### 2018

• de Villemereuil, P., Mouterde, M., Gaggiotti, O. E., & Till-Bottraud, I.. (2018). Patterns of phenotypic plasticity and local adaptation in the wide elevation range of the alpine plant Arabis alpina. Journal of Ecology.

1. Local adaptation and phenotypic plasticity are two important characteristics of alpine plants to overcome the threats caused by global changes. Among alpine species, Arabis alpina is characterised by an unusually wide altitudinal amplitude, ranging from 800m to 3100m of elevation in the French Alps. Two non-exclusive hypotheses can explain the presence of A. alpina across this broad ecological gradient: adaptive phenotypic plasticity or local adaptation, making this species especially useful to better understand these phenomena in alpine plant species. 2. We carried out common garden experiments at two different elevations with maternal progenies from 6 sites that differed in altitude. We showed that (i) key phenotypic traits (morphotype, total fruit length, growth, height) display significant signs of local adaptation, (ii) most traits studied are characterised by a high phenotypic plasticity between the two experimental gardens, and (iii) the two populations from the highest elevations lacked morphological plasticity compared to the other populations. 3. By combining two genome scan approaches (detection of selection and association methods), we isolated a candidate gene (SPS1). This gene was associated with height and local average temperature in our studied populations, consistent with previous studies on this gene in A. thaliana. Synthesis Given the nature of the traits involved in the detected pattern of local adaptation and the relative lack of plasticity of the two most extreme populations, our findings are consistent with a scenario of a locally adaptive stress response syndrome in high elevation populations. Due to a reduced phenotypic plasticity, an overall low intra-population genetic diversity of the adaptive traits and weak gene flow, populations of high altitude might have difficulties to cope with e.g. a rise of temperature.

@article{de_villemereuil_patterns_2018,
title = {Patterns of phenotypic plasticity and local adaptation in the wide elevation range of the alpine plant {Arabis} alpina},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2018 - Patterns of phenotypic plasticity and local adapta.pdf},
abstract = {1. Local adaptation and phenotypic plasticity are two important characteristics of alpine plants to overcome the threats
caused by global changes.
Among alpine species, Arabis alpina is characterised by an unusually wide altitudinal amplitude, ranging from 800m to 3100m of elevation in the French Alps.
Two non-exclusive hypotheses can explain the presence of A. alpina across this broad ecological gradient: adaptive phenotypic plasticity or local adaptation, making this species especially useful to better understand these phenomena in alpine plant species.
2. We carried out common garden experiments at two different elevations with maternal progenies from 6 sites that differed in altitude. We showed that (i) key phenotypic traits (morphotype, total fruit length, growth, height) display significant signs of local adaptation, (ii) most traits studied are characterised by a high phenotypic plasticity between the two experimental gardens, and (iii) the two populations from the highest elevations lacked morphological plasticity compared to the other populations.
3. By combining two genome scan approaches (detection of selection and association methods), we isolated a candidate gene (SPS1). This gene was associated with height and local average temperature in our studied populations, consistent with previous studies on this gene in A. thaliana.
Synthesis Given the nature of the traits involved in the detected pattern of local adaptation and the relative lack of plasticity of the two most extreme populations, our findings are consistent with a scenario of a locally adaptive stress response syndrome in high elevation populations.
Due to a reduced phenotypic plasticity, an overall low intra-population genetic diversity of the adaptive traits and weak gene flow, populations of high altitude might have difficulties to cope with e.g. a rise of temperature.},
journal = {Journal of Ecology},
author = {de Villemereuil, Pierre and Mouterde, Médéric and Gaggiotti, Oscar E. and Till-Bottraud, Irène},
year = {2018},
note = {IF: 6.49, Q1
bibtex: devillemereuil\_patterns\_2018-perso},
keywords = {unpublished},
file = {de Villemereuil et al. - 2018 - Patterns of phenotypic plasticity and local adapta.pdf:/home/pierre/Zotero/storage/4598AIYC/de Villemereuil et al. - 2018 - Patterns of phenotypic plasticity and local adapta.pdf:application/pdf}
}

• de Villemereuil, P., Morrissey, M. B., Nakagawa, S., & Schielzeth, H.. (2018). Fixed effect variance and the estimation of repeatabilities and heritabilities: Issues and solutions. Journal of Evolutionary Biology. doi:10.1111/jeb.13232

Linear mixed effects models are frequently used for estimating quantitative genetic parameters, including the heritability, of traits of interest. Heritability is an important metric, because it acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change. As a quantity of biological interest, it is important that the denominator, the phenotypic variance, actually reflects the amount of phenotypic variance in the relevant ecological stetting. The current practice of quantifying heritability from mixed effects models frequently deprives the heritability of variance explained by fixed effects (often leading to upward-bias) and it has been suggested to omit fixed effects when estimating heritabilities. We advocate an alternative option of fitting complex models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of heritabilities. The approach is easily implemented (an example is provided) and allows corrections for the estimation of heritability, such as the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We explore the complications arising depending on the nature of the covariates included as fixed effects (e.g. biological or experimental origin, characteristics of biological covariates). Furthermore, we discuss fixed effects in non-linear and generalized linear models when fixed effects. In these cases, the variance parameters depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.

