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Understanding effects of harvest on population dynamics is of major interest, especially for declining species. European lapwing Vanellus vanellus populations increased from the 1960s until the 1980s and declined strongly thereafter. About 400,000 lapwings are harvested annually and it is thus of high conservation relevance to assess whether hunting was a main cause for the observed changes in lapwing population trends. We developed a multi-event cause-specific mortality model which we applied to a long-term ring-recovery data set (1960-2010) of > 360,000 records to estimate survival and cause-specific mortalities. We found no temporal change in survival over the last 50 years for first-year (FY) and older birds (after first-year; AFY) originating from different ringing areas. Mean survival was high, around 0.60 and 0.80 for FY and AFY individuals, respectively. The proportion of total mortality due to hunting was <0.10 over the study period and the estimated proportion of harvested individuals (kill rate) was <0.05 in each year. Our result of constant survival indicates that demographic processes other than survival were responsible for the pronounced change in lapwing population trends in the 1980s. Our findings lend support to the hypothesis that hunting was not a significant contributor to the large-scale decline of lapwing populations. To halt the ongoing decline of European lapwing populations management should focus on life history stages other than survival (e.g. productivity). Further analyses are required to investigate the contribution of other demographic rates to the decline of lapwings and to identify the most efficient conservation actions.

In migrant animals, conditions encountered at various times and places throughout their annual cycle may affect breeding success. Yet, most studies so far have only investigated the effect of specific parts of the annual cycle, despite the importance to understand how different stages can interact and how these stages compare to intrinsic quality to properly modulate breeding success. Using a structural equation modelling approach, we investigated drivers of breeding success (migration cycle, individual quality, breeding conditions) in hoopoes Upupa epops, a long-distant migrant. Our causal framework explained 75% of the variation in breeding success. The effect of the migration schedule was negligible, whereas the previous breeding attempt strongly influenced current breeding success. We suggest that the interplay of individual quality and environmental conditions during both previous and current breeding season may be more important drivers of breeding success than migration schedules, even in a long-distance migrant. We conclude that structural equation modeling is a promising tool to investigate causal relationships. Applied to hoopoes, we demonstrated that current breeding success is strongly linked to previous breeding success. Complementary analysis integrating weather and climate conditions during migration and the breeding season may provide a deeper and wider overview of the annual cycle of hoopoes and additional insights into the existence of carry-over effects in breeding success.

Assessing trends in the relative abundance of populations is a key yet complex issue for management and conservation. This is a major aim of many large‐scale censusing schemes such as the International Waterbird Count (IWC). However, owing to the lack of sampling strategy and standardization, such schemes likely suffer from biases due to spatial heterogeneity in sampling effort. Despite huge improvements of the statistical tools that allow tackling these statistical issues (e.g., GLMM, Bayesian inference), many conservationists still prefer to rely on stand‐alone turn‐key statistical tools, often violating the prerequisites put forward by the developers of these tools. Here, we propose a straightforward and flexible approach to tackle the typical statistical issues one can encounter when analyzing count data of monitoring schemes such as the IWC. We rely on IWC counts of the declining common pochard populations of the Northwest European flyway as a case study (period 2002–2012). To standardize the size of sampling units and mitigate spatial autocorrelation, we grouped sampling sites using a 75 × 75 km grid cells overlaid over the flyway of interest. Then, we used a hierarchical modeling approach, assessing population trends with random effects at two spatial scales (grid cells, and sites within grid cells) in order to derive spatialized values and to compute the average population trend at the whole flyway scale. Our approach allowed to tackle many statistical issues inherent to this type of analysis but often neglected, including spatial autocorrelation. Concerning the case study, our main findings are that: (1) the northwestern population of common pochards experienced a steep decline (4.9% per year over the 2002–2012 period); (2) the decline was more pronounced at high than low latitude (11.6% and 0.5% per year at 60° and 46° of latitude, respectively); and, (3) the decline was independent of the initial number of individuals in a given site (random across sites). Beyond the case study of the common pochard, our study provides a conceptual statistical framework for estimating and assessing potential drivers of population trends at various spatial scales. In this paper, we focused on analysing winter count data for common Pochard to estimate the trend in population size between the years 2002 and 2012. These data are routinely analysed to inform the IUCN conservation status of species. However, we highlight the existence of statistical biases that need to be taken into account to make the analyses more robust. For the species studied, our analysis shows a significant decrease in population size over the period studied, justifying the vulnerable nature of its conservation status. Moreover, the analysis tool we developed allowed us to highlight a more profound decrease to the north of the flyway than to the south, evidence that could not be highlighted with the classic tools usually used. Hence, given the growing interest by conservationists, we developed an R package to allow anyone using this model and reproducing the analysis done.

