaks and improve public health. [ABSTRACT FROM AUTHOR]

ABSTRACT The Gulf of Maine (GOM) northern shrimp, Pandalus borealis, once supported a significant winter fishery, but a moratorium has been placed on the fishery since 2014 because of a population collapse and recruitment failures that have been attributed to unfavorably warm water temperatures. The GOM is at the southernmost end of the northern shrimp's range, suggesting its population dynamics and distribution may be vulnerable to warming water temperatures. In this study, we used survey data to estimate spatial indicators for GOM northern shrimp at four life history stages to identify possible temporal trends and examine relationships between the indicators and northern shrimp abundance and bottom temperature. We observed patchier distributions over time, which were related to declining population abundance, and a distributional shift northward that was associated with warming bottom water temperatures. Northern shrimp habitat distribution was strongly associated with bottom temperature. Shrimp of all life stages were found in bottom waters cooler than the station's average bottom temperature. [ABSTRACT FROM AUTHOR]

Wildlife‐vehicle collisions have significant consequences for both humans and animals, including injuries, deaths, and vehicle damage. Therefore, analysis of accident data is important for planning countermeasures and appropriate wildlife management. In this research field, roadkill incidents have been extensively studied in many taxa, while railway accidents have received less attention despite their obvious impact on wildlife. Here we applied a Bayesian state‐space model to 31 years of collision data, both on railways and on roads, collected by the Ministry of the Environment in Hokkaido prefecture, Japan, to reveal the spatiotemporal dynamics of accidents for white‐tailed eagles, Steller's sea eagles, and red‐crowned cranes, for which over hundred accidents were reported in the data. Our analysis suggested that the mean annual number of individuals collected per collision site across Hokkaido increased 47,377‐fold in the white‐tailed eagle, 40,277‐fold in the Steller's sea eagle, and 50,584‐fold in the red‐crowned crane between 1991 and 2021. There have been concerns about the impact of traffic accidents on the population dynamics of these endangered birds, but no formal analyses have been conducted. Our analysis showed numerically that the negative impact has been increasing annually. These results suggest that long‐term data accumulation over large spatial scales allows us to understand the dynamics of accidents and predict potential factors underlying collision risks. [ABSTRACT FROM AUTHOR]

Introduction: Porcine circovirus 2 (PCV2) emerged more than three decades ago as one of the most impactful pathogens in the swine industry. Despite being a DNA virus, one of the hallmarks of PCV2 is its high evolutionary rate, which has led to the emergence of different genotypes, each exhibiting varying degrees of evolutionary success. Current knowledge suggests the occurrence of three main waves of genotype dominance, alternating over time (i.e., PCV2a, PCV2b, and PCV2d), alongside less prevalent genotypes. However, although PCV2d is currently the most common genotype nowadays, the others continue being circulating in the pig population. Methods: The present study reconsidered the epidemiological and evolutionary patterns of PCV2 genotypes using phylodynamic analyses, benefiting from an almost 10-fold increase in ORF2 sequence availability compared to previous studies. Additionally, a phylogeographic analysis was performed to investigate viral dispersal patterns and frequency, and the selective pressures acting on the capsid protein were estimated and compared among genotypes. Results: While successive emergence of major genotypes was confirmed, this study extends previous findings by revealing subsequent prevalence fluctuations of PCV2a and PCV2b after their initial decline. This evolutionary process may represent an example of balancing selection, specifically negative frequency-dependent selection, where a genotype fitness diminishes as it becomes more common, and vice versa. Variations in genotype- or clade-specific immunity—affected by the local prevalence of viral groups—combined with the periodic introduction of strains that have independently evolved in different regions, may have led to fluctuations in the population dynamics of major genotypes over time. These fluctuations were associated with ongoing evolution and variations in the capsid amino acid profile. Discussion: These findings have profound implications for future control strategies. Although PCV2d remains the most prevalent and widespread genotype, other genotypes should not be neglected. Control strategies should thus target the entire PCV2 population, with a focus on fostering broader and more cross-protective immunity. [ABSTRACT FROM AUTHOR]

