scholarly journals Basic Conceptual Structure of Evolutionary Ecology

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rogério Parentoni Martins
Keyword(s):  
Evolutionary Biology ◽  
Evolutionary Ecology ◽  
Genotypic Variation ◽  
Structured Populations ◽  
Phenotypic Variance ◽  
Selective Agents ◽  
Survival And Reproduction ◽  
Environmental Variations ◽  
Successive Generations ◽  
Evolutionary Consequences

Concepts are linguistic structures with specific syntax and semantics used as sources of communicating ideas. Concepts can be simple (e.g., tree), complex (e.g., adaptation) and be part of a network of interactions that characterize an area of scientific research. The conceptual interrelationships and some evolutionary consequences upon which these interrelations are based will be addressed here. The evolutionary ecology is an area of research from the population evolutionary biology that deals mainly with the effect of positive natural selection on panmictic and structured populations. Environmental factors, conditions and variable resources in time and space, constitute the selective agents that act on the phenotypic and genotypic variation of populations in a single generation, could result in evolutionary adaptations, which are simply those traits that are most likely to confer survival and reproduction (evolutionary fitness) of the phenotypes that carry them in successive generations. The bases of adaptation are mainly genetic and transmitted vertically (classical Mendelian mechanisms) or horizontally (in the case of microorganisms). The phenotypic variance of the population is a conjoint consequence of the additive genotypic variance (heritability), nonadditive variance (dominance and epistasis), pleiotropy and the interaction between genotype and environment. The ability of the same genotype to respond to spatial environmental variations can result in phenotypic plasticity that manifests itself through reaction norms. The total phenotypic variation and its genetic and environmental components influence the ability of a population to evolve (evolvability).

scholarly journals AEGIS: An In Silico Tool to model Genome Evolution in Age-Structured Populations

2019 ◽  
Author(s):  
William J. Bradshaw ◽  
Arian Šajina ◽  
Dario Riccardo Valenzano
Keyword(s):  
Life History ◽  
In Silico ◽  
Evolutionary Biology ◽  
Life History Evolution ◽  
Structured Populations ◽  
Time Simulation ◽  
Sexual And Asexual Reproduction ◽  
Wide Range ◽  
Survival And Reproduction ◽  
Age Structured

AbstractAEGIS (Ageing of Evolving Genomes In Silico) is a versatile population-genetics numerical-simulation tool that enables the evolution of life history trajectories under sexual and asexual reproduction and a wide variety of evolutionary constraints. By encoding age-specific survival and reproduction probabilities as discrete genomic elements, AEGIS allows these probabilities to evolve freely and independently over time. Simulation of population evolution with AEGIS demonstrates that ageing-like phenotypes evolve in stable environments under a wide range of conditions, that life history trajectories depend heavily on mutation rates, and that sexual populations are better able to accumulate high levels of beneficial mutations affecting early-life survival and reproduction. AEGIS is free and open-source, and aims to become a standard reference tool in the study of life-history evolution and the evolutionary biology of ageing.

scholarly journals Morphological integration during postnatal ontogeny: implications for evolutionary biology

2021 ◽  
Author(s):  
Alex Hubbe ◽  
Guilherme Garcia ◽  
Harley Sebastiao ◽  
Arthur Porto ◽  
Fabio Andrade Machado ◽  
...  
Keyword(s):  
Evolutionary Biology ◽  
Additive Genetic Variance ◽  
Structural Similarity ◽  
Morphological Integration ◽  
Ontogenetic Stage ◽  
Postnatal Ontogeny ◽  
Phenotypic Variance ◽  
Skull Morphology ◽  
Genetic Covariance ◽  
Ontogenetic Stages

Understanding how development changes the genetic covariance of complex phenotypes is fundamental for the study of evolution. If the genetic covariance changes dramatically during postnatal ontogeny, one cannot infer confidently evolutionary responses based on the genetic covariance estimated from a single postnatal ontogenetic stage. Mammalian skull morphology is a common model system for studying the evolution of complex structures. These studies often involve estimating covariance between traits based on adult individuals. There is robust evidence that covariances changes during ontogeny. However, it is unknown whether differences in age-specific covariances can, in fact, bias evolutionary analyses made at subadult ages. To explore this issue, we sampled two marsupials from the order Didelphimorphia, and one precocial and one altricial placental at different stages of postnatal ontogeny. We calculated the phenotypic variance-covariance matrix (P-matrix) for each genus at these postnatal ontogenetic stages. Then, we compared within genus P-matrices and also P-matrices with available congeneric additive genetic variance-covariance matrices (G-matrices) using Random Skewers and the Krzanowsky projection methods. Our results show that the structural similarity between matrices is in general high (> 0.7). Our study supports that the G-matrix in therian mammals is conserved during most of the postnatal ontogeny. Thus it is feasible to study life-history changes and evolutionary responses based on the covariance estimated from a single ontogenetic stage. Our results also suggest that at least for some marsupials the G-matrix varies considerably prior to weaning, which does not invalidate our previous conclusion because specimens at this stage would experience striking differences in selective regimes than during later ontogenetic stages.

