Reduction In Population Size Research Articles

Current Status of Pulsatilla patens in Latvia—Population Size, Demographic and Seed Viability Indicators, Soil Parameters and Their Relationships

Pulsatilla patens (L.) Mill. (Eastern pasque flower) is classified as a highly endangered and declining species in Europe. The present research assessed the current status of P. patens in Latvia by collecting data on its distribution in historical places, Natura 2000 territories, and other areas, largely covering the entire country. We aimed to analyze the relationships between P. patens populations size, demographic indicators, and soil parameters, in order to gain knowledge on the impact of local ecological factors and optimal growth conditions, which are important for conservation and potential reintroduction. Although P. patens was not detected in more than a third of the surveyed 624 locations, more than 18 thousand individuals were recorded. Our results indicate that optimal growth conditions for P. patens occurred near highways, forest roads, and paths, that is, in places with reduced competition from other species and improved lighting conditions. The seed viability ranging from 22% to 62% can be considered potentially sufficient for the continuation of the species if enough flowering plants and moss-free spaces for germination are maintained. Although P. patens tolerates a broad soil pH range, in Latvia this species mainly grows in acidic sandy soils with an average pHKCl of 4.07. The soil parameters that most strongly positively correlated with P. patens regional population size and performance included higher soil pH level and plant available nutrient content, particularly P, K, Ca, Mg, Mn and B. Increased soil P and Mn levels significantly enhanced flowering, while high organic matter content could be associated with reduced population sizes. Despite its still large current population, long-term risks remain without active management. Conservation measures, such as creating open soil areas, where vegetation is removed and shading is reduced, are necessary to mitigate population decline.

Integrating a novel algorithm in assessing the impact of floods on the genetic diversity of a high commercial value fish (Cyprinidae: Spinibarbus sp.) in Lang Son province of Vietnam.

Floods, which occur when the amount of precipitation surpasses the capacity of an area to drain it adequately, have detrimental consequences on the survival and future generations of fishes. However, few works have reported the prediction of this natural phenomenon in a relation to certain fish species, especially in fast-flowing rivers. In the specific context of the northern mountainous provinces of Vietnam, where the Spinibarbus sp. fish species resides, it has been observed through the current study that the fish population in Lang Son exhibits the lowest genetic diversity and genetic distance. Consequently, the population of Spinibarbus sp. in Lang Son shows a heightened susceptibility to floods, resulting in reduction in population size and compromised population resilience. In order to provide decision support information for managers, conservationists, and researchers, we have employed a genetic algorithm-support vector machine regression (GA-SVR) predictive model to map flood vulnerability using thirteen dependent variables. The study findings have unveiled a significant negative correlation between flood-sensitive regions and genetic diversity. These discoveries emphasize the significance of considering the impact of floods on the genetic diversity of Spinibarbus sp. in Lang Son through flood vulnerability mapping. This underscores the value of establishing a comprehensive framework based on the GA-SVR algorithm for early flood detection, thereby facilitating the implementation of effective measures to minimize damages and conserve this commercial fish species.

Inferring the Demographic History of Aye-Ayes (Daubentonia madagascariensis) from High-Quality, Whole-Genome, Population-Level Data.

The nocturnal aye-aye, Daubentonia madagascariensis, is one of the most elusive lemurs on the island of Madagascar. The timing of its activity and arboreal lifestyle has generally made it difficult to obtain accurate assessments of population size using traditional census methods. Therefore, alternative estimates provided by population genetic inference are essential for yielding much needed information for conservation measures and for enabling ecological and evolutionary studies of this species. Here, we utilize genomic data from 17 individuals-including 5 newly sequenced, high-coverage genomes-to estimate this history. Essential to this estimation are recently published annotations of the aye-aye genome which allow for variation at putatively neutral genomic regions to be included in the estimation procedures, and regions subject to selective constraints, or in linkage to such sites, to be excluded owing to the biasing effects of selection on demographic inference. By comparing a variety of demographic estimation tools to develop a well-supported model of population history, we find strong support for two demes, separating northern Madagascar from the rest of the island. Additionally, we find that the aye-aye has experienced two severe reductions in population size. The first occurred rapidly, ∼3,000 to 5,000 years ago, and likely corresponded with the arrival of humans to Madagascar. The second occurred over the past few decades and is likely related to substantial habitat loss, suggesting that the species is still undergoing population decline and remains at great risk for extinction.

