Variation In Population Size Research Articles

Varying population size of the Cape Royds Adélie penguin colony, 1955–2020: a synthesis

AbstractAmong the longest Antarctic biological time series is that of Adélie penguin Pygoscelis adeliae population size at Cape Royds, 1955 to the present. Demographic trends over the 66 years fall into five periods: 1) decrease then recovery due to control of tourism from McMurdo Station/Scott Base; 2) further increase responding to the removal of > 20 000 trophically competing Antarctic minke whales Balaenoptera bonaerensis from the colony's wintering area; 3) stabilization but not decrease upon the ban of whaling in 1982, and whale recovery, owing to increased winds facilitating McMurdo Sound Polynya presence (easier ocean access during nesting); 4) decrease in 2001–2005 when two mega-icebergs, B15A/C16, opposed the wind effect by increasing sea-ice cover, thus limiting ocean access; and 5) after iceberg departure, minimal recovery due to the increased velocity of the wind-generated Ross Gyre reducing penguin breeding probability. A multivariant model using 1998–2018 data confirmed the roles of gyre speed (negative) and open water (positive) in colony growth. Additional negative influence came from high nest predation by south polar skuas Stercorarius maccormicki, reducing chick production, as well as perhaps increased trophic competition from nearby Weddell seals Leptonychotes weddellii. Clearly, long time series increase our understanding of penguin population dynamics responding to a complexity of factors.

Analysis of the Effect of Population on Unemployment in West Lombok Regency 2012-2021

This research is entitled "Analysis of the Effect of Population on Unemployment in West Lombok Regency 2012-2021". In this study, researchers wanted to see how the population compared to unemployment in West Lombok Regency from 2012-2021. This type of research is quantitative research with an associative approach. The analytical tool used in this research is simple linear regression analysis. The results of this research are an R-square value of 0.545 with a significance value (α) or alpha of 0.05 (5%). The R-square value in this study has meaning, namely that the influence of the population variable is able to explain the unemployment variable in West Lombok Regency by 54.5%. That is, partially the influence of the independent variable population size on unemployment in West Lombok Regency is not significant. This is indicated by the calculated T value being greater than the T table value. T calculated < T table (T calculated 0.737 > T table 2.306).

High germline mutation rates, but not extreme population outbreaks, influence genetic diversity in a keystone coral predator.

Lewontin's paradox, the observation that levels of genetic diversity (π) do not scale linearly with census population size (Nc) variation, is an evolutionary conundrum. The most extreme mismatches between π and Nc are found for highly abundant marine invertebrates. Yet, the influences of new mutations on π relative to extrinsic processes such as Nc fluctuations are unknown. Here, we provide the first germline mutation rate (μ) estimate for a marine invertebrate in corallivorous crown-of-thorns sea stars (Acanthaster cf. solaris). We use high-coverage whole-genome sequencing of 14 parent-offspring trios alongside empirical estimates of Nc in Australia's Great Barrier Reef to jointly examine the determinants of π in populations undergoing extreme Nc fluctuations. The A. cf. solaris mean μ was 9.13 x 10-09 mutations per-site per-generation (95% CI: 6.51 x 10-09 to 1.18 x 10-08), exceeding estimates for other invertebrates and showing greater concordance with vertebrate mutation rates. Lower-than-expected Ne (~70,000-180,000) and low Ne/Nc values (0.0047-0.048) indicated weak influences of population outbreaks on long-term π. Our findings are consistent with elevated μ evolving in response to reduced Ne and generation time length, with important implications for explaining high mutational loads and the determinants of genetic diversity in marine invertebrate taxa.

Multiobjective trajectory optimization algorithms for solving multi-UAV-assisted mobile edge computing problem

The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs using the k-means clustering algorithm. Then, a low-complexity greedy mechanism is used to generate the order of HPs assigned to each UAV, determining its trajectory. Several experiments are conducted to assess the effectiveness of the MTPAs with variable population size and cyclic selection mechanisms. The experimental findings demonstrate that the MTPAs with the cyclic selection mechanism outperform all competing algorithms, achieving better outcomes.

