Search Results (43)

During public health emergencies, individuals typically obtain epidemic-related information through mass media channels and personal social media platforms. This information enables them to monitor epidemic progression and adjust their preventive behaviors accordingly to mitigate infection risks. To capture these processes, this paper proposes a three-layer coupled metapopulation network model that investigates the effects of regional mass media and social information propagation on the spatial spread of epidemic. The mass media layer represents regional outlets that propagate epidemic-related information to individuals within corresponding patches. Migrant individuals not only follow mass media information of the residential patch, but also continue to follow mass media information from their destination patch. The information layer captures the dynamics of information exchange on social media platforms. The epidemic layer depicts the spread of the epidemic within the metapopulation network and simulates the reaction-diffusion dynamics of migrating individuals across different patches through a Migration-Interaction-Return (MIR) mechanism; the coupling between the information layer and the epidemic layer is asymmetric. Theoretical analysis using the Microscopic Markov Chain Approach (MMCA) derives the evolution equation and determines the epidemic thresholds, while Monte Carlo (MC) simulations validate the model and explore factors influencing propagation dynamics. Our research indicates that when migrants simultaneously receive mass media information from both residential and destination patches, it significantly enhances information coverage and promotes protective behaviors, thereby effectively suppressing epidemic spread. Furthermore, promoting information propagation—particularly the communication among individuals within a patch—significantly increases the proportion of aware individuals, reduces the infection scale, and raises the epidemic threshold. Notably, population migration would originally lead to an increase in infection scale, but as the intensity of information propagation strengthens, migration instead has a good effect on controlling epidemic spread. These results provide deeper insights into the role of awareness propagation and human mobility in epidemic containment. Full article

Due to small population size, Population Viability Analysis (PVA) of endangered species often pools all individuals into a single matrix to decrease variation in estimation of transition rates. These pooled populations may mask significant environmental variation among populations, affecting estimates. Using 10 years of population data (2000–2010) on the endangered plant Jacquemontia reclinata in Southeastern Florida, USA, I parameterized a stage-structured matrix model and calculated annual growth rates (lambdas)and elasticity for each year using stochastic matrix models. The metapopulation model incorporating actual dynamics of the two largest populations showed a lower occupancy rate and higher risk of extinction at an earlier time compared to a model that used the average of all natural populations. Analyses were consistent that incorporating population variation versus average dynamics in modeling J. reclinata demography results in more variation and greater extinction risk. Local variation may be due to both weather (including minimum winter temperature and total annual precipitation) and local disturbance dynamics in these urban preserves. Full article

Species are disappearing worldwide, and it is likely that the rate of their disappearance will increase. The most important factors responsible for this are assumed to be changes in climate and land use. To determine the probability of extinction of a given species, it must be viewed as a metapopulation composed of many populations. In plants, seeds are spread by wind or water (passive dispersers), unlike active dispersers, which can actively look for a suitable site of their species. Thus, while active dispersers can locate a suitable site, passive dispersers often fail to arrive at a suitable site. The following question arises: is it better for the survival of a metapopulation of passive dispersers to concentrate on conserving a few large populations, each of which will produce many propagules, or on many small populations, each of which will produce only few propagules? Here, we address the question of which of these strategies will maximize the likelihood of the survival of such a metapopulation, using orchids as a model. We concluded that small populations should be preferentially preserved. Small populations are more numerous and more likely to occur more widely in the region studied and therefore a larger proportion of the seeds they produce is more likely to land in suitable habitats than that produced by the fewer large populations. For conservation, there is a possibility to extend the results to other taxa. However, this must be carried out with caution and must consider the taxon in question. Full article

The Australian lungfish, Neoceratodus forsteri, is one of the world’s oldest vertebrate lineages, with a slow life-history and threatened status, requiring immediate conservation efforts. The main threats to lungfish populations are degradation and availability of key macrophyte habitats, water regulation and flow modification. As this long-lived species (at least 77 years) has delayed maturity (mature at 10 years), field monitoring alone will not be enough to inform the challenge of ensuring sustainable populations. A stochastic metapopulation model was developed for the Burnett River (Southeast Queensland, Australia), an important habitat for the lungfish that is a highly regulated system with extensive water infrastructure. The model consists of three interacting populations, where the ecology and biology of the species were translated into an 80-year-class population projection matrix for each population, each with post-development streamflow, habitat and movement rules. The model highlights the longer-term interaction between dams and stream flows on habitat availability and subsequent recruitment. Through a pre-development streamflow, we quantify the impact of high regulation and development on the lungfish population in the Burnett River: a minor decline in the upstream population (e.g., 9.8% decline), a large decline in the middle population (64.2% decline), virtually no change in the downstream population (e.g., 1.2% decline) and a moderate decline in the overall metapopulation (e.g., 22.3% decline). The loss of spawning and feeding habitat remains the main reason for population decline, with implications that the loss will lead to greater pressure on remaining downstream habitat due to combined flow and dam effects and, in turn, to extended periods of recovery of spawning habitat. Our modeling approach substantially advances conservation management of this species, as it can be adapted to suit other populations in other river systems and used to test sensitivity to recovery actions. Full article