@article{de_villemereuil_fixed_2018,
title = {Fixed effect variance and the estimation of repeatabilities and heritabilities: {Issues} and solutions},
issn = {1420-9101},
shorttitle = {Fixed effect variance and the estimation of repeatabilities and heritabilities},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2017 - Fixed effect variance and the estimation of repeat.pdf},
doi = {10.1111/jeb.13232},
abstract = {Linear mixed effects models are frequently used for estimating quantitative genetic parameters, including the heritability, of traits of interest. Heritability is an important metric, because it acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change. As a quantity of biological interest, it is important that the denominator, the phenotypic variance, actually reflects the amount of phenotypic variance in the relevant ecological stetting. The current practice of quantifying heritability from mixed effects models frequently deprives the heritability of variance explained by fixed effects (often leading to upward-bias) and it has been suggested to omit fixed effects when estimating heritabilities. We advocate an alternative option of fitting complex models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of heritabilities. The approach is easily implemented (an example is provided) and allows corrections for the estimation of heritability, such as the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We explore the complications arising depending on the nature of the covariates included as fixed effects (e.g. biological or experimental origin, characteristics of biological covariates). Furthermore, we discuss fixed effects in non-linear and generalized linear models when fixed effects. In these cases, the variance parameters depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.},
language = {en},
urldate = {2017-07-06},
journal = {Journal of Evolutionary Biology},
author = {de Villemereuil, Pierre and Morrissey, Michael B. and Nakagawa, Shinichi and Schielzeth, Holger},
year = {2018},
note = {bibtex: devillemereuil\_fixed\_2017-perso
IF: 3.13, Q1 (sous presse)},
keywords = {fixed effects, heritability, linear mixed modelling, quantitative genetics},
file = {de Villemereuil et al. - 2017 - Fixed effect variance and the estimation of repeat.pdf:/home/pierre/Zotero/storage/DJR9GDWQ/de Villemereuil et al. - 2017 - Fixed effect variance and the estimation of repeat.pdf:application/pdf}
}

• de Villemereuil, P.. (2018). Quantitative genetics methods depending on the nature of the phenotypic trait. Annals of the New York Academy of Sciences. doi:10.1111/nyas.13571
@article{de_villemereuil_quantitative_2018,
series = {The {Year} in {Evolutionary} {Biology}},
title = {Quantitative genetics methods depending on the nature of the phenotypic trait},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil - 2017 - Quantitative genetics methods depending on the nat.pdf},
doi = {10.1111/nyas.13571},
journal = {Annals of the New York Academy of Sciences},
author = {de Villemereuil, Pierre},
year = {2018},
note = {bibtex: devillemereuil\_quantitative\_2017-perso
IF: 4.47, Q1},
file = {de Villemereuil - 2017 - Quantitative genetics methods depending on the nat.pdf:/home/pierre/Zotero/storage/TZNLGJ38/de Villemereuil - 2017 - Quantitative genetics methods depending on the nat.pdf:application/pdf}
}

• Alberto, F. J., Boyer, F., Orozco-terWengel, P., Streeter, I., Servin, B., de Villemereuil, P., Benjelloun, B., Librado, P., Biscarini, F., Colli, L., Zamani, W., Alberti, A., Engelen, S., Stella, A., Joost, S., Ajmone-Marsan, P., Negrini, R., Orlando, L., Rezaei, H. R., Naderi, S., Bruford, M., Clarke, L., Flicek, P., Wincker, P., Coissac, E., Kijas, J., Tosser-Klopp, G., Chikhi, A., Taberlet, P., & Pompanon, F.. (2018). Convergent genomic signatures of domestication in sheep and goats. Nature Communications.
[BibTeX]
@article{alberto_convergent_2018,
title = {Convergent genomic signatures of domestication in sheep and goats},
journal = {Nature Communications},
author = {Alberto, Florian J. and Boyer, Frédéric and Orozco-terWengel, Pablo and Streeter, Ian and Servin, Bertrand and de Villemereuil, Pierre and Benjelloun, Badr and Librado, Pablo and Biscarini, Filippo and Colli, Licia and Zamani, Wahid and Alberti, Adriana and Engelen, Stefan and Stella, Alessandra and Joost, Stéphane and Ajmone-Marsan, Paolo and Negrini, Riccardo and Orlando, Ludovic and Rezaei, Hamid Reza and Naderi, Saeid and Bruford, Mike and Clarke, Laura and Flicek, Paul and Wincker, Patrick and Coissac, Eric and Kijas, James and Tosser-Klopp, Gwenola and Chikhi, Abdelkader and Taberlet, Pierre and Pompanon, François},
year = {2018},
note = {bibtex: alberto\_convergent\_2018-perso
IF: 13.09, Q1}
}

### 2017

• Garamszegi, L. Z., & de Villemereuil, P.. (2017). Perturbations on the uniform distribution of p-values can lead to misleading inferences from null-hypothesis testing. Trends in Neuroscience and Education, 8-9, 18-27. doi:10.1016/j.tine.2017.10.001

Null-hypothesis testing (NHT) based on statistical significance is the most conventional statistical framework, on which neuroscientists rely for the analysis of their data. However, this approach can provide misleading results if p-values are wrongly interpreted, as often done in practice. Misconceptions can arise, in particular, when i) wrong null-hypothesis is chosen for reference; ii) the assumptions of the statistical model are not met; iii) p-values are interpreted as the probability of the null- or alternative hypotheses or as the measure of the importance of findings; iv) statistical thresholds guide scientific conclusions and decision making; v) one applies multiple testing or p-hacking. In this commentary, we address these issues by bringing into the focus the uniform distribution of p-values with the hope of enhancing the appreciation and proper use of the NHT approach among neuroscientists. We propose guidelines for the correct interpretations of p-values that brain and behavioural scientists may adopt to improve both the transparency of statistical reports and the value of scientific conclusions drawn from them.