Assessing genome-wide adaptations associated with range expansion in the pink rice borer, Sesamia inferens.

Understanding the genetic basis of adaptive evolution following habitat expansion can have important implications for pest management. The pink rice borer (PRB), Sesamia inferens (Walker), is a destructive pest of rice that was historically restricted to regions south of 34°N latitude in China. However, with changes in global climate and farming practices, the distribution of this moth has progressively expanded, encompassing most regions in North China. Here, 3 highly differentiated subpopulations were discovered using high-quality single-nucleotide polymorphism and structural variant datasets across China, corresponding to northern, southern China regions, and the Yunnan-Guizhou Plateau, with significant patterns of isolation by geographic and environmental distances. Our estimates of evolutionary history indicate asymmetric migration with varying population sizes across the 3 subpopulations. Selective sweep analyses estimated strong selection at insect cuticle glycine-rich cuticular protein genes which are associated with enhanced desiccation adaptability in the northern group, and at the histone-lysine-N-methyltransferase gene associated with range expansion and local adaptation in the Shandong population. Our findings have significant implications for the development of effective strategies to control this pest.

Assessment of annual variability in the population status and reproductive cycle of purple sea urchins (Heliocidaris crassispina, Agassiz, 1864) in Daya Bay, China

The purple sea urchin (Heliocidaris crassispina) is one of the main drivers of ecosystem dynamics, and its reproductive cycle plays an important role in population structure and size variability. In this study, we analyzed the population structure, gonadal developmental characteristics, reproductive cycle and other factors influencing wild purple sea urchins from December 2021 to November 2022 in Daya Bay, Guangdong, China. The results showed that purple sea urchins showed an allometric growth pattern, there were obvious seasonal variations in the population size, and fishing pressure reduced the sea urchin populations; two-factor analysis of variance (ANOVA) and correlation analyses showed negative correlations between month (M), test diameter (TD), weight (W) and gonadal index (GI) (P> 0.01), and a positive correlation between TD, weight (W), and GI (P<0.01); the gonadal development process can be divided into recovery, growth, prematuration, maturation, and late discharge, with almost synchronous development of male and female gametes; the spawning period is from April to November, which is longer than in the temperate zones, showing two spawning peaks in April and August. The time of spawning is influenced by temperature and food. Data on the breeding cycle of sea urchins in Daya Bay were obtained in this study. This information can offer theoretical assistance in the preservation and management of both artificial nurseries and wild resources.

More management, less damage? With increasing population size, economic costs of managing geese to minimize yield losses may outweigh benefits

Conflicts between farmers and geese are intensifying; yet, it remains unclear how interactions between goose population size and management regimes affect yield loss and economic costs. We investigate the cost-effectiveness of accommodation and scaring areas in relation to barnacle goose (Branta leucopsis) population size. We use an existing individual-based model of barnacle geese foraging in nature, accommodation, and scaring areas in Friesland, the Netherlands, to study the most cost-effective management under varying population sizes (i.e., between 20 and 200% of the current size). Our study shows that population size non-linearly affects yield loss costs and total costs per goose. The most cost-effective management scenario for intermediate to large populations is to avoid scaring of geese. For small populations, intensive scaring resulted in minimized yield loss costs and total costs, but also substantially lower goose body mass. Our results strongly suggest that scaring becomes a less effective management measure as goose populations increase.