Rice ranks as the third most important staple cereal food crop in Kenya, following maize and wheat. The demand for rice has been increasing at a faster rate than most cereals, occasionally requiring imports to meet local needs. In response, the Kenyan government has been implementing a programme to increase rice production by expanding irrigated lowland rice schemes from 32 988 to 171 676 ha, while increasing rain-fed upland production areas four-fold to 42 000 ha by 2030. However, with continued rice monocropping in irrigated lowland fields and intensification in rain-fed upland ecosystems, there is a growing concern that plant-parasitic nematodes may emerge as a significant problem across rice ecosystems. This study characterised the nematode genera associated with rice in Kenya and assessed their diversity and distribution across rice ecosystems, with a focus on Hirschmanniella species. Eleven nematode genera were identified in both ecosystems, indicating a relatively high diversity. These genera, ranked by prominence, included Hirschmanniella, Hoplolaimus, Pratylenchus, Helicotylenchus, Meloidogyne, Ditylenchus, Trichodorus, Xiphinema, Rotylenchus, Longidorus and Paratylenchus. The family Tylenchidae was also found to be an important component of the nematofauna. Both morphological and molecular analyses confirmed the presence of Hirschmanniella oryzae in irrigated lowland and rain-fed upland rice, which represents the first documented report for Kenya. A notably large molecular variation was also observed for these Kenyan populations on the 18S region, with intraspecific variation up to 10 bp difference. Understanding the diversity, distribution, and species composition of nematodes in rice ecosystems is important for raising awareness among plant protection services, farmers, cooperatives and extension agents about plant-parasitic nematodes and their challenges in rice production. Future research should build on this knowledge to support extensive studies on population dynamics and damage threshold, focusing on local rice cultivars to develop effective management strategies, including breeding programmes and other cultural practices. [ABSTRACT FROM AUTHOR]

This paper studies the bounded confidence model on growing fully-mixed populations. In this model, in addition to the usual opinion clusters, significant secondary clusters of smaller size appear systematically, while those secondary clusters appear erratically and include much fewer agents when the population is fixed. Through simulations, we derive the bifurcation diagram of the growing population model and compare it to the diagram obtained with an evolving probability density instead of agents, and with their equivalent having a fixed population. Our tests, when changing the usual bounded confidence function into a smooth bounded confidence function, suggest that these secondary clusters are mainly generated by a different mechanism when the population is growing than when it is fixed. [ABSTRACT FROM AUTHOR]

Fluctuation dynamics of an experimentally measured observable offer a primary signal for nonequilibrium systems, along with dynamics of the mean. While universal speed limits for the mean have actively been studied recently, constraints for the speed of the fluctuation have been elusive. Here, we develop a theory concerning rigorous limits to the rate of fluctuation growth. We find a principle that the speed of an observable's fluctuation is upper bounded by the fluctuation of an appropriate observable describing velocity, which also indicates a tradeoff relation between the changes for the mean and fluctuation. We demonstrate the advantages of our inequalities for processes with non-negligible dispersion of observables, quantum work extraction, and the entanglement growth in free fermionic systems. Our results open an avenue toward a quantitative theory of fluctuation dynamics in various non-equilibrium systems encompassing quantum many-body systems and nonlinear population dynamics. Fluctuation dynamics of an observable offers a primary signal for understanding non-equilibrium statistical mechanics. Here, the author derives a principle that the speed of an observable's fluctuation is upper bounded by the fluctuation of an observable describing velocity, which is valid for various non-equilibrium systems from quantum many-body systems to nonlinear population dynamics. [ABSTRACT FROM AUTHOR]

Public health events (PHEs) have emerged as significant threats to human life, health, and economic growth. PHEs, such as COVID-19, have prompted a reevaluation for enhanced regular prevention and control (RPC). In this study, we focus on the core concept of prevention and control intensity (PCI), and establish a neoclassical economic growth model from the long-term and macro perspective to balance life protection and economic growth. The model construct the mechanism of PCI on economic growth through population dynamics and capital accumulation under the backdrop of RPC for PHEs. We find the conditions for PCI when the economy achieves steady state, and provides an algorithm establishing the optimal strategy that maximises per capita disposable income based on the optimal PCI and consumption. Simulation result quantifies an inverted U-shaped relationship between PCI and capital per capita, output per capita and consumption per capita in the steady state. The model suggests that, given the PHEs of inducing potential unemployment shock, it is worthwhile to combine the implementation of moderate PCI with coordinated policies of income distribution. Highlights: • Building a Neoclassical economic growth model under public health events (PHEs). • Model links regular prevention and control (RPC) to macroeconomic factors. • Exploring mechanism of RPC for PHEs on economic growth. • Appropriate prevention and control intensity can engender economic growth. [ABSTRACT FROM AUTHOR]