@article{garamszegi_perturbations_2017,
title = {Perturbations on the uniform distribution of p-values can lead to misleading inferences from null-hypothesis testing},
volume = {8-9},
issn = {2211-9493},
url = {http://devillemereuil.legtux.org/publis/Zsolt Garamszegi and de Villemereuil - 2017 - Perturbations on the uniform distribution of p-val.pdf},
doi = {10.1016/j.tine.2017.10.001},
abstract = {Null-hypothesis testing (NHT) based on statistical significance is the most conventional statistical framework, on which neuroscientists rely for the analysis of their data. However, this approach can provide misleading results if p-values are wrongly interpreted, as often done in practice. Misconceptions can arise, in particular, when i) wrong null-hypothesis is chosen for reference; ii) the assumptions of the statistical model are not met; iii) p-values are interpreted as the probability of the null- or alternative hypotheses or as the measure of the importance of findings; iv) statistical thresholds guide scientific conclusions and decision making; v) one applies multiple testing or p-hacking. In this commentary, we address these issues by bringing into the focus the uniform distribution of p-values with the hope of enhancing the appreciation and proper use of the NHT approach among neuroscientists. We propose guidelines for the correct interpretations of p-values that brain and behavioural scientists may adopt to improve both the transparency of statistical reports and the value of scientific conclusions drawn from them.},
urldate = {2017-12-26},
journal = {Trends in Neuroscience and Education},
author = {Garamszegi, László Zsolt and de Villemereuil, Pierre},
month = dec,
year = {2017},
note = {bibtex: garamszegi\_perturbations\_2017-perso
IF: 4.47, Q1 (Cit: 0)},
keywords = {Statistics, -hacking, -values, Null-hypothesis testing, p-hacking, p-values},
pages = {18--27},
file = {Zsolt Garamszegi and de Villemereuil - 2017 - Perturbations on the uniform distribution of p-val.pdf:/home/pierre/Zotero/storage/TB6TKTTV/Zsolt Garamszegi and de Villemereuil - 2017 - Perturbations on the uniform distribution of p-val.pdf:application/pdf}
}

### 2016

• de Villemereuil, P., Schielzeth, H., Nakagawa, S., & Morrissey, M. B.. (2016). General methods for evolutionary quantitative genetic inference from generalised mixed models. Genetics, 204(3), 1281-1294. doi:10.1534/genetics.115.186536

Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population.

@article{de_villemereuil_general_2016,
title = {General methods for evolutionary quantitative genetic inference from generalised mixed models},
volume = {204},
copyright = {Copyright © 2016 de Villemereuil et al.. Available freely online through the author-supported open access option.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.},
issn = {0016-6731, 1943-2631},
url = {http://devillemereuil.legtux.org/publis/Villemereuil et al. - 2016 - General methods for evolutionary quantitative gene.pdf},
doi = {10.1534/genetics.115.186536},
abstract = {Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population.},
language = {en},
number = {3},
urldate = {2016-12-27},
journal = {Genetics},
author = {de Villemereuil, Pierre and Schielzeth, Holger and Nakagawa, Shinichi and Morrissey, Michael B.},
month = nov,
year = {2016},
note = {bibtex: devillemereuil\_general\_2016-perso
IF: 5.18, Q1 (Cit: 10)},
keywords = {evolution, Quantitative Genetics, Statistics, additive genetic variance, Generalized linear model, G matrix, generalised linear mixed model, theory},
pages = {1281--1294},
file = {Villemereuil et al. - 2016 - General methods for evolutionary quantitative gene.pdf:/home/pierre/Zotero/storage/VT9IFDR5/Villemereuil et al. - 2016 - General methods for evolutionary quantitative gene.pdf:application/pdf}
}

• Andrello, M., de Villemereuil, P., Busson, D., Gaggiotti, O. E., & Till-Bottraud, I.. (2016). Population dynamics of \textitArabis alpina in the French Alps: evidence for demographic compensation?. bioRxiv, 70847. doi:10.1101/070847

Due to its genetic proximity with Arabidopsis thaliana, Arabis alpina (Brassicaceae) is increasingly used as a perennial model species in studies of molecular evolution and adaptation. We studied the demography of A. alpina in six natural sites widely differing in their degree of disturbance, slope and vegetation, and encompassing the full altitudinal range of the species. We estimated three vital rates (growth, reproductive effort and survival) for individually-marked plants, studied for six years (2008-2014). We characterized the thermic conditions of each site with different thermal variables obtained using in situ continuous-time data loggers. Although A. alpina is described as a perennial species, the average life expectancy was only 1.82 years and most plants died before setting seeds. Plant size was a strong predictor of all three vital rates. Mean daily temperature showed a positive effect on growth and a negative effect on survival. Furthermore, reproductive effort covaried negatively with survival, suggesting a mechanism of demographic compensation acting on an elevational gradient. Synthesis. These results are informative of the selective pressures experienced by A. alpina in natural conditions and will help design experimental and molecular studies of local adaptation in this species.