Camera trapping is the standard field method of monitoring cryptic, low-density mammal populations. Typically, researchers run camera surveys for 60 to 90 days and estimate density using closed population spatially explicit capture-recapture (SCR) models. The SCR models estimate density, capture probability (g0), and a scale parameter (σ) that reflects ranging behaviour. We used a year of camera data from 20 camera stations to estimate the density of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary in Belize, using closed population SCR models. We subsampled the dataset into 276 90-day sessions and 186 180-day sessions. Estimated density fluctuated from 0.51 to 5.30 male jaguars / 100 km2 between the 90-day sessions, with comparatively robust and precise estimates for the 180-day sessions (0.73 to 3.75 male jaguars / 100 km2). We explain the variation in density estimates from the 90-day sessions in terms of temporal variation in social behaviour, specifically male competition and mating events during the three-month wet season. Density estimates from the 90-day sessions varied with σ, but not with the number of individuals detected, suggesting that variation in density was almost fully attributable to changes in estimated ranging behaviour. We found that the models overestimated σ when compared to the mean ranging distance derived from GPS tracking data from two collared individuals in the camera grid. Overestimation of σ when compared to GPS collar data was more pronounced for the 180-day sessions than the 90-day sessions. We conclude that one-off ('snap-shot') short-term, small-scale camera trap surveys do not sufficiently sample wide-ranging large carnivores. When using SCR models to estimate the density from these data, we caution against the use of poor sampling designs and/or misinterpretation of scope of inference. Although the density estimates from one-off, short-term, small-scale camera trap surveys may be statistically accurate within each short-term sampling period, the variation between density estimates from multiple sessions throughout the year illustrate that the estimates obtained should be carefully interpreted and extrapolated, because different factors, such as temporal stochasticity in behaviour of a few individuals, may have strong repercussions on density estimates. Because of temporal variation in behaviour, reliable density estimates will require larger samples of individuals and spatial recaptures than those presented in this study (mean +/- SD = 14.2 +/- 1.2 individuals, 37.7 +/- 4.7 spatial recaptures, N = 276 sessions), which are representative of, or higher than published sample sizes. To satisfy the need for larger samples, camera surveys will need to be more expansive with a higher density of stations. In the absence of this, we advocate longer sampling periods and subsampling through time as a means of understanding and describing stability or variation between density estimates.

Lethal control is widely employed to suppress the numbers of target wildlife species within restricted management areas. The success of such measures is expected to vary with local circumstances affecting rates of removal and replacement. There is a need both to evaluate success in individual cases and to understand variability and its causes. In Britain, red fox (Vulpes vulpes) populations are culled within the confines of shooting estates to benefit game and wildlife prey species. We developed a Bayesian state-space model for within-year fox population dynamics within such restricted areas and fitted it to data on culling effort and success obtained from gamekeepers on 22 shooting estates of 2 to 36 km2. We used informative priors for key population processes-immigration, cub recruitment and non-culling mortality-that could not be quantified in the field. Using simulated datasets we showed that the model reliably estimated fox density and demographic parameters, and we showed that conclusions drawn from real data were robust to alternative model assumptions. All estates achieved suppression of the fox population, with pre-breeding fox density on average 47% (range 20%-90%) of estimated carrying capacity. As expected, the number of foxes killed was a poor indicator of effectiveness. Estimated rates of immigration were variable among estates, but in most cases indicated rapid replacement of culled foxes so that intensive culling efforts were required to maintain low fox densities. Due to this short-term impact, control effort focussed on the spring and summer period may be essential to achieve management goals for prey species. During the critical March-July breeding period, mean fox densities on all estates were suppressed below carrying capacity, and some maintained consistently low fox densities throughout this period. A similar model will be useful in other situations to quantify the effectiveness of lethal control on restricted areas.

