Author: "Sarah Cubaynes" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Sarah Cubaynes"' showing total 30 results

Start Over Author "Sarah Cubaynes"

30 results on '"Sarah Cubaynes"'

1. Building spaces of interactions between researchers and managers: Case studies with wildlife monitoring and conservation in France

Author: Thibaut Couturier, Sarah Bauduin, Guillelme Astruc, Aurélie Blanck, Coline Canonne, Thierry Chambert, Jules Chiffard, Alix Cosquer, Sarah Cubaynes, Laurence Curtet, Emmanuelle Dortel, Nolwenn Drouet‐Hoguet, Christophe Duchamp, Charlotte Francesiaz, Oksana Grente, Adrien Jailloux, Maëlis Kervellec, Valentin Lauret, Jean‐Dominique Lebreton, Julie Louvrier, Lucile Marescot, Raphaël Mathevet, Marie‐Laure Navas, Charlotte Perrot, Nicolas Poulet, Pierre‐Yves Quenette, Michel Salas, Guillaume Souchay, Cécile Vanpé, Aurélien Besnard, and Olivier Gimenez
Subjects: boundary science, environmental management, research–implementation gap, science‐conservation interface, Environmental sciences, GE1-350, Ecology, QH540-549.5
Abstract: Abstract To document and halt biodiversity loss, monitoring, quantifying trends and assessing management and conservation strategies on wildlife populations and communities are crucial steps. With increasing technological innovations, more and more data are collected and new quantitative methods are constantly developed. These rapid developments come with an increasing need for analytical skills, which are hardly accessible to managers. On the other hand, researchers spend more and more time on research grant applications and administrative tasks, which leaves fewer opportunities for knowledge transfer. This situation tends to increase the gap between researchers and managers. Here, we illustrate how to fill this gap by presenting two long‐term collaborations between a research unit—Centre for Functional and Evolutionary Ecology; CEFE—and a national agency—French Biodiversity Agency; OFB. The first example is a collaboration providing statistical support to national parks for the design and implementation of scientific monitoring protocols. It relies on the recruitment of a research engineer funded by OFB and physically based at CEFE, who works closely with OFB and managers. The second example is a collaboration on the management of large carnivores. For more than 10 years, it has involved several PhD students and post‐doctoral fellows co‐supervised by CEFE and OFB, and has recently resulted in the recruitment of a permanent OFB researcher who works half‐time at CEFE and half‐time at OFB. These case studies illustrate the modalities of collaborative work between public institutions acting at different levels of biodiversity conservation for the co‐construction of research agendas and the exchange of knowledge. These collaborations also bring out some challenges. Inter‐knowledge and mutual learning remain difficult at scales larger than that of the teams concerned. The staff working at this interface needs to possess good listening skills, respect all partners' needs and demonstrate flexibility. Knowledge exchanges require time, thus reducing productivity according to quantitative metrics such as scientific publications or institutional reports. These collaborations can therefore be difficult to assume socially, and remain tenuous because they rely on a good understanding of the differences in governance of the various partners. Based on our experience, success is favoured by long‐term and close relationships, and by co‐construction of projects at early stage. Sharing a space (i.e. office or building) facilitates face‐to‐face interactions during planned work sessions and casual meetings that build up a shared scientific culture and mutual trust.
Published: 2023
Full Text: View/download PDF

2. Reproductive senescence in polar bears in a variable environment

Author: Marwan Naciri, Jon Aars, Marie-Anne Blanchet, Olivier Gimenez, and Sarah Cubaynes
Subjects: polar bears, sea ice, fertility, reproductive senescence, litter size, offspring survival, Evolution, QH359-425, Ecology, QH540-549.5
Abstract: Reproductive senescence is ubiquitous in mammals. However, patterns of senescence vary across reproductive traits, even within populations, perhaps because of differences in selection pressures, physiological constraints, and responses to environmental conditions. We investigated reproductive senescence in wild female polar bears (Ursus maritimus), using 31 years of capture-recapture data from the Svalbard area. We studied the influence of environmental conditions on age-specific litter production and litter size using generalized linear mixed models. Further, using a capture-recapture model that handles the dependency between vital rates of individuals belonging to the same family unit, we assessed maternal-age-related changes in first year cub and litter survival. We provide clear evidence for reproductive senescence in female polar bears. Litter production and litter size peaked in middle-aged females and declined sharply afterward. By contrast cub and litter survival did not decline after prime age. We found no evidence of terminal investment. The reproductive output of all females was affected by sea-ice conditions during the previous year and the Arctic Oscillation, with some effects differing greatly between age groups. Old females were affected the most by environmental conditions. Our results suggest that the decline in reproductive output is a combination of fertility and body-condition senescence, with a weak contribution of maternal-effect senescence, possibly due to benefits of experience. Further, as predicted by evolutionary theory, senescence appears to be a consequence of failures in early stages of the reproductive cycle rather than in late stages, and environmental variation affected old females more than prime-aged females. Our study emphasizes the need to study several reproductive traits and account for environmental variation when investigating reproductive senescence. Differences in senescence patterns across reproductive traits should be interpreted in light of evolutionary theory and while considering underlying physiological drivers.
Published: 2022
Full Text: View/download PDF

3. Modeling the demography of species providing extended parental care: A capture–recapture multievent model with a case study on polar bears (Ursus maritimus)

Author: Sarah Cubaynes, Jon Aars, Nigel G. Yoccoz, Roger Pradel, Øystein Wiig, Rolf A. Ims, and Olivier Gimenez
Subjects: apex predator, arctic ecosystem, Bayesian modeling, capture–recapture, dependency among individuals, family structure, Ecology, QH540-549.5
Abstract: Abstract In species providing extended parental care, one or both parents care for altricial young over a period including more than one breeding season. We expect large parental investment and long‐term dependency within family units to cause high variability in life trajectories among individuals with complex consequences at the population level. So far, models for estimating demographic parameters in free‐ranging animal populations mostly ignore extended parental care, thereby limiting our understanding of its consequences on parents and offspring life histories. We designed a capture–recapture multievent model for studying the demography of species providing extended parental care. It handles statistical multiple‐year dependency among individual demographic parameters grouped within family units, variable litter size, and uncertainty on the timing at offspring independence. It allows for the evaluation of trade‐offs among demographic parameters, the influence of past reproductive history on the caring parent's survival status, breeding probability, and litter size probability, while accounting for imperfect detection of family units. We assess the model performance using simulated data and illustrate its use with a long‐term dataset collected on the Svalbard polar bears (Ursus maritimus). Our model performed well in terms of bias and mean square error and in estimating demographic parameters in all simulated scenarios, both when offspring departure probability from the family unit occurred at a constant rate or varied during the field season depending on the date of capture. For the polar bear case study, we provide estimates of adult and dependent offspring survival rates, breeding probability, and litter size probability. Results showed that the outcome of the previous reproduction influenced breeding probability. Overall, our results show the importance of accounting for i) the multiple‐year statistical dependency within family units, ii) uncertainty on the timing at offspring independence, and iii) past reproductive history of the caring parent. If ignored, estimates obtained for breeding probability, litter size, and survival can be biased. This is of interest in terms of conservation because species providing extended parental care are often long‐living mammals vulnerable or threatened with extinction.
Published: 2021
Full Text: View/download PDF