@article{andrello_population_2016,
title = {Population dynamics of \textit{{Arabis} alpina} in the {French} {Alps}: evidence for demographic compensation?},
shorttitle = {Population dynamics of {Arabis} alpina in the {French} {Alps}},
url = {http://www.biorxiv.org/content/early/2016/08/22/070847},
doi = {10.1101/070847},
abstract = {Due to its genetic proximity with Arabidopsis thaliana, Arabis alpina (Brassicaceae) is increasingly used as a perennial model species in studies of molecular evolution and adaptation. We studied the demography of A. alpina in six natural sites widely differing in their degree of disturbance, slope and vegetation, and encompassing the full altitudinal range of the species. We estimated three vital rates (growth, reproductive effort and survival) for individually-marked plants, studied for six years (2008-2014). We characterized the thermic conditions of each site with different thermal variables obtained using in situ continuous-time data loggers. Although A. alpina is described as a perennial species, the average life expectancy was only 1.82 years and most plants died before setting seeds. Plant size was a strong predictor of all three vital rates. Mean daily temperature showed a positive effect on growth and a negative effect on survival. Furthermore, reproductive effort covaried negatively with survival, suggesting a mechanism of demographic compensation acting on an elevational gradient. Synthesis. These results are informative of the selective pressures experienced by A. alpina in natural conditions and will help design experimental and molecular studies of local adaptation in this species.},
language = {en},
urldate = {2016-08-23},
journal = {bioRxiv},
author = {Andrello, Marco and de Villemereuil, Pierre and Busson, Delphine and Gaggiotti, Oscar E. and Till-Bottraud, Irene},
year = {2016},
note = {bibtex: andrello\_population\_2016-perso
(en preparation)},
keywords = {unpublished},
pages = {070847},
file = {Andrello et al. - 2016 - Population dynamics of Arabis alpina in the French.pdf:/home/pierre/Zotero/storage/V6EJMQN9/Andrello et al. - 2016 - Population dynamics of Arabis alpina in the French.pdf:application/pdf}
}

• de Villemereuil, P., Gaggiotti, O. E., Mouterde, M., & Till-Bottraud, I.. (2016). Common garden experiments in the genomic era: new perspectives and opportunities. Heredity, 116(3), 249-254. doi:10.1038/hdy.2015.93

The study of local adaptation is rendered difficult by many evolutionary confounding phenomena (for example, genetic drift and demographic history). When complex traits are involved in local adaptation, phenomena such as phenotypic plasticity further hamper evolutionary biologists to study the complex relationships between phenotype, genotype and environment. In this perspective paper, we suggest that the common garden experiment, specifically designed to deal with phenotypic plasticity, has a clear role to play in the study of local adaptation, even (if not specifically) in the genomic era. After a quick review of some high-throughput genotyping protocols relevant in the context of a common garden, we explore how to improve common garden analyses with dense marker panel data and recent statistical methods. We then show how combining approaches from population genomics and genome-wide association studies with the settings of a common garden can yield to a very efficient, thorough and integrative study of local adaptation. Especially, evidence from genomic (for example, genome scan) and phenotypic origins constitute independent insights into the possibility of local adaptation scenarios, and genome-wide association studies in the context of a common garden experiment allow to decipher the genetic bases of adaptive traits.

@article{de_villemereuil_common_2016,
title = {Common garden experiments in the genomic era: new perspectives and opportunities},
volume = {116},
issn = {0018-067X},
shorttitle = {Common garden experiments in the genomic era},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2015 - Common garden experiments in the genomic era new.pdf},
doi = {10.1038/hdy.2015.93},
abstract = {The study of local adaptation is rendered difficult by many evolutionary confounding phenomena (for example, genetic drift and demographic history). When complex traits are involved in local adaptation, phenomena such as phenotypic plasticity further hamper evolutionary biologists to study the complex relationships between phenotype, genotype and environment. In this perspective paper, we suggest that the common garden experiment, specifically designed to deal with phenotypic plasticity, has a clear role to play in the study of local adaptation, even (if not specifically) in the genomic era. After a quick review of some high-throughput genotyping protocols relevant in the context of a common garden, we explore how to improve common garden analyses with dense marker panel data and recent statistical methods. We then show how combining approaches from population genomics and genome-wide association studies with the settings of a common garden can yield to a very efficient, thorough and integrative study of local adaptation. Especially, evidence from genomic (for example, genome scan) and phenotypic origins constitute independent insights into the possibility of local adaptation scenarios, and genome-wide association studies in the context of a common garden experiment allow to decipher the genetic bases of adaptive traits.},
language = {en},
number = {3},
urldate = {2016-04-08},
journal = {Heredity},
author = {de Villemereuil, Pierre and Gaggiotti, Oscar E. and Mouterde, Médéric and Till-Bottraud, Irène},
month = mar,
year = {2016},
note = {bibtex: devillemereuil\_common\_2016-perso
IF: 3.82, Q1 (Cit: 25)},
pages = {249--254},
file = {de Villemereuil et al. - 2015 - Common garden experiments in the genomic era new .pdf:/home/pierre/Zotero/storage/HKQMUCWI/de Villemereuil et al. - 2015 - Common garden experiments in the genomic era new .pdf:application/pdf}
}