Abstract There is growing evidence that the Earth's climate is undergoing profound changes that are affecting biodiversity worldwide. This gives rise to the pressing need to develop robust predictions on how species will respond in order to inform conservation strategies and allow managers to adapt mitigation measures accordingly. While predictions have begun to emerge on how species at the extremes of the so‐called slow‐fast continuum might respond to climate change, empirical studies for species for which all demographic traits contribute relatively equally to population dynamics are lacking. Yet, climate change is expected to strongly affect them throughout their entire lifecycle. We built a 21‐year integrated population model to characterize the population dynamics of the rock partridge ( Alectoris graeca ) in France, and tested the influence of nine weather covariates on demographic parameters. As predicted, both annual survival and breeding success were affected by weather covariates. Thick snow cover during winter was associated with low survival and small brood size the following breeding season. Brood size was higher with intermediate winter temperatures and snowmelt timing, positively correlated to breeding period temperature, but negatively correlated to temperature during the coldest fortnight and precipitation during the breeding period. Survival was positively correlated to winter temperature, but negatively to breeding period precipitation. Large‐scale indices indicated that cold and wet winters were associated with small brood size the following breeding season but with high survival. Expected changes of weather conditions due to climate change are likely to impact demographic traits of the rock partridge both positively and negatively depending on the traits and on the affected weather variables. Future population dynamics will thus depend on the magnitude of these different impacts. Our study illustrates the difficulty to make strong predictions about how species with a population dynamic influenced by both survival and fecundity will respond to climate change.

Breeding propensity, i.e., the probability that a mature female attempts to breed in a given year, is a critical demographic parameter in long‐lived species. Life‐history theory predicts that this trait should be affected by reproductive trade‐offs so that the probability of future reproduction should depend on the current reproductive investment. However, breeding propensity is one of the most difficult parameters to estimate because nonbreeders are often absent from the breeding area, thereby requiring the inclusion of unobservable states in the analysis. We developed a new methodological approach by integrating a robust design sampling scheme within the multi‐event capture–recapture framework. Our new model accounted for uncertainty in state assignation while allowing for departure of individuals between secondary sampling occasions. We applied this model to a long‐term data set of female Greater Snow Geese (Chen caerulescens atlantica) to estimate breeding propensity and to investigate potential reproductive costs. We combined resightings during the nesting stage and recapture at the end of the breeding season to estimate breeding propensity and nesting success, and added recoveries to improve survival probability estimates. We found that both breeding propensity and nesting success depended upon breeding status in the previous year, though not survival. Successful breeders had a lower breeding propensity than failed breeders in the following year, but a higher nesting success. Individuals absent from the breeding colony had a low breeding propensity, but a high nesting success the following year. Our results suggest a cost of reproduction on breeding propensity in the next year, but once females decide to breed, nesting success is likely driven by individual quality. An added benefit of our model is that, unlike previous models with unobservable states, all parameters were identifiable when survival and breeding probabilities were fully state dependent. Our new multi‐event framework is a flexible tool that can be applied to a large range of species to estimate breeding propensity and to investigate reproductive trade‐offs.

Breeding propensity, i.e., the probability that a mature female attempts to breed in a given year, is a critical demographic parameter in long-lived species. Life-history theory predicts that this trait should be affected by reproductive trade-offs so that the probability of future reproduction should depend on the current reproductive investment. However, breeding propensity is one of the most difficult parameters to estimate because nonbreeders are often absent from the breeding area, thereby requiring the inclusion of unobservable states in the analysis. We developed a new methodological approach by integrating a robust design sampling scheme within the multi-event capture-recapture framework. Our new model accounted for uncertainty in state assignation while allowing for departure of individuals between secondary sampling occasions. We applied this model to a long-term data set of female Greater Snow Geese ( Chen caerulescens atlantica ) to estimate breeding propensity and to investigate potential reproductive costs. We combined resightings during the nesting stage and recapture at the end of the breeding season to estimate breeding propensity and nesting success, and added recoveries to improve survival probability estimates. We found that both breeding propensity and nesting success depended upon breeding status in the previous year, though not survival. Successful breeders had a lower breeding propensity than failed breeders in the following year, but a higher nesting success. Individuals absent from the breeding colony had a low breeding propensity, but a high nesting success the following year. Our results suggest a cost of reproduction on breeding propensity in the next year, but once females decide to breed, nesting success is likely driven by individual quality. An added benefit of our model is that, unlike previous models with unobservable states, all parameters were identifiable when survival and breeding probabilities were fully state dependent. Our new multi-event framework is a flexible tool that can be applied to a large range of species to estimate breeding propensity and to investigate reproductive trade-offs.

