Population Research Articles

Multi-input Fourier neural network and its sparrow search optimization

In engineering applications, the back-propagation (BP) neural network often encounters many limitations due to its slow convergence and high noise sensitivity, and meanwhile the reported Fourier neural networks have no ability to extract the features of multi-attribute input data. Hereby, This work proposes a gradient descent-based multi-input Fourier neural network after integrating the multi-layer perceptron with an overlapping Fourier neural network. Thereafter, related to the difficulty of deciding the global optimal parameter settings, an improved sparrow search algorithm is developed to optimize the parameter settings and solve high dimensional function optimization problems, after the Cat chaotic map and the mechanisms of population-size adjustment and parameter adaptiveness are designed to promote the sparrow search algorithm's ability to balance global exploration and local exploitation. The theoretical analysis shows that the improved algorithm's computational complexity is decided by its population size and the optimization problem's dimension. Numerically comparative experiments have validated that not only the acquired Fourier neural network can effectively extract the features of multi-attribute data with strong generalization ability, but also the improved algorithm has significant advantages in coping with high dimensional function optimization problems.

Resilience of genetic diversity in forest trees over the Quaternary

The effect of past environmental changes on the demography and genetic diversity of natural populations remains a contentious issue and has rarely been investigated across multiple, phylogenetically distant species. Here, we perform comparative population genomic analyses and demographic inferences for seven widely distributed and ecologically contrasting European forest tree species based on concerted sampling of 164 populations across their natural ranges. For all seven species, the effective population size, Ne, increased or remained stable over many glacial cycles and up to 15 million years in the most extreme cases. Surprisingly, the drastic environmental changes associated with the Pleistocene glacial cycles have had little impact on the level of genetic diversity of dominant forest tree species, despite major shifts in their geographic ranges. Based on their trajectories of Ne over time, the seven tree species can be divided into three major groups, highlighting the importance of life history and range size in determining synchronous variation in genetic diversity over time. Altogether, our results indicate that forest trees have been able to retain their evolutionary potential over very long periods of time despite strong environmental changes.

Population Genomics of Adaptive Radiations: Exceptionally High Levels of Genetic Diversity and Recombination in an Endemic Spider From the Canary Islands.

The spider genus Dysdera has undergone a remarkable diversification in the oceanic archipelago of the Canary Islands, with ~60 endemic species having originated during the 20 million years since the origin of the archipelago. This evolutionary radiation has been accompanied by substantial dietary shifts, often characterised by phenotypic modifications encompassing morphological, metabolic and behavioural changes. Hence, these endemic spiders represent an excellent model for understanding the evolutionary drivers and to pinpoint the genomic determinants underlying adaptive radiations. Recently, we achieved the first chromosome-level genome assembly of one of the endemic species, D. silvatica, providing a high-quality reference sequence for evolutionary genomics studies. Here, we conducted a low coverage-based resequencing study of a natural population of D. silvatica from La Gomera island. Taking advantage of the new high-quality genome, we characterised genome-wide levels of nucleotide polymorphism, divergence and linkage disequilibrium, and inferred the demographic history of this population. We also performed comprehensive genome-wide scans for recent positive selection. Our findings uncovered exceptionally high levels of nucleotide diversity and recombination in this geographically restricted endemic species, indicative of large historical effective population sizes. We also identified several candidate genomic regions that are potentially under positive selection, highlighting relevant biological processes, such as vision and nitrogen extraction as potential adaptation targets. These processes may ultimately drive species diversification in this genus. This pioneering study of spiders that are endemic to an oceanic archipelago lays the groundwork for broader population genomics analyses aimed at understanding the genetic mechanisms driving adaptive radiation in island ecosystems.

Reducing ACO Population Size to Increase Computational Speed

Ant Colony Optimization (ACO) is a powerful metaheuristic algorithm widely used to solve complex optimization problems in production and logistics. This paper presents a methodology for enhancing the ACO performance when applied to Traveling Salesman Problems (TSP). By reducing the number of ants in the colony, the algorithm's computational speed improves but solution quality is sacrificed. An optimal number of ants to produce the best results in the shortest time is specific to the problem at hand and can't be defined generally. This paper investigates the effect of ant population reduction relative to the problem size by measuring its impact on solution quality and execution time. Results show that for certain problem sizes ant population and execution time can be halved with practically no reduction in solution quality, or they can be reduced 5 times at the price of slightly worse solution quality. Reduction of ant population is much more impactful on reduction of execution time than it is on solution quality.