Genetic diversity and signatures of selection in Anglo-Arabian racehorses based on medium-density genotype data

The Anglo-Arabian horse breed (AA) is the result of crossing between Thoroughbred and Arabian horses renowned for versatility in show-jumping and racing. Southwest France and Sardinia in Italy are considered the historical nucleus of European AA breeding. Since little is known on genetic variability in the AA breed, especially those bred in the Sardinia region, this study investigates: i) inbreeding levels via pedigree and SNP genotyping; ii) runs of homozygosity (ROH) and selection signatures; iii) effective population size; and iv) distinctiveness of AA compared to Arabian and Thoroughbred horses. The ROH revealed an excessive amount of homozygosity, with a high kurtosis value observed in several chromosomes and an average inbreeding based on ROH of 21.1%. This latter value is higher compared to what was found based on pedigree data, highlighting the importance of complete pedigree records when estimating inbreeding based on genealogical data. The genomic inbreeding in AA horses was higher compared to other Italian horse breeds but lower than what was found in pure Thoroughbred and Arabian horses. This result suggests that the selection process for AA horses, may have prioritised racing ability over breed preservation which is the main goal in most local Italian breeds. The reduction in effective population size found in the latest 20 generations corroborates the potential presence of genetic diversity reduction due to selection in the AA breed. Remarkably, a large ROH island was identified on ECA17, which overlapped with two QTLs associated with racing performance.

Short-term responses of identified soil beneficial-bacteria to the insecticide fipronil: toxicological impacts.

Pesticides including insecticides are often applied to prevent distortion posed by plant insect pests. However, the application of these chemicals detrimentally affected the non-target organisms including soil biota. Fipronil (FIP), a broad-spectrum insecticide, is extensively used to control pests across the globe. The frequent usage calls for attention regarding risk assessment of undesirable effects on non-target microorganisms. Here, laboratory-based experiments were conducted to assess the effect of FIP on plant-beneficial bacteria (PBB); Rhizobium leguminosarum (Acc. No. PQ578652), Azotobacter salinestris (Acc. No. PQ578649) and Serratia marcescens (Acc. No. PQ578651). PBB synthesized growth regulating substances were negatively affected by increasing fipronil concentrations. For instance, at 100 µg FIPmL-1, a decrease in indole-3-acetic acid (IAA) synthesis by bacterial strains followed the order: A. salinestris (95.6%) S. marcescens (91.6%) > R. leguminosarum (87%). Also, exposure of bacteria cells to FIP hindered the growth and morphology of PBB observed as distortion, cracking, and aberrant structure under scanning electron microscopy (SEM). Moreover, FIP-treated and propidium iodide (PI)-stained bacterial cells displayed an insecticide dose-dependent increase in cellular permeability as observed under a confocal laser microscope (CLSM). Colony counts (log10 CFU mL-1) and growth of A. salinestris was completely inhibited at 150 µg FIPmL-1. The surface adhering ability (biofilm formation) of PBB was also disrupted/inhibited in a FIP dose-related manner. The respiration loss due to FIP was coupled with a reduction in population size. Fipronil at 150 µgmL-1 decreased cellular respiration in A. salinestris (72%) S. marcescens (53%) and R. leguminosarum (85%). Additionally, biomarker enzymes; lactate dehydrogenase (LDH), lipid peroxidation (LPO), and oxidative stress (catalase; CAT) induced by FIP represented significant (p ≤ 0.05) toxicity towards PBB strains. Conclusively, fipronil suggests a toxic effect that emphasizes their careful monitoring in soils before application and their optimum addition in the soil-plant system. It is high time to prepare both target-specific and slow-released agrochemical formulation for crop protection with concurrent safeguarding of soils.

