When can cultural selection explain adaptation?

Cultural selection models aim to explain cultural phenomena as the products of a selective process, often characterising institutions, practices, norms or behaviours as adaptations. I argue that a lack of attention has been paid to the explanatory power of cultural selection frameworks. Arguments for cultural selection frequently depend on demonstrating only that selection models can in principle be applied to culture, rather than explicitly demonstrating the explanatory payoffs that could arise from their application. Understanding when and how cultural selection generates powerful explanations is crucial to evaluating cultural selection, as well as realising its promised epistemic and practical benefits. I argue that the ability for cultural selection to explain ‘design without a designer’ is crucial to successful and powerful cultural selection explanations. I introduce the strategy of comparing cultural selection to goal-directed agent accounts in order to evaluate when cultural selection can provide distinctive explanatory payoffs, drawing on two case studies to illustrate the benefits of this strategy. I argue that a focus on phenomena which cannot be explained through intention or agency-based explanations in particular could provide a fruitful avenue to identifying the cases where cultural selection can be insightfully applied.

Using Selection Models to Assess Sensitivity to Publication Bias: A Tutorial and Call for More Routine Use

In meta-analyses, it is critical to assess the extent to which publication bias might have compromised the results. Classical methods based on the funnel plot, including Egger’s test and Trim-and-Fill, have become the de facto default methods to do so, with a large majority of recent meta-analyses in top medical journals (85%) assessing for publication bias exclusively using these methods. However, these classical funnel plot methods have important limitations when used as the sole means of assessing publication bias: they essentially assume that the publication process favors large point estimates for small studies and does not affect the largest studies, and they can perform poorly when effects are heterogeneous. In light of these limitations, we recommend that meta-analyses routinely apply other publication bias methods in addition to or instead of classical funnel plot methods. To this end, we describe how to use and interpret selection models. These methods make the often more realistic assumption that publication bias favors ``statistically significant'' results and that also directly accommodate effect heterogeneity. Selection models are well-established in the statistics literature and are supported by user-friendly software, yet remain rarely reported in many disciplines. We use previously published meta-analyses to demonstrate that selection models can yield insights that extend beyond those provided by funnel plot methods, suggesting the importance of establishing more comprehensive reporting practices for publication bias assessment.

Genomic prediction with allele dosage information in highly polyploid species

Several studies have shown how to leverage allele dosage information to improve the accuracy of genomic selection models in autotetraploids. In this study we expanded the methodology used for genomic selection in autotetraploids to higher (and mixed) ploidy levels. We adapted the models to build covariance matrices of both additive and digenic dominance effects that are subsequently used in genomic selection models. We applied these models using estimates of ploidy and allele dosage to sugarcane and sweet potato datasets and validated our results by also applying the models in simulated data. For the simulated datasets, including allele dosage information led up to 140% higher mean predictive abilities in comparison to using diploidized markers. Including dominance effects was highly advantageous when using diploidized markers, leading to mean predictive abilities which were up to 115% higher in comparison to only including additive effects. When the frequency of heterozygous genotypes in the population was low, such as in the sugarcane and sweet potato datasets, there was little advantage in including allele dosage information in the models. Overall, we show that including allele dosage can improve genomic selection in highly polyploid species under higher frequency of different heterozygous genotypic classes and high dominance degree levels.