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The exchange of carbon between soil organic carbon (SOC) and the atmosphere affects the climate 1 , 2 and—because of the importance of organic matter to soil fertility—agricultural productivity 3 . The dynamics of topsoil carbon has been relatively well quantified 4 , but half of the soil carbon is located in deeper soil layers (below 30 centimetres) 5 – 7 , and many questions remain regarding the exchange of this deep carbon with the atmosphere 8 . This knowledge gap restricts soil carbon management policies and limits global carbon models 1 , 9 , 10 . Here we quantify the recent incorporation of atmosphere-derived carbon atoms into whole-soil profiles, through a meta-analysis of changes in stable carbon isotope signatures at 112 grassland, forest and cropland sites, across different climatic zones, from 1965 to 2015. We find, in agreement with previous work 5 , 6 , that soil at a depth of 30–100 centimetres beneath the surface (the subsoil) contains on average 47 per cent of the topmost metre’s SOC stocks. However, we show that this subsoil accounts for just 19 per cent of the SOC that has been recently incorporated (within the past 50 years) into the topmost metre. Globally, the median depth of recent carbon incorporation into mineral soil is 10 centimetres. Variations in the relative allocation of carbon to deep soil layers are better explained by the aridity index than by mean annual temperature. Land use for crops reduces the incorporation of carbon into the soil surface layer, but not into deeper layers. Our results suggest that SOC dynamics and its responses to climatic control or land use are strongly dependent on soil depth. We propose that using multilayer soil modules in global carbon models, tested with our data, could help to improve our understanding of soil–atmosphere carbon exchange. This study of whole-soil carbon dynamics finds that, of the atmospheric carbon that is incorporated into the topmost metre of soil over 50 years, just 19 per cent reaches the subsoil, in a manner that depends on land use and aridity.

The mechanisms leading to megafauna (>44 kg) extinctions in Late Pleistocene (126,000—12,000 years ago) Australia are highly contested because standard chronological analyses rely on scarce data of varying quality and ignore spatial complexity. Relevant archaeological and palaeontological records are most often also biased by differential preservation resulting in under-representated older events. Chronological analyses have attributed megafaunal extinctions to climate change, humans, or a combination of the two, but rarely consider spatial variation in extinction patterns, initial human appearance trajectories, and palaeoclimate change together. Here we develop a statistical approach to infer spatio-temporal trajectories of megafauna extirpations (local extinctions) and initial human appearance in south-eastern Australia. We identify a combined climate-human effect on regional extirpation patterns suggesting that small, mobile Aboriginal populations potentially needed access to drinkable water to survive arid ecosystems, but were simultaneously constrained by climate-dependent net landscape primary productivity. Thus, the co-drivers of megafauna extirpations were themselves constrained by the spatial distribution of climate-dependent water sources.

Weed control is generally considered to be essential for crop production and herbicides have become the main method used for weed control in developed countries. However, concerns about harmful environmental consequences have led to strong pressure on farmers to reduce the use of herbicides. As food demand is forecast to increase by 50% over the next century, an in-depth quantitative analysis of crop yields, weeds and herbicides is required to balance economic and environmental issues. This study analysed the relationship between weeds, herbicides and winter wheat yields using data from 150 winter wheat fields in western France. A Bayesian hierarchical model was built to take account of farmers’ behaviour, including implicitly their perception of weeds and weed control practices, on the effectiveness of treatment. No relationship was detected between crop yields and herbicide use. Herbicides were found to be more effective at controlling rare plant species than abundant weed species. These results suggest that reducing the use of herbicides by up to 50% could maintain crop production, a result confirmed by previous studies, while encouraging weed biodiversity. Food security and biodiversity conservation may, therefore, be achieved simultaneously in intensive agriculture simply by reducing the use of herbicides.

Gene flow is a key factor in the evolution of species, influencing effective population size, hybridisation and local adaptation. We analysed local gene flow in eight stands of white oak (mostly Quercus petraea and Q. robur, but also Q. pubescens and Q. faginea) distributed across Europe. Adult trees within a given area in each stand were exhaustively sampled (range [239, 754], mean 423), mapped, and acorns were collected ([17,147], 51) from several mother trees ([3], [47], 23). Seedlings ([65,387], 178) were harvested and geo-referenced in six of the eight stands. Genetic information was obtained from screening distinct molecular markers spread across the genome, genotyping each tree, acorn or seedling. All samples were thus genotyped at 5–8 nuclear microsatellite loci. Fathers/parents were assigned to acorns and seedlings using likelihood methods. Mating success of male and female parents, pollen and seed dispersal curves, and also hybridisation rates were estimated in each stand and compared on a continental scale. On average, the percentage of the wind-borne pollen from outside the stand was 60%, with large variation among stands (21–88%). Mean seed immigration into the stand was 40%, a high value for oaks that are generally considered to have limited seed dispersal. However, this estimate varied greatly among stands (20–66%). Gene flow was mostly intraspecific, with large variation, as some trees and stands showed particularly high rates of hybridisation. Our results show that mating success was unevenly distributed among trees. The high levels of gene flow suggest that geographically remote oak stands are unlikely to be genetically isolated, questioning the static definition of gene reserves and seed stands.