4. Climate change does not decouple interactions between a central‐place‐foraging predator and its migratory prey

Author: Morgane Gicquel, Marion L. East, Heribert Hofer, Sarah Cubaynes, and Sarah Benhaiem
Subjects: climate change, herbivore migration, predator–prey interaction, rainfall, seasonality, Serengeti National Park, Ecology, QH540-549.5
Abstract: Abstract Little is known about potential cascading effects of climate change on the ability of predators to exploit mobile aggregations of prey with a spatiotemporal distribution largely determined by climatic conditions. If predators employ central‐place foraging when rearing offspring, the ability of parents to locate sufficient prey could be reduced by climate change. In the Serengeti National Park, Tanzania, migratory species dominate mammalian herbivore biomass. These migratory herds exploit nutrient‐rich vegetation on the southern plains in the rainy season and surface water in the northwest in the dry season. Female spotted hyenas Crocuta crocuta breed throughout the year and use long‐distance central‐place‐foraging “commuting trips” to migratory herds to fuel lactation for ≥12 months. Changes in rainfall patterns that alter prey movements may decrease the ability of mothers to locate profitable foraging areas and thus increase their overall commuting effort, particularly for high‐ranking females that have priority of access to food resources within their clan territory and thus less commuting experience. In hyena clan territories, this may be reflected by a decrease in migratory herd presence and a decrease in the presence of lactating females, as maternal den presence represents the opposite of commuting effort. We investigated the strength of the relationship between rainfall volume, migratory herd presence in three hyena clan territories, and the responses of lactating females to this climate/prey relationship in terms of maternal den presence, using an observation‐based dataset spanning three decades. The probability of migratory herd presence in hyena clan territories increased with the amount of rainfall 2 months earlier, and maternal den presence increased with migratory herd presence. Rainfall volume substantially increased over 30 years, whereas the presence of migratory herds in hyena clans and the strength of the relationship between rainfall and migratory herd presence decreased. Hyenas thus adjusted well to the climate change‐induced decreased the presence of migratory herds in their territories, since maternal den presence did not decrease over 30 years and still matched periods of high prey abundance, irrespective of female social status. These results suggest a high plasticity in the response of this keystone predator to environmental variability.
Published: 2022
Full Text: View/download PDF

5. A meta-analysis of genome-wide association studies identifies multiple longevity genes

Author: Joris Deelen, Daniel S. Evans, Dan E. Arking, Niccolò Tesi, Marianne Nygaard, Xiaomin Liu, Mary K. Wojczynski, Mary L. Biggs, Ashley van der Spek, Gil Atzmon, Erin B. Ware, Chloé Sarnowski, Albert V. Smith, Ilkka Seppälä, Heather J. Cordell, Janina Dose, Najaf Amin, Alice M. Arnold, Kristin L. Ayers, Nir Barzilai, Elizabeth J. Becker, Marian Beekman, Hélène Blanché, Kaare Christensen, Lene Christiansen, Joanna C. Collerton, Sarah Cubaynes, Steven R. Cummings, Karen Davies, Birgit Debrabant, Jean-François Deleuze, Rachel Duncan, Jessica D. Faul, Claudio Franceschi, Pilar Galan, Vilmundur Gudnason, Tamara B. Harris, Martijn Huisman, Mikko A. Hurme, Carol Jagger, Iris Jansen, Marja Jylhä, Mika Kähönen, David Karasik, Sharon L. R. Kardia, Andrew Kingston, Thomas B. L. Kirkwood, Lenore J. Launer, Terho Lehtimäki, Wolfgang Lieb, Leo-Pekka Lyytikäinen, Carmen Martin-Ruiz, Junxia Min, Almut Nebel, Anne B. Newman, Chao Nie, Ellen A. Nohr, Eric S. Orwoll, Thomas T. Perls, Michael A. Province, Bruce M. Psaty, Olli T. Raitakari, Marcel J. T. Reinders, Jean-Marie Robine, Jerome I. Rotter, Paola Sebastiani, Jennifer Smith, Thorkild I. A. Sørensen, Kent D. Taylor, André G. Uitterlinden, Wiesje van der Flier, Sven J. van der Lee, Cornelia M. van Duijn, Diana van Heemst, James W. Vaupel, David Weir, Kenny Ye, Yi Zeng, Wanlin Zheng, Henne Holstege, Douglas P. Kiel, Kathryn L. Lunetta, P. Eline Slagboom, and Joanne M. Murabito
Subjects: Science
Abstract: Genome-wide association studies have only revealed a handful of genetic loci for longevity. Here, in a case–control design based on phenotype definitions of individuals surviving at or beyond the age corresponding to the 90th and 99th survival percentile, the authors report two additional loci located in the APOE locus and near GPR78.
Published: 2019
Full Text: View/download PDF

6. Publisher Correction: A meta-analysis of genome-wide association studies identifies multiple longevity genes

Author: Joris Deelen, Daniel S. Evans, Dan E. Arking, Niccolò Tesi, Marianne Nygaard, Xiaomin Liu, Mary K. Wojczynski, Mary L. Biggs, Ashley van der Spek, Gil Atzmon, Erin B. Ware, Chloé Sarnowski, Albert V. Smith, Ilkka Seppälä, Heather J. Cordell, Janina Dose, Najaf Amin, Alice M. Arnold, Kristin L. Ayers, Nir Barzilai, Elizabeth J. Becker, Marian Beekman, Hélène Blanché, Kaare Christensen, Lene Christiansen, Joanna C. Collerton, Sarah Cubaynes, Steven R. Cummings, Karen Davies, Birgit Debrabant, Jean-François Deleuze, Rachel Duncan, Jessica D. Faul, Claudio Franceschi, Pilar Galan, Vilmundur Gudnason, Tamara B. Harris, Martijn Huisman, Mikko A. Hurme, Carol Jagger, Iris Jansen, Marja Jylhä, Mika Kähönen, David Karasik, Sharon L. R. Kardia, Andrew Kingston, Thomas B. L. Kirkwood, Lenore J. Launer, Terho Lehtimäki, Wolfgang Lieb, Leo-Pekka Lyytikäinen, Carmen Martin-Ruiz, Junxia Min, Almut Nebel, Anne B. Newman, Chao Nie, Ellen A. Nohr, Eric S. Orwoll, Thomas T. Perls, Michael A. Province, Bruce M. Psaty, Olli T. Raitakari, Marcel J. T. Reinders, Jean-Marie Robine, Jerome I. Rotter, Paola Sebastiani, Jennifer Smith, Thorkild I. A. Sørensen, Kent D. Taylor, André G. Uitterlinden, Wiesje van der Flier, Sven J. van der Lee, Cornelia M. van Duijn, Diana van Heemst, James W. Vaupel, David Weir, Kenny Ye, Yi Zeng, Wanlin Zheng, Henne Holstege, Douglas P. Kiel, Kathryn L. Lunetta, P. Eline Slagboom, and Joanne M. Murabito
Subjects: Science
Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22613-2
Published: 2021
Full Text: View/download PDF

7. Detection of Eco‐Evolutionary Processes in the Wild: Evolutionary Trade‐Offs Between Life History Traits

Author: Valentin Journé, Sarah Cubaynes, Julien Papaïx, and Mathieu Buoro
Published: 2022