• Till-Bottraud, I., & de Villemereuil, P.. (2016). Kin recognition or phenotype matching?. New Phytologist, 209(1), 13-14. doi:10.1111/nph.13554
@article{till-bottraud_kin_2016,
title = {Kin recognition or phenotype matching?},
volume = {209},
issn = {1469-8137},
url = {http://devillemereuil.legtux.org/publis/Till-Bottraud et de Villemereuil - 2016 - Kin recognition or phenotype matching.pdf},
doi = {10.1111/nph.13554},
language = {en},
number = {1},
urldate = {2016-01-23},
journal = {New Phytologist},
author = {Till-Bottraud, Irène and de Villemereuil, Pierre},
month = jan,
year = {2016},
note = {bibtex: tillbottraud\_kin\_2016-perso
IF: 7.92, Q1 (Cit: 5)},
keywords = {Arabidopsis thaliana, kin selection, light signal, neighbor recognition, phenotype matching, shade avoidance},
pages = {13--14},
file = {Till-Bottraud et de Villemereuil - 2016 - Kin recognition or phenotype matching.pdf:/home/pierre/Zotero/storage/QVMF9EJS/Till-Bottraud et de Villemereuil - 2016 - Kin recognition or phenotype matching.pdf:application/pdf}
}

### 2015

• Frichot, É., Schoville, S. D., de Villemereuil, P., Gaggiotti, O. E., & François, O.. (2015). Detecting adaptive evolution based on association with ecological gradients: Orientation matters!. Heredity, 115(1), 22-28. doi:10.1038/hdy.2015.7
[BibTeX] [Abstract]

Population genetic signatures of local adaptation are frequently investigated by identifying loci with allele frequencies that exhibit high correlation with ecological variables. One difficulty with this approach is that ecological associations might be confounded by geographic variation at selectively neutral loci. Here, we consider populations that underwent spatial expansion from their original range, and for which geographical variation of adaptive allele frequency coincides with habitat gradients. Using range expansion simulations, we asked whether our ability to detect genomic regions involved in adaptation could be impacted by the orientation of the ecological gradients. For three ecological association methods tested, we found, counter-intuitively, fewer false-positive associations when ecological gradients aligned along the main axis of expansion than when they aligned along any other direction. This result has important consequences for the analysis of genomic data under non-equilibrium population genetic models. Alignment of gradients with expansion axes is likely to be common in scenarios in which expanding species track their ecological niche during climate change while adapting to changing environments at their rear edge.

@article{frichot_detecting_2015,
title = {Detecting adaptive evolution based on association with ecological gradients: {Orientation} matters!},
volume = {115},
issn = {0018-067X},
doi = {10.1038/hdy.2015.7},
abstract = {Population genetic signatures of local adaptation are frequently investigated by identifying loci with allele frequencies that exhibit high correlation with ecological variables. One difficulty with this approach is that ecological associations might be confounded by geographic variation at selectively neutral loci. Here, we consider populations that underwent spatial expansion from their original range, and for which geographical variation of adaptive allele frequency coincides with habitat gradients. Using range expansion simulations, we asked whether our ability to detect genomic regions involved in adaptation could be impacted by the orientation of the ecological gradients. For three ecological association methods tested, we found, counter-intuitively, fewer false-positive associations when ecological gradients aligned along the main axis of expansion than when they aligned along any other direction. This result has important consequences for the analysis of genomic data under non-equilibrium population genetic models. Alignment of gradients with expansion axes is likely to be common in scenarios in which expanding species track their ecological niche during climate change while adapting to changing environments at their rear edge.},
language = {en},
number = {1},
urldate = {2015-06-15},
journal = {Heredity},
author = {Frichot, Éric and Schoville, Sean D. and de Villemereuil, Pierre and Gaggiotti, Oscar E. and François, Olivier},
month = jul,
year = {2015},
note = {bibtex: frichot\_detecting\_2015-perso
IF: 3.82, Q1 (Cit: 20)},
pages = {22--28},
file = {Frichot et al. - 2015 - Detecting adaptive evolution based on association .pdf:/home/pierre/Zotero/storage/HZKXA6HA/Frichot et al. - 2015 - Detecting adaptive evolution based on association .pdf:application/pdf}
}

• Aguilée, R., de Villemereuil, P., & Guillon, J.. (2015). Dispersal evolution and resource matching in a spatially and temporally variable environment. Journal of Theoretical Biology, 370, 184-196. doi:10.1016/j.jtbi.2015.01.018

Metapopulations may consist of patches of different quality, and are often disturbed by extrinsic processes causing variation of patch quality. The persistence of such metapopulations then depends on the species׳ dispersal strategy. In a temporally constant environment, the evolution of dispersal rates follows the resource matching rule, i.e. at the evolutionarily stable dispersal strategy the number of competitors in each patch matches the resource availability in each patch. Here, we investigate how the distribution of individuals resulting from convergence stable dispersal strategies would match the distribution of resources in an environment which is temporally variable due to extrinsic disturbance. We develop an analytically tractable asexual model with two qualities of patches. We show that convergence stable dispersal rates are such that resource matching is predicted in expectation before habitat quality variation, and that the distribution of individuals undermatches resources after habitat quality variation. The overall flow of individuals between patches matches the overall flow of resources between patches resulting from environmental variation. We show that these conclusions can be generalized to organisms with sexual reproduction, and to a metapopulation with three qualities of patches when there is no mutational correlation between dispersal rates.