Contrasting Genomic Diversity and Inbreeding Levels Among Two Closely Related Falcon Species With Overlapping Geographic Distributions.

Genomic resources are valuable to examine historical demographic patterns and their effects to better inform management and conservation of threatened species. We evaluated population trends and genome-wide variation in the near-threatened Orange-breasted Falcon (Falco deiroleucus) and its more common sister species, the Bat Falcon (F. rufigularis), to explore how the two species differ in genomic diversity as influenced by their contrasting long-term demographic histories. We generated and aligned whole genome resequencing data for 12 Orange-breasted Falcons and 9 Bat Falcons to an annotated Gyrfalcon (F. rusticolus) reference genome that retained approximately 22.4 million biallelic autosomal SNPs (chromosomes 1-22). Our analyses indicated much lower genomic diversity in Orange-breasted Falcons compared to Bat Falcons. All sampled Orange-breasted Falcons were significantly more inbred than the sampled Bat Falcons, with values similar to those observed in island-mainland species comparisons. The distribution of runs of homozygosity showed variation suggesting long-term low population size and the possibility of bottlenecks in Orange-breasted Falcons contrasting with consistently larger populations in Bat Falcons. Analysis of genetic load suggests that Orange-breasted Falcons are less likely to experience inbreeding depression than Bat Falcons due to reduced inbreeding load but are at elevated risk from fixation of deleterious gene variants and perhaps a reduced adaptive potential. These genomic analyses highlight differences in the historical demography of two closely related species that have influenced their current genomic diversity and should result in differing strategies for their continued conservation.

Effects of selection stringency on the outcomes of directed evolution.

Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.

Comparative Study of Single-Trait and Multi-Trait Genomic Prediction Models.

Conventional genomic selection models trait individually, neglecting complex trait interactions. Multi-trait models address this by considering genetic correlations, thus improving breeding value accuracy. Despite their theoretical benefits, quantifying these models' breeding advantages across genetic backgrounds is essential. This study evaluates the benefits of multi-trait models under varying population sizes and three levels of genetic correlations (low, medium, high) using simulations based on 50 K chip data from 5000 individuals. In equal heritability scenarios, the multi-trait GBLUP model consistently outperforms single-trait models, with breeding advantages increasing with heritability. For example, with a reference population of 4500, improvements range from 0.3% to 4.1%. Notably, trait combinations with low heritability are insensitive to changes in genetic correlation, with gains remaining ≤ 0.1% across different genetic correlations under low heritability conditions. In differing heritability scenarios, the multi-trait model's benefits vary, particularly enhancing low-heritability traits when paired with high-heritability ones. Additionally, modeling time increases as genetic correlation decreases. The results of this study indicate that multi-trait models improve breeding accuracy but require more modeling time and place higher demands on algorithms and software. We recommend breeding strategies tailored to different phenotypes and genetic backgrounds to balance efficiency and accuracy.

Economic Development of the Major Cities in the Context of Demographic Challenges

In recent years, Russia has faced serious global challenges. Particular attention is required for demographic challenges, which are caused by natural population decline, ambiguous migration processes, and their exacerbation due to the spread of the coronavirus infection and subsequently the aggravation of the foreign policy situation. The largest Russian cities are centers of economic growth with significant potential for solving regional livelihood problems. An important indicator of the state and development of a city as a socio-economic system is the change in population size. The dynamics of demographic and other socio-economic indicators deserve detailed analysis and study. The results obtained must be considered when making strategic decisions and developing adaptation mechanisms. In the past five years, a natural population decline has prevailed in the studied largest municipalities, which is only partially offset by migration growth. In most cities with a population of over one million, there is a correlation between the dynamics of population growth (both natural and migration), the average wage level, and the quality of the urban environment. In cities with migration outflow, measures are needed to improve these parameters. Since all cities have developed industries, including due to natural and historical factors, systematic monitoring of the relevant indicators and a detailed analysis of the reasons for negative trends, including demographic ones, are important for ensuring balanced economic development. To ensure the balance of supply and demand in the labor market in terms of qualification and professional requirements and expectations, the implementation of regional programs and projects with the participation of government and business is proposed. In the context of the economic development of the largest cities and adaptation to global challenges, it is proposed that government authorities, local governments, the scientific community, and businesses identify new promising specializations for cities, including the need to enhance import substitution and implement high-tech, science-intensive industries.

