Summary1. Studies reporting phenological changes in response to climate change are numerous and concern all groups of living organisms. Phenological changes could cause mismatching in food chains, thus inducing important perturbations in ecosystem functioning. Nevertheless, the relevancy of the conclusions drawn from phenological studies strongly depends on the estimation accuracy of such phenological changes. Many different estimators exist and some have already raised major criticism, although they continue to be used. Therefore, there is a crucial need for an extensive study documenting the behaviour of phenological estimators. 2. Here, we compare the estimation efficiency of 10 phenological estimators: different first appearance dates, mean dates, different percentile dates and a smoothing method based on spline functions using simulated phenological data. Root mean-squared errors and bias of the phenological estimations are calculated in relation to different parameters of the simulated phenological data. 3. Results show that first appearance dates behave as a very inaccurate and biased estimator regarding any phenological data set. Mean dates and estimates calculated using the smoothing method provided in general the most accurate estimates of phenological shifts. They were also the most robust to variation in sample sizes and to imperfect detectability. 4. Our results allow us to warn against the use of first appearance dates in future phenological studies and to recommend using mean dates or smoothing techniques to estimate phenological change of entire distributions. We also provide advice concerning phenological monitoring effort. These recommendations should most importantly apply to studies aiming at comparing phenological variation among sites or among species.

Problems induced by heterogeneity in species and individuals detectability are now well recognized when analysing count data. Yet, most recent techniques developed to handle this problem are still hardly applicable to many monitoring schemes, and do not provide abundance estimates at the point count scale. Here, we show how using simple weather variables can be a useful surrogate to detect variability in species detectability. We further look for a potential bias or loss in statistical power based on count data while ignoring weather and time-of-day variables. We first used the French Breeding Bird Survey to test how each of the counts of the 97 most common breeding species was influenced by weather and time-of-day variables. We assessed how the estimation of each species response to fragmentation could be influenced by correcting counts with such variables. Among 97 species, 75 were affected by at least one of the five weather and time-of-day variables considered. Despite these strong influences, the relationship between species abundance and fragmentation was not biased when not controlling counts for weather and time-of-day variables and further found no improvement in statistical power when accounting for these variables. Our results show that simple variables can be very powerful to assess how species detectability is influenced by weather conditions but they are inconsistent with any specific bias due to heterogeneous detectability. We suggest that raw count data can be used without any correction in case the sources of variation in detectability could be considered independent to the factor of interest.

One of the most consensual ecological effects of the current climate warming is the alteration of the environmental timing of ecosystems. Phenological shifts, at different levels of food webs, are predicted to have major effects on species assemblages. Indeed it is unlikely that all species should be able to respond to the phenological shifts of their environment evenly. Yet questions remain about the specific traits that predict the ability of a species to track the temporal fluctuations of its environment. In this study, we use data from the French Constant Effort Site ringing program over a 20 years period (1989–2008) to estimate the ability of 20 common passerine species to adjust their breeding phenology to spring temperature variations. We show that the sensitivity of species breeding phenology to climate relates to species mean migration distance, species’ thermal and habitat niche breadth and brain mass. Species with the broadest ecological and thermal niches, the shortest mean migration distances and the largest brains were most able to adjust their breeding phenology to temperature variations. Our results thus identify long distance migrants and ecological specialists as species that could most suffer from the future expected climate change and suggests phenological adjustment as one possible mechanism underlying the replacement of specialist species by more generalist ones, the so called functional biotic homogenization.

International audienceEvidences for phenological changes in response to climate change are now numerous. One of the most documented changes has been the advance of spring arrival dates in migratory birds. However, the effects of climate change on subsequent events of the annual cycle remain poorly studied and understood. Moreover, the rare studies on autumn migration have mainly concerned passerines. Here, we investigated whether raptor species have changed their autumn migratory phenology during the past 30 years at one of the most important convergent points of western European migration routes in France, the Organbidexka pass, in the Western Pyrenees. Eight out of the 14 studied raptor species showed significant phenological shifts during 1981–2008. Long-distance migrants displayed stronger phenological responses than short-distance migrants, and advanced their mean passage dates significantly. As only some short-distance migrants were found to delay their autumn migration and as their trends in breeding and migrating numbers were not significantly negative, we were not able to show any possible settling process of raptor populations. Negative trends in numbers of migrating raptors were found to be related to weaker phenological responses. Further studies using data from other migration sites are necessary to investigate eventual changes in migration routes and possible settling process