In fisheries’ stock assessments, the concept of “growth plasticity”—the ability of organisms to modulate their growth rates in response to environmental conditions—has gained attention in recent years. Historically, the impacts of fishing activities and environmental fluctuations were considered separately, while their combined effects have recently come into focus. This study collected 834 adult small yellow croakers (Larimichthys polyactis) from the northern Yellow Sea, the central Yellow Sea, the southern Yellow Sea, and the northern East Sea by trawling during 2020–2021. Using otolith increments as a proxy for annual somatic growth, the study reconstructed otolith chronologies during 2015–2020 for these four stocks. The results of the mixed-effects modeling suggested that temperature during spawning and previous overwintering seasons had comparable importance for the annual growth of small yellow croakers, with higher temperature promoting growth. The growth of small yellow croakers was also found to be correlated with ENSO events, with a lag of 1 to 2 years. A further investigation into combined effects revealed that higher fishing pressure might inhibit the small yellow croaker’s response to favorable environmental conditions. Furthermore, considering the potential differences in growth plasticity among stocks, an analysis was conducted on the spatial variations in growth response to these factors. The analysis revealed that, compared to the stocks in the Yellow Sea, the stock from the East China Sea could exhibit higher growth, superior adaptability to temperature, and a distinctive response to fishing pressure. In conclusion, the present study, while primarily focusing on temperature, preliminarily analyzed the combined effects of fishing and environment and underscored the differences in growth plasticity between stocks in the Yellow Sea and the East China Sea. Despite the limited factors analyzed in this study, it suggests a direction for future studies, highlighting the necessity to include more environmental factors, and even population factors (e.g., the biomass of preys), for a more comprehensive understanding of the combined effects. Based on the observed differences between the two potential subpopulations, this study also provides new insights for the management of the small yellow croaker based on metapopulation dynamics. Full article

This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements and enables the simultaneous estimation of transmission topology and epidemic factors. The use of the joint estimation scheme reduces the estimation error. The stability of equilibria and convergence behaviors of proposed dynamics are then analyzed. Furthermore, the sensitivity of the proposed model to population movements is evaluated in terms of the basic reproduction number. This article also rigorously investigates the effectiveness of non-pharmaceutical interventions via distributively controlling population flow in curbing virus transmission. It is found that the population flow control strategy reduces the number of infections during the epidemic. Full article

Accurate epidemic forecasting plays a vital role for governments to develop effective prevention measures for suppressing epidemics. Most of the present spatio–temporal models cannot provide a general framework for stable and accurate forecasting of epidemics with diverse evolutionary trends. Incorporating epidemiological domain knowledge ranging from single-patch to multi-patch into neural networks is expected to improve forecasting accuracy. However, relying solely on single-patch knowledge neglects inter-patch interactions, while constructing multi-patch knowledge is challenging without population mobility data. To address the aforementioned problems, we propose a novel hybrid model called metapopulation-based spatio–temporal attention network (MPSTAN). This model aims to improve the accuracy of epidemic forecasting by incorporating multi-patch epidemiological knowledge into a spatio–temporal model and adaptively defining inter-patch interactions. Moreover, we incorporate inter-patch epidemiological knowledge into both model construction and the loss function to help the model learn epidemic transmission dynamics. Extensive experiments conducted on two representative datasets with different epidemiological evolution trends demonstrate that our proposed model outperforms the baselines and provides more accurate and stable short- and long-term forecasting. We confirm the effectiveness of domain knowledge in the learning model and investigate the impact of different ways of integrating domain knowledge on forecasting. We observe that using domain knowledge in both model construction and the loss function leads to more efficient forecasting, and selecting appropriate domain knowledge can improve accuracy further. Full article

Despite previous concerns regarding the survival of Asiatic Lions confined to the Gir Protected Area, their dispersal into surrounding landscapes has become a subject of considerable research and discussion. This study employs species distribution modeling, corridor analysis, and additional landscape assessment using satellite-based temperatures and Land Cover statistics to investigate this dispersal and identify potential corridors based on extensive field data. The results reveal the identification of a potential corridor from Gir Wildlife Sanctuary towards Velavadar Blackbuck National Park, indicating the expansion of the Asiatic Lion’s range in the Eastern Greater Gir Landscape. These findings highlight the significance of resilience in Lion dispersal and corridor expansion, with implications for conservation and potential regional benefits, including ecosystem services and eco-tourism for sustainable development of the region. Full article