Background: Although metabarcoding and metagenomic approaches have generated large datasets on worldwide phytoplankton species diversity, the intraspecific genetic diversity underlying the genetic adaptation of marine phytoplankton to specific environmental niches remains largely unexplored. This is mainly due to the lack of biological resources and tools for monitoring the dynamics of this diversity in space and time. Results: To gain insight into population diversity, a novel method based on INDEL markers was developed on Bathycoccus prasinos (Mamiellophyceae), an abundant and cosmopolitan species with strong seasonal patterns. Long read sequencing was first used to characterize structural variants among the genomes of six B. prasinos strains sampled from geographically distinct regions in the world ocean. Markers derived from identified insertions/deletions were validated by PCR then used to genotype 55 B. prasinos strains isolated during the winter bloom 2018–2019 in the bay of Banyuls-sur-Mer (Mediterranean Sea, France). This led to their classification into eight multi-loci genotypes and the sequencing of strains representative of local diversity, further improving the available genetic diversity of B. prasinos. Finally, selected markers were directly tracked on environmental DNA sampled during 3 successive blooms from 2018 to 2021, showcasing a fast and cost-effective approach to follow local population dynamics. Conclusions: This method, which involves (i) pre-identifying the genetic diversity of B. prasinos in environmental samples by PCR, (ii) isolating cells from selected environmental samples and (iii) identifying genotypes representative of B. prasinos diversity for sequencing, can be used to comprehensively describe the diversity and population dynamics not only in B. prasinos but also potentially in other generalist phytoplankton species. [ABSTRACT FROM AUTHOR]

Understanding the dynamic states and transitions of heterogeneous cell populations is crucial for addressing fundamental biological questions. High-content imaging provides rich datasets, but it remains increasingly difficult to integrate and annotate high-dimensional and time-resolved datasets to profile heterogeneous cell population dynamics in different microenvironments. Using hepatic stellate cells (HSCs) LX-2 as model, we proposed a novel analytical strategy for image-based integration and annotation to profile dynamics of heterogeneous cell populations in 2D/3D microenvironments. High-dimensional features were extracted from extensive image datasets, and cellular states were identified based on feature profiles. Time-series clustering revealed distinct temporal patterns of cell shape and actin cytoskeleton reorganization. We found LX-2 showed more complex membrane dynamics and contractile systems with an M-shaped actin compactness trend in 3D culture, while they displayed rapid spreading in early 2D culture. This image-based integration and annotation strategy enhances our understanding of HSCs heterogeneity and dynamics in complex extracellular microenvironments. [ABSTRACT FROM AUTHOR]

Background: Five species of the Phortica genus (Diptera: Drosophilidae) are known in Europe and the Middle East. Among these, Phortica variegata and Phortica okadai are better known for their role as vectors of the zoonotic eyeworm Thelazia callipaeda. Other species, such as Phortica semivirgo and Phortica oldenbergi, have been studied less. Given the paucity of data about these Phortica spp. vectors, we explored the population dynamics and ecology of Phortica spp. in an area highly endemic for T. callipeada (Manziana, Rome, Central Italy). Methods: Phortica spp. flies were collected over a 3-year period (2018–2020) during their active season (April–October) with a sweep net while hovering around fermenting fruits or a human operator acting as baits. Collected flies were morphologically identified and tested for a T. callipeada infection and for the presence of Wolbachia, by polymerase chain reaction (PCR). Population dynamics of species collected was associated to environmental drivers through generalized additive models. Results: Of the 5564 flies collected, 90.8% were P. variegata, 9.1% were P. oldenbergi, 0.05% were P. semivirgo, and one specimen was P. okadai. Only P. variegata scored molecularly infected with T. callipeada throughout the 3-year sampling period (1.8%). Phortica oldenbergi, observed consistently during the entire sampling period, exhibited a marked preference for fruit traps, contrasting with the lachryphagous activity of P. variegata. Analysis of environmental drivers of P. oldenbergi and P. variegata population dynamics indicated temperature, wind speed, and pressure as significant factors. In addition, Wolbachia pipientis endosymbiont was detected in P. oldenbergi and P. okadai. Conclusions: For the first time, this study analysed several ecological aspects of Phortica species coexisting in a T. callipeada endemic area, highlighting different behaviors in the same environment and their vectorial role. Notably, this is also the first report of the presence of P. oldenbergi in Italy and P. okadai in Europe, underscoring the importance of extensive sampling for detecting potential vectors and alien species with direct implications for vector-borne disease epidemiology. [ABSTRACT FROM AUTHOR]