8. The kinship matrix: inferring the kinship structure of a population from its demography

Author: Sarah Cubaynes, Samuel Pavard, Victor Ronget, François Bienvenu, Christophe Coste, Department of Biology, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim, Norway, Department of Mathematics and Computer Science, Transilvania University, of Brasov, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Éco-Anthropologie (EAE), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-18-CE02-0011,MathKinD,Mathématiques de la démographie de l'apparentement : nouveaux développements et application à l'humain(2018), ANR-16-CE27-0013,VINICULTURE,Vignes et vins en France du Néolithique au Moyen Age. Approche intégrée en archéosciences(2016), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Department of Biology [Trondheim] (IBI NTNU), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Department of Statistics [Oxford], University of Oxford, Éco-Anthropologie (EA), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro
Subjects: Population projection matrices, 0106 biological sciences, Familial structure, Class (set theory), [SDV]Life Sciences [q-bio], Ecology (disciplines), Population Dynamics, Population, [SHS.DEMO]Humanities and Social Sciences/Demography, 010603 evolutionary biology, 01 natural sciences, [SHS]Humanities and Social Sciences, Nephew and niece, Matrix (mathematics), 03 medical and health sciences, Id, ego and super-ego, Kinship, Humans, Sociology, [MATH]Mathematics [math], education, ComputingMilieux_MISCELLANEOUS, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Structure (mathematical logic), 0303 health sciences, education.field_of_study, Kinship demography, Structured populations, Population model, Relatedness, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Demography
Abstract: The familial structure of a population and the relatedness of its individuals are determined by its demography. There is, however, no general method to infer kinship directly from the life-cycle of a structured population. Yet this question is central to fields such as ecology, evolution and conservation, especially in contexts where there is a strong interdependence between familial structure and population dynamics. Here, we give a general formula to compute, from any matrix population model, the expected number of arbitrary kin (sisters, nieces, cousins, etc) of a focal individual ego, structured by the class of ego and of its kin. Central to our approach are classic but little-used tools known as genealogical matrices. Our method can be used to obtain both individual-based and population-wide metrics of kinship, as we illustrate. It also makes it possible to analyze the sensitivity of the kinship structure to the traits implemented in the model.
Published: 2021

9. Identification des compromis évolutifs entre traits d’histoire de vie en milieu naturel

Author: Valentin JOURNÉ, Sarah CUBAYNES, Julien PAPAÏX, and Mathieu BUORO
Abstract: Les biologistes n’ont souvent qu’une vision partielle des processus éco-évolutifs à l’œuvre en population naturelle. Tout ce qu’il observe n’est qu’une mesure bruitée ou incomplète des mécanismes sous-jacents. Nous montrons que les processus d’intérêt tels que les compromis évolutifs peuvent être représentés par des variables cachées qui ne sont pas mesurées directement mais sont partiellement renseignés par des variables observées.
Published: 2022

10. Publisher Correction: A meta-analysis of genome-wide association studies identifies multiple longevity genes

Author: Sarah Cubaynes, Joanne M. Murabito, Eric S. Orwoll, Iris E. Jansen, Joanna Collerton, Vilmundur Gudnason, Mikko Hurme, Paola Sebastiani, Carol Jagger, Carmen Martin-Ruiz, Douglas P. Kiel, Henne Holstege, Kenny Ye, Pilar Galan, Wolfgang Lieb, Diana van Heemst, Janina Dose, Wanlin Zheng, Ilkka Seppälä, Ashley van der Spek, Jessica D. Faul, Nir Barzilai, David R. Weir, James W. Vaupel, Wiesje M. van der Flier, Dan E. Arking, Terho Lehtimäki, Marja Jylhä, Thomas B. L. Kirkwood, Andrew Kingston, Marcel J. T. Reinders, Jean-François Deleuze, Cornelia M. van Duijn, Hélène Blanché, Anne B. Newman, Chao Nie, Elizabeth J. Becker, Daniel S. Evans, P. Eline Slagboom, Marianne Nygaard, Lene Christiansen, Jean Marie Robine, Junxia Min, Almut Nebel, Tamara B. Harris, Lenore J. Launer, Mary K. Wojczynski, Ellen A. Nohr, Alice M. Arnold, Olli T. Raitakari, Birgit Debrabant, Jerome I. Rotter, Thorkild I. A. Sørensen, André G. Uitterlinden, Steven R. Cummings, Kristin L. Ayers, Jennifer A. Smith, Joris Deelen, Yi Zeng, Chloé Sarnowski, Heather J. Cordell, Karen Davies, Kent D. Taylor, Mika Kähönen, Albert V. Smith, Najaf Amin, Rachel Duncan, Michael A. Province, Leo-Pekka Lyytikäinen, Kathryn L. Lunetta, Kaare Christensen, Sharon L.R. Kardia, Thomas T. Perls, Erin B. Ware, Martijn Huisman, Mary L. Biggs, Marian Beekman, Sven J. van der Lee, David Karasik, Niccolò Tesi, Xiaomin Liu, Gil Atzmon, Claudio Franceschi, and Bruce M. Psaty
Subjects: Apolipoprotein E2, Science, Apolipoprotein E4, Longevity, MEDLINE, General Physics and Astronomy, Genome-wide association study, Diseases, Computational biology, Biology, Longevity genes, Genome-wide association studies, GeneralLiterature_MISCELLANEOUS, General Biochemistry, Genetics and Molecular Biology, SNP, Humans, Endoplasmic Reticulum Chaperone BiP, Heat-Shock Proteins, Multidisciplinary, Published Erratum, General Chemistry, Publisher Correction, Ageing, ComputingMethodologies_PATTERNRECOGNITION, Risk factors, Meta-analysis, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Genome-Wide Association Study
Abstract: Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity.
Published: 2021