@article{aguilee_dispersal_2015,
title = {Dispersal evolution and resource matching in a spatially and temporally variable environment},
volume = {370},
issn = {0022-5193},
url = {http://devillemereuil.legtux.org/publis/Aguil%C3%A9e%20et%20al.%20-%202015%20-%20Dispersal%20evolution%20and%20resource%20matching%20in%20a%20spa.pdf},
doi = {10.1016/j.jtbi.2015.01.018},
abstract = {Metapopulations may consist of patches of different quality, and are often disturbed by extrinsic processes causing variation of patch quality. The persistence of such metapopulations then depends on the species׳ dispersal strategy. In a temporally constant environment, the evolution of dispersal rates follows the resource matching rule, i.e. at the evolutionarily stable dispersal strategy the number of competitors in each patch matches the resource availability in each patch. Here, we investigate how the distribution of individuals resulting from convergence stable dispersal strategies would match the distribution of resources in an environment which is temporally variable due to extrinsic disturbance. We develop an analytically tractable asexual model with two qualities of patches. We show that convergence stable dispersal rates are such that resource matching is predicted in expectation before habitat quality variation, and that the distribution of individuals undermatches resources after habitat quality variation. The overall flow of individuals between patches matches the overall flow of resources between patches resulting from environmental variation. We show that these conclusions can be generalized to organisms with sexual reproduction, and to a metapopulation with three qualities of patches when there is no mutational correlation between dispersal rates.},
urldate = {2015-02-25},
journal = {Journal of Theoretical Biology},
author = {Aguilée, Robin and de Villemereuil, Pierre and Guillon, Jean-Michel},
year = {2015},
note = {bibtex: aguilee\_dispersal\_2015-perso
IF: 2.26, Q1 (Cit: 1)},
pages = {184--196},
file = {Aguilée et al. - 2015 - Dispersal evolution and resource matching in a spa.pdf:/home/pierre/Zotero/storage/FDGSMM6P/Aguilée et al. - 2015 - Dispersal evolution and resource matching in a spa.pdf:application/pdf}
}

• de Villemereuil, P., & Gaggiotti, O. E.. (2015). A new FST-based method to uncover local adaptation using environmental variables. Methods in Ecology and Evolution, 6(11), 1248-1258. doi:10.1111/2041-210X.12418

@article{de_villemereuil_new_2015,
title = {A new {FST}-based method to uncover local adaptation using environmental variables},
volume = {6},
issn = {2041210X},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil et Gaggiotti - 2015 - A new FST-based method to uncover local adaptation.pdf},
doi = {10.1111/2041-210X.12418},
language = {en},
number = {11},
urldate = {2015-07-13},
journal = {Methods in Ecology and Evolution},
author = {de Villemereuil, Pierre and Gaggiotti, Oscar E.},
month = nov,
year = {2015},
note = {bibtex: devillemereuil\_new\_2015-perso
IF: 8.73, Q1 (Cit: 34)},
keywords = {local adaptation, Bayesian methods, false discovery rate, environment, F model, genome-scan},
pages = {1248--1258},
file = {de Villemereuil et Gaggiotti - 2015 - A new FST-based method to uncover local adaptation.pdf:/home/pierre/Zotero/storage/KQFNXIXM/de Villemereuil et Gaggiotti - 2015 - A new FST-based method to uncover local adaptation.pdf:application/pdf}
}

### 2014

• Morrissey, M. B., de Villemereuil, P., Doligez, B., & Gimenez, O.. (2014). Bayesian approaches to the quantitative genetic analysis of natural populations. In Charmantier, A., Garant, D., & Kruuk, L. E. B. (Eds.), In Quantitative Genetics in the Wild (, pp. 228-253). Oxford (UK): Oxford University Press.
[BibTeX]
@incollection{morrissey_bayesian_2014,
title = {Bayesian approaches to the quantitative genetic analysis of natural populations},
isbn = {978-0-19-165595-1},
language = {en},
booktitle = {Quantitative {Genetics} in the {Wild}},
publisher = {Oxford University Press},
author = {Morrissey, Michael B. and de Villemereuil, Pierre and Doligez, Blandine and Gimenez, Olivier},
editor = {Charmantier, Anne and Garant, Dany and Kruuk, Loeske E.B.},
month = apr,
year = {2014},
note = {bibtex: morrissey\_bayesian\_2014-perso
(Cit: 13)},
pages = {228--253},
file = {Morrissey et al. - 2014 - Bayesian approaches to the quantitative genetic an.pdf:/home/pierre/Zotero/storage/8DKX8JG9/Morrissey et al. - 2014 - Bayesian approaches to the quantitative genetic an.pdf:application/pdf}
}

• de Villemereuil, P., & Nakagawa, S.. (2014). General Quantitative Genetic Methods for Comparative Biology. In Garamszegi, L. Z. (Ed.), In Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (, pp. 287-303). Berlin, Heidelberg: Springer Berlin Heidelberg.
@incollection{garamszegi_general_2014,
title = {General {Quantitative} {Genetic} {Methods} for {Comparative} {Biology}},
isbn = {978-3-662-43550-2},
language = {en},
urldate = {2014-08-14},
booktitle = {Modern {Phylogenetic} {Comparative} {Methods} and {Their} {Application} in {Evolutionary} {Biology}},
publisher = {Springer Berlin Heidelberg},
author = {de Villemereuil, Pierre and Nakagawa, Shinichi},
editor = {Garamszegi, László Zsolt},
year = {2014},
note = {bibtex: devillemereuil\_general\_2014-perso
(Cit: 11)},
pages = {287--303},
file = {de Villemereuil et Nakagawa - 2014 - General Quantitative Genetic Methods for Comparati.pdf:/home/pierre/Zotero/storage/UIU9TSZK/de Villemereuil et Nakagawa - 2014 - General Quantitative Genetic Methods for Comparati.pdf:application/pdf}
}

• de Villemereuil, P., Frichot, É., Bazin, É., François, O., & Gaggiotti, O. E.. (2014). Genome scan methods against more complex models: when and how much should we trust them?. Molecular Ecology, 23(8), 2006-2019. doi:10.1111/mec.12705

The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.