Chromosomal-Level Genome Suggests Adaptive Constraints Leading to the Historical Population Decline in an Extremely Endangered Plant.

Increased human activity and climate change have significantly impacted wild habitats and increased the number of endangered species. Exploring evolutionary history and predicting adaptive potential using genomic data will facilitate species conservation and biodiversity recovery. Here, we examined the genome evolution of a critically endangered tree Pellacalyx yunnanensis, a plant species with extremely small populations (PSESP) that is narrowly distributed in Xishuangbanna, China. The species has neared extinction due to economic exploitation in recent decades. We assembled a chromosome-level genome of 334 Mb, with the N50 length of 20.5 Mb. Using the genome, we discovered that P. yunnanensis has undergone several population size reductions, leading to excess deleterious mutations. The species may possess low adaptive potential due to reduced genetic diversity and the loss of stress-responsive genes. We estimate that P. yunnanensis is the basal species of its genus and diverged from its relatives during global cooling, suggesting it was stranded in unsuitable environments during periods of dramatic climate change. In particular, the loss of seed dormancy leads to germination under unfavourable conditions and reproduction challenges. This dormancy loss may have occurred through genetic changes that suppress ABA signalling and the loss of genes involved in seed maturation. The high-quality genome has also enabled us to reveal phenotypic trait evolution in Rhizophoraceae and identify divergent adaptation to intertidal and inland habitats. In summary, our study elucidates mechanisms underlying the decline and evaluates the adaptive potential of P. yunnanensis to future climate change, informing future conservation efforts.

First record of a white phenotype Egyptian weasel (Mustela subpalmata) in Tahta, Sohag, Egypt

Abstract Aberrant pelage colourations have been occasionally detected in carnivores, including mustelids. However, to our knowledge, no cases of leucism have been reported in the genus Mustela to date. On May 1, 2024, an Egyptian weasel (Mustela subpalmata) killed by locals was discovered and photographed in the town of Tahta, Egypt. The specimen exhibited predominantly white fur on its left side and head. Although eye colour was not inspected, the normal lip colouration, dirty white fur hue, and dark brown tail tip suggest a case of leucism rather than albinism. Leucism is often linked to inbreeding caused by population isolation or size reduction, which could arise through urban development, considering that roads can act as social and physical barriers to gene flow. Genetic studies are needed to elucidate the population dynamics and conservation implications for urban Egyptian weasels.

Automated search of an optimal configuration of FETI-based algorithms with the swarm and evolutionary algorithms

Finite Element Tearing and Interconnecting (FETI) methods are used in the engineering community to solve extremely large engineering simulations on clusters and supercomputers with thousands of computational nodes. This paper focuses on minimizing the execution time of these methods by searching for an optimal configuration with swarm and evolutionary algorithms (SEAs). It incorporates optimization of an expensive cost function (taking tens to hundreds of seconds) of discrete search space with up to hundreds of thousands of combinations constrained by its boundaries and incompatible individuals (invalid combination of FETI solver parameters). In addition, the optimization occurs in real time, i.e., during a simulation. Hence, the number of objective function evaluations must remain low (tens to lower hundreds). The paper compares the performance of 3 basic SEAs with a reduced population size (Differential Evolution, Particle Swarm Optimization, and Self-Organizing Migrating Algorithm), 4 micro SEAs (Micro Differential Evolution Ray, Improved Micro-Particle Swarm Optimization, Micro-Particle Swarm Optimization, and Micro-Genetic Algorithm), and a random search on 4 distinct time-dependent simulations of a heat transfer. The experiments show that basic SEAs with a small population (about 5 individuals) and a penalty system as protection against incompatible configurations represent the most effective solution. One can use them to find the optimal configuration of FETI-based methods in approximately 130 evaluations. It can improve the utilization of expensive hardware resources of modern computational clusters.

Inferring the demographic history of aye-ayes (Daubentonia madagascariensis) from high-quality, whole-genome, population-level data.

