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The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha(-1) year(-1) in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale.

Background Social behavior and social organization have major influences on individual health and fitness. Yet, biomedical research focuses on studying a few genotypes under impoverished social conditions. Understanding how lab conditions have modified social organizations of model organisms, such as lab mice, relative to natural populations is a missing link between socioecology and biomedical science. Results Using a common garden design, we describe the formation of social structure in the well-studied laboratory mouse strain, C57BL/6J, in replicated mixed-sex populations over 10-day trials compared to control trials with wild-derived outbred house mice in outdoor field enclosures. We focus on three key features of mouse social systems: (i) territory establishment in males, (ii) female social relationships, and (iii) the social networks formed by the populations. Male territorial behaviors were similar but muted in C57 compared to wild-derived mice. Female C57 sharply differed from wild-derived females, showing little social bias toward cage mates and exploring substantially more of the enclosures compared to all other groups. Female behavior consistently generated denser social networks in C57 than in wild-derived mice. Conclusions C57 and wild-derived mice individually vary in their social and spatial behaviors which scale to shape overall social organization. The repeatable societies formed under field conditions highlights opportunities to experimentally study the interplay between society and individual biology using model organisms.

The parasitic Varroa destructor is considered a major pathogenic threat to honey bees and to beekeeping. Without regular treatment against this mite, honey bee colonies can collapse within a 2–3-year period in temperate climates. Beyond this dramatic scenario, Varroa induces reductions in colony performance, which can have significant economic impacts for beekeepers. Unfortunately, until now, it has not been possible to predict the summer Varroa population size from its initial load in early spring. Here, we present models that use the Varroa load observed in the spring to predict the Varroa load one or three months later by using easily and quickly measurable data: phoretic Varroa load and capped brood cell numbers. Built on 1030 commercial colonies located in three regions in the south of France and sampled over a three-year period, these predictive models are tools designed to help professional beekeepers’ decision making regarding treatments against Varroa. Using these models, beekeepers will either be able to evaluate the risks and benefits of treating against Varroa or to anticipate the reduction in colony performance due to the mite during the beekeeping season.

Pollutant biomonitoring demands analytical methods to cover a wide range of target compounds, work with minimal sample amounts, and apply least invasive and reproducible sampling procedures. We developed a method to analyse 68 bioaccumulative organic pollutants in three seabird matrices: plasma, liver, and stomach oil, representing different exposure phases. Extraction efficiency was assessed based on recoveries of spiked surrogate samples, then the method was applied to environmental samples collected from Scopoli’s shearwater ( Calonectris diomedea ). Extraction was performed in an ultrasonic bath, purification with Florisil cartridges (5 g, 20 mL), and analysis by GC–Orbitrap–MS. Quality controls at 5 ng yielded satisfactory recoveries (80–120%) although signal intensification was found for some compounds. The method permitted the detection of 28 targeted pollutants in the environmental samples. The mean sum of organic pollutants was 4.25 ± 4.83 ng/g in plasma, 1634 ± 2990 ng/g in liver, and 233 ± 111 ng/g in stomach oil (all wet weight). Pollutant profiles varied among the matrices, although 4,4′-DDE was the dominant compound overall. This method is useful for pollutant biomonitoring in seabirds and discusses the interest of analysing different matrices.

Conventional bio-logging techniques used for ethological studies of seabirds have their limitations when studying detailed behaviours at sea. This study uses animal-borne video cameras to reveal fine-scale behaviours, associations with conspecifics and other species and interactions with fishery vessels during foraging of a Mediterranean seabird. The study was conducted on Scopoli's shearwaters (Calonectris diomedea) breeding in Linosa island (35°51′33″ N; 12°51′34″ E) during summer 2020. Foraging events were video recorded from a seabirds' view with lightweight cameras attached to the birds' back. Foraging always occurred in association with other shearwaters. Competitive events between shearwaters were observed, and their frequency was positively correlated to the number of birds in the foraging aggregation. Associations with tunas and sea turtles have been frequent observations at natural foraging sites. During foraging events, video recordings allowed observations of fine-scale behaviours, which would have remained unnoticed with conventional tracking devices. Foraging events could be categorised by prey type into “natural prey” and “fishery discards”. Analysis of the video footage suggests behavioural differences between the two prey type categories. Those differences suggest that the foraging effort between natural prey and fishery discards consumption can vary, which adds new arguments to the discussion about energy trade-offs and choice of foraging strategy. These observations highlight the importance of combining tracking technologies to obtain a complete picture of the at-sea behaviours of seabirds, which is essential for understanding the impact of foraging strategies and seabird-fishery interactions.