scholarly journals Fluctuating selection: the perpetual renewal of adaptation in variable environments

2010 ◽  
Vol 365 (1537) ◽  
pp. 87-97 ◽  
Author(s):  
Graham Bell
Keyword(s):  
Natural Selection ◽  
Evolutionary Biology ◽  
Natural Populations ◽  
Phenotypic Variance ◽  
Weak Selection ◽  
Fluctuating Selection ◽  
Environmental Variance ◽  
Strong Selection ◽  
Genomic Studies ◽  
Over Time

Darwin insisted that evolutionary change occurs very slowly over long periods of time, and this gradualist view was accepted by his supporters and incorporated into the infinitesimal model of quantitative genetics developed by R. A. Fisher and others. It dominated the first century of evolutionary biology, but has been challenged in more recent years both by field surveys demonstrating strong selection in natural populations and by quantitative trait loci and genomic studies, indicating that adaptation is often attributable to mutations in a few genes. The prevalence of strong selection seems inconsistent, however, with the high heritability often observed in natural populations, and with the claim that the amount of morphological change in contemporary and fossil lineages is independent of elapsed time. I argue that these discrepancies are resolved by realistic accounts of environmental and evolutionary changes. First, the physical and biotic environment varies on all time-scales, leading to an indefinite increase in environmental variance over time. Secondly, the intensity and direction of natural selection are also likely to fluctuate over time, leading to an indefinite increase in phenotypic variance in any given evolving lineage. Finally, detailed long-term studies of selection in natural populations demonstrate that selection often changes in direction. I conclude that the traditional gradualist scheme of weak selection acting on polygenic variation should be supplemented by the view that adaptation is often based on oligogenic variation exposed to commonplace, strong, fluctuating natural selection.

Selection

1996 ◽  
Author(s):  
Thomas Bäck
Keyword(s):  
Model Selection ◽  
Evolutionary Algorithms ◽  
Evolutionary Biology ◽  
Disruptive Selection ◽  
Stabilizing Selection ◽  
Intermediate Phenotypes ◽  
Biological Reality ◽  
Survival And Reproduction ◽  
Implementation Models ◽  
Selection Operator

The genetic operators summarized in the set Ω, i.e. mutation and recombination (and probably others, e.g. inversion) create new individuals in a completely undirected way. In Evolutionary Algorithms, the selection operator plays a major role by imposing a direction on the search process, i.e. a clear preference of those individuals which perform better according to the fitness measure Φ. Selection is the only component of Evolutionary Algorithms where the fitness of individuals has an impact on the evolution process. The practical implementations of selection as discussed in sections 2.1.4, 2.2.4, and 2.3.4 seemingly contradict the biological viewpoint presented in section 1.1, where natural selection was emphasized not to be an active force but instead to be characterized by different survival and reproduction rates. However, artificial implementation models and biological reality are not necessarily contradicting each other. While in biological systems fitness can only be measured indirectly by differences in growth rates, fitness in Evolutionary Algorithms is a direct, well-defined and evaluable property of individuals. The biological struggle for existence (e.g. by predator-prey interactions, capabilities of somatic adaptation, and the particular physical properties of individuals) has no counterpart in computer implementations of standard Evolutionary Algorithms. Therefore, an artificial abstraction of these mechanisms can use fitness measures to determine survival and reproduction a posteriori, since the struggle for existence is completely hidden in the evaluation process of individuals. The fact that different survival and reproduction constitute selection is valid in both cases, but in Evolutionary Algorithms fitness is measurable and implies the survival and reproduction behavior, which is just opposite to biological reality. This is simply an implication of the fitness-centered intention which necessarily prevails design and application of these algorithms. Therefore, it is just a logic consequence to model selection as an active, fitness-based component of Evolutionary Algorithms. However, how to model selection is by no means a simple problem. In evolutionary biology, it is usually distinguished between stabilizing, directed, and disruptive selection (see [Fut90], pp. 174–175). In the case of stabilizing selection, intermediate phenotypes have best fitness values, while disruptive selection is characterized by two or more distinct phenotypes that are highly fit and by intermediate phenotypes of low fitness (this assumes an - albeit unknown - ordering of phenotypes).