An outbreak species exhibits extreme, rapid population fluctuations that can be qualified as discrete events within a continuous dynamic. When outbreaks occur they may appear novel and disconcerting because the limiting factors of their dynamics are not readily identifiable. We present the first population hybrid dynamic model that combines continuous and discrete processes, designed to simulate marine species outbreaks. The deterministic framework was tested using the case of an unexploited benthic invertebrate species: the small, serpulid polychaete Ditrupa arietina. This species is distributed throughout the northeast Atlantic Ocean and Mediterranean Sea; it has a life cycle characterised by a pelagic dispersive larval stage, while juveniles and adults are sedentary. Sporadic reports of extremely high, variable densities (from <10 to >10,000 $ind.m^{-2}$) have attracted attention from marine ecologists for a century. However, except for one decade-long field study from the Bay of Banyuls (France, Gulf of Lions, Mediterranean Sea), observations are sparse. Minimal formulations quantified the processes governing the population dynamics. Local population continuous dynamics were simulated from a size-structured model with a null immigration–emigration flux balance. The mathematical properties, based on the derived hybrid model, demonstrated the possibilities of reaching an equilibrium for the population using a single number of recruits per reproducer. Two extrapolations were made: (1) local population dynamics were simulated over 180 years using North Atlantic Oscillation indices to force recruitment variability and (2) steady-state population densities over the Gulf of Lions were calculated from a connectivity matrix in a metapopulation. The dynamics reach a macroscopic stability in both extrapolations, despite the absence of density regulating mechanisms. This ensures the persistence of D. arietina, even when strong, irregular oscillations characteristic of an outbreak species are observed. The hybrid model suggests that a macroscopic equilibrium for a population with variable recruitment conditions can only be characterised for time periods which contain several outbreak occurrences distributed over a regional scale. Full article

Since the droughts of the 1970s–1980s, populations in the Sahel region have opted for a mass exodus to more humid urban or rural centers. Migrations or exoduses have accelerated in recent decades due to environmental degradation and unfavorable climatic conditions. Insufficient harvests are the main reason for migration for the majority of migrants in the Sahelian areas. Migration is a major adaptation strategy to cope with extreme climatic conditions, thus requiring quantification in the destination area. The aim of this paper is to propose a metapopulation model to approximate reality by identifying the transition from one socioeconomic vulnerability group to another, from a less favorable area to favorable area in terms of natural resources, depending on the strategies, policies, and climate variability. The model was used to analyze the dynamics of socioeconomic vulnerability to study the impact of migration on the dynamics of socioeconomic vulnerability. The developed mathematical model was analyzed. Up to 2050, simulations applied to the Tougou village in northern Burkina Faso show that migration has a positive impact on the socioeconomic vulnerability of the destination area, thereby reducing the vulnerability of the population by 10% when resources are increased by up to 30%. Full article

The large population movement during the Spring Festival travel in China can considerably accelerate the spread of epidemics, especially after the relaxation of strict control measures against COVID-19. This study aims to assess the impact of population migration in Spring Festival holiday on epidemic spread under different scenarios. Using inter-city population movement data, we construct the population flow network during the non-holiday time as well as the Spring Festival holiday. We build a large-scale metapopulation model to simulate the epidemic spread among 371 Chinese cities. We analyze the impact of Spring Festival travel on the peak timing and peak magnitude nationally and in each city. Assuming an R₀ (basic reproduction number) of 15 and the initial conditions as the reported COVID-19 infections on 17 December 2022, model simulations indicate that the Spring Festival travel can substantially increase the national peak magnitude of infection. The infection peaks arrive at most cities 1–4 days earlier as compared to those of the non-holiday time. While peak infections in certain large cities, such as Beijing and Shanghai, are decreased due to the massive migration of people to smaller cities during the pre-Spring Festival period, peak infections increase significantly in small- or medium-sized cities. For a less transmissible disease (R₀ = 5), infection peaks in large cities are delayed until after the Spring Festival. Small- or medium-sized cities may experience a larger infection due to the large-scale population migration from metropolitan areas. The increased disease burden may impose considerable strain on the healthcare systems in these resource-limited areas. For a less transmissible disease, particular attention needs to be paid to outbreaks in large cities when people resume work after holidays. Full article

Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)—that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model. Full article