The nocturnal aye-aye, Daubentonia madagascariensis, is one of the most elusive lemurs on the island of Madagascar. The timing of its activity and arboreal lifestyle has generally made it difficult to obtain accurate assessments of population size using traditional census methods. Therefore, alternative estimates provided by population genetic inference are essential for yielding much needed information for conservation measures and for enabling ecological and evolutionary studies of this species. Here, we utilize genomic data from 17 unrelated individuals - including 5 newly sequenced, high-coverage genomes - to estimate this history. Essential to this estimation are recently published annotations of the aye-aye genome which allow for variation at putatively neutral genomic regions to be included in the estimation procedures, and regions subject to selective constraints, or in linkage to such sites, to be excluded owing to the biasing effects of selection on demographic inference. By comparing a variety of demographic estimation tools to develop a well-supported model of population history, we find strong support for the species to consist of two demes, separating northern Madagascar from the rest of the island. Additionally, we find that the aye-aye has experienced two severe reductions in population size. The first occurred rapidly, approximately 3,000 to 5,000 years ago, and likely corresponded with the arrival of humans to Madagascar. The second occurred over the past few decades and is likely related to substantial habitat loss, suggesting that the species is still undergoing population decline and remains at great risk for extinction.

Repeated evolution on oceanic islands: comparative genomics reveals species-specific processes in birds

Understanding the interplay between genetic drift, natural selection, gene flow, and demographic history in driving phenotypic and genomic differentiation of insular populations can help us gain insight into the speciation process. Comparing patterns across different insular taxa subjected to similar selective pressures upon colonizing oceanic islands provides the opportunity to study repeated evolution and identify shared patterns in their genomic landscapes of differentiation. We selected four species of passerine birds (Common Chaffinch Fringilla coelebs/canariensis, Red-billed Chough Pyrrhocorax pyrrhocorax, House Finch Haemorhous mexicanus and Dark-eyed/island Junco Junco hyemalis/insularis) that have both mainland and insular populations. Changes in body size between island and mainland populations were consistent with the island rule. For each species, we sequenced whole genomes from mainland and insular individuals to infer their demographic history, characterize their genomic differentiation, and identify the factors shaping them. We estimated the relative (Fst) and absolute (dxy) differentiation, nucleotide diversity (π), Tajima’s D, gene density and recombination rate. We also searched for selective sweeps and chromosomal inversions along the genome. All species shared a marked reduction in effective population size (Ne) upon island colonization. We found diverse patterns of differentiated genomic regions relative to the genome average in all four species, suggesting the role of selection in island-mainland differentiation, yet the lack of congruence in the location of these regions indicates that each species evolved differently in insular environments. Our results suggest that the genomic mechanisms involved in the divergence upon island colonization—such as chromosomal inversions, and historical factors like recurrent selection—differ in each species, despite the highly conserved structure of avian genomes and the similar selective factors involved. These differences are likely influenced by factors such as genetic drift, the polygenic nature of fitness traits and the action of case-specific selective pressures.

Spatio-temporal heterogeneity and scenario prediction of influencing factors of transportation carbon emissions in the Yangtze River Economic Belt, China

Abstract Reducing carbon emissions in the transportation sector is a crucial aspect of China achieving its carbon peak and carbon neutrality goals. This study investigates the spatiotemporal differentiation characteristics of carbon emissions from transportation in the Yangtze River Economic Belt. Using the Geographically and Temporally Weighted Regression(GTWR) model to reveal the spatio-temporal heterogeneity of factors influencing transportation carbon emissions. Additionally, the Support Vector Regression(SVR) is trained to predict the carbon emissions reduction potential of transportation under different scenarios. The results showed that: From 2000 to 2021, the transportation emissions of the Yangtze River economic belt showed an overall upward trend. The high carbon emission regions are Jiangsu Province, Shanghai, Zhejiang Province and Hubei Province, and the emission center is located in Hubei Province. The total population, urbanization rate, per capita GDP, carbon emission intensity, passenger turnover volume, and civilian vehicle ownership all have a positive effect on transportation carbon emissions, while energy structure has a negative impact. Moreover, the influence of each factor exhibits significant spatial heterogeneity. Under three scenarios: baseline, low-carbon scenario I, and low-carbon scenario II, transportation carbon emissions in the Yangtze River Economic Belt are projected to peak by 2030. With the application of clean energy and a reduction in population size, low carbon scenario II demonstrates greater potential for carbon emission reduction, with a projected value of 88.552 million tons by 2032.