Information on seabird diet is key to understanding their ecological role in the marine food web. The Mediterranean Sea is a biodiversity hotspot that is experiencing a series of growing threats, including overfishing and climate change. The Scopoli’s ( Calonectris diomedea ) and Yelkouan shearwaters ( Puffinus yelkouan ), two marine predators in the region, are expected to have a piscivorous diet and exploit fishery discards, but their exact reliance on different resources is still unclear. We sampled four populations in the central Mediterranean Sea and used a combination of DNA metabarcoding and stable isotopes to compare their diets and assess trophic niches. We found prey items from 38 families belonging to 21 orders. Clupeiformes and Perciformes were the main prey groups identified in both shearwater species. In fact, diet composition largely overlapped and differed by only 3% variation in the diet consumed at order level and 16% at genus level, despite sampling different populations. The results suggest high overlap of dietary and isotopic niches, while Yelkouan shearwaters occupied a wider niche space overall. Certain taxa were potentially derived from discards but are also available naturally as juvenile fish or in foraging associations with marine megafauna such as predatory fish and turtles. These findings highlight the strong dietary overlap and ecological similarities between Scopoli’s and Yelkouan shearwaters, emphasising the importance of understanding their foraging dynamics in the context of resource competition and the increasing pressures on Mediterranean marine ecosystems.

Viruses occupy a large proportion of the pathogen communities within honey bee colonies, with more than 80 species detected in Apis mellifera . Honey bee viruses are globally distributed and several species have been linked to honey bee diseases that threaten colony health. However, less is known about the ecology and epidemiology of viruses within honey bee colonies, and in particular whether a link exists between virus temporal dynamics and seasonal variations and/or colony dynamics. Using a large-scale longitudinal survey conducted over three years, we report the prevalence and abundance of deformed wing virus, acute bee paralysis virus, black queen cell virus, chronic bee paralysis virus and sacbrood virus (DWV, ABPV complex, BQCV, CBPV and SBV) in more than 300 colonies located in two different environments of southern Europe (Provence in France, Piemonte in Italy), and exhibiting contrasted climatic conditions. Monthly measurements performed throughout the beekeeping seasons indicate distinct seasonal trends in prevalence and abundance of the five virus species: DWV, SBV and ABPV complex displayed marked seasonal variations, that were similar in both environments tested. We also highlight the link between seasonal virus dynamics and colony dynamics for SBV/BQCV, and parasite dynamics for DWV. This study improves our understanding of virus ecology within honey bee colonies.

Viruses occupy a large proportion of the pathogen communities within honey bee colonies, with more than 80 species detected in Apis mellifera. Honey bee viruses are globally distributed and several species have been linked to honey bee diseases that threaten colony health. However, less is known about the ecology and epidemiology of viruses within honey bee colonies, and in particular whether a link exists between virus temporal dynamics and seasonal variations and/or colony dynamics. Using a large-scale longitudinal survey conducted over three years, we report the prevalence and abundance of deformed wing virus, acute bee paralysis virus, black queen cell virus, chronic bee paralysis virus and sacbrood virus (DWV, ABPV complex, BQCV, CBPV and SBV) in more than 300 colonies located in two different environments of southern Europe (Provence in France, Piemonte in Italy), and exhibiting contrasted climatic conditions. Monthly measurements performed throughout the beekeeping seasons indicate distinct seasonal trends in prevalence and abundance of the five virus species: DWV, SBV and ABPV complex displayed marked seasonal variations, that were similar in both environments tested. We also highlight the link between seasonal virus dynamics and colony dynamics for SBV/BQCV, and parasite dynamics for DWV. This study improves our understanding of virus ecology within honey bee colonies.

In several countries, regional surveys are carried out to detect the presence of pests and diseases in crops. During these surveys, the incidence of major diseases and the presence of pests are recorded on various dates during the growing season. In this study, we aim to develop a framework to make better use of these regional surveys to estimate pest and disease dynamics, to analyse their variability across sites and years, and to assess uncertainty. Our framework is illustrated in four case studies: Septoria leaf blotch on wheat, downy mildew on grapevine, yellow sigatoka on banana and weevils on sweet potato. We showed that frequentist and Bayesian generalised linear mixed models gave similar results. This type of models is flexible enough to handle different types of data. They can be used to estimate disease and pest dynamics from observations collected in regional surveys and could help regional extension services evaluate risk levels at the regional scale.