@article{de_villemereuil_genome_2014,
title = {Genome scan methods against more complex models: when and how much should we trust them?},
volume = {23},
issn = {1365-294X},
shorttitle = {Genome scan methods against more complex models},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2014 - Genome scan methods against more complex models w.pdf},
doi = {10.1111/mec.12705},
abstract = {The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.},
language = {en},
number = {8},
urldate = {2014-04-09},
journal = {Molecular Ecology},
author = {de Villemereuil, Pierre and Frichot, Éric and Bazin, Éric and François, Olivier and Gaggiotti, Oscar E.},
year = {2014},
note = {bibtex: devillemereuil\_genome\_2014-perso
IF: 6.26, Q1 (Cit: 101)},
keywords = {genome scan, Adaptation, Bayesian methods, Genome scan, false discovery rate, power simulation study},
pages = {2006--2019},
file = {de Villemereuil et al. - 2014 - Genome scan methods against more complex models w.pdf:/home/pierre/Zotero/storage/X4E5F8MS/de Villemereuil et al. - 2014 - Genome scan methods against more complex models w.pdf:application/pdf}
}

### 2013

• de Villemereuil, P., Gimenez, O., & Doligez, B.. (2013). Comparing parent–offspring regression with frequentist and Bayesian animal models to estimate heritability in wild populations: a simulation study for Gaussian and binary traits. Methods in Ecology and Evolution, 4(3), 260-275. doi:10.1111/2041-210X.12011

* Estimating heritability of traits in wild populations is a major prerequisite to understand their evolution. Until recently, most heritability estimates had been obtained using parent-offspring regressions. However, the popularity of animal models, that is, (generalized) linear mixed models assessing the genetic variance component based on population pedigree information, has markedly increased in the past few years. Animal models are claimed to perform better than parent–offspring regressions mainly because they use full between-individual relatedness information and they allow explicit modelling of the environmental effects shared by individuals. However, the differences between heritability estimates obtained using both approaches are not straight forward, and the factors influencing these differences remain unclear. * We performed a simulation study to evaluate and compare the accuracy and precision of estimates obtained from parent–offspring regressions and animal models using both Frequentist (REML, PQL) and Bayesian (MCMC) estimation methods. We explored the influence of (i) the presence and type of shared environmental effects (non-transgenerational or transgenerational), (ii) the distribution of the phenotypic trait considered (Gaussian or binary trait) and (iii) data quantity and quality (sample size, pedigree connectivity) on heritability estimates obtained from the two approaches for different levels of true heritability. * In the absence of shared environmental effects, the animal model using the REML method performed best for a Gaussian trait, while the animal model using MCMC was more appropriate for a binary trait. For low quantity and quality data, and a binary trait, the parent–offspring regression yielded very imprecise estimates. * Estimates from the parent–offspring regression were not influenced by a non-transgenerational shared environmental effect, whereas estimates from animal models in which environmental effects are ignored were affected by both non-transgenerational and transgenerational effects. * We discuss the relevance of each approach and estimation method for estimating heritability in wild populations. Importantly, because most effects fitted in animal models are, in fact, non-transgenerational (including environmental maternal effects), we advocate a systematic comparison between parent–offspring regression and animal model estimates to detect potentially missing non-transgenerational environmental effects.

@article{de_villemereuil_comparing_2013,
title = {Comparing parent–offspring regression with frequentist and {Bayesian} animal models to estimate heritability in wild populations: a simulation study for {Gaussian} and binary traits},
volume = {4},
issn = {2041-210X},
shorttitle = {Comparing parent–offspring regression with frequentist and {Bayesian} animal models to estimate heritability in wild populations},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2013 - Comparing parent–offspring regression with frequen.pdf},
doi = {10.1111/2041-210X.12011},
abstract = {* Estimating heritability of traits in wild populations is a major prerequisite to understand their evolution. Until recently, most heritability estimates had been obtained using parent-offspring regressions. However, the popularity of animal models, that is, (generalized) linear mixed models assessing the genetic variance component based on population pedigree information, has markedly increased in the past few years. Animal models are claimed to perform better than parent–offspring regressions mainly because they use full between-individual relatedness information and they allow explicit modelling of the environmental effects shared by individuals. However, the differences between heritability estimates obtained using both approaches are not straight forward, and the factors influencing these differences remain unclear. * We performed a simulation study to evaluate and compare the accuracy and precision of estimates obtained from parent–offspring regressions and animal models using both Frequentist (REML, PQL) and Bayesian (MCMC) estimation methods. We explored the influence of (i) the presence and type of shared environmental effects (non-transgenerational or transgenerational), (ii) the distribution of the phenotypic trait considered (Gaussian or binary trait) and (iii) data quantity and quality (sample size, pedigree connectivity) on heritability estimates obtained from the two approaches for different levels of true heritability. * In the absence of shared environmental effects, the animal model using the REML method performed best for a Gaussian trait, while the animal model using MCMC was more appropriate for a binary trait. For low quantity and quality data, and a binary trait, the parent–offspring regression yielded very imprecise estimates. * Estimates from the parent–offspring regression were not influenced by a non-transgenerational shared environmental effect, whereas estimates from animal models in which environmental effects are ignored were affected by both non-transgenerational and transgenerational effects. * We discuss the relevance of each approach and estimation method for estimating heritability in wild populations. Importantly, because most effects fitted in animal models are, in fact, non-transgenerational (including environmental maternal effects), we advocate a systematic comparison between parent–offspring regression and animal model estimates to detect potentially missing non-transgenerational environmental effects.},
language = {en},
number = {3},
urldate = {2013-03-15},
journal = {Methods in Ecology and Evolution},
author = {de Villemereuil, Pierre and Gimenez, Olivier and Doligez, Blandine},
year = {2013},
note = {bibtex: devillemereuil\_comparing\_2013-perso
IF: 8.73, Q1 (Cit: 54)},
keywords = {Statistics, Bayesian methods {\textbackslash}textless Statistics, Quantitative genetics {\textbackslash}textless Population Genetics},
pages = {260--275},
file = {de Villemereuil et al. - 2013 - Comparing parent–offspring regression with frequen.pdf:/home/pierre/Zotero/storage/ITH4ZWVP/de Villemereuil et al. - 2013 - Comparing parent–offspring regression with frequen.pdf:application/pdf}
}

