Feral populations of Brassica oleracea along Atlantic coasts in western Europe

EAM was funded by a University of Glasgow Lord Kelvin Adam Smith PhD studentship; UZI was funded by a NERC Independent Research Fellowship (NE/L011956); CAC is supported by the BBSRC (BB/P004202/1); KAM utilized equipment funded by the Wellcome Trust Institutional Strategic Support Fund (WT097835MF), Wellcome Trust Multi‐User Equipment Award (WT101650MA), and BBSRC LOLA award (BB/K003240/1). Part of the work was supported by a British Society for Plant Pathology summer studentship, and grants from the Botanical Research Fund, and the Blodwen Lloyd Bins trust funded through the Glasgow Natural History Society.There has been growing emphasis on the role that crop wild relatives might play in supporting highly selected agriculturally valuable species in the face of climate change. In species that were domesticated many thousands of years ago, distinguishing wild populations from escaped feral forms can be challenging, but reintroducing variation from either source could supplement current cultivated forms. For economically important cabbages (Brassicaceae: Brassica oleracea), “wild” populations occur throughout Europe but little is known about their genetic variation or potential as resources for breeding more resilient crop varieties. The main aim of this study was to characterize the population structure of geographically isolated wild cabbage populations along the coasts of the UK and Spain, including the Atlantic range edges. Double-digest restriction-site-associated DNA sequencing was used to sample individual cabbage genomes, assess the similarity of plants from 20 populations, and explore environment–genotype associations across varying climatic conditions. Interestingly, there were no indications of isolation by distance; several geographically close populations were genetically more distinct from each other than to distant populations. Furthermore, several distant populations shared genetic ancestry, which could indicate that they were established by escapees of similar source cultivars. However, there were signals of local adaptation to different environments, including a possible relationship between genetic diversity and soil pH. Overall, these results highlight wild cabbages in the Atlantic region as an important genetic resource worthy of further research into their relationship with existing crop varieties.Publisher PDFPeer reviewe

Re-identification of individuals from images using spot constellations : a case study in Arctic charr (Salvelinus alpinus)

The long-term monitoring of Arctic charr in lava caves is funded by the Icelandic Research Fund, RANNÍS (research grant nos. 120227 and 162893). E.A.M. was supported by the Icelandic Research Fund, RANNÍS (grant no. 162893) and NERC research grant awarded to M.B.M. (grant no. NE/R011109/1). M.B.M. was supported by a University Research Fellowship from the Royal Society (London). C.A.L. and B.K.K. were supported by Hólar University, Iceland. The Titan Xp GPU used for this research was donated to K.T. by the NVIDIA Corporation.The ability to re-identify individuals is fundamental to the individual-based studies that are required to estimate many important ecological and evolutionary parameters in wild populations. Traditional methods of marking individuals and tracking them through time can be invasive and imperfect, which can affect these estimates and create uncertainties for population management. Here we present a photographic re-identification method that uses spot constellations in images to match specimens through time. Photographs of Arctic charr (Salvelinus alpinus) were used as a case study. Classical computer vision techniques were compared with new deep-learning techniques for masks and spot extraction. We found that a U-Net approach trained on a small set of human-annotated photographs performed substantially better than a baseline feature engineering approach. For matching the spot constellations, two algorithms were adapted, and, depending on whether a fully or semi-automated set-up is preferred, we show how either one or a combination of these algorithms can be implemented. Within our case study, our pipeline both successfully identified unmarked individuals from photographs alone and re-identified individuals that had lost tags, resulting in an approximately 4 our multi-step pipeline involves little human supervision and could be applied to many organisms.Publisher PDFPeer reviewe

Data from: Are molecular markers useful predictors of adaptive potential?

Estimates of molecular genetic variation are often used as a cheap and simple surrogate for a population's adaptive potential, yet empirical evidence suggests they are unlikely to be a valid proxy. However, this evidence is based on molecular genetic variation poorly predicting estimates of adaptive potential rather than how well it predicts true values. As a consequence, the relationship has been systematically underestimated and the precision with which it could be measured severely overstated. By collating a large database, and using suitable statistical methods, we obtain a 95% upper bound of 0.26 for the proportion of variance in quantitative genetic variation explained by molecular diversity. The relationship is probably too weak to be useful, but this conclusion must be taken as provisional: less noisy estimates of quantitative genetic variation are required. In contrast, and perhaps surprisingly, current sampling strategies appear sufficient for characterising a population's molecular genetic variation at comparable markers

Data from: Disentangling genetic and prenatal sources of familial resemblance across ontogeny in a wild passerine.