Design of a novel multimodal optimization algorithm and its application in logistics optimization

<abstract> <p>This study was purposed to design a multimodal continuous optimization algorithm based on a scheme agent to address the multidimensional complexity of optimization. An evolutionary sampling method of subarea exploration and multiple exploitations was developed by employing the scheme with variable population size so as to obtain higher optimization speed and accuracy. Second, the distribution plan was quantified into high-dimensional variable parameters based on the characteristics of logistics distribution optimization problems, and a high-dimensional discrete optimization model was constructed. Then, we identified and addressed the prominent issues and malignant virtual changes in the application of continuous algorithms to discrete problems. We have introduced a reasonable mutation mechanism during the optimization sampling process to mitigate this issue. Continuous real coordinate points were transformed across the neighborhood to standard discrete integer coordinate points by normalizing and logicizing the optimization sampling coordinates; also, the discretization of the continuous algorithm was realized. This approach could effectively prevent the algorithm from searching for targets in continuous optimization space, thereby fully reducing the complexity of the objective function distribution after conversion. The experiments showed that the transformed multimodal discrete optimization algorithm effectively addressed the optimization design problem of logistics distribution.</p> </abstract>

A Λ-Fleming-Viot type model with intrinsically varying population size

A Λ-Fleming-Viot type model with intrinsically varying population size

PEMODELAN DETERMINAN TINGKAT PENGANGGURAN TERDIDIK DI KABUPATEN SUMBAWA BARAT MENGGUNAKAN METODE REGRESI

This study aims to examine the determinants of the level of educated unemployment in West Sumbawa Regency in 2012-2021, which consists of population and minimum wages using the regression method. The type of this study was associative with the type of data used quantitative data in the form of population data, minimum wages and educated unemployment in West Sumbawa Regency for the last ten years, namely 2012-2021. The data used in this study were obtained from documents or archives that have been published on the website of the West Sumbawa Regency Central Statistics Agency. The collected data was analyzed using multiple linear regression analysis techniques, partial hypothesis testing (t test), simultaneous hypothesis testing (F test), and determinant coefficient test (R2). The results of this study showed that the population had no partial effect on the level of educated unemployment, while the minimum wage has a negative and significant effect on the level of educated unemployment in West Sumbawa Regency in 2012-2021. Simultaneously, population size and minimum wage had a significant effect on the level of educated unemployment in West Sumbawa Regency in 2012-2021. The ability of the variables population size and minimum wage to influence changes in the level of educated unemployment in West Sumbawa Regency in 2012-2021 is 42.8%, while the remaining 57.2% is influenced by other variables outside this research model, such as the education level, wages, age, gender, work experience, training, marital status, and area of residence.

A variable population size opposition-based learning for differential evolution algorithm and its applications on feature selection

A variable population size opposition-based learning for differential evolution algorithm and its applications on feature selection

Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size

A Bayesian network (BN) is a probabilistic graphical model that can model complex and nonlinear relationships. Its structural learning from data is an NP-hard problem because of its search-space size. One method to perform structural learning is a search and score approach, which uses a search algorithm and structural score. A study comparing 15 algorithms showed that hill climbing (HC) and tabu search (TABU) performed the best overall on the tests. This work performs a deeper analysis of the application of the adaptive genetic algorithm with varying population size (AGAVaPS) on the BN structural learning problem, which a preliminary test showed that it had the potential to perform well on. AGAVaPS is a genetic algorithm that uses the concept of life, where each solution is in the population for a number of iterations. Each individual also has its own mutation rate, and there is a small probability of undergoing mutation twice. Parameter analysis of AGAVaPS in BN structural leaning was performed. Also, AGAVaPS was compared to HC and TABU for six literature datasets considering F1 score, structural Hamming distance (SHD), balanced scoring function (BSF), Bayesian information criterion (BIC), and execution time. HC and TABU performed basically the same for all the tests made. AGAVaPS performed better than the other algorithms for F1 score, SHD, and BIC, showing that it can perform well and is a good choice for BN structural learning.

