scholarly journals Rapid evolution of learning and reproduction in natural populations of Drosophila melanogaster

2018 ◽  
Author(s):  
Emily L. Behrman ◽  
Tadeusz J. Kawecki ◽  
Paul Schmidt
Keyword(s):  
Drosophila Melanogaster ◽  
Evolutionary Dynamics ◽  
Rna Binding ◽  
Rapid Evolution ◽  
Learning Ability ◽  
Learning Performance ◽  
General Mechanism ◽  
Behavioural Plasticity ◽  
Trade Off ◽  
Trade Offs

AbstractLearning is a general mechanism of adaptive behavioural plasticity whose benefits and costs depend on the environment. Thus, seasonal oscillations in temperate environments between winter and summer might produce cyclical selection pressures that would drive rapid evolution of learning performance in multivoltine populations. To test this hypothesis, we investigated the evolutionary dynamics of learning ability over this rapid seasonal timescale in a natural population of Drosophila melanogaster. Associative learning was tested in common garden-raised flies collected from nature in the spring and fall over three consecutive years. The spring flies consistently learned better than fall flies, revealing seasonal evolution of improved learning performance in nature. Fecundity showed the opposite seasonal pattern, suggesting a trade-off between learning and reproduction. This trade-off also held within population: more fecund individual females learned less well. This trade-off is mediated at least in part by natural polymorphism in the RNA binding protein couch potato (cpo), with a genotype favoured during summer showing poorer learning performance and higher fecundity than a genotype favoured over winter. Thus, seasonal environments can drive rapid cyclical evolution of learning performance, but the evolutionary dynamics may be driven by trade-offs generated by pleiotropic effects of causative alleles selected for other reasons.

scholarly journals Grazing resistance in phytoplankton

Hydrobiologia ◽  
2020 ◽  
Vol 848 (1) ◽  
pp. 237-249 ◽  
Author(s):  
Miquel Lürling
Keyword(s):  
Community Dynamics ◽  
Phytoplankton Community ◽  
Rapid Evolution ◽  
Variable Environment ◽  
Trade Off ◽  
Defense Strategies ◽  
Trade Offs ◽  
Grazing Resistance ◽  
Population And Community Dynamics

AbstractPhytoplankton is confronted with a variable assemblage of zooplankton grazers that create a strong selection pressure for traits that reduce mortality. Phytoplankton is, however, also challenged to remain suspended and to acquire sufficient resources for growth. Consequently, phytoplanktic organisms have evolved a variety of strategies to survive in a variable environment. An overview is presented of the various phytoplankton defense strategies, and costs and benefits of phytoplankton defenses with a zooming in on grazer-induced colony formation. The trade-off between phytoplankton competitive abilities and defenses against grazing favor adaptive trait changes—rapid evolution and phenotypic plasticity—that have the potential to influence population and community dynamics, as exemplified by controlled chemostat experiments. An interspecific defense–growth trade-off could explain seasonal shifts in the species composition of an in situ phytoplankton community yielding defense and growth rate as key traits of the phytoplankton. The importance of grazing and protection against grazing in shaping the phytoplankton community structure should not be underestimated. The trade-offs between nutrient acquisition, remaining suspended, and grazing resistance generate the dynamic phytoplankton community composition.

scholarly journals Step-specific adaptation and trade-off over the course of an infection by GASP-mutation small colony variants

2020 ◽  
Author(s):  
Faucher Christian ◽  
Mazana Vincent ◽  
Kardacz Marion ◽  
Parthuisot Nathalie ◽  
Ferdy Jean-Baptiste ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Chronic Phase ◽  
Infection Process ◽  
Infection Model ◽  
Xenorhabdus Nematophila ◽  
Trade Off ◽  
Small Colony Variants ◽  
Trade Offs ◽  
Small Colony ◽  
Host Evolution