We consider a linear system based on a population dynamics model dependent on age, space and nonlocal boundary conditions. Note that our population dynamics model is a four‐step model with a second derivative with respect to the age variable and a second derivative with respect to the space variable. Population growth at each stage depends on time and space. We prove the uniqueness and existence of a positive solution by combining the Galerkin's variational method and the Banach's fixed point theorem. [ABSTRACT FROM AUTHOR]

In recent years, conventional logistic maps have been applied across various fields including modeling and security, owing to their versatility and utility. However, their reliance on a single modifiable parameter limits their adaptability. This paper aims to explore generalized logistic maps with arbitrary powers, which offer greater flexibility compared to the standard logistic map. By introducing additional parameters in the form of arbitrary powers, these maps exhibit increased degrees of freedom, thus expanding their applicability across a wider spectrum of scenarios. Consequently, the conventional logistic map emerges as a specific instance within the proposed framework. The inclusion of arbitrary powers enriches system dynamics, enabling a more nuanced exploration of system behavior in diverse contexts. Through a series of illustrations, this study investigates the influence of arbitrary powers and equation parameters on equilibrium points, their positions, stability conditions, basin of attraction, and bifurcation diagrams, including the emergence of chaotic behavior. [ABSTRACT FROM AUTHOR]

Summary Validated predictions of wildlife population density would be very useful for managers of overabundant wildlife and their effects on biodiversity. In this study such predictions were generated by modelling population dynamics of 18 non‐culled populations of Eastern Grey Kangaroos (Macropus giganteus) across small biodiversity conservation reserves in Canberra. Predictions were validated using three analyses of independent, out‐of‐sample, data from 11 populations which were non‐culled or culled. Association (analysis 1) showed observed and predicted densities were significantly positively correlated (R2 = 0.79, P = 0.045, n = 5) with unbiased slope and y intercept for non‐culled populations, though observed and predicted densities of culled populations were unrelated (R2 = 0.32, P = 0.24, n = 6). Coverage (analysis 2) showed predicted densities were within the 95% confidence interval of observed densities in five of five non‐culled populations and four of six culled populations, with one underestimate and one overestimate in the latter group. Bias (analysis 3) showed the mean bias (=observed – predicted) was 0.18 (±0.23 SE) kangaroos/ha for non‐culled and 0.08 (±0.23 SE) for culled populations. The results have been used to adjust kangaroo management approaches in Canberra as part of adaptive management. [ABSTRACT FROM AUTHOR]

Survival is a key demographic parameter for long‐lived bird species as it strongly influences their population dynamics and persistence. In recent decades, several studies have focused on unravelling differential patterns of survival by sex or age in bird populations, as each group may be affected by different ecological and anthropogenic pressures. Vultures are a highly threatened group of birds where age‐ and especially sex‐dependent survival patterns have been understudied, and therefore, obtaining robust estimates and understanding which factors modulate them is crucial for developing management and conservation strategies. Here, we used a long‐term dataset (1990–2023) from a wild colony of griffon vultures Gyps fulvus in central Spain and a capture‐mark‐recapture framework to address potential sex‐ and age‐mediated patterns of apparent survival and resighting. Resighting probabilities were lower for individuals in their first year of life than for older individuals. Apparent survival probabilities increased with age and were generally higher for males than for females, particularly in subadults. Disentangling whether an unequal survival between sexes is due to female‐biased dispersal or to true mortality is necessary to understand population dynamics and to be able to implement adequate conservation management actions. Our research underlines the importance of considering sex and age interactions in demographic analyses of long‐lived, usually threatened species. [ABSTRACT FROM AUTHOR]