The inhomogeneous population distribution appears as various population densities or different types of dynamics in distant sites of the extended habitat and may arise due to, for example, the resettlement features, the internal population structure, and the population dynamics synchronization mechanisms between adjacent subpopulations. In this paper, we propose the model of the spatio-temporal dynamics of two-age-structured populations coupled by migration (metapopulation) with long-range displacement. We study mechanisms leading to inhomogeneous spatial distribution as a type of cluster synchronization of population dynamics. To study the spatial patterns and synchronization, we use the method of constructing spatio-temporal profiles and spatial return maps. We found that patterns with spots or stripes are typical spatial structures with synchronous dynamics. In most cases, the spatio-temporal dynamics are mixed with randomly located single populations with strong burst (outbreak) of population size (solitary states). As the coupling parameters decrease, the number of solitary states grows, and they increasingly synchronize and form the clusters of solitary states. As a result, there are the several clusters with different dynamics. The appearance of these spatial patterns most likely occurs due to the multistability of the local age-structured population, leading to the spatio-temporal multistability. Full article

(1) Background: Is variation among the communities of a metacommunity higher than within the communities? Community ecologists and population geneticists often characterize the structure of metacommunities by partitioning variation (diversity) into the two following components using measures such as $F_{ST}$ or $G_{ST}$ and $\alpha$- and $\beta$-diversity. The within-communities component is usually some average of (type, species, genetic) diversities within the communities, and the among-communities component is the additive or multiplicative complement of the overall diversity. Such an among-communities component lacks independent conceptual specification, a matter of long-standing dispute. Only if the two components are independently and commensurably specified can the central question of comparability be answered meaningfully. (2) Methods: A novel approach to overcoming this conceptual weakness identifies two principles of the partitioning of variation among communities (concentration and division) then relates these principles to the common notions of variation (diversity) within and among communities, distinguishes primary indicators to quantify the partitioning principles, transforms the indicators into conceptually independent measures (indices) of variation within and among communities, and by this attains their commensurability and thus comparability. The application of the methods to quantifying the effects of evolutionary mechanisms is outlined. (3) Results: Common approaches are corrected and extended. (a) Analyses of metacommunity/metapopulation structures that rely on apportionment or related indices and take its complement to be differentiation yield incomparable measures of variation within and among communities. (b) The common practice of partitioning the total diversity into additive or multiplicative components produces the inconsistent ranking of the two components. (c) Community concentration and division can result from elementary processes of adaptive differentiation and migration (gene flow) among communities, where the (commensurable) amounts of community concentration and division reflect the relative participation of these processes in metacommunity structuring and translate directly into the measures of diversity within and among communities. (d) The modelling of the contributions of the two partitioning principles to the metacommunity structure is restricted by the marginal distributions of types and community affiliation. (e) The model demonstrates the degree to which adaptational processes at the metacommunity level are mixtures of adaptational events within and among communities. Full article

Whale migrations are poorly understood. Two competing hypotheses dominate the literature: 1. moving between feeding and breeding grounds increases population fitness, 2. migration is driven by dynamic environmental gradients, without consideration of fitness. Other hypotheses invoke communication and learned behaviors. In this article, their migration was investigated with a minimal individual-based model at the scale of the Global Ocean. Our aim is to test if global migration patterns can emerge from only the local, individual perception of environmental change. The humpback whale (Megaptera novaeangliae) meta-population is used as a case study. This species reproduces in 14 zones spread across tropical latitudes. From these breeding areas, humpback whales are observed to move to higher latitudes seasonally, where they feed, storing energy in their blubber, before returning to lower latitudes. For the model, we developed a simplified ethogram that conditions the individual activity. Then trajectories of 420 whales (30 per DPS) were simulated in two oceanic configurations. The first is a homogeneous ocean basin without landmasses and a constant depth of −1000 m. The second configuration used the actual Earth topography and coastlines. Results show that a global migration pattern can emerge from the movements of a set of individuals which perceive their environment only locally and without a pre-determined destination. This emerging property is the conjunction of individual behaviors and the bathymetric configuration of the Earth’s oceanic basins. Topographic constraints also maintain a limited connectivity between the 14 DPSs. An important consequence of invoking a local perception of environmental change is that the predicted routes are loxodromic and not orthodromic. In an ocean without landmasses, ecophysiological processes tended to over-estimate individual weights. With the actual ocean configuration, the excess weight gain was mitigated and also produced increased heterogeneity among the individuals. Developing a model of individual whale dynamics has also highlighted where the understanding of whales’ individual behaviors and population dynamic processes is incomplete. Our new simulation framework is a step toward being able to anticipate migration events and trajectories to minimize negative interactions and could facilitate improved data collection on these movements. Full article