AbstractDuring an infection, parasites face a succession of challenges, each decisive for disease outcome. The diversity of challenges requires a series of parasite adaptations to successfully multiply and transmit from host to host. Thus, the pathogen genotypes which succeed during one step might be counter-selected in later stages of the infection. Using the bacteria Xenorhabdus nematophila and adult Drosophila melanogaster as hosts, we showed that such step-specific adaptations, here linked to GASP mutations in the X. nematophila master gene regulator lrp, exist and can trade-off with each other. We found that nonsense lrp mutations had lowered ability to resist the host immune response, while all classes of mutations in lrp were associated with a decrease in the ability to proliferate during early infection. We demonstrate that reduced proliferation of X. nematophila best explains diminished virulence in this infection model. Finally, decreased proliferation during the first step of infection is accompanied with improved proliferation during late infection, suggesting a trade-off between the adaptations to each step. Step-specific adaptations could play a crucial role in the chronic phase of infections in any diseases that show similar small colony variants (also known as SCV) to X. nematophila.ImportanceWithin-host evolution has been described in many bacterial diseases, and the genetic basis behind the adaptations stimulated a lot of interest. Yet, the studied adaptations are generally focused on antibiotic resistance, rarely on the adaptation to the environment given by the host, and the potential trade-off hindering adaptations to each step of the infection are rarely considered. Those trade-offs are key to understand intra-host evolution, and thus the dynamics of the infection. However, the understanding of these trade-offs supposes a detailed study of host-pathogen interactions at each step of the infection process, with for each step an adapted methodology. Using Drosophila melanogaster as host and the bacteria Xenorhabdus nematophila, we investigated the bacterial adaptations resulting from GASP mutations known to induce small colony variant (SCV) phenotype positively selected within-the-host over the course of an infection, and the trade-off between step-specific adaptations.

scholarly journals Plant eco-evolution weakens mutualistic interaction with declining pollinator populations

2019 ◽  
Author(s):  
Avril Weinbach ◽  
Nicolas Loeuille ◽  
Rudolf P. Rohr
Keyword(s):  
Growth Rates ◽  
Evolutionary Dynamics ◽  
Plant Evolution ◽  
Population Declines ◽  
Pollination Services ◽  
Nectar Production ◽  
Trade Off ◽  
Trade Offs ◽  
Mutualistic Interaction ◽  
Plant Pollinator

AbstractRecent pollinator population declines threaten pollination services and greatly impact plant-pollinator coevolution. We investigate how such evolutionary effects affect plant-pollinator coexistence. Using eco-evolutionary dynamics, we study the evolution of plant attractiveness in a simple pollinator-plant model, assuming an allocation trade-off between attractiveness (e.g. nectar production, flower shape and size) and plant intrinsic growth rates. First, we investigated how attractiveness evolution changes species persistence, biomass production, and the intensity of the mutualism (as a proxy for pollination services). We show that the shape of the allocation trade-off is key in determining the outcome of the eco-evolutionary dynamics and that concave trade-offs allow convergence to stable plant-pollinator coexistence. Then we analyse the effect of pollinator population declines on the eco-evolutionary dynamics. Decreasing intrinsic growth rates of pollinator population results in a plant-evolution driven disappearance of the mutualistic interaction, eventually leading to pollinator extinction. With asymmetric mutualism favouring the pollinator, the evolutionary disappearance of the mutualistic interaction is delayed. Our results suggest that evolution may account for the current collapse of pollination systems and that restoration attempts should be enforced early enough to prevent potential negative effects driven by plant evolution.

scholarly journals Attracting pollinators vs escaping herbivores: eco-evolutionary dynamics of plants confronted with an ecological trade-off

2021 ◽  
Author(s):  
Youssef Yacine ◽  
Nicolas Loeuille
Keyword(s):  
Evolutionary Dynamics ◽  
Plant Traits ◽  
Adaptive Dynamics ◽  
Disruptive Selection ◽  
Stabilizing Selection ◽  
Ecological Communities ◽  
Trade Off ◽  
Trade Offs ◽  
Herbivore Community ◽  
Plant Animal Interactions

AbstractA large number of plant traits are subject to an ecological trade-off between attracting pollinators and escaping herbivores. The interplay of both plant-animal interactions determines their evolution. Within a plant-pollinator-herbivore community in which interaction strengths depend on trait-matching, eco-evolutionary dynamics are studied using the framework of adaptive dynamics. We characterize the type of selection acting on the plant phenotype and the consequences for multispecies coexistence. We find that pollination favors stabilizing selection and coexistence. In contrast, herbivory fosters runaway selection, which threatens plant-animal coexistence. These contrasting dynamics highlight the key role of ecological trade-offs in structuring ecological communities. In particular, we show that disruptive selection is possible when such trade-offs are strong. While the interplay of pollination and herbivory is known to maintain plant polymorphism in several cases, our work suggests that it might also have fueled the diversification process itself.

scholarly journals Honeybee workers with higher reproductive potential have a greater learning ability

Apidologie ◽  
2021 ◽  
Author(s):  
Karolina Kuszewska ◽  
Wiktoria Rojek
Keyword(s):  
Positive Impact ◽  
Reproductive Potential ◽  
Learning Ability ◽  
Biological Functions ◽  
Learning Skills ◽  
Trade Off ◽  
Individual Fitness ◽  
Trade Offs ◽  
Rebel Workers ◽  
Separate Regression