The temporal stability of plant productivity affects species' access to resources, exposure to stressors and strength of interactions with other species in the community, including support to the food web. The magnitude of temporal stability depends on how a species allocates resources among tissues and across phenological stages, such as vegetative growth versus reproduction. Understanding how plant phenological traits correlate with the long‐term stability of plant biomass is particularly important in highly variable ecosystems, such as drylands. We evaluated whether phenological traits predict the temporal stability of plant species productivity by correlating 18 years of monthly phenology observations with biannual estimates of above‐ground plant biomass for 98 plant species from semi‐arid drylands. We then paired these phenological traits with potential climate drivers to identify abiotic contexts that favour specific phenological strategies among plant species. Phenological traits predicted the stability of plant species above‐ground biomass. Plant species with longer vegetative phenophases not only had more stable biomass production over time but also failed to fruit in a greater proportion of years, indicating a growth–reproduction trade‐off. Earlier leaf‐out dates, longer fruiting duration and longer time lags between leaf and fruit production also predicted greater temporal stability. Species with stability‐promoting traits began greening in drier conditions than their unstable counterparts and experienced unexpectedly greater exposure to climate stress, indicated by the wider range of temperatures and precipitation experienced during biologically active periods. Our results suggest that bet‐hedging strategies that spread resource acquisition and reproduction over long time periods help to stabilize plant species productivity in variable environments, such as drylands. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

ABSTRACT Pomacea canaliculata (Lamarck 1822), a freshwater gastropod indigenous to lower Del Plata Basin of Argentina, has become the most destructive and invasive rice pests in south China since its introduction in the 1980s. In Guangdong, the main production areas for double rice, most of P. canaliculata overwinter in paddy field ditches after late‐rice harvesting in mid‐November and diapause to temporarily to avoid the damaging effects of extreme low temperatures. This pest aroused from diapause and migrated to the paddy field after early‐rice reviving in next late March. Overwintering and cold tolerance of natural P. canaliculata have a non‐negligible impact on population dynamics and distribution in the following year. We tested the supercooling capability, levels of cryoprotectant synthesis, activity of antioxidant defense system (antioxidant enzymes and reduced glutathione), and degree of oxidative damage (concentration of malondialdehyde as an index of lipid peroxidation) monthly, using natural P. canaliculata samples with a size‐gender structure (i.e., juveniles, female, and male adults) from experimental ponds during the period of mid‐November to the following April. P. canaliculata survived the winter with a monthly death rate of 7%–16.5% in coldest January. The supercooling point (SCP) of overwintering P. canaliculata decreased initially before increasing subsequently with monthly changes in water temperature. P. canaliculata accumulated a high glycogen content before December, which depleted towards the end of January, while lipid content reached peak in January and depleted since February. Activity of antioxidant defense system of P. canaliculata exhibited significant monthly differences and showed relatively higher size heterogeneity than monthly variations. The results contribute to the knowledge of adaptability in overwintering P. canaliculata and help to understand the mechanism of the invasive success of this species. [ABSTRACT FROM AUTHOR]

The focus of this selection of papers is the linkage of habitat and population dynamics for the purpose of conservation. We thus provide a general framework for making conservation decisions, emphasizing how knowledge of habitat–population linkages fits into this framework. We begin by describing structured decision-making (SDM) as a general approach to making conservation decisions. SDM requires the development of the following elements: objectives, actions, model(s), monitoring, and decision algorithm. We then describe adaptive resource management (ARM), a specific type of SDM developed for recurrent decisions characterized by potentially resolvable uncertainty. Many different classes of actions can be used to influence animal population dynamics, and modification of habitat is one class of action that is frequently used. Habitat management requires models for predicting responses of the managed system to management actions, and these models are based on our knowledge of habitat–population linkages. Frequently, these models are decomposed into two submodels: one used to predict habitat changes expected to result from management actions and another used to predict population responses to habitat changes. This latter modeling focuses generally on the influence of habitat change on vital rates governing the dynamics of population state variables (variables such as population size or density that describe the status or health of a population). Specific recommendations depend on 1) the vital rates and state variable(s) being considered, 2) the relative spatial scales of animal movement and habitat measurement, and 3) the relative temporal scales of habitat change and vital rate estimation. Finally, we present an example of an ARM program for habitat management, highlighting the role of habitat-linked population modeling in this effort. [ABSTRACT FROM AUTHOR]

Predation reduces the population density of prey, affecting its fitness and population dynamics. Few studies have connected trait changes with fitness consequences in prey and the molecular basis and metabolic mechanisms of such changes in bat-insect systems. This study focuses on the responses of Helicoverpa armigera to different predation risks, focusing on echolocating bats and their calls. Substantial modifications were observed in the nocturnal and diurnal activities of H. armigera under predation risk, with enhanced evasion behaviors. Accelerated development and decreased fitness were observed under predation risks. Transcriptomic and metabolomic analyses indicated that exposure to bats induced the upregulation of amino acid metabolism- and antioxidant pathway-related genes, reflecting shifts in resource utilization in response to oxidative stress. Exposure to bat predation risks enhanced the activity of DNA damage repair pathways and suppressed energy metabolism, contributing to the observed trait changes and fitness decreases. The current results underscore the complex adaptive strategies that prey species evolve in response to predation risk, enhancing our understanding of the predator–prey dynamic and offering valuable insights for innovative and ecologically informed pest management strategies. Helicoverpa armigera shows adaptive trait changes under bat predation risk, with increased evasion, accelerated development, and fitness decreases. Molecular analyses reveal shifts in metabolism and stress response pathways linked to these changes. [ABSTRACT FROM AUTHOR]