11. A meta-analysis of genome-wide association studies identifies multiple longevity genes

Author: Jerome I. Rotter, Rachel Duncan, Lenore J. Launer, Iris E. Jansen, Sarah Cubaynes, Nir Barzilai, Elizabeth J. Becker, Wiesje M. van der Flier, Joris Deelen, Sharon L.R. Kardia, Birgit Debrabant, Wanlin Zheng, Michael A. Province, Janina Dose, Anne B. Newman, Vilmundur Gudnason, Carol Jagger, Kenny Ye, Gil Atzmon, Carmen Martin-Ruiz, Kathryn L. Lunetta, Claudio Franceschi, Marianne Nygaard, Diana van Heemst, Najaf Amin, P. Eline Slagboom, Thomas B. L. Kirkwood, Ilkka Seppälä, Chloé Sarnowski, Heather J. Cordell, Terho Lehtimäki, Jennifer A. Smith, Cornelia M. van Duijn, Marja Jylhä, Dan E. Arking, Hélène Blanché, Jessica D. Faul, Yi Zeng, Henne Holstege, Kent D. Taylor, Junxia Min, Alice M. Arnold, Kristin L. Ayers, Mika Kähönen, Olli T. Raitakari, Pilar Galan, Mary K. Wojczynski, Jean-Marie Robine, Lene Christiansen, David R. Weir, Karen Davies, J. Collerton, Kaare Christensen, Douglas P. Kiel, Niccolò Tesi, Andrew Kingston, Bruce M. Psaty, Joanne M. Murabito, Marcel J. T. Reinders, Albert V. Smith, Xiaomin Liu, Leo-Pekka Lyytikäinen, Mary L. Biggs, Almut Nebel, Sven J. van der Lee, Mikko Hurme, David Karasik, Jean-François Deleuze, Thomas T. Perls, Erin B. Ware, Martijn Huisman, Marian Beekman, Chao Nie, Tamara B. Harris, Ellen A. Nohr, Eric S. Orwoll, Steven R. Cummings, Paola Sebastiani, James W. Vaupel, Wolfgang Lieb, Daniel S. Evans, Thorkild I. A. Sørensen, André G. Uitterlinden, Ashley van der Spek, Max planck Institute for Biology of Ageing [Cologne], Leiden University Medical Center (LUMC), California Pacific Medical Center Research Institute, Johns Hopkins University School of Medicine [Baltimore], McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Department of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, Department of Clinical Genetics (Academic Medical Center, University of Amsterdam), VU University Medical Center [Amsterdam], Delft Bioinformatics Lab [Delft], Delft University of Technology (TU Delft), University of Southern Denmark (SDU), Beijing Genomics Institute [Shenzhen] (BGI), China National GeneBank, Division of Statistical Genomics, Washington University School of Medicine, Department of Biostatistics, University of Washington [Seattle], Department of Medicine, Department of Epidemiology [Rotterdam], Erasmus University Medical Center [Rotterdam] (Erasmus MC), Department of Medicine [Bronx, NY, USA], Albert Einstein College of Medicine [New York], Department of Biology, Faculty of Sciences and Science Education, Haifa University, University of Haifa [Haifa], Institute for Social Research, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Department of Biostatistics [Boston, MA, USA], Harvard T.H. Chan School of Public Health, Icelandic Heart Association [Kopavogur, Iceland] (IHA), Department of Clinical Chemistry, Tempere University, Institute of Genetic Medicine [Newcastle], Newcastle University [Newcastle], Institute of Clinical Molecular Biology, Kiel University, SEMA4, Department of Biostatistics, University of Washington, Boston University [Boston] (BU), Fondation Jean Dausset CEPH, Odense University Hospital, Odense, Copenhagen University Hospital, Institute of Health and Society, Mécanismes moléculaires dans les démences neurodégénératives (MMDN), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Institute of Neuroscience [Newcastle] (ION), Institute of Clinical Research, Centre National de Génotypage (CNG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institute of Ageing and Health, Newcastle University, Newcastle Upon Tyne, Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University [Nizhni Novgorod], Institute of Neurological Sciences of Bologna IRCCS, Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Icelandic Heart Association, Heart Preventive Clinic and Research Institute, University of Iceland [Reykjavik], National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Department of Clinical Epidemiology and Biostatistics, VU Medical Centre, Amsterdam, Amsterdam Public Health Research Institute [The Netherlands], Department of Microbiology and Immunology, University of Tampere [Finland], Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, Institute of Health & Society, Department of Complex Trait Genetics, Vrije Universiteit Amsterdam [Amsterdam] (VU), Tampere University Hospital, School of Public Health, University of Michigan [Dearborn], Faculty of Medicine and Life Sciences [Tampere], Institute of Epidemiology and Biobank PopGen, Faculty of Medicine and Health Technology [Tampere, Finland], Zhejiang University School of Medicine [China], University of Pittsburgh Graduate School of Public Health, Oregon Health & Sciences University, Oregon Health and Science University [Portland] (OHSU), Boston University School of Medicine (BUSM), Department of Medicine, University of Washington, Department of Epidemiology, University of Washington, Kaiser Permanente Washington Health Research Institute [Seattle] (KPWHRI), Turku University Hospital (TYKS), University of Turku, School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), UCLA Medical Center, University of Michigan System, University of Copenhage, Faculty of Health and Medical Sciences, MRC Integrative Epidemiology Unit [Bristol, Royaume-Uni] (MRC IEU), University of Bristol [Bristol], Los Angeles Biomedical Research Institute (LA BioMed), Harbor UCLA Medical Center [Torrance, Ca.], Netherlands Genomics Initiative, Netherlands Consortium for Healthy Aging [Leiden, Netherlands] (NCHA), Department of Internal Medicine (ROTTERDAM - Med Int), Maasstad Ziekenhuis = Maasstad Hospital, Alzheimercentre, department of Neurology, VU University Medical Center, Nuffield Department of Population Health [Oxford], University of Oxford, Max Planck Institute for Demographic Research (MPIDR), Max-Planck-Gesellschaft, Beijing Forestry University, Medical School of Duke University, Hinda and Arthur Marcus Institute for Aging Research,Hebrew SeniorLife, Harvard Medical School [Boston] (HMS), Broad Institute of MIT & Harvard, AUDOUX, Michèle, Université de Montpellier (UM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris 13 (UP13)-Institut National de la Recherche Agronomique (INRA)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), University of California-University of California, Maasstad Hospital, University of Oxford [Oxford], Epidemiology and Data Science, APH - Aging & Later Life, APH - Societal Participation & Health, Neurology, Amsterdam Neuroscience - Complex Trait Genetics, Human genetics, APH - Personalized Medicine, APH - Methodology, Læknadeild (HÍ), Faculty of Medicine (UI), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, University of Iceland, Epidemiology, Internal Medicine, Universiteit Leiden, and École Pratique des Hautes Études (EPHE)
Subjects: 0301 basic medicine, Percentile, Aging, Apolipoprotein E2, media_common.quotation_subject, Science, [SDV]Life Sciences [q-bio], Longevity, Apolipoprotein E4, Áhættuþættir, General Physics and Astronomy, Genome-wide association study, Diseases, [SDV.GEN] Life Sciences [q-bio]/Genetics, Biology, Genetic correlation, Genome-wide association studies, Article, General Biochemistry, Genetics and Molecular Biology, Odds, 03 medical and health sciences, 0302 clinical medicine, Genetics, Humans, lcsh:Science, Endoplasmic Reticulum Chaperone BiP, Heat-Shock Proteins, media_common, Genetic association, [SDV.GEN]Life Sciences [q-bio]/Genetics, Multidisciplinary, Öldrun, Sjúkdómar, Human Genome, General Chemistry, Genetic architecture, [SDV] Life Sciences [q-bio], Ageing, 030104 developmental biology, Risk factors, Meta-analysis, lcsh:Q, Erfðarannsóknir, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: Publisher's version (útgefin grein)., Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity., Alexander von Humboldt-Stiftung
Published: 2019

12. Worldwide demography of centenarians

Author: Jean-Marie Robine, Sarah Cubaynes, Mécanismes moléculaires dans les démences neurodégénératives (MMDN), Université de Montpellier (UM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), CERMES3 - Centre de recherche Médecine, sciences, santé, santé mentale, société (CERMES3 - UMR 8211 / U988 / UM 7), École des hautes études en sciences sociales (EHESS)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Université Paris sciences et lettres (PSL)
Subjects: Male, Aging, media_common.quotation_subject, Longevity, Population Dynamics, Population, Demographic transition, Context (language use), 03 medical and health sciences, 0302 clinical medicine, Japan, Humans, 030212 general & internal medicine, education, media_common, Aged, 80 and over, Sex Characteristics, education.field_of_study, Limiting, Europe, Geography, Mortality level, Life expectancy, Female, Centenarian, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, 030217 neurology & neurosurgery, Developmental Biology, Demography
Abstract: International audience; The global number of centenarians should strongly increase during the 21 st century. According to the Population Division of the United Nations it should reach more than 25 million people in 2100. To better understand the dynamics of the emergence and growth of the centenarian population, we focused on four European countries having long chronological series and high quality data about centenarians, Denmark, France, Sweden, and Switzerland, and Japan which has had the highest life expectancy at birth for several years. we analysed the emergence of the centenarian populations and their pace of growth in the wider context of the adult longevity revolution, as well as the trends in mortality level among these new populations. We found that out of the 5 countries studied, the decrease in mortality at age 100 for females who are leading the adult longevity revolution, seems to be interrupted in 4 countries, including in Japan. These results are in favour of the scenario of “compression of mortality”, possibly limiting the future number of centenarians. However, previous studies have shown that demographic transitions are not linear and, after periods of interruption, trends can resume towards an always greater longevity.
Published: 2017