Proteomic and metabolomic profiles of plasma-derived Extracellular Vesicles differentiate melanoma patients from healthy controls

BackgroundPlasma-derived Extracellular Vesicles (EVs) have been suggested as novel biomarkers in melanoma, due to their ability to reflect the cell of origin and ease of collection. This study aimed to identify novel EV biomarkers that can discriminate between disease stages. This was achieved by characterising the plasma-derived EVs of patients with melanoma, and comparing their proteomic and metabolomic profile to those from healthy controls. MethodsEVs were isolated from the plasma of 36 patients with melanoma and 13 healthy controls using Size Exclusion Chromatography. Proteomic and Metabolomic Analyses were performed, and machine learning algorithms were used to identify potential proteins and metabolites to differentiate the plasma-derived EVs from melanoma patients of different disease stages. ResultsThe concentration and size of the EV population isolated was similar between groups. Proteins (APOC4, PRG4, PLG, TNC, VWF and SERPIND1) and metabolites (lyso PC a C18:2, PC ae C44:3) previously associated with melanoma pathogenesis were identified as relevant in differentiating between disease stages. ConclusionThe results further support the continued investigation of circulating plasma-derived EVs as biomarkers in melanoma. Furthermore, the potential of combined proteo-metabolomic signatures for differentiation between disease stages may provide valuable insights into early detection, prognosis, and personalised treatment strategies.

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Abstract Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (0.25 m tall) varied between sites within 0.52 < < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Too big to purge: persistence of deleterious Mutations in Island populations of the European Barn Owl (Tyto alba).

A key aspect of assessing the risk of extinction/extirpation for a particular wild species or population is the status of inbreeding, but the origin of inbreeding and the current mutational load are also two crucial factors to consider when determining survival probability of a population. In this study, we used samples from 502 barn owls from continental and island populations across Europe, with the aim of quantifying and comparing the level of inbreeding between populations with differing demographic histories. In addition to comparing inbreeding status, we determined whether inbreeding is due to non-random mating or high co-ancestry within the population. We show that islands have higher levels of inbreeding than continental populations, and that this is mainly due to small effective population sizes rather than recent consanguineous mating. We assess the probability that a region is autozygous along the genome and show that this probability decreased as the number of genes present in that region increased. Finally, we looked for evidence of reduced selection efficiency and purging in island populations. Among island populations, we found an increase in numbers of both neutral and deleterious minor alleles, possibly as a result of drift and decreased selection efficiency but we found no evidence of purging.

Mutation potentiates migration swamping in polygenic local adaptation.

Locally adapted traits can exhibit a wide range of genetic architectures, from pronounced divergence at a few loci to small frequency divergence at many loci. The type of architecture that evolves depends strongly on the migration rate, as weakly selected loci experience swamping and do not make lasting contributions to divergence. Simulations from previous studies showed that even when mutations are strongly selected and should resist migration swamping, the architecture of adaptation can collapse and become transient at high mutation rates. Here, we use an analytical two-population model to study how this transition in genetic architecture depends upon population size, strength of selection, and parameters describing the mutation process. To do this, we develop a mathematical theory based on the diffusion approximation to predict the threshold mutation rate above which the transition occurs. We find that this performs well across a wide range of parameter space, based on comparisons with individual-based simulations. The threshold mutation rate depends most strongly on the average effect size of mutations, weakly on the strength of selection, and marginally on the population size. Across a wide range of the parameter space, we observe that the transition to a transient architecture occurs when the trait-wide mutation rate is 10-3-10-2, suggesting that this phenomenon is potentially relevant to complex traits with a large mutational target. On the other hand, based on the apparent stability of genetic architecture in many classic examples of local adaptation, our theory suggests that per-trait mutation rates are often relatively low.

Lost in space and time: robust demography and enhanced resilience buffer adverse environmental effects in a highly isolated and sedentary pre-pleistocene landscape vertebrate