Haplotype-based inference of recent effective population size in modern and ancient DNA samples

Individuals sharing recent ancestors are likely to co-inherit large identical-by-descent (IBD) genomic regions. The distribution of these IBD segments in a population may be used to reconstruct past demographic events such as effective population size variation, but accurate IBD detection is difficult in ancient DNA data and in underrepresented populations with limited reference data. In this work, we introduce an accurate method for inferring effective population size variation during the past ~2000 years in both modern and ancient DNA data, called HapNe. HapNe infers recent population size fluctuations using either IBD sharing (HapNe-IBD) or linkage disequilibrium (HapNe-LD), which does not require phasing and can be computed in low coverage data, including data sets with heterogeneous sampling times. HapNe shows improved accuracy in a range of simulated demographic scenarios compared to currently available methods for IBD-based and LD-based inference of recent effective population size, while requiring fewer computational resources. We apply HapNe to several modern populations from the 1,000 Genomes Project, the UK Biobank, the Allen Ancient DNA Resource, and recently published samples from Iron Age Britain, detecting multiple instances of recent effective population size variation across these groups.

Variation in local population size predicts social network structure in wild songbirds.

The structure of animal societies is a key determinant of many ecological and evolutionary processes. Yet, we know relatively little about the factors and mechanisms that underpin detailed social structure. Among other factors, social structure can be influenced by habitat configuration. By shaping animal movement decisions, heterogeneity in habitat features, such as vegetation and the availability of resources, can influence the spatiotemporal distribution of individuals and subsequently key socioecological properties such as the local population size and density. Differences in local population size and density can impact opportunities for social associations and may thus drive substantial variation in local social structure. Here, we investigated spatiotemporal variation in population size at 65 distinct locations in a small songbird, the great tit (Parus major) and its effect on social network structure. We first explored the within-location consistency of population size from weekly samples and whether the observed variation in local population size was predicted by the underlying habitat configuration. Next, we created social networks from the birds' foraging associations at each location for each week and examined if local population size affected social structure. We show that population size is highly repeatable within locations across weeks and years and that some of the observed variation in local population size was predicted by the underlying habitat, with locations closer to the forest edge having on average larger population sizes. Furthermore, we show that local population size affected social structure inferred by four global network metrics. Using simple simulations, we then reveal that much of the observed social structure is shaped by social processes. Across different population sizes, the birds' social structure was largely explained by their preference to forage in flocks. In addition, over and above effects of social foraging, social preferences between birds (i.e. social relationships) shaped certain network features such as the extent of realized social connections. Our findings thus suggest that individual social decisions substantially contribute to shaping certain social network features over and above effects of population size alone.

Risk of Hysterectomy After Endometrial Ablation: A Systematic Review and Meta-analysis

ABSTRACT A common gynecological problem for approximately 30% of women at reproductive age in European countries is heavy menstrual bleeding (HMB). Although hysterectomy is a highly successful treatment for this benign problem, it also risks serious complications due to its nature as a major operation. Less invasive HMB treatment options include insertion of a levonorgestrel-releasing intrauterine system, medical treatment (such as tranexamic acid and oral contraceptive pill), or endometrial ablation (which aims to destroy endometrial tissue and the superficial myometrium to reduce/stop menstrual bleeding). Endometrial ablation failure may result in the objective outcome of hysterectomy. This meta-analysis and review aimed to assess hysterectomy risk following nonresectoscopic endometrial ablation treatment to improve understanding and HMB patient counseling. Various nonresectoscopic ablation techniques versus their associated hysterectomy rates were investigated, and subgroup analyses were performed. Following a comprehensive and thorough search process of the MEDLINE, CENTRAL, and EMBASE databases, 53 articles ultimately met the inclusion criteria for inclusion in the systematic review. Between 1992 and 2017, in the included studies, 48,071 patients underwent endometrial ablation. A high risk of bias was found in 13 studies (mainly due to selection or reporting bias), whereas 12 studies maintained low risk of bias. However, exclusion of the 13 high-risk studies for a subgroup analysis yielded similar results to the original meta-analysis. Results of the analysis indicated a consistently increasing post–endometrial ablation hysterectomy, with 2% increments annually between 1 and 5 years following the procedure, rising to 4.3% after 1 year and 12.4% after 5 years. In 2 studies, a post–10-year follow-up found a mean hysterectomy rate of 21.3%. Between both various study designs and the different varieties of devices used, no major differences in hysterectomy rates were found, respectively. Limitations of the review include a high risk for heterogeneity found among studies in almost all analyses utilized by this analysis. Publication bias and methodological issues (variation of population size and study type) lent to the heterogeneity. Because of this variation, the authors performed analyses of subgroups with different study designs. In addition, of the 53 studies included, 15 of them included fewer than 50 participants, which was corrected in this analysis via an inverse variance. Overall, the study indicated that hysterectomy risk following endometrial ablation increases from 4.3% at the 1-year mark to 12.4% at the post–5-year mark. Neither differences in nonresectoscopic endometrial ablation techniques nor study design seemed to affect hysterectomy rates. This systematic review's data can be applied to clinical practice and used for counseling patients about hysterectomy risks within 5 years of endometrial ablation.