Shuffled multi-evolutionary algorithm with linear population size reduction

Shuffled multi-evolutionary algorithm with linear population size reduction

Combining graph theory and spatially-explicit, individual-based models to improve invasive species control strategies at a regional scale

ContextInvasive species cause widespread species extinction and economic loss. There is an increasing need to identify ways to efficiently target control efforts from local to regional scales.ObjectivesOur goal was to test whether prioritizing managed habitat using different treatments based on spatial measures of connectivity, including graph-theoretic measures, can improve management of invasive species and whether the level of control effort affects treatment performance. We also explored how uncertainty in biological variables, such as dispersal ability, affects measures performance.MethodsWe used a spatially-explicit, individual-based model (sIBM) based on the American bullfrog (Lithobates catesbeianus), a globally pervasive invasive species. Simulations were informed by geographic data from part of the American bullfrog’s non-native range in southeastern Arizona, USA where they are known to pose a threat to native species.ResultsWe found that total bullfrog populations and occupancy declined in response to all treatments regardless of effort level or patch prioritization methods. The most effective spatial prioritization was effort-dependent and varied depending on spatial context, but frequently a buffer strategy was most effective. Treatments were also sensitive to dispersal ability. Performance of treatments prioritizing habitat patches using betweenness centrality improved with increasing dispersal ability, while performance of eigenvalue centrality improved as dispersal ability decreased.ConclusionsWith the careful application of connectivity measures to prioritize control efforts, similar reductions in invasive species population size and occupancy could be achieved with less than half the effort of sub-optimal connectivity measures at higher effort rates. More work is needed to determine if trait-based generalities may define appropriate connectivity measures for specific suites of dispersal abilities, demographic traits, and population dynamics.

Differential evolution-based topology optimization using Karhunen-Loève expansion

Topology optimization is a widely adopted structural optimization approach that aims to maximize desired performance based on mathematical and physical principles. Gradient-based methods are common for obtaining optimal configurations; however, they struggle with problems involving numerous local optima and non-differentiable problems, which prevent the derivation of design sensitivity. To address these issues, this study explores the application of the Success History-Based Differential Evolution with Linear Population Size Reduction and Semi-Parameter Adaptation, which is regarded as LSHADE-SPA algorithm, known for its strong search capabilities. Nevertheless, its computational cost makes it impractical for topology optimization, especially as the number of design variables increases. To overcome this challenge, a novel structure representation method utilizing the Karhunen-Loève (KL) expansion is constructed. Applying truncated KL expansion instead of conventional density methods to represent structural configurations reduces the number of design variables. This article introduces a differential evolution-based topology optimization method utilizing truncated KL expansion and validates its effectiveness through applications in various structural and fluid problems. The method is particularly suited to highly nonlinear problems, such as negative Poisson's ratio structures and micro-mixer designs. The findings here indicate that differential evolution with truncated KL expansion can serve as an efficient and robust topology optimization method, proving particularly effective for solving optimization problems that are challenging for conventional gradient-based approaches. Notably, the proposed methodology not only delivers superior results but also alleviates computational burdens, thereby advancing the prospects of topology optimization in various engineering applications.