Inbreeding and Outbreeding

1947 ◽  
Vol 1947 (01) ◽  
pp. 7-27 ◽  
Author(s):  
Forbes W. Robertson
Keyword(s):  
Mating System ◽  
Mating Systems ◽  
Careful Consideration ◽  
Population Variance ◽  
Rule Of Thumb ◽  
Cell Divisions ◽  
Particulate Nature ◽  
Genetic Complexity ◽  
Environmental Variations ◽  
Successive Generations

The type of mating system we should use in any plan of livestock improvement requires very careful consideration. By reason of the particulate nature of inheritance and the behaviour of chromosomes in the cell divisions preceding the formation of eggs and sperm, the various mating systems differ in their influence upon the uniformity or otherwise of successive generations, the chances of securing improvement, the scope for control by selection, and finally, our ability to discriminate between the relative contributions of genetic and environmental variations to the population variance. Because of the genetic complexity many misconceptions have flourished about what we may expect with different mating systems, about the effects of inbreeding and the advantages and dangers of outbreeding. Different breeders have often secured different results with similar mating systems and the search for a rule of thumb guide has proved fruitless.

scholarly journals Cetaceans as Exemplars of Evolution and Evolutionary Ecology: A Glossary

Oceans ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 56-76
Author(s):  
Alexander J. Werth
Keyword(s):  
Case Studies ◽  
Evolutionary Biology ◽  
Evolutionary Ecology ◽  
Current Knowledge ◽  
Brute Fact ◽  
Research Studies

Extant cetaceans (whales, dolphins, and porpoises) and their extinct ancestors offer some of the strongest and best-known examples of macroevolutionary transition as well as microevolutionary adaptation. Unlike most reviews of cetacean evolution, which are intended to chronicle the timeline of cetacean ancestry, document the current knowledge of cetacean adaptations, or simply validate the brute fact of evolution, this review is instead intended to demonstrate how cetaceans fittingly illustrate hundreds of specific, detailed terms and concepts within evolutionary biology and evolutionary ecology. This review, arrayed in alphabetical glossary format, is not meant to offer an exhaustive listing of case studies or scholarly sources, but aims to show the breadth and depth of cetacean research studies supporting and investigating numerous evolutionary themes.

scholarly journals Kin and multilevel selection in social evolution: a never-ending controversy?

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 776 ◽  
Author(s):  
Jos Kramer ◽  
Joël Meunier
Keyword(s):  
Kin Selection ◽  
Group Selection ◽  
Evolutionary Biology ◽  
Social Evolution ◽  
Selection Process ◽  
Structured Populations ◽  
Multilevel Selection ◽  
Current Debate ◽  
The Relationship ◽  
Shed Light

Kin selection and multilevel selection are two major frameworks in evolutionary biology that aim at explaining the evolution of social behaviors. However, the relationship between these two theories has been plagued by controversy for almost half a century and debates about their relevance and usefulness in explaining social evolution seem to rekindle at regular intervals. Here, we first provide a concise introduction into the kin selection and multilevel selection theories and shed light onto the roots of the controversy surrounding them. We then review two major aspects of the current debate: the presumed formal equivalency of the two theories and the question whether group selection can lead to group adaptation. We conclude by arguing that the two theories can offer complementary approaches to the study of social evolution: kin selection approaches usually focus on the identification of optimal phenotypes and thus on the endresult of a selection process, whereas multilevel selection approaches focus on the ongoing selection process itself. The two theories thus provide different perspectives that might be fruitfully combined to promote our understanding of the evolution in group-structured populations.

scholarly journals Cognitive innovations and the evolutionary biology of expertise

2017 ◽  
Vol 372 (1735) ◽  
pp. 20160427 ◽  
Author(s):  
Reuven Dukas
Keyword(s):  
Social Interactions ◽  
Cognitive Abilities ◽  
Evolutionary Biology ◽  
Environmental Changes ◽  
Internal Communication ◽  
Genetic Networks ◽  
Evolution Of Language ◽  
Survival And Reproduction ◽  
Biochemical Mechanisms ◽  
Communication And Coordination