Cross-fostering experiments are widely used by quantitative geneticists to study genetics and by behavioral ecologists to study the effects of prenatal in- vestment. Generally, the effects of genes and prenatal investment are confounded and the interpretation given to such experiments is largely dependent on the in- terests of the researcher. Using a large-scale well controlled experiment on a wild population of blue tits (Cyanistes caeruleus) we are able to partition variation in body mass across ontogeny into the effects of genes and the effects of between- clutch variation in egg characteristics. We show that although egg effects are important early in ontogeny they quickly dissipate, suggesting that the genetic interpretation of cross-fostering experiments may be preferable for many types of trait. However, the heritability of body mass is smaller than has previously been reported. Our results suggest that this is due to a combination of control- ling postnatal environmental effects more carefully and accounting for viability selection operating early in ontogeny

R Scripts

R code for performing all analyses in Hadfield at al. (2013) `Intra-clutch differences in egg characteristics mitigate the consequences of age-related hierarchies in a wild passerine

Data from: Intra-clutch differences in egg characteristics mitigate the consequences of age-related hierarchies in a wild passerine.

The relative age of an individual's siblings is a major cause of fitness variation in many species. In Blue tits (Cyanistes caeruleus) we show that age hierarchies are predominantly caused by incubation pre-clutch completion, such that last laid eggs hatch later than early laid eggs. However, after statistically controlling for incubation behavior late laid eggs are shown to hatch more quickly than early laid eggs reducing the amount of asynchrony. By experimentally switching early and late laid eggs between nests on the day they were laid we controlled for the effect of differential incubation and found that the faster hatching times of late laid eggs remains. Chicks that hatched earlier were heavier and had higher probability of fledgling, and chicks that hatched from experimental eggs had patterns of growth and survival consistent with this. Egg mass explained a small part of this variation, but the remainder must be due to egg composition. These results are consistent with the idea that intrinsic differences between eggs across the laying sequence serve to mitigate the effects of age-related hierarchies. We also show that between-clutch variation in prenatal developmental rate exists and that it is mainly environmental in origin rather than genetic

Script

R script to process data and run models

Loci

Loci contains information from both single locus and multiple loci estimates with the following fields: # Study: Reference # Population: Population description given in the source paper # Species: Species # Year: Year measurements made # Season: Season measurements made # Age.Class: Adults or chicks # Sample.size: Number of individuals the estimate is based on # Type.of.loci: Molecular marker used # Total.loci: Number of loci the estimate is based on # Number of sites: Only relevant for nucleotide diversity estimates # Locus: Name of the locus # Mean.diversity: Average diversity for either single or multilocus estimates # SE.Mean.Diversity: Any standard errors reported # SD.Mean.Diversity: Any standard deviations reported # Diversity.Measure: Heterozygosity (Microsatellite, Allozyme, AFLP) or pi (Nucleotide diversities) # Type.of.chromosome: Autosome or sex # Type.of.site: For nucleotide diversities, i.e. silent, intronic, non-coding, synonymous, mixed sites # Sampling strategy: Estimate from single or multiple population

Quan

Quan contains the heritability and coefficients of additive genetic variance estimates with the following fields: # Study: Reference # Population: Population description given in the source paper # Type.of.relative: The type of relatives used in the estimation. AM = animal model, C = clonal, FS = full-sib, HS = half-sib, MPO = mid-parent-offspring, SPO = single-parent-offspring # Male.Female: Was a maternal or paternal parent used in SPO or forming a HS family # Lab.Field: Lab = laboratory estimates, Field = wild estimates, Field/Lab = parental measurements made in the wild and F1 estimates made in a laboratory # Species: Species # Trait: The trait measured (this is copied and pasted from the original study removing case and white space is necessary) # Group: Information on the experimental groups used. E.g. experimentally different temperatures # Trait.From.Hansen.et.al: Is the initial information for an estimate sourced from Hansen et al. (2011)? Additional information has been added here. # Trait.Type: Classification of the trait into B = behaviour, LH = life-history, M = morphological, P = physiological # Sample.size: Number of individuals used in the estimation # Trait.Mean: Average value for the trait # SD.Trait.Mean: Any standard deviations reported # Precision.Trait.Mean: Any precision reported # Precision.Measure.Trait.Mean: SE = standard error, CI - Confidence intervals, Range # Heritability: Heritability estimate reported # SD.Heritability: Any standard deviations reported # Precision.Heritability: Any precision reported # Precision.Measure.Heritability: SE = standard error, CI - Confidence intervals # Var.H2: Variation in heritability estimate # Cva: Coefficient of additive genetic variance # SE.ia: Standard error reported for CV