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Highlights • The GP method provides an accurate measure of acorn crop size despite seed predation. • The method is highly reliable when applied in French temperate forests. • This method can be applied at the individual (tree) and population (forest) levels. • The method offers a robust and easy-to-implement tool to managers and scientists. Abstract Masting, or mast-seeding, defined as a synchronized and highly variable seed production from year-to-year within a population of plants, is one of the most common example of pulsed resources in terrestrial ecosystems. In oaks, the dramatic fluctuations of acorn production impact its reproductive success and regeneration, the dynamics of a large diversity of seed consumers that rely on it, and, by cascade effects, the dynamics of the entire forest community. However, reproductive effort is difficult to quantify and there is therefore an urgent need of a reliable assessment of the dynamic of acorn production based on a low-cost, unbiased, and robust tool. One of the most commonly used method, the "visual on-tree" method, is very easy and quick to carry out, but is biased under high seed production or when branches are difficult to see. We here assessed the robustness of an alternative method, the "ground plot" (GP), based on a unique annual ground survey after peak of acorn fall, which has not been tested so far. We compared this method at tree and site levels (10 forests throughout France) with the costly and time-consuming trap acorn collection (TNR) method (used here as a reference method). We show that results from the GP method closely matched with those obtained using the TNR method, which demonstrates the efficiency and robustness of the GP method at both tree and forest site levels. Despite some limitations in specific environmental contexts we review, this GP method offers a powerful tool to quantify acorn production and should be deployed to understand mechanisms underlying oak masting and/or to assess its ecological or economic consequences. [ABSTRACT FROM AUTHOR]

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• Monitoring exploited population trends is a timely issue for sustainable management. • Some hunter associations collect routinely hunting logs of wild boar drive hunting. • A simple reproductible Bayesian catch-effort conceptual framework was used. • Catchability allows to link hunting components in order to assess population trends. • Our framework can be used immediately by manager to adapt their wild boar harvest. For sustainable management of exploited populations, it is required to have good knowledge on temporal trends in population density to adapt the harvest. In this regard, hunting statistics are often collected routinely by government agencies and associations. These data are used to assess demographic trends through the development of indices, which are in turn used to manage exploited populations in a sustainable way. However, these population indices depend on features of the hunting process (e.g. hunting effort, hunting conditions, probability of catch). In this study, we show how to use hunting logs to assess demographic trends in exploited populations while accounting for the components of the hunting process. In particular, we developed a catch-effort model to study how the hunting effort leads to mortality rate – hunting pressure – within a given habitat type and during a given period. We illustrated the usefulness of this approach using exploited wild boar (Sus scrofa) populations as a case study. We used a large hunting logs dataset to perform our study, with several hundreds of thousands hunting events for more than 10 years in two French departments in France, including information about the number of hunters, of wild boars culled and the date of the hunt. We showed that catchability is a key parameter to assess hunting pressure at a given time and place. This parameter varies both within the hunting season and between habitat types. Once this variation in catchability was accounted for, our catch-effort model allowed us to obtain estimates of relative densities of wild boar populations over the study period at the management unit scale. Thus, catch-effort models are powerful tools to assess population density and to understand the underlying hunting process. Our study offers straightforward and reproducible conceptual framework that can be applied routinely by wildlife managers on exploited populations and practitioners from hunting statistics logs. [ABSTRACT FROM AUTHOR]

Wild boar populations have increased dramatically over the last decades throughout Europe and in France in particular. While hunting is considered the most efficient way to control game populations, many local conflicts persist after the hunting period due to remaining high densities of wild boar despite the large number of animals culled every year. Therefore, increasing the efficiency of hunting is a timely issue. Herein, we assessed how hunting effort can be measured, and we determined whether the hunting effort carried out by hunters explains the observed hunting pressure. We measured the characteristics and results of all hunts that occurred in the experimental forest of Châteauvillain-Arc-en-Barrois (Northeastern France), and we modelled the number of animals culled as a function of the hunting effort, measured by the number of beaters, hunters, and dogs, as well as the size of the hunting area. We also accounted for variables suspected to affect the hunting efficiency achieved with a given effort, such as time of day (AM/PM), the month during which hunting occurred. We found that more posted hunters, larger hunted areas, and hunts carried out early in the season, i.e. before February, increased the number of culled animals. Our model can be used by wildlife managers to adjust hunting effort in order to reach the hunting pressure expected to meet management objectives. Unlabelled Image • Increase of wild boar population causes many conflicts that must be managed. • Understanding harvest through hunting effort is a timely issue for wild boar management. • A drive hunt with more hunters leads to a higher number of culled animals. • Hunting is less efficient on small areas and during the end of the hunting season. • Our drive hunt model can be used as a predictive tool in wildlife management. [ABSTRACT FROM AUTHOR]