Characterizing phenotypic diversity in marine populations of the threespine stickleback

Fraser, Ainsley Lilias; El‐Sabaawi, Rana W.

doi:10.1038/s41598-022-22872-z

Cited by 2 publications

(2 citation statements)

References 93 publications

(148 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the adaptive significance of morphology with regards to temperature conditions within this species is still unclear [ 73 , 88 ]. It is possible that different body morphologies favor different swimming styles or influence ability to occupy different water depths or current strengths [ 1 , 90 , 91 ].…”

Section: Discussionmentioning

confidence: 99%

Intergenerational effects of ocean temperature variation: Early life benefits are short-lived in threespine stickleback

Spence-Jones,

Pein,

Shama

2024

PLoS ONE

View full text Add to dashboard Cite

Current climate change models predict an increase in temperature variability and extreme events such as heatwaves, and organisms need to cope with consequent changes to environmental variation. Non-genetic inheritance mechanisms can enable parental generations to prime their offspring’s abilities to acclimate to environmental change–but they may also be deleterious. When parents are exposed to predictable environments, intergenerational plasticity can lead to better offspring trait performance in matching environments. Alternatively, parents exposed to variable or unpredictable environments may use plastic bet-hedging strategies to adjust the phenotypic variance among offspring. Here, we used a model species, the threespine stickleback (Gasterosteus aculeatus), to test whether putatively adaptive intergenerational effects can occur in response to shifts in environmental variation as well as to shifts in environmental mean, and whether parents employ plastic bet-hedging strategies in response to increasing environmental variation. We used a full-factorial, split-clutch experiment with parents and offspring exposed to three temperature regimes: constant, natural variation, and increased variation. We show that within-generation exposure to increased temperature variation reduces growth of offspring, but having parents that were exposed to natural temperature variation during gametogenesis may offset some early-life negative growth effects. However, these mitigating intergenerational effects do not appear to persist later in life. We found no indication that stickleback mothers plastically altered offspring phenotypic variance (egg size or clutch size) in response to temperature variation. However, lower inter-individual variance of juvenile fish morphology in offspring of increased variation parents may imply the presence of conservative bet-hedging strategies in natural populations. Overall, in our experiment, parental exposure to temperature variation had limited effects on offspring fitness-related traits. Natural levels of environmental variation promoted a potentially adaptive intergenerational response in early life development, but under more challenging conditions associated with increased environmental variation, the effect was lost.

show abstract

Section: Discussionmentioning

confidence: 99%

Intergenerational effects of ocean temperature variation: Early life benefits are short-lived in threespine stickleback

Spence-Jones,

Pein,

Shama

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…(1) (Schutz et al, 2022), Gasterosteidae (2) (Fraser & El-Sabaawi, 2022), Hominidae (Krenn et al, 2022), Hynobiidae (Jia et al, 2022), Muridae (a/b) (Devine et al, 2022), Poeciliidae (Riesch et al, 2016), Serranidae (Alós et al, 2014), Cichlidae (Ronco et al, 2020), Colubridae+ (Head & Polly, 2015), Crocodylidae (Watanabe & Slice, 2014), Formicidae (Kennedy et al, 2014), Ocypodidae (Hopkins et al, 2016), Percidae (Martin & Mendelson, 2014), Vespidae (Perrard et al, 2014), andViviparidae (Van Bocxlaer &Hunt, 2013). Dataset details are listed in Table S1.…”

Section: Conflict Of Interest Statementmentioning

confidence: 99%

Classifying high-dimensional phenotypes with ensemble learning

Devine

Kurki

Epp

et al. 2023

Preprint

View full text Add to dashboard Cite

Classification is a fundamental task in biology used to assign members to a class. While linear discriminant functions have long been effective, advances in phenotypic data collection are yielding increasingly high-dimensional datasets with more classes, unequal class covariances, and non-linear distributions. Numerous studies have deployed machine learning techniques to classify such distributions, but they are often restricted to a particular organism, a limited set of algorithms, and/or a specific classification task. In addition, the utility of ensemble learning or the strategic combination of models has not been fully explored.We performed a meta-analysis of 33 algorithms across 20 datasets containing over 20,000 high-dimensional shape phenotypes using an ensemble learning framework. Both binary (e.g., sex, environment) and multi-class (e.g., species, genotype, population) classification tasks were considered. The ensemble workflow contains functions for preprocessing, training individual learners and ensembles, and model evaluation. We evaluated algorithm performance within and among datasets. Furthermore, we quantified the extent to which various dataset and phenotypic properties impact performance.We found that discriminant analysis variants and neural networks were the most accurate base learners on average. However, their performance varied substantially between datasets. Ensemble models achieved the highest performance on average, both within and among datasets, increasing average accuracy by up to 3% over the top base learner. Higher class R2values, mean class shape distances, and between– vs. within-class variances were positively associated with performance, whereas higher class covariance distances were negatively associated. Class balance and total sample size were not predictive.Learning-based classification is a complex task driven by many hyperparameters. We demonstrate that selecting and optimizing an algorithm based on the results of another study is a flawed strategy. Ensemble models instead offer a flexible approach that is data agnostic and exceptionally accurate. By assessing the impact of various dataset and phenotypic properties on classification performance, we also offer potential explanations for variation in performance. Researchers interested in maximizing performance stand to benefit from the simplicity and effectiveness of our approach made accessible via the R packagepheble.

show abstract

Characterizing phenotypic diversity in marine populations of the threespine stickleback

Cited by 2 publications

References 93 publications

Intergenerational effects of ocean temperature variation: Early life benefits are short-lived in threespine stickleback

Intergenerational effects of ocean temperature variation: Early life benefits are short-lived in threespine stickleback

Classifying high-dimensional phenotypes with ensemble learning

Contact Info

Product

Resources

About