BackgroundFew animal populations have been studied under the framework of the OCBIL theory, which addresses the ecology and evolution of biodiversity on old climatically buffered infertile landscapes. Available genetic data challenge the low connectivity and high genetic differentiation predicted for isolated tepui-summit vertebrate communities, suggesting potential dispersal among summits. However, the OCBIL theory posits reduced dispersibility, enhanced resilience to habitat fragmentation and inbreeding due to small populations. We tested these hypotheses by conducting the first analytic evaluation of the spatial ecology and population biology of a tepui-summit vertebrate at multiple spatial scales.ResultsWe used harmonic radar tracking (100 individuals/448 points of contact) and capture-mark-recapture data (596 individuals captured/52 recaptured) to reveal the temporal niche, microhabitat use, population size, and dispersal abilities of the tepui-summit endemic toad Oreophrynella quelchii on Roraima-tepui. Abundance was determined using a closed population model incorporating sources of variation in capture probability. We tested the relative influence of biotic and abiotic variables on distances moved through model selection. Our data indicate that the population size of O. quelchii is remarkably large (ca. 12 million individuals), with strong seasonal demographic fluctuations. Ecology and observed limited spatial movements challenge the likelihood of active dispersal among tepui tops in this species. Our results are counter to those predicted by the available genetic data but support two hypotheses of the OCBIL theory: reduced dispersibility and enhanced resilience. However, they do not support the expectation of a small refugial population size.ConclusionWe postulate that the insular, hostile tepui-summit environment tends to produce robust demographic populations, likely to buffer stochastic adverse environmental effects, rather than diversity, as observed in much younger post-Pleistocene Neotropical landscapes. Our results draw attention to the value of faunal studies using an OCBIL framework to better understand the ecology and evolution of this unique biota worldwide.

The influence of a coordination training model that uses various manipulative movements on futsal kick accuracy

Achieving precise kick accuracy and coordinated movements in futsal was essential for competitive success. This study aimed to evaluate the differences between various futsal training approaches that incorporated distinct coordination exercises involving manipulative movements, specifically assessing their impact on kick accuracy. A quasi-experimental design with a control group was employed, focusing on shooting skills within the futsal context. The population consisted of 70 members from the Universitas Muhammadiyah Kalimantan Timur (UMKT) futsal team, from which 40 players were purposively selected based on their participation duration of over six months and their tier status within the university's core teams. Participants were divided into two groups: one received static coordination training (control group), and the other underwent dynamic coordination training (experimental group), each comprising 20 players. The intervention spanned 18 sessions, including pre- and post-tests. Data were collected through a modified shooting kick test at the futsal goal, utilizing a point distribution system. Analysis revealed significant differences in kick performance between the two training methods, with dynamic coordination training yielding superior results (t-count = 3.010 t-table = 2.024). These findings indicated that dynamic coordination exercises were more effective than static ones in enhancing futsal kick accuracy. Future research should have considered expanding the population and sample size to include amateur players, introducing additional variables and diverse training methodologies, extending training duration and frequency, conducting long-term evaluations, measuring shooting technique quality, and employing quantitative research methods. By addressing these suggestions, subsequent studies could contribute more comprehensively to the understanding of effective training strategies in futsal.

Chromosome-level genome of diamondback terrapin provides insight into the genetic basis of salinity adaptation.

Diamondback terrapins (Malaclemys terrapin centrata) exhibit strong environmental adaptability and live in both freshwater and saltwater. However, the genetic basis of this adaptability has not been the focus of research. In this study, we successfully constructed a ∼2.21-Gb chromosome-level genome assembly for M. t. centrata using high-coverage and high-depth genomic sequencing data generated on multiple platforms. The M. t. centrata genome contains 25 chromosomes and the scaffold N50 of ∼143.75 Mb, demonstrating high continuity and accuracy. In total, 53.82% of the genome assembly was composed of repetitive sequences, and 22435 protein-coding genes were predicted. Our phylogenetic analysis indicated that M. t. centrata was closely related to the red-eared slider turtle (Trachemys scripta elegans), with divergence approximately ∼23.6 million years ago (Mya) during the early Neogene period of the Cenozoic era. The population size of M. t. centrata decreased significantly over the past ∼14 Mya during the Cenozoic era. Comparative genomic analysis indicated that 36 gene families related to ion transport were expanded and several genes (AQP3, solute carrier subfamily, and potassium channel genes) underwent specific amino acid site mutations in the M. t. centrata genome. Changes to these ion transport-related genes may have contributed to the remarkable salinity adaptability of diamondback terrapin. The results of this study not only provide a high-quality reference genome for M. t. centrata but also elucidate the possible genetic basis for salinity adaptation in this species.