13. How many cubs can a mum nurse? Maternal age and size influence litter size in polar bears

Author: Øystein Wiig, Olivier Gimenez, Andrew E. Derocher, Dorinda Marie Folio, Sarah Cubaynes, Jon Aars, FRAM Centre, Norwegian Polar Institute, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Natural History Museum [Oslo], University of Oslo (UiO), Institut de Mathématiques et de Modélisation de Montpellier (I3M), Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), and Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)
Subjects: 0106 biological sciences, Litter (animal), 010504 meteorology & atmospheric sciences, Litter Size, Ursus maritimus, media_common.quotation_subject, Affect (psychology), 010603 evolutionary biology, 01 natural sciences, Reproductive senescence, Pregnancy, biology.animal, Individual data, Animals, Body Size, Stabilizing selection, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, media_common, Evolutionary Biology, biology, Reproductive success, Reproduction, Agricultural and Biological Sciences (miscellaneous), Female, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, General Agricultural and Biological Sciences, Ursidae, Demography, Maternal Age
Abstract: Life-history theory predicts that females' age and size affect the level of maternal investment in current reproduction, balanced against the future reproductive effort, maintenance and survival. Using long-term (30 years) individual data on 193 female polar bears ( Ursus maritimus ), we assessed age- and size-specific variation on litter size. Litter size varied with maternal age, younger females had higher chances of losing a cub during their first months of life. Results suggest an improvement in reproductive abilities early in life due to experience with subsequent reproductive senescence. Litter size increased with maternal size, indicating that size may reflect individual quality. We also found an optimum in the probability of having twins, suggesting stabilizing selection on female body size. Heterogeneity was observed among the largest females, suggesting that large size comes at a cost.
Published: 2019

14. Unsuccessful dispersal affects life history characteristics of natal populations: The role of dispersal related variation in vital rates

Author: Sarah Cubaynes, Tim Coulson, Isabel M. Smallegange, Jacques A. Deere, Department of Zoology [Oxford], University of Oxford [Oxford], Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam [Amsterdam] (UvA), Mécanismes moléculaires dans les démences neurodégénératives (MMDN), Université de Montpellier (UM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), and Evolutionary and Population Biology (IBED, FNWI)
Subjects: 0106 biological sciences, education.field_of_study, Reproductive success, Ecology, Ecological Modeling, Population, Rhizoglyphus robini, Biology, 010603 evolutionary biology, 01 natural sciences, Article, Gene flow, 010601 ecology, Habitat, Biological dispersal, Population growth, Vital rates, education, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: International audience; Individuals that disperse from one habitat to another has consequences for individual fitness, population dynamics and gene flow. The fitness benefits accrued in the new habitat are traded off against costs associated with dispersal. Most studies focus on costs at settlement and effects on settlement populations; the influence of dispersal to natal populations is assessed by monitoring change in numbers due to emigration. However, the extent to which natal populations are affected when individuals that invest in dispersal fail to disperse/emigrate is unclear. Here, we use an Integral Projection Model (IPM) to assess how developing into a disperser affects natal population structure and growth. We do so using the bulb mite (Rhizoglyphus robini) as a study system. Bulb mites, in unfavourable environments, develop into a dispersal (deutonymph) stage during ontogeny; these individuals are called dispersers with individuals not developing into this stage called non-dispersers. We varied disperser expression and parameterised IPMs to describe three simulations of successful and unsuccessful dispersal: (i) ‘no dispersal’ - dispersal stage is excluded and demographic data are from non-disperser individuals; (ii) ‘false dispersal’ - dispersal stage included and demographic data from non-disperser individuals are used; (iii) ‘true dispersal’ - dispersal stage included and demographic data are from individuals that go through the dispersal stage and from non-disperser individuals. We found that the type of dispersal simulation (no dispersal < false dispersal < true dispersal) and disperser expression increases generation time and reduces lifetime reproductive success and population growth rate. Our findings show that disperser individuals that fail to leave, can change the structure and growth of natal populations.
Published: 2019

15. How many cubs can a mum raise? Maternal age and size influence litter size in polar bears

Author: Andrew E. Derocher, Olivier Gimenez, Dorinda Marie Folio, Jon Aars, Sarah Cubaynes, and Øystein Wiig
Subjects: Litter (animal), Reproductive senescence, Ursus maritimus, media_common.quotation_subject, biology.animal, Individual data, Stabilizing selection, Biology, Reproduction, Affect (psychology), Life history theory, media_common, Demography
Abstract: Life history theory predicts that females’ age and size affect the level of maternal investment in current reproduction, balanced against future reproductive effort, maintenance and survival. Using long-term (30 years) individual data on 231 female polar bears (Ursus maritimus), we assessed age- and size-specific variation on litter size. Litter size varied with maternal age, younger females had higher chances of losing a cub during their first months of life. Results suggest an improvement of breeding abilities early in life due to experience with subsequent reproductive senescence. Litter size increased with maternal size, indicating that size may reflect individual quality. Heterogeneity was observed among the largest females, suggesting that large size comes at a cost. Maternal traits and environmental conditions may act together to influence reproductive success.
Published: 2019

16. Modeling the impact of selective harvesting on red deer antlers

Author: Sarah Cubaynes, Susanne Schindler, Aurelio F. Malo, Rocío A. Pozo, Jeremy J. Cusack, and Tim Coulson
Subjects: 0106 biological sciences, education.field_of_study, Ungulate, Ecology, Population, Wildlife, Phenotypic trait, Biology, biology.organism_classification, 010603 evolutionary biology, 01 natural sciences, Antler, Trophy, 010601 ecology, Sexual selection, General Earth and Planetary Sciences, Reproductive value, education, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation, General Environmental Science
Abstract: Hunting is a common component in the management of ungulate species. Despite its widespread use, the influence of selective harvesting on phenotypic trait change is still ambiguous, and represents a critical gap in our understanding of the responses of wild populations under harvest. Using the long-term red deer (Cervus elaphus) dataset (1972–2012) from the Isle of Rum National Nature Reserve, Scotland, we assessed the relationship between antler length and key demographic processes (i.e., survival, recruitment, antler growth, parent-offspring trait correlation) for the male component of the population. We then constructed the first integral projection model for this species to examine the effects of simulated trophy hunting on 2 population-level parameters: the stable antler size distribution and the relative reproductive value of males. When male mortality rates due to hunting were
Published: 2016

17. Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

Author: Valentin Lauret, Eric Marboutin, Christian Miquel, Olivier Gimenez, Julie Louvrier, Rémi Choquet, Christophe Duchamp, Sarah Cubaynes, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Paul-Valéry - Montpellier 3 (UM3), Office National de la Chasse et de la Faune Sauvage, CNERA Prédateurs Animaux déprédateurs, Mécanismes moléculaires dans les démences neurodégénératives (MMDN), Université Montpellier 2 - Sciences et Techniques (UM2)-École pratique des hautes études (EPHE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Laboratoire d'Ecologie Alpine (LECA), Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Joseph Fourier - Grenoble 1 (UJF)-Université Grenoble Alpes (UGA), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Université de Montpellier (UM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
Subjects: 0106 biological sciences, biology, Occupancy, Range (biology), ved/biology, Ecology, 010604 marine biology & hydrobiology, [SDV.BA]Life Sciences [q-bio]/Animal biology, Species distribution, ved/biology.organism_classification_rank.species, Sampling (statistics), 15. Life on land, biology.organism_classification, Gray wolf, 010603 evolutionary biology, 01 natural sciences, Canis, Altitude, Geography, 13. Climate action, Ecology, Evolution, Behavior and Systematics, Cell based
Abstract: While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves (Canis lupus) that have been recolonizing France since the early 90’s. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500m and positively influenced by the number of observed occupied sites at short and long-distances, forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decision-makers to target sites where large carnivores are likely to occur and mitigate conflicts. This article is protected by copyright. All rights reserved.
Published: 2018

18. Espérances de vie, espérances de santé : tendances convergentes ou divergentes sur le plan mondial ?

Author: Jean-Marie Robine and Sarah Cubaynes
Subjects: 03 medical and health sciences, 0302 clinical medicine, 05 social sciences, 0507 social and economic geography, 050702 demography, 030212 general & internal medicine
Published: 2017

19. Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

Author: Sarah Cubaynes, Eric Marboutin, Christophe Duchamp, Olivier Gimenez, Christian Miquel, Rémi Choquet, and Julie Louvrier
Subjects: biology, Occupancy, business.industry, Range (biology), Species distribution, Sampling (statistics), Distribution (economics), biology.organism_classification, Canis, Altitude, Geography, Statistics, business, Cell based
Abstract: While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Not accounting for these issues can lead to flawed inference about the distribution.Here, we focused on the wolf (Canis lupus) that has been recolonizing France since the early 90’s. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1993 to 2014, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models.Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by 50% on average. Colonization increased with increasing number of occupied sites at short and long-distances, as well as with increasing forest cover, farmland cover and mean altitude. Colonization decreased when high-altitude increased. The growth rate, defined as the number of sites newly occupied in a given year divided by the number of occupied sites in the previous year, decreased over time, from over 100% in 1994 to 5% in 2014. This suggests that wolves are expanding in France but at a rate that is slowing down. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decision-makers to target sites where large carnivores are likely to occur and mitigate conflicts.
Published: 2017
Full Text: View/download PDF

20. Density-dependent intraspecific aggression regulates survival in northern Yellowstone wolves (Canis lupus)

Author: Kira A. Quimby, Tim Coulson, Daniel R. Stahler, Daniel R. MacNulty, Sarah Cubaynes, and Douglas W. Smith
Subjects: Male, Wyoming, Food Chain, Climate, Longevity, Population, Population density, Intraspecific competition, Predation, Age Distribution, Animals, Sex Distribution, education, Ecology, Evolution, Behavior and Systematics, Apex predator, Population Density, Spatial Analysis, education.field_of_study, Wolves, Montana, biology, National park, Ecology, biology.organism_classification, Aggression, Canis, Female, Animal Science and Zoology, Seasons, Vital rates
Abstract: Summary 1. Understanding the population dynamics of top-predators is essential to assess their impact on ecosystems and to guide their management. Key to this understanding is identifying the mechanisms regulating vital rates. 2. Determining the influence of density on survival is necessary to understand the extent to which human-caused mortality is compensatory or additive. In wolves (Canis lupus), empirical evidence for density-dependent survival is lacking. Dispersal is considered the principal way in which wolves adjust their numbers to prey supply or compensate for human exploitation. However, studies to date have primarily focused on exploited wolf populations, in which density-dependent mechanisms are likely weak due to artificially low wolf densities. 3. Using 13 years of data on 280 collared wolves in Yellowstone National Park, we assessed the effect of wolf density, prey abundance and population structure, as well as winter severity, on age-specific survival in two areas (prey-rich vs. prey-poor) of the national park. We further analysed cause-specific mortality and explored the factors driving intraspecific aggression in the prey-rich northern area of the park. 4. Overall, survival rates decreased during the study. In northern Yellowstone, density dependence regulated adult survival through an increase in intraspecific aggression, independent of prey availability. In the interior of the park, adult survival was less variable and density-independent, despite reduced prey availability. There was no effect of prey population structure in northern Yellowstone, or of winter severity in either area. Survival was similar among yearlings and adults, but lower for adults older than 6 years. 5. Our results indicate that density-dependent intraspecific aggression is a major driver of adult wolf survival in northern Yellowstone, suggesting intrinsic density-dependent mechanisms have the potential to regulate wolf populations at high ungulate densities. When low prey availability or high removal rates maintain wolves at lower densities, limited inter-pack interactions may prevent density-dependent survival, consistent with our findings in the interior of the park.
Published: 2014

21. Fitting animal survival models with temporal random effects

Author: Sarah Cubaynes, Christian Lavergne, Olivier Gimenez, Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques et de Modélisation de Montpellier (I3M), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Université Paul-Valéry - Montpellier 3 (UPVM), Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Université Paul-Valéry - Montpellier 3 (UM3)
Subjects: [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Statistics and Probability, [STAT.AP]Statistics [stat]/Applications [stat.AP], Band return, Link function, Generalized linear mixed models, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Variance (accounting), Random effects model, Schall's algorithm, R software, Generalized linear mixed model, Ring recoveries, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Linearization, Frequentist inference, Statistics, %22">Fish , [MATH]Mathematics [math], Statistics, Probability and Uncertainty, ComputingMilieux_MISCELLANEOUS, Survival analysis, General Environmental Science, Mathematics
Abstract: International audience; Estimating temporal variance in animal demographic parameters is of particular importance in population biology. We implement the Schall's algorithm for incorporating temporal random effects in survival models using recovery data. Our frequentist approach is based on a formulation of band-recovery models with random effects as generalized linear mixed models and a linearization of the link function conditional on the random effects. A simulation study shows that our procedure provides unbiased and precise estimates. The method is then implemented on two case studies using recovery data on fish and birds.
Published: 2013

22. Will central Wyoming elk stop migrating to Yellowstone, and should we care?

Author: Tim Coulson, Jack Massey, and Sarah Cubaynes
Subjects: Geography, Ecology, Ecology, Evolution, Behavior and Systematics
Published: 2016

23. An improved procedure to estimate wolf abundance using non-invasive genetic sampling and capture–recapture mixture models