'Evolutionary poker': an agent-based model of interactome emergence and epistasis tested against Lenski's long-term E. coli experiments.

A simple agent-based model is presented that produces results matching the experimental data found by Lenski's group for ≤50,000 generations of Escherichia coli bacteria under continuous selective pressure. Although various mathematical models have been devised previously to model the Lenski data, the present model has advantages in terms of overall simplicity and conceptual accessibility. The model also clearly illustrates a number of features of the evolutionary process that are otherwise not obvious, such as the roles of epistasis and historical contingency in adaptation and why evolution is time irreversible ('Dollo's law'). The reason for this irreversibility is that genomes become increasingly integrated or organized, and this organization becomes a novel selective factor itself, against which future generations must compete. Selection for integrated or synergistic networks, systems or sets of mutations or traits, not for individual mutations, confers the main adaptive advantage. The result is a punctuated form of evolution that follows a logarithmic occurrence probability, in which evolution proceeds very quickly when interactomes begin to form but which slows as interactomes become more robust and the difficulty of integrating new mutations increases. Sufficient parameters exist in the game to suggest not only how equilibrium or stasis is reached but also the conditions in which it will be punctuated, the factors governing the rate at which genomic organization occurs and novel traits appear, and how population size, genome size and gene variability affect these.

Genomic diversity landscapes in outcrossing and selfing Caenorhabditis nematodes.

Caenorhabditis nematodes form an excellent model for studying how the mode of reproduction affects genetic diversity, as some species reproduce via outcrossing whereas others can self-fertilize. Currently, chromosome-level patterns of diversity and recombination are only available for self-reproducing Caenorhabditis, making the generality of genomic patterns across the genus unclear given the profound potential influence of reproductive mode. Here we present a whole-genome diversity landscape, coupled with a new genetic map, for the outcrossing nematode C. remanei. We demonstrate that the genomic distribution of recombination in C. remanei, like the model nematode C. elegans, shows high recombination rates on chromosome arms and low rates toward the central regions. Patterns of genetic variation across the genome are also similar between these species, but differ dramatically in scale, being tenfold greater for C. remanei. Historical reconstructions of variation in effective population size over the past million generations echo this difference in polymorphism. Evolutionary simulations demonstrate how selection, recombination, mutation, and selfing shape variation along the genome, and that multiple drivers can produce patterns similar to those observed in natural populations. The results illustrate how genome organization and selection play a crucial role in shaping the genomic pattern of diversity whereas demographic processes scale the level of diversity across the genome as a whole.