Animal life can be perceived as the selective use of information for maximizing survival and reproduction. All organisms including bacteria and protists rely on genetic networks to build and modulate sophisticated structures and biochemical mechanisms for perceiving information and responding to environmental changes. Animals, however, have gone through a series of innovations that dramatically increased their capacity to acquire, retain and act upon information. Multicellularity was associated with the evolution of the nervous system, which took over many tasks of internal communication and coordination. This paved the way for the evolution of learning, initially based on individual experience and later also via social interactions. The increased importance of social learning also led to the evolution of language in a single lineage. Individuals' ability to dramatically increase performance via learning may have led to an evolutionary cycle of increased lifespan and greater investment in cognitive abilities, as well as in the time necessary for the development and refinement of expertise. We still know little, however, about the evolutionary biology, genetics and neurobiological mechanisms that underlie such expertise and its development. This article is part of the themed issue ‘Process and pattern in innovations from cells to societies’.

scholarly journals Evaluation of reciprocal cross populations for spike-related traits in early consecutive generations of bread wheat (Triticum aestivum L.)

Genetika ◽  
2017 ◽  
Vol 49 (2) ◽  
pp. 511-528
Author(s):  
Imren Kutlu ◽  
Alpay Balkan ◽  
Kayıhan Korkut ◽  
Oguz Bilgin ◽  
Ismet Baser
Keyword(s):  
Grain Yield ◽  
Maternal Effects ◽  
Genotypic Variation ◽  
Selection Criterion ◽  
Triticum Aestivum L ◽  
High Yield ◽  
Group Method ◽  
Genetic Advance ◽  
Spike Density ◽  
Successive Generations

Breeding effort on increasing grain yield of wheat will incessantly continue because it is indispensable product. Obtaining the genetic information such as genotypic variation, heritability, genetic advance is the fundamental components of these studies. It is important that the maternal effects are put forward throughout successive generations because of genotypic and/or environmental effects as far as variation. This research was conducted to investigate changes of reciprocal crosses throughout successive generations and determine selection criteria for high yield in early generations. For this purpose, the populations were analyzed with regard to genotypic and phenotypic variation coefficient, heritability, genetic advance and Unweighted Pair Group Method (UPGMA) cluster analysis for real crosses, reciprocals and all genotypes separately. According to the results, heritability and genetic advance values of traits investigated were highly varied throughout successive generations among real crosses, reciprocals and all genotypes. This finding indicated that non-additive gen effects or epitasis played a role in inheritance of all traits. Dissimilarity of crosses than their reciprocals indicated variation of successive generation. Dissimilarity value of each parent differed as generation progresses according to combination created. This condition suggested that there were maternal effects in this population throughout successive generations. Grain weight per spike, spike harvest index and spike density had high direct and indirect effects on the grain yield at all of three generations, it proved that these traits can be a selection criterion for early generations. Sana was the best parent and ?Bezostaja x Krasunia? and ?Krasunia x Pehlivan? were best performance in most of traits at all generations.

scholarly journals Epidemiological and evolutionary consequences of periodicity in treatment coverage

2021 ◽  
Vol 288 (1946) ◽  
pp. 20203007
Author(s):  
Alicia Walter ◽  
Sébastien Lion
Keyword(s):  
Evolutionary Dynamics ◽  
Treatment Strategies ◽  
Structured Populations ◽  
Disease Spread ◽  
Reproductive Value ◽  
Selection Gradient ◽  
Treatment Coverage ◽  
Pathogen Prevalence ◽  
Evolutionary Consequences ◽  
Pulsed Treatment

Host heterogeneity is a key driver of host–pathogen dynamics. In particular, the use of treatments against infectious diseases creates variation in quality among hosts, which can have both epidemiological and evolutionary consequences. We present a general theoretical model to highlight the consequences of different imperfect treatments on pathogen prevalence and evolution. These treatments differ in their action on host and pathogen traits. In contrast with previous studies, we assume that treatment coverage can vary in time, as in seasonal or pulsed treatment strategies. We show that periodic treatment strategies can limit both disease spread and virulence evolution, depending on the type of treatment. We also introduce a new method to analytically calculate the selection gradient in periodic environments, which allows our predictions to be interpreted using the concept of reproductive value, and can be applied more generally to analyse eco-evolutionary dynamics in class-structured populations and fluctuating environments.

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