Population connectivity and size reductions in the Anthropocene: the consequence of landscapes and historical bottlenecks in white forsythia fragmented habitats

BackgroundWhite forsythia (Abeliophyllum distichum) is an endangered Korean Peninsula endemic that has been subjected to recent population genomics studies using SNPs via RAD sequencing. Here, we primarily employed the often underutilized haplotype information from RAD loci to further describe the species’ previously uninvestigated haplotype-based genomic variation and structure, and genetic-geographic characteristics and gene flow patterns among its five earlier identified genetic groups. We also inferred the time of past events that may have impacted the effective population size of these groups, as well as the species’ potential future distribution amidst the warming climate and anthropogenic threats.ResultsOur findings emphasized the recognition of the species’ regional patterns of genetic structure, and the role of topography and its associated gene flow patterns as some of the possible factors that may have influenced the species’ present-day fragmented population distribution. The inferred bottleneck events during the Anthropocene, some of which aligned with the time of historical catastrophic events on the Peninsula (e.g., the Korean War), were revealed to have contributed to the generally low effective population size of its five lineages, particularly those with marginal distributional range. Future distribution under both optimistic and pessimistic climatic scenarios suggests unlikely suitable habitats for these populations to expand from their current range limits, at least in the next 80 years.ConclusionsThe small effective population size and landscape-driven limited gene flow among white forsythia populations will remain a big challenge for the conservation management of the species’ already fragmented population distribution. To help mitigate these impacts, the merging of various research approaches and the use of genomic data to their full potential is recommended to provide the optimized knowledge-based tools for the conservation of this endangered species, and other similar plants under pressure.

Airfoil Optimization Using Deep Learning Models and Evolutionary Algorithms for the Case Large-Endurance UAVs Design

This article presents the development of the AZTLI-NN network and the evaluation of this network as a set of evolutionary algorithms in airfoil optimization tasks. AZTLI-NN has the characteristic of predicting the aerodynamic coefficients of the airfoils in the form of images (graphs of the aerodynamic coefficients as a function of the angle of attack) from parameter vectors corresponding to the parameterization method CST. This feature allows the network to achieve good performance when generalizing the predictions of the aerodynamic coefficients, being on par with neural networks that have the aerodynamic coefficients encoded in the form of structured data, and has the ability to handle a wide range of usage airfoils in general aviation. In addition, a case of how AZTLI-NN together with an adaptive evolutionary algorithm and population size reduction methods achieve good performance in finding the airfoil that provides the highest possible endurance value is shown, so this work is considered as an option in the early stages of the design for the selection of airfoils in the design of large-endurance UAVs.

A novel hybrid algorithm based on improved marine predators algorithm and equilibrium optimizer for parameter extraction of solar photovoltaic models

In the field of solar photovoltaic (PV) systems, the accurate and reliable extraction of parameters from PV models is crucial for effective simulation, evaluation, and control. Although various optimization algorithms have been widely used for parameter extraction in solar PV systems, the accuracy and reliability of the parameters extracted by these methods usually fall short of the expected standards. To address these shortcomings, a novel hybrid algorithm that combines the improved marine predators algorithm (MPA) with the equilibrium optimizer (EO), named IMPAEO, is proposed. In the IMPAEO, an elite opposition-based learning strategy is introduced to expand the exploration range of the algorithm to enhance population diversity; an adaptive weight coefficient is employed to improve the updating rule of the MPA, facilitating a balance between global exploration and local exploitation; the EO operator is integrated to enhance the performance of the MPA in the local exploitation phase to improve the solution quality and convergence speed; and the linear population size reduction (LPSR) technique is introduced to dynamically reduce the population size and improve the search performance. The effectiveness of the proposed IMPAEO is validated using the CEC2017 benchmark test suite, which is also applied to extract parameters of the single diode model, the double diode model, and three different PV modules. Comparative analysis with other algorithms demonstrates that the IMPAEO exhibits significant advantages in terms of solution quality, convergence speed, and algorithm stability. Consequently, the proposed IMPAEO not only proves to be efficient and reliable in parameter extraction for solar PV models but also offers a promising alternative in this field.

Conservation genomic study of Hopea hainanensis (Dipterocarpaceae), an endangered tree with extremely small populations on Hainan Island, China.