Natal Habitat Preference Induction (NHPI) plays a significant role in shaping settlement decisions in dispersive animals. Despite its importance, limited research has explored how NHPI varies during natal dispersal phases and across different types of natal habitats. In this study, we examined NHPI in 77 GPS-tagged juvenile red kites (Milvus milvus) originating from different natal habitats along an elevational gradient in Switzerland. We applied individual-based step selection analysis to investigate habitat selection from independence to settlement. We found that during the prospecting phase, individuals predominantly selected habitats similar to their natal environments. However, this pattern changed in the settlement phase: individuals fledged from habitats at higher elevations or closer to urban areas mostly avoided similar habitats (negative NHPI), while those from areas with more farmlands or pastures (combined with forests) showed a preference for similar habitats (positive NHPI). Moreover, the magnitude and individual variation in NHPI differed depending on the natal habitat types from which individuals originated. These findings highlight that strength, direction, and individual variation in NHPI differ between natal habitat types and dispersal phases. Natal habitats therefore can have pervasive legacy effects on subsequent habitat selection, likely affecting population and range dynamics. [ABSTRACT FROM AUTHOR]

ABSTRACT Habitat association has been proposed to affect evolutionary dynamics through its control on dispersal propensity, which is considered a key trait for lineage survival in habitats of low durational stability. The Habitat Constraint hypothesis predicts different micro‐ and macroevolutionary patterns for stable versus dynamic habitat specialists, but the empirical evidence remains controversial and in insects mostly derives from winged lineages. We here use genome‐wide SNP data to assess the effect of habitat association on the population dynamics of two closely related flightless lineages of the genus Eutagenia (Coleoptera: Tenebrionidae), which are co‐distributed across the Cyclades islands in the Eastern Mediterranean but are associated with habitat types of different presumed stability: the psammophilous lineage is associated with dynamic sandy coastal habitats, while the geophilous lineage is associated with comparatively stable compact soil habitats. Our comparative population genomic and demographic analyses support higher inter‐island gene flow in the psammophilous lineage, presumably due to the physical properties of dynamic sand‐dune habitats that promote passive dispersal. We also find consistent bottlenecks in the psammophilous demes, suggesting that lineage evolution in the dynamic habitat is punctuated by local extinction and recolonisation events. The inferred demographic processes are surprisingly uniform among psammophilous demes, but vary considerably among geophilous demes depending on historical island connectivity, indicating more stringent constraints on the dynamic habitat lineage. This study extends the Habitat Constraint hypothesis by demonstrating that selection on dispersal traits is not the only mechanism that can drive consistent differences in evolutionary dynamics between stable versus dynamic habitat specialists. [ABSTRACT FROM AUTHOR]

Clear‐fell harvesting of non‐native plantation forests in the southern hemisphere creates large amounts of woody debris that is used for breeding by non‐native bark beetles and wood borers. Limited competition and a shortage of natural enemies allow some invasive pine bark beetles and wood borers to become highly abundant and cause phytosanitary issues. Replicated transects (550–750 m long) of funnel traps baited with alpha‐pinene and ethanol spanning even‐aged Pinus radiata stands were established in New Zealand's largest plantation forest and monitored from pre‐harvest until up to 5 years post‐harvesting to test the effects of harvesting residue resource pulses on the spatio‐temporal population dynamics of Hylurgus ligniperda, Hylastes ater and Arhopalus ferus. Generalised additive mixed model analyses of trap captures of >687,000 H. ligniperda, >7600 H. ater and >5000 A. ferus individuals showed greatly elevated populations for the first 2–3 years post‐harvest. Subsequently, populations returned to very low levels once the resource pulse was exhausted. Spatial patterns of captures reflecting beetle dispersal from breeding material showed a curvilinear decline along the breeding resource gradient by up to 95% from the interior of a recently harvested stand into an adjacent, previously harvested stand with exploited resources. Synthesis and applications. The results show that forest management characterised by ongoing harvesting of neighbouring stands facilitates the build‐up of large populations of bark beetles and wood borers of phytosanitary concern. This is due to recurrent resource pulses caused by the supply of substantial amounts of breeding material from tree felling. Our findings can inform forest planning aimed at avoiding such harvesting patterns, by assisting with the development and implementation of strategies for the management of beetle populations across the landscape. Most beetles dispersed <400 m beyond the source stand edge, although some beetles are likely to have dispersed further. Keeping a distance of 1–2 km between recently felled stands and those scheduled for felling would be expected to reduce rapid population growth. This knowledge can also be used to reduce seedling attack, minimise infestation of logs, and ultimately, mitigate pathway and biosecurity risks, with important implications for forest product exports and quarantine measures. [ABSTRACT FROM AUTHOR]