The impact of COVID-19 on life expectancy among four Asian American subgroups

Background and objectiveTo date, the impact of the COVID-19 pandemic on life expectancy for the Asian American (AA) population has been reported in aggregate. This study provides estimates of life expectancy at birth before and during the pandemic, with a set of demographic, health, and socioeconomic risk factors for the four largest subgroups: Asian Indians, Chinese, Filipinos, and Vietnamese. These estimates are placed in context of the broader U.S. population. MethodsThis study uses age-specific all-cause mortality from CDC WONDER and population counts from the American Community Surveys. We apply methodologies to address variability in population sizes over time (smoothing) and data quality issues at older ages (Brass relational model life table system) to produce three sets of sex-specific life expectancy estimates by subgroup for 2019, 2020, and 2021. ResultsAlmost all estimates show that the four AA subgroups experienced greater losses between 2019 and 2020 than Whites. These losses ranged from 1.1 to 3.9 years, with the largest drops among Chinese women (2.0–2.4) and Filipino men (3.5–3.9). Under all scenarios, losses in life expectancy were considerably smaller in 2021 than in 2020 among the four subgroups – with several subgroups experiencing a modest rebound – but these improvements did not compensate for the large increases in death rates in 2020. Filipino men had the largest decline in life expectancy from 2019 to 2021 among the four subgroups (3.4–4.2 years), and Vietnamese men and women experienced large losses which continued into the second year of the pandemic. ConclusionsDespite high pre-pandemic life expectancies, AAs suffered a large, and rarely acknowledged, increase in mortality during the pandemic, with substantial heterogeneity across subgroups and between women and men. This variability is due in part to the pronounced differences in risk factors for infection and severity which have been documented within the AA population.

Efficient Evaluation of Incident Power Density by Millimeter-Wave MIMO User Equipment Using Vectorized Field Superposition and Stochastic Population Optimizers

In this work, stochastic population optimizers are applied with vectorized field values to determine the peak exposure of user equipment (UE) installed with a multiple-input multiple-output (MIMO) antenna operating at 28 GHz. This problem presents a significant challenge due to the difficulty in evaluating all possible configurations of the input values. By aids of optimization on initial population size as well as the sampling spacing, two optimizers incorporating variable population size exhibit good robustness and efficiency, i.e., an error of 1% compared to the results by Monte Carlo (MC) simulation of 1 000 000 times. A reduction in the simulation time of 90% compared to MC simulation of 10 000 times. The proposed method demonstrates its performance in evaluating exposure at both planar and nonplanar surface according to the standards. Besides, the method can be applied in postprocessing of the numerical simulation and the measured data for maximized spatial-average power density. The study provides a new and easy to implement method for efficiently characterizing the maximum exposure from millimeter wave MIMO UE.

Population size affected by environmental variability impacts genetics, traits, and plant performance in Trifolium montanum L.

Population size, genetic diversity, and performance have fundamental importance for ecology, evolution, and nature conservation of plant species. Despite well-studied relationships among environmental, genetic, and intraspecific trait variation (ITV), the influence of population size on these aspects is less understood. To assess the sources of population size variation, but also its impact on genetic, functional trait, and performance aspects, we conducted detailed population size estimations, assessed 23 abiotic and biotic environmental habitat factors, performed population genetic analyses using nine microsatellite markers, and recorded nine functional traits based on 260 Trifolium montanum individuals from 13 semi-dry grassland locations of Central Europe. Modern statistical analyses based on a multivariate framework (path analysis) with preselected linear regression models revealed that the variation of abiotic factors (in contrast to factors per se) almost completely, significantly explained fluctuations in population size (R 2 = .93). In general, abiotic habitat variation (heterogeneity)was not affected by habitat area. Population size significantly explained genetic diversity(N A: R 2 = .42, H o: R 2 = .67, H e: R 2 = .43, and I: R 2 = .59), inbreeding (F IS: R 2 = .35), and differentiation (G ST: R 2 = .20). We also found that iFDCV (ITV) was significantly explained by abiotic habitat heterogeneity, and to a lesser extent by genetic diversity H e (R 2 = .81). Nevertheless, habitat heterogeneity did not statistically affect genetic diversity. This may be due to the use of selectively neutral microsatellite markers, and possibly by insufficient abiotic selective pressures on habitats examined. Small T. montanum populations in nonoptimal habitats were characterized by reduced genetic and functional trait diversity, and elevated genetic inbreeding and differentiation. This indicates reduced adaptability to current and future environmental changes. The long-term survival of small populations with reduced genetic diversity and beginning inbreeding will be highly dependent on habitat protection and adequate land-use actions.