### 2012

• de Villemereuil, P., Wells, J. A., Edwards, R. D., & Blomberg, S. P.. (2012). Bayesian models for comparative analysis integrating phylogenetic uncertainty. BMC Evolutionary Biology, 12(1), 102. doi:10.1186/1471-2148-12-102

Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable.

@article{de_villemereuil_bayesian_2012,
title = {Bayesian models for comparative analysis integrating phylogenetic uncertainty},
volume = {12},
issn = {1471-2148},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2012 - Bayesian models for comparative analysis integrati.pdf},
doi = {10.1186/1471-2148-12-102},
abstract = {Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable.},
language = {en},
number = {1},
urldate = {2013-02-27},
journal = {BMC Evolutionary Biology},
author = {de Villemereuil, Pierre and Wells, Jessie A. and Edwards, Robert D. and Blomberg, Simon P.},
month = jun,
year = {2012},
note = {bibtex: devillemereuil\_bayesian\_2012-perso
IF: 3.31, Q1 (Cit: 44)},
pages = {102},
file = {de Villemereuil et al. - 2012 - Bayesian models for comparative analysis integrati.pdf:/home/pierre/Zotero/storage/QUBVS2JU/de Villemereuil et al. - 2012 - Bayesian models for comparative analysis integrati.pdf:application/pdf}
}

### 2011

• de Villemereuil, P., & López-Sepulcre, A.. (2011). Consumer functional responses under intra- and inter-specific interference competition. Ecological Modelling, 222(3), 419-426. doi:10.1016/j.ecolmodel.2010.10.011
@article{de_villemereuil_consumer_2011,
title = {Consumer functional responses under intra- and inter-specific interference competition},
volume = {222},
issn = {03043800},
url = {http://devillemereuil.legtux.org/publis/de Villemereuil et López-Sepulcre - 2011 - Consumer functional responses under intra- and int.pdf},
doi = {10.1016/j.ecolmodel.2010.10.011},
number = {3},
urldate = {2011-02-23},
journal = {Ecological Modelling},
author = {de Villemereuil, Pierre and López-Sepulcre, Andrés},
month = feb,
year = {2011},
note = {bibtex: devillemereuil\_consumer\_2011-perso
IF: 2.62, Q2 (Cit: 26)},
pages = {419--426},
file = {de Villemereuil et López-Sepulcre - 2011 - Consumer functional responses under intra- and int.pdf:/home/pierre/Zotero/storage/T4GGW89J/de Villemereuil et López-Sepulcre - 2011 - Consumer functional responses under intra- and int.pdf:application/pdf}
}

• Nakagawa, S., & de Villemereuil, P.. A simple and general method for accounting for phylogenetic uncertainty via Rubin’s rules in comparative analysis. Systematic Biology.
[BibTeX]
@article{nakagawa_simple_nodate,
title = {A simple and general method for accounting for phylogenetic uncertainty via {Rubin}’s rules in comparative analysis},
journal = {Systematic Biology},
author = {Nakagawa, Shinichi and de Villemereuil, Pierre},
note = {IF: 13.67, Q1, (soumis, révisions majeures)},
keywords = {unpublished}
}

• de Villemereuil, P., Rutschmann, A., Ewen, J. G., Santure, A. W., & Brekke, P.. Can threatened species keep up with climate change in sub-standard habitat? No observed or expected evolutionary response in lay date for the New Zealand hihi. Evolution.
[BibTeX]
@article{de_villemereuil_can_nodate,
title = {Can threatened species keep up with climate change in sub-standard habitat? {No} observed or expected evolutionary response in lay date for the {New} {Zealand} hihi},
journal = {Evolution},
author = {de Villemereuil, Pierre and Rutschmann, Alexis and Ewen, John G. and Santure, Anna W. and Brekke, Patricia},
note = {IF: 4.56, Q1 (soumis, révisions majeures)},
keywords = {unpublished}
}