Author: Sarah Cubaynes, Olivier Gimenez, Jean-Dominique Lebreton, Elena Fabbri, Romolo Caniglia, and Ettore Randi
Subjects: education.field_of_study, biology, Ecology, Population, Biodiversity, Sampling (statistics), Population ecology, biology.organism_classification, Mark and recapture, Canis, Habitat, Abundance (ecology), Genetics, education, Ecology, Evolution, Behavior and Systematics
Abstract: Non-invasive genetic sampling (NGS) is increasingly used to estimate the abundance of rare or elusive species such as the wolf (Canis lupus), which cannot be directly counted in forested mountain habitats. Wolf individual and familial home ranges are wide, potentially connected by long-range dispersers, and their populations are intrinsically open. Appropriate demo- graphic estimators are needed, because the assumptions of homogeneous detection probability and demographic closeness are violated. We compiled the capture-recapture record of 418 individual wolf genotypes identified from ca. 4,900 non-invasive samples, collected in the northern Italian Apennines from January 2002 to June 2009. We analysed this dataset using novel capture-recapture multi- event models for open populations that explicitly account for individual detection heterogeneity (IDH). Overall, the detection probability of the weakly detectable individuals, probably pups, juveniles and migrants (P = 0.08), was ca. six times lower than that of the highly detectable wolves (P = 0.44), probably adults and dominants. The apparent annual survival rate of weakly detectable individuals was lower (U = 0.66) than those of highly detectable wolves (U = 0.75). The population mean annual finite rate of increase was k = 1.05 ± 0.11, and the mean annual size ranged from N = 117 wolves in 2003 to N = 233 wolves in 2007. This procedure, combining large-scale NGS and multievent IDH demographic models, provides the first estimates of abundance, multi-annual trend and survival rates for an open large wolf population in the Apennines. These results contribute to deepen our understanding of wolf population ecology and dynamics, and provide new information to implement sound long-term conservation plans.
Published: 2011

24. To breed or not to breed: a seabird's response to extreme climatic events

Author: Sarah Cubaynes, Paul F. Doherty, Olivier Gimenez, and E. A. Schreiber
Subjects: Adaptive strategies, media_common.quotation_subject, Breeding, Booby, Models, Biological, Competition (biology), Birds, Mark and recapture, Sexual Behavior, Animal, biology.animal, Animals, Seawater, media_common, El Nino-Southern Oscillation, Breeding in the wild, biology, Ecology, Temperature, biology.organism_classification, Adaptation, Physiological, Agricultural and Biological Sciences (miscellaneous), Breed, Seasons, Population Ecology, Reproduction, Seabird, General Agricultural and Biological Sciences
Abstract: Intermittent breeding is an important life-history strategy that has rarely been quantified in the wild and for which drivers remain unclear. It may be the result of a trade-off between survival and reproduction, with individuals skipping breeding when breeding conditions are below a certain threshold. Heterogeneity in individual quality can also lead to heterogeneity in intermittent breeding. We modelled survival, recruitment and breeding probability of the red-footed booby ( Sula sula ), using a 19 year mark–recapture dataset involving more than 11 000 birds. We showed that skipping breeding was more likely in El-Niño years, correlated with an increase in the local sea surface temperature, supporting the hypothesis that it may be partly an adaptive strategy of birds to face the trade-off between survival and reproduction owing to environmental constraints. We also showed that the age-specific probability of first breeding attempt was synchronized among different age-classes and higher in El-Niño years. This result suggested that pre-breeders may benefit from lowered competition with experienced breeders in years of high skipping probabilities.
Published: 2010

25. Combining capture–recapture data and pedigree information to assess heritability of demographic parameters in the wild

Author: Julien Papaïx, Anne Charmantier, Sarah Cubaynes, Philippe Perret, Mathieu Buoro, Olivier Gimenez, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM), and French ANR ANR-08-JCJC-0028-01
Subjects: 0106 biological sciences, Population, Biology, 010603 evolutionary biology, 01 natural sciences, Songbirds, Mark and recapture, 03 medical and health sciences, Bayes' theorem, Quantitative Trait, Heritable, Genetic variation, Statistics, Animals, Threshold model, Computer Simulation, Cormack-Jolly-Seber model, education, Ecology, Evolution, Behavior and Systematics, Selection (genetic algorithm), 030304 developmental biology, [STAT.AP]Statistics [stat]/Applications [stat.AP], 0303 health sciences, education.field_of_study, Models, Statistical, Ecology, [SDV.BA]Life Sciences [q-bio]/Animal biology, State-space model, Bayes Theorem, Quantitative genetics, Heritability, Binary trait, Markov Chains, Pedigree, Capture-Recapture Animal Model, Individual heterogeneity, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract: International audience; Quantitative genetic analyses have been increasingly used to estimate the genetic basis of life-history traits in natural populations. Imperfect detection of individuals is inherent to studies that monitor populations in the wild, yet it is seldom accounted for by quantitative genetic studies, perhaps leading to flawed inference. To facilitate the inclusion of imperfect detection of individuals in such studies, we develop a method to estimate additive genetic variance and assess heritability for binary traits such as survival, using capture–recapture (CR) data. Our approach combines mixed-effects CR models with a threshold model to incorporate discrete data in a standard ‘animal model’ approach. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data from a wild population of blue tits (Cyanistes caeruleus) and present the first estimate of heritability of adult survival in the wild. In agreement with the prediction that selection should deplete additive genetic variance in fitness, we found that survival had low heritability. Because the detection process is incorporated, capture–recapture animal models (CRAM) provide unbiased quantitative genetics analyses of longitudinal data collected in the wild.
Published: 2010

26. How can quantitative ecology be attractive to young scientists? Balancing computer/desk work with fieldwork

Author: Eleni Papadatou, Lucile Marescot, Marlène Gamelon, Sabrina Servanty, Sarah Cubaynes, François Guilhaumon, Jean-Yves Barnagaud, Guillaume Péron, Marine Desprez, Mathieu Buoro, Fitsum Abadi, Laetitia Blanc, Olivier Gimenez, Julien Papaïx, Paméla Lagrange, and Bénédicte Madon
Subjects: 0106 biological sciences, Engineering, Ecology, Management science, business.industry, 010604 marine biology & hydrobiology, 010603 evolutionary biology, 01 natural sciences, Quantitative ecology, Adaptive management, Work (electrical), business, Nature and Landscape Conservation, Desk
Published: 2012

27. Age-specific cost of first reproduction in female southern elephant seals

Author: Sarah Cubaynes, Marine Desprez, Olivier Gimenez, Clive R. McMahon, Mark A. Hindell, and Robert Harcourt
Subjects: education.field_of_study, Ecology, Seals, Earless, media_common.quotation_subject, Reproduction, Population, Age Factors, Capture mark recapture, Mean age, Biology, Agricultural and Biological Sciences (miscellaneous), Age specific, Animals, Female, Population Ecology, Life history, Mortality, General Agricultural and Biological Sciences, education, media_common, Demography
Abstract: When to commence breeding is a crucial life-history decision that may be the most important determinant of an individual's lifetime reproductive output and can have major consequences on population dynamics. The age at which individuals first reproduce is an important factor influencing the intensity of potential costs (e.g. reduced survival) involved in the first breeding event. However, quantifying age-related variation in the cost of first reproduction in wild animals remains challenging because of the difficulty in reliably recording the first breeding event. Here, using a multi-event capture–recapture model that accounts for both imperfect detection and uncertainty in the breeding status on an 18-year dataset involving 6637 individuals, we estimated age and state-specific survival of female elephant seals ( Mirounga leonina ) in the declining Macquarie Island population. We detected a clear cost of first reproduction on survival. This cost was higher for both younger first-time breeders and older first-time breeders compared with females recruiting at age four, the overall mean age at first reproduction. Neither earlier primiparity nor delaying primiparity appear to confer any evolutionary advantage, rather the optimal strategy seems to be to start breeding at a single age, 4 years.
Published: 2014

28. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

Author: Arni Magnusson, Mark N. Maunder, Hans J. Skaug, Anders Nielsen, Perry de Valpine, Eun Jung Kim, Elizabeth E. Crone, Cleridy E. Lennert-Cody, Benjamin M. Bolker, Liza S. Comita, Jim Regetz, Olivier Gimenez, John C. Nash, Elise F. Zipkin, Casper Willestofte Berg, Sarah Cubaynes, Jessica Ford, Marc Kéry, Trevor D. Davies, Beth Gardner, Steve Martell, Mollie Elizabeth Brooks, University of Zurich, and Bolker, Benjamin M
Subjects: Flexibility (engineering), Source code, Estimation theory, Computer science, Ecology, Ecological Modeling, media_common.quotation_subject, Advice (programming), Variety (cybernetics), 10127 Institute of Evolutionary Biology and Environmental Studies, AD Model Builder, Nonlinear system, Range (mathematics), 1105 Ecology, Evolution, Behavior and Systematics, 2302 Ecological Modeling, Learning curve, 570 Life sciences, biology, 590 Animals (Zoology), JAGS, WinBUGS, parameter estimation, optimization, Ecology, Evolution, Behavior and Systematics, media_common
Abstract: 1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data.
Published: 2013

29. Testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling

Author: Bruno Faivre, Arnaud Grégoire, Claire Doutrelant, Olivier Gimenez, Sarah Cubaynes, Philippe Perret, Centre d’Ecologie Fonctionnelle et Evolutive ( CEFE ), Institut de Recherche pour le Développement ( IRD [France-Sud] ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ) -Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -École pratique des hautes études ( EPHE ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Université Paul-Valéry - Montpellier 3 ( UM3 ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Institut de Mathématiques et de Modélisation de Montpellier ( I3M ), Université Montpellier 2 - Sciences et Techniques ( UM2 ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Research supported by OSU OREME., Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Mathématiques et de Modélisation de Montpellier (I3M), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), Biogéosciences [UMR 6282] [Dijon] (BGS), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Paul-Valéry - Montpellier 3 (UM3), Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement
Subjects: 0106 biological sciences, Population Dynamics, selection gradient analyses, Biology, state-space models, 010603 evolutionary biology, 01 natural sciences, Models, Biological, Structural equation modeling, Mark and recapture, 010104 statistics & probability, structural equation models, life history tradeoffs, Animals, Passeriformes, 0101 mathematics, Set (psychology), Ecology, Evolution, Behavior and Systematics, Selection (genetic algorithm), Ecosystem, capture-recapture models, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, Ecology, Model selection, Cyanistes, individual heterogeneity, biology.organism_classification, Causality, Biological Evolution, evolutionary ecology, Evolutionary ecology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract: 8 pages; International audience; Studying evolutionary mechanisms in natural populations often requires testing multifactorial scenarios of causality involving direct and indirect relationships among individual and environmental variables. It is also essential to account for the imperfect detection of individuals to provide unbiased demographic parameter estimates. To cope with these issues, we developed a new approach combining structural equation models with capture-recapture models (CR-SEM) that allows the investigation of competing hypotheses about individual and environmental variability observed in demographic parameters. We employ Markov chain Monte Carlo sampling in a Bayesian framework to (1) estimate model parameters, (2) implement a model selection procedure to evaluate competing hypotheses about causal mechanisms, and (3) assess the fit of models to data using posterior predictive checks. We illustrate the value of our approach using two case studies on wild bird populations. We first show that CR-SEM can be useful to quantify the action of selection on a set of phenotypic traits with an analysis of selection gradients on morphological traits in Common Blackbirds (Turdus merula). In a second case study on Blue Tits (Cyanistes caeruleus), we illustrate the use of CR-SEM to study evolutionary trade-offs in the wild, while accounting for varying environmental conditions.
Published: 2012

30. Importance of Accounting for Detection Heterogeneity When Estimating Abundance: the Case of French Wolves

Author: Carole Poillot, Eric Marboutin, Olivier Gimenez, Anne-Marie Reboulet, Rémi Choquet, Christophe Duchamp, Pierre Taberlet, Jean-Michel Gaillard, Sarah Cubaynes, Christian Miquel, Roger Pradel, Jean-Dominique Lebreton, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Biodémographie évolutive, Département écologie évolutive [LBBE], Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Oncfs, ONCFS, Laboratoire d'Ecologie Alpine (LECA), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Joseph Fourier - Grenoble 1 (UJF)-Université Grenoble Alpes (UGA), Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], Biometry, Animal Identification Systems, 010603 evolutionary biology, 01 natural sciences, Mark and recapture, Feces, Abundance (ecology), Animals, mixture models, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation, Population Density, Models, Statistical, Wolves, Ecology, Individual heterogeneity, 010604 marine biology & hydrobiology, Welfare economics, Population size, modelos mezclados, individual heterogeneity, Canis lupus, multievent model, tamaño poblacional, Geography, modelo multievento, [SDE]Environmental Sciences, marca-recaptura, France, heterogeneidad individual, mark-recapture, E-SURGE, population size canis lupus, Hair
Abstract: Assessing conservation strategies requires reliable estimates of abundance. Because detecting all individuals is most often impossible in free-ranging populations, estimation procedures have to account for a
Published: 2010

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources

Refine your results

30 results on '"Sarah Cubaynes"'

1. Building spaces of interactions between researchers and managers: Case studies with wildlife monitoring and conservation in France

2. Reproductive senescence in polar bears in a variable environment

3. Modeling the demography of species providing extended parental care: A capture–recapture multievent model with a case study on polar bears (Ursus maritimus)

4. Climate change does not decouple interactions between a central‐place‐foraging predator and its migratory prey

5. A meta-analysis of genome-wide association studies identifies multiple longevity genes

6. Publisher Correction: A meta-analysis of genome-wide association studies identifies multiple longevity genes

7. Detection of Eco‐Evolutionary Processes in the Wild: Evolutionary Trade‐Offs Between Life History Traits

8. The kinship matrix: inferring the kinship structure of a population from its demography

9. Identification des compromis évolutifs entre traits d’histoire de vie en milieu naturel

10. Publisher Correction: A meta-analysis of genome-wide association studies identifies multiple longevity genes

11. A meta-analysis of genome-wide association studies identifies multiple longevity genes

12. Worldwide demography of centenarians

13. How many cubs can a mum nurse? Maternal age and size influence litter size in polar bears

14. Unsuccessful dispersal affects life history characteristics of natal populations: The role of dispersal related variation in vital rates

15. How many cubs can a mum raise? Maternal age and size influence litter size in polar bears

16. Modeling the impact of selective harvesting on red deer antlers

17. Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

18. Espérances de vie, espérances de santé : tendances convergentes ou divergentes sur le plan mondial ?

19. Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

20. Density-dependent intraspecific aggression regulates survival in northern Yellowstone wolves (Canis lupus)

21. Fitting animal survival models with temporal random effects

22. Will central Wyoming elk stop migrating to Yellowstone, and should we care?

23. An improved procedure to estimate wolf abundance using non-invasive genetic sampling and capture–recapture mixture models

24. To breed or not to breed: a seabird's response to extreme climatic events

25. Combining capture–recapture data and pedigree information to assess heritability of demographic parameters in the wild

26. How can quantitative ecology be attractive to young scientists? Balancing computer/desk work with fieldwork

27. Age-specific cost of first reproduction in female southern elephant seals

28. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

29. Testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling

30. Importance of Accounting for Detection Heterogeneity When Estimating Abundance: the Case of French Wolves

Catalog

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Database

Publisher

30 results on '"Sarah Cubaynes"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources