de Bem Oliveira, I. | Search bioRxiv

bioRxiv2026

Endangered Species Act listing is linked with greater research effort for U.S. butterflies

Conservation strategies for at-risk species can be aided significantly by research on topics such as ecology, life history, and threats, yet research effort is lacking for many species facing elevated extinction risk. Here we investigated whether listing under the U.S. Endangered Species Act (ESA) was associated with research effort for U.S. butterflies, and whether that effort was higher before or after ESA listing. We found that ESA-listed species had significantly more peer-reviewed publications than non-listed species after accounting for species range and taxonomic family. Further, we showed that more papers were published per year after ESA listing than before. These findings confirm that ESA-listed species benefit from greater research attention that can support data-informed conservation efforts. However, the relative scarcity of studies prior to ESA listing, as well as the lack of research for many unlisted, at-risk taxa, underscores the need for proactive, strategic research effort to inform conservation action.

zoology↗

bioRxiv2018

Genomic prediction of autotetraploids; influence of relationship matrices, allele dosage, and continuous genotyping calls in phenotype prediction

Estimation of allele dosage in autopolyploids is challenging and current methods often result in the misclassification of genotypes. Here we propose and compare the use of next generation sequencing read depth as continuous parameterization for autotetraploid genomic prediction of breeding values, using blueberry (Vaccinium corybosum spp.) as a model. Additionally, we investigated the influence of different sources of information to build relationship matrices in phenotype prediction; no relationship, pedigree, and genomic information, considering either diploid or tetraploid parameterizations. A real breeding population composed of 1,847 individuals was phenotyped for eight yield and fruit quality traits over two years. Analyses were based on extensive pedigree (since 1908) and high-density marker data (86K markers). Our results show that marker-based matrices can yield significantly better prediction than pedigree for most of the traits, based on model fitting and expected genetic gain. Continuous genotypic based models performed as well as the current best models and presented a significantly better goodness-of-fit for all traits analyzed. This approach also reduces the computational time required for marker calling and avoids problems associated with misclassification of genotypic classes when assigning dosage in polyploid species. Accuracies are encouraging for application of genomic selection (GS) for blueberry breeding. Conservatively, GS could reduce the time for cultivar release by three years. GS could increase the genetic gain per cycle by 86% on average when compared to phenotypic selection, and 32% when compared with pedigree-based selection.

genomics↗