Hybridization and inbreeding affect the survival of a critically endangered coral

Coral reefs face escalating pressures leading to unprecedented declines in the populations of reef-building corals.1,2 Conservation genomic studies are critical in understanding and formulating interventions to reverse such declines but thus far have only focused on a handful of broadcast-spawning species in shallow waters.3,4 The recent International Union for Conservation of Nature (IUCN) reclassification of six different brooding corals as "critically endangered"5 indicates that such species are equally threatened. However, we lack a thorough understanding of the factors underlying their decline. Here, we comprehensively examine the genetic impacts of a severe population decline in the brooding coral Helioseris cucullata, once a major contributor to Caribbean reefs but now critically endangered.6,7,8 Genome-wide sequencing of colonies across five locations revealed the presence of two distinct species, indicating that the remaining population sizes are even smaller than previously estimated. Using an exhaustive, spatially explicit sampling approach (across a total of ∼2.5 hectares), we observed extremely low genetic diversity and identified how localized dispersal, excessive inbreeding, and prevalent asexual reproduction may contribute to an extinction vortex. However, we also found evidence for recent hybridization and introgression, providing an avenue for the reintroduction of genetic diversity into both lineages. With many brooding species now under threat, these results highlight the critical need to assess the genetic processes associated with their declines so that these can be harnessed or mitigated to deliver effective conservation outcomes.

A Butterfly Algorithm That Combines Chaos Mapping and Fused Particle Swarm Optimization for UAV Path Planning

Effective path planning is essential for autonomous drone flight to enhance task efficiency. Many researchers have applied swarm intelligence algorithms to drone path planning. For instance, the traditional Butterfly Optimization Algorithm (BOA) has been used for this purpose. However, traditional BOA faces challenges such as slow convergence and susceptibility to being trapped in local optima. An Improved Butterfly Optimization Algorithm (IBOA) has been developed to identify optimal routes to address these limitations. Initially, ICMIC mapping is utilized to establish the butterfly community, enhancing the initial population’s diversity and preventing premature algorithm convergence. Following this, a population reset strategy is introduced, replacing weaker individuals over a specified number of iterations while maintaining a constant population size. This strategy enhances the algorithm’s ability to avoid local optima and increases its robustness. Additionally, characteristics of the Particle Swarm Optimization (PSO) algorithm are integrated to enhance the butterfly’s location update mechanism, accelerating the algorithm’s convergence rate. To evaluate the performance of the IBOA algorithm, this study designed a CEC2020 function test experiment and compared it with several swarm intelligence algorithms. The results showed that IBOA achieved the best performance in 70% of the function tests, outperforming 75% of the other algorithms. In the path planning experiments within a simulated environment, IBOA quickly converged to the optimal path, and the paths it planned were the shortest and safest compared to those generated by other algorithms.

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.

Population Health Implications of Medicaid Prerelease and Transition Services for Incarcerated Populations.

As states expand prerelease and transition services for incarcerated individuals under the Medicaid Reentry Section 1115 Demonstration Opportunity, we sought to systematically inform Medicaid state and plan administrators regarding the population size and burden of disease data available on incarcerated populations in both jails and prisons in the United States. We analyzed data on eligibility criteria for new Medicaid prerelease and transition services based on incarceration length and health conditions across states. We estimated the potentially eligible populations in prisons and jails, considering various incarceration lengths and health status requirements. We also compared disease prevalence in the incarcerated population with that of the existing civilian Medicaid population. We found that rural and smaller states would experience a disproportionately large proportion of their Medicaid populations to be eligible for prerelease and transition services if new Medicaid eligibility rules were broadly applied. Self-reported psychological distress was notably higher among incarcerated individuals compared with those currently on Medicaid. The prevalence rates of previously diagnosed chronic hepatitis C and kidney disease were also much higher in the incarcerated population than the existing civilian Medicaid population. We estimated large volumes of potentially Medicaid-eligible entrants as coverage policy changes take effect over the coming years, particularly impacting smaller and more rural states. Our findings reveal very high disease prevalence rates among the incarcerated population subject to new Medicaid coverage, including specific chronic, infectious, and behavioral health conditions that state Medicaid programs, health plans, and providers may benefit from advanced planning to address.

On the aquatic phenology of sympatric crested and marbled newts (genus Triturus)

Abstract The large-bodied newts Triturus cristatus and T. marmoratus possess ranges that overlap in western France where they frequently share breeding ponds. I test the hypothesis that the aquatic phenology differs between them, as associated with a more aquatic and terrestrial life style respectively. Total adult population sizes were determined in spring in 13 experiments by the capture-mark-recapture methodology, followed by equivalent experiments in summer. Population sizes dropped by an order of magnitude on average whereas species frequencies changed in favour of T. cristatus, suggesting that T. marmoratus leaves the breeding site earlier than T. cristatus. Interspecies hybrids behaved like T. marmoratus. Descriptions are provided of other features (in ecology, morphology, demography, and colouration pattern) that support the different, aquatic versus terrestrial life styles of T. cristatus and T. marmoratus.