AbstractLearning ability, which allows individuals to adjust their behaviour to changing environmental conditions, has a considerable positive impact on individual fitness. However, in addition to benefits, learning also incurs a cost, which means that investment in learning and maintaining learned skills can lead to trade-offs impacting other biological functions. Here, we tested whether a trade-off exists between learning skills and reproductive potential in honeybee workers. For this purpose, we compared learning ability between two groups of workers that differed in reproductive potential—normal and rebel workers. The results showed that workers with high reproductive potential (rebels), measured according to the number of ovarioles in the ovary, learned faster than normal workers with low reproductive potential. Moreover, by performing separate regression analyses within the rebel and non-rebel worker groups, we found that the reproductive potential of workers was positively correlated with their learning ability. The results show that in honeybees, there is no trade-off in resource allocation between two costly biological functions, learning and reproduction.

scholarly journals No trade-off between learning ability and parasitoid resistance in Drosophila melanogaster

2006 ◽  
Vol 19 (4) ◽  
pp. 1359-1363 ◽  
Author(s):  
M. KOLSS ◽  
A. R. KRAAIJEVELD ◽  
F. MERY ◽  
T. J. KAWECKI
Keyword(s):  
Drosophila Melanogaster ◽  
Learning Ability ◽  
Trade Off

scholarly journals No evidence for an evolutionary trade-off between learning and immunity in a social insect

2008 ◽  
Vol 5 (1) ◽  
pp. 55-57 ◽  
Author(s):  
A Alghamdi ◽  
N.E Raine ◽  
E Rosato ◽  
E.B Mallon
Keyword(s):  
Immune Response ◽  
Bombus Terrestris ◽  
A Priori ◽  
Evolutionary Relationship ◽  
Learning Ability ◽  
Learning Performance ◽  
Fitness Costs ◽  
Zone Of Inhibition ◽  
Trade Off ◽  
Fast Learning

The immune response affects learning and memory in insects. Given this and the known fitness costs of both the immune system and learning, does an evolutionary trade-off exist between these two systems? We tested this by measuring the learning ability of 12 bumble-bee ( Bombus terrestris ) colonies in a free-flying paradigm. We then tested their immune response using the zone of inhibition assay. We found a positive relationship between colony learning performance and immune response, that is, fast-learning colonies also show high levels of antimicrobial activity. We conclude that there is no a priori reason to demand an evolutionary relationship between two traits that are linked physiologically.

Dual Goals for Speed and Accuracy on the Same Performance Task

2012 ◽  
Vol 11 (3) ◽  
pp. 118-126 ◽  
Author(s):  
Olive Emil Wetter ◽  
Jürgen Wegge ◽  
Klaus Jonas ◽  
Klaus-Helmut Schmidt
Keyword(s):  
Memory Scanning ◽  
Performance Task ◽  
Performance Tasks ◽  
Trade Off ◽  
Test Experiment ◽  
Trade Offs ◽  
New Finding ◽  
Sternberg Paradigm ◽  
Speed Accuracy ◽  
Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

The Effects of Decision Time on Perceptions of Decisions and Decision Makers in (Moral) Trade-Off Scenarios

2019 ◽  
Author(s):  
Anna Katharina Spälti ◽  
Mark John Brandt ◽  
Marcel Zeelenberg
Keyword(s):  
Decision Process ◽  
Decision Time ◽  
Decision Makers ◽  
Process Information ◽  
Total N ◽  
Trade Off ◽  
Financial Gain ◽  
Trade Offs ◽  
Sacred Values ◽  
Moral Context

People often have to make trade-offs. We study three types of trade-offs: 1) "secular trade-offs" where no moral or sacred values are at stake, 2) "taboo trade-offs" where sacred values are pitted against financial gain, and 3) "tragic trade-offs" where sacred values are pitted against other sacred values. Previous research (Critcher et al., 2011; Tetlock et al., 2000) demonstrated that tragic and taboo trade-offs are not only evaluated by their outcomes, but are also evaluated based on the time it took to make the choice. We investigate two outstanding questions: 1) whether the effect of decision time differs for evaluations of decisions compared to decision makers and 2) whether moral contexts are unique in their ability to influence character evaluations through decision process information. In two experiments (total N = 1434) we find that decision time affects character evaluations, but not evaluations of the decision itself. There were no significant differences between tragic trade-offs and secular trade-offs, suggesting that the decisions structure may be more important in evaluations than moral context. Additionally, the magnitude of the effect of decision time shows us that decision time, may be of less practical use than expected. We thus urge, to take a closer examination of the processes underlying decision time and its perception.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

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