Studies of population dynamics of long-lived species have generally focused on adult survival because population growth should be most sensitive to this parameter. However, actual variations in population size can often be driven by other demographic parameters, such as juvenile survival, when they show high temporal variability. We used capture—recapture data from a long-term study of a hunted, migratory species, the greater snow goose (Chen caerulescens atlantica), to assess temporal variability in first-year survival and the relative importance of natural and hunting mortality. We also conducted a parasite-removal experiment to determine the effect of internal parasites and body condition on temporal variation in juvenile survival. We found that juvenile survival showed a higher temporal variability than adult survival and that natural mortality was more important than hunting mortality, unlike in adults. Parasite removal increased first-year survival and reduced its annual variability in females only. Body condition at fledging was also positively correlated with first-year survival in treated females. With reduced parasite load, females, which are thought to invest more in their immune system than males according to Bateman's principle, could probably reallocate more energy to growth than males, leading to a higher survival. Treated birds also had a higher survival than control ones during their second year, suggesting a developmental effect that manifested later in life. Our study shows that natural factors such as internal parasites may be a major source of variation in juvenile survival of a long-lived, migratory bird, which has implications for its population dynamics.

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. Résumé Pour documenter et enrayer la perte de biodiversité, plusieurs étapes clés consistent à surveiller, quantifier les tendances et évaluer les stratégies de gestion des populations d’espèces sauvages. Grâce aux innovations technologiques croissantes, de plus en plus de données sont collectées et de nouvelles approches quantitatives sont constamment proposées. Ces développements rapides s’accompagnent d’un besoin croissant de compétences analytiques. Cette situation tend à creuser le fossé entre les chercheurs et les gestionnaires. Nous illustrons ici la manière de combler ce fossé en présentant deux collaborations de long terme entre une unité de recherche – le Centre d’Écologie Fonctionnelle et Évolutive (CEFE) – et un organisme public – l’Office Français de la Biodiversité (OFB). Le premier exemple est une collaboration visant à fournir un soutien statistique aux parcs nationaux pour la conception et la mise en œuvre de protocoles de suivi scientifique. Cette collaboration s’appuie sur le recrutement d’un ingénieur de recherche financé par l’OFB, basé physiquement au CEFE, et qui travaille en étroite collaboration avec l’OFB. Le deuxième exemple est une collaboration sur la gestion des grands carnivores. Depuis plus de 10 ans, cette collaboration a impliqué plusieurs personnes en doctorat et post‐doctorat co‐encadrées par le CEFE et l’OFB, et a récemment abouti au recrutement d’une chercheuse permanente par l’OFB qui partage son temps de travail entre le CEFE et l’OFB. Ces études de cas illustrent les modalités d’un travail collaboratif entre des institutions publiques opérant à différents niveaux de la conservation de la biodiversité pour la co‐construction d’agendas de recherche et le partage de connaissances. Ces collaborations s’accompagnent aussi de plusieurs défis. L'interconnaissance et l’apprentissage mutuel restent difficiles à des échelles plus grandes que celles des équipes concernées. Le personnel travaillant à cette interface doit faire preuve d'une bonne capacité d’écoute, respecter les besoins de tous les partenaires et faire preuve de flexibilité. L’échange de connaissances nécessite du temps, ce qui réduit la productivité mesurée selon des indicateurs quantitatifs basés sur les publications scientifiques ou les rapports institutionnels. Ces collaborations peuvent être difficiles à assumer socialement et restent fragiles car elles dépendent d’une bonne compréhension des différences de gouvernance entre les différents partenaires. Selon notre expérience, le succès d’une telle collaboration est favorisé par des relations étroites et à long terme, et par la co‐construction de projets à un stade précoce. Le partage d’un espace facilite les interactions en face‐à‐face lors des sessions de travail planifiées et les rencontres occasionnelles qui construisent une culture scientifique partagée et une confiance mutuelle.