Summary Ralstonia pseudosolanacearum, a plant pathogen responsible for bacterial wilt in numerous plant species, exhibits paradoxical growth in the host by achieving high bacterial densities in xylem sap, an environment traditionally considered nutrient‐poor. This study combined in vitro experiments and mathematical modeling to elucidate the population dynamics of R. pseudosolanacearum within plants. To simulate the xylem environment, a tomato xylem‐mimicking medium was developed. Then, a mathematical model was constructed using in vitro data and employed to simulate the dynamics of bacterial density and xylem sap composition during plant infection. The model accurately reproduced in planta experimental observations, including high bacterial densities and the depletion of glutamine and asparagine. Additionally, the model estimated the minimal number of bacteria required to initiate infection, the timing of infection post‐inoculation, the bacterial mortality rate within the plant and the rate at which bacterial putrescine is assimilated by the plant. The findings demonstrate that xylem sap can sustain high bacterial densities, provides an explanatory framework for the presence of acetate, putrescine and 3‐hydroxybutyrate in the sap of infected xylem and give clues as to the role of putrescine in the virulence of R. pseudosolanacearum. [ABSTRACT FROM AUTHOR]

ABSTRACT Invasive species pose a significant threat to global biodiversity and ecological services. The specific threshold of the initial population size necessary for the successful establishment of an invasive species remains uncertain. This research delves into the effects of different initial population sizes on the colonisation and population dynamics of the globally recognised invasive pest, Cydia pomonella, within a controlled laboratory setting. The results revealed that even a single pair of C. pomonella displayed notable eggs laid (fecundity) and hatched (fertility) by producing offspring upon mating. Larger initial population size exhibited increased mating rates, leading to enhanced fecundity and subsequently more offspring, indicating that the development of C. pomonella population is directly impacted by the mating rate of the initial individuals. Projections based on F1 data indicate that the C. pomonella population size could persistently increase over a span of 150 days, with varying growth rates influenced by the initial population size. These findings indicate the importance of heightened monitoring and management in non‐invaded regions to avert early colonisation of C. pomonella, thus mitigating the threat of invasion and subsequent population expansion. [ABSTRACT FROM AUTHOR]

Introduction: As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Methods: Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity. Results: A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Discussion: Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas. [ABSTRACT FROM AUTHOR]

Individual vital rates, such as mortality and birth rates, are key determinants of lifetime reproductive success, and variability in these rates shapes population dynamics. Previous studies have found that this vital rate heterogeneity can influence demographic properties, including population growth rates. However, the explicit effects of the variation within and the covariance between vital rates that can also vary throughout the lifespan on population growth remain unknown. Here, we explore the analytical consequences of nongenetic heterogeneity on long-term population growth rates and rates of evolution by modifying traditional age-structured population projection matrices to incorporate variation among individual vital rates. The model allows vital rates to be permanent throughout life ("fixed condition") or to change over the lifespan ("dynamic condition"). We reduce the complexity associated with adding individual heterogeneity to age-structured models through a novel application of matrix collapsing ("phenotypic collapsing"), showing how to collapse in a manner that preserves the asymptotic and transient dynamics of the original matrix. The main conclusion is that nongenetic individual heterogeneity can strongly impact the long-term growth rate and rates of evolution. The magnitude and sign of this impact depend heavily on how the heterogeneity covaries across the lifespan of an organism. Our results emphasize that nongenetic variation cannot simply be viewed as random noise, but rather that it has consistent, predictable effects on fitness and evolvability. [ABSTRACT FROM AUTHOR]