Aim Processes and variables measured in ecology are almost always spatially autocorrelated, potentially leading to the choice of overly complex models when performing variable selection. One way to solve this problem is to account for residual spatial autocorrelation (RSA) for each subset of variables considered and then use a classical model selection criterion such as the Akaike information criterion (AIC). However, this method can be laborious and it raises other concerns such as which spatial model to use or how to compare different spatial models. To improve the accuracy of variable selection in ecology, this study evaluates an alternative method based on a spatial cross-validation procedure. Such a procedure is usually used for model evaluation but can also provide interesting outcomes for variable selection in the presence of spatial autocorrelation. Innovation We propose to use a special case of spatial cross-validation, spatial leave-one-out (SLOO), giving a criterion equivalent to the AIC in the absence of spatial autocorrelation. SLOO only computes non-spatial models and uses a threshold distance (equal to the range of RSA) to keep each point left out spatially independent from the others. We first provide some simulations to evaluate how SLOO performs compared with AIC.We then assess the robustness of SLOO on a large-scale dataset. R software codes are provided for generalized linear models. Main conclusions The AIC was relevant for variable selection in the presence of RSA if the independent variables considered were not spatially autocorrelated. It otherwise failed because highly spatially autocorrelated variables were more often selected than others. Conversely, SLOO had similar performances whether the variables were themselves spatially autocorrelated or not. It was particularly useful when the range of RSA was small, which is a common property of spatial tools. SLOO appears to be a promising solution for selecting relevant variables from most ecological spatial datasets.

The ability of our ancestors to switch food sources and to migrate to more favourable environments enabled the rapid global expansion of anatomically modern humans beyond Africa as early as 120,000 years ago. Whether this versatility was largely the result of environmentally determined processes or was instead dominated by cultural drivers, social structures, and interactions among different groups, is unclear. We develop a statistical approach that combines both archaeological and genetic data to infer the more-likely initial expansion routes in northern Eurasia and the Americas. We then quantify the main differences in past environmental conditions between the more-likely routes and other potential (less-likely) routes of expansion. We establish that, even though cultural drivers remain plausible at finer scales, the emergent migration corridors were predominantly constrained by a combination of regional environmental conditions, including the presence of a forest-grassland ecotone, changes in temperature and precipitation, and proximity to rivers.

Conservation management becomes complicated when globally threatened species reach high densities locally, exceeding the carrying capacity of the ecosystem and causing damage. Managing high‐profile native species is particularly challenging, because ethical debates and public opposition to traditional control methods often prompt shifts toward strategies that prevent environmental harm rather than reducing populations. The koala (Phascolarctos cinereus) in South Australia exemplifies these challenges because, although it can damage the vegetation from high browsing pressure, culling is avoided due to public resistance. Therefore, managers have to consider costly and logistically constrained alternatives such as fertility control and translocation. Demographic models are valuable tools for predicting population dynamics, but their effectiveness depends on reliable population density estimates, often biased by expert‐elicited and citizen‐science data. We combined a point‐process model, an ensemble species distribution model, and a demographic model to project koala populations in the Mount Lofty Ranges over the next 25 years to assess the efficiency and cost‐effectiveness of fertility‐control interventions while accounting for sampling biases, habitat suitability, and local densities. We tested two hypotheses: (1) koala distribution is driven by rainfall, temperature, and soil acidity, with summer rainfall boosting habitat suitability, and (2) spatially targeted fertility interventions in high‐suitability areas are more cost‐effective than generalised strategies due to subpopulation connectivity. Our models confirmed that these three environmental factors shape koala distribution and that, in the absence of intervention, the koala population could increase by ~17‐25% in 25 years. Fertility control focusing on adult females emerged as the most cost‐effective (~AU $34 million) strategy, although it was slower at reducing population size compared to an intervention also sterilising female back young. While the choice of sterilisation scenario has minimal impact on overall costs, ethical considerations and long‐term conservation goals such as population density thresholds will have more influence on managing expenses effectively. The koala population in South Australia's Mount Lofty Ranges is increasing, raising concerns about overbrowsing and the need for sustainable management. Using combined demographic, point‐process, and species distribution models, we projected koala populations over 25 years to evaluate fertility‐control strategies. Our findings highlight rainfall, temperature, and vegetation as key drivers of habitat suitability, with targeted fertility control for adult females emerging as the most cost‐effective intervention (~AU$ 