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Coulon A.,Cornell University | Coulon A.,CNRS Mechanical Adaptation and Evolution
Molecular Ecology Resources | Year: 2010

Genhet is an R function which calculates the five most used estimates of individual heterozygosity. The advantage of this program is that it can be applied to any diploid genotype dataset, without any limitation in the number of individuals, loci or alleles. Its detailed manual should allow people who have never used R before to make the function work quite easily. The program is freely available at http://www.aureliecoulon.net/research/ac-computer-programs. html. © 2009 Blackwell Publishing Ltd. Source

Herrel A.,CNRS Mechanical Adaptation and Evolution | Bonneaud C.,French National Center for Scientific Research
Journal of Experimental Biology | Year: 2012

Amphibians are ideal taxa with which to investigate the effects of climate change on physiology, dispersal capacity and distributional ranges as their physiological performance and fitness is highly dependent on temperature. Moreover, amphibians are among the most endangered vertebrate taxa. Here we use the tropical clawed frog, Xenopus tropicalis, as a model system to explore effects of temperature on locomotor performance. Our analyses show that locomotion is thermally sensitive, as illustrated by significant effects of temperature on terrestrial exertion capacity (time until exhaustion) and aquatic burst speed (maximal burst swimming velocity and maximal burst swimming acceleration capacity). Exertion performance measures had relatively lower temperature optima and narrower performance breadth ranges than measures of burst speed. The narrow 80% performance breadths confirm predictions that animals from stable environments should display high thermal sensitivity and, combined with the divergent temperature optima for exertion capacity and burst speed, underscore the vulnerability of tropical species such as X. tropicalis to even relatively small temperature changes. The temperature sensitivity of locomotor performance traits in X. tropicalis suggests that tropical ectotherms may be impacted by predicted changes in climate. © 2012. Published by The Company of Biologists Ltd. Source

Ponge J.-F.,CNRS Mechanical Adaptation and Evolution
Soil Biology and Biochemistry | Year: 2013

The present review was undertaken to add more information on the place taken by humus forms in plant-soil interactions. Three questions were asked: (i) are humus forms under the control of plant-soil relationships, (ii) are humus forms the main seat of these relationships, and (iii) can humus forms explain interactions between aboveground and belowground biodiversity. Some conflicting views about humped-back models of species richness may be resolved by considering a limited number of stable humus forms (here considered as ecosystem strategies) which should be treated separately rather than in a single model. Mull, moder and mor pathways are each characterized by a fine tuning between aboveground and belowground communities, the humus form (including litter) being the place where resonance between these communities takes place, both in functional and evolutionary sense. © 2012 Elsevier Ltd. Source

Stevens V.M.,CNRS Mechanical Adaptation and Evolution | Stevens V.M.,University of Liege | Turlure C.,CNRS Mechanical Adaptation and Evolution | Baguette M.,CNRS Mechanical Adaptation and Evolution
Biological Reviews | Year: 2010

Dispersal has recently gained much attention because of its crucial role in the conservation and evolution of species facing major environmental changes such as habitat loss and fragmentation, climate change, and their interactions. Butterflies have long been recognized as ideal model systems for the study of dispersal and a huge amount of data on their ability to disperse has been collected under various conditions. However, no single 'best' method seems to exist leading to the co-occurrence of various approaches to study butterfly mobility, and therefore a high heterogeneity among data on dispersal across this group. Accordingly, we here reviewed the knowledge accumulated on dispersal and mobility in butterflies, to detect general patterns. This meta-analysis specifically addressed two questions. Firstly, do the various methods provide a congruent picture of how dispersal ability is distributed across species? Secondly, is dispersal species-specific? Five sources of data were analysed: multisite mark-recapture experiments, genetic studies, experimental assessments, expert opinions, and transect surveys. We accounted for potential biases due to variation in genetic markers, sample sizes, spatial scales or the level of habitat fragmentation. We showed that the various dispersal estimates generally converged, and that the relative dispersal ability of species could reliably be predicted from their relative vagrancy (records of butterflies outside their normal habitat). Expert opinions gave much less reliable estimates of realized dispersal but instead reflected migration propensity of butterflies. Within-species comparisons showed that genetic estimates were relatively invariable, while other dispersal estimates were highly variable. This latter point questions dispersal as a species-specific, invariant trait. © 2010 Cambridge Philosophical Society. Source

Archaux F.,IRSTEA | Henry P.-Y.,CNRS Mechanical Adaptation and Evolution | Gimenez O.,CNRS Center of Evolutionary and Functional Ecology
Methods in Ecology and Evolution | Year: 2012

Numbers of individuals or species are often recorded to test for variations in abundance or richness between treatments, habitat types, ecosystem management types, experimental treatments, time periods, etc. However, a difference in mean detectability among treatments is likely to lead to the erroneous conclusion that mean abundance differs among treatments. No guidelines exist to determine the maximum acceptable difference in detectability. In this study, we simulated count data with imperfect detectability for two treatments with identical mean abundance (N) and number of plots (nplots) but different mean detectability (p). We then estimated the risk of erroneously concluding that N differed between treatments because the difference in p was ignored. The magnitude of the risk depended on p, N and nplots. Our simulations showed that even small differences in p can dramatically increase this risk. A detectability difference as small as 4-8% can lead to a 50-90% risk of erroneously concluding that a significant difference in N exists among treatments with identical N=50 and nplots=50. Yet, differences in p of this magnitude among treatments or along gradients are commonplace in ecological studies. Fortunately, simple methods of accounting for imperfect detectability prove effective at removing detectability difference between treatments. Considering the high sensitivity of statistical tests to detectability differences among treatments, we conclude that accounting for detectability by setting up a replicated design, applied to at least part of the design scheme and analysing data with appropriate statistical tools, is always worthwhile when comparing count data (abundance, richness). © 2011 The Authors. Methods in Ecology and Evolution © 2011 British Ecological Society. Source

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