Hopea hainanensis Merrill & Chun is considered a keystone and indicator species in the tropical lowland rainforests of Hainan Island. Owing to its high-quality timber, H. hainanensis has been heavily exploited, leading to its classification as a first-class national protected plant in China and a plant species with extremely small populations (PSESPs). This study analyzed genome-wide single nucleotide polymorphisms obtained through restriction site-associated DNA sequencing from 78 adult trees across 10 H. hainanensis populations on Hainan Island. The nucleotide diversity of the sampled populations ranged from 0.00096 to 0.00138, which is lower than that observed in several other PSESPs and endangered tree species. Bayesian unsupervised clustering, principal component analysis, and neighbor-joining tree reconstruction identified three to five genetic clusters in H. hainanensis, most of which were geographically widespread and shared by multiple populations. Demographic history analysis based on pooled samples indicated that the decline in the H. hainanensis population began approximately 20,000 years ago, starting from an ancestral population size of approximately 10,000 individuals. The reduction in population size accelerated approximately 4,000 years ago and has continued to the present, resulting in a severely reduced population on Hainan Island. Intensified genetic drift in small and isolated H. hainanensis populations may contribute to moderate differentiation between some of them, as revealed by pairwise F st. In conclusion, our conservation genomic study confirms a severe population decline and an extremely low level of nucleotide variation in H. hainanensis on Hainan Island. These findings provide critical insights for the sustainable management and genetic restoration of H. hainanensis on Hainan Island.

Inbreeding and high developmental temperatures affect cognition and boldness in guppies (Poecilia reticulata).

Inbreeding impairs the cognitive abilities of humans, but its impact on cognition in other animals is poorly studied. For example, environmental stress (e.g. food limitation and extreme temperatures) often amplifies inbreeding depression in morphological traits, but whether cognition is similarly affected is unclear. We, therefore, tested if a higher temperature (30°C versus 26°C) during development exacerbates any difference in inhibitory control between inbred (f = 0.25) and outbred guppies (Poecilia reticulata). Inhibitory control is an aspect of cognition that is often measured in vertebrates using a detour test, in which animals have to navigate around a transparent barrier to reach a reward. We also tested if inbreeding and temperature affect 'boldness', which is a putative personality trait in guppies. Inbreeding lowered inhibitory control of guppies raised at the higher temperature but not those raised at the control temperature. Inbred fish were significantly less bold than outbred fish. In addition, males, but not females, raised at the higher temperature had significantly lower inhibitory control. There was no effect of temperature on the boldness of either sex. Our study is among the first to test if experimentally induced inbreeding impairs cognition in a non-domesticated vertebrate. We show that both inbreeding and higher temperatures during development can affect the behaviour and cognitive abilities of fish. These findings are noteworthy given the twin threats of rising global temperatures and more frequent inbreeding as habitat fragmentation reduces population sizes.

A triple population adaptive differential evolution

The Differential Evolution (DE) algorithm is one of the most efficient algorithms for complex numerical optimization. However, the nature of differential mutation and crossover hinders the individuals from a major change and always guides them toward their superior neighbors. There's a lack of useful directional information to help the population escape from early convergence. To solve the above problem, this paper proposes a novel Triple-population-based Adaptive Differential Evolution (TPADE) to enhance the evolutionary efficiency in solving various complex numerical optimization problems. First, a population division method with symmetrical linear reduction is designed to divide the parent population of each iteration into three sub-populations of different sizes, i.e., superior sub-population, medium sub-population, and inferior sub-population. Each sub-population adopts distinct differential mutation and crossover operators to maintain balanced search directions. Second, a superior-trial-preserved selection mechanism is proposed to screen useful directional information to guide the next iteration of evolution. Third, an effective parameter adaptation strategy is designed with the linear population size reduction strategy to avoid redundant search. Experiments are then conducted to show that the TPADE exhibits well performance compared with eleven state-of-the-art DE variants, CEC winners, and their variants on the CEC'2014, CEC'2017, and CEC'2022 benchmark suites. The C++ source code of TPADE can be downloaded from https://github.com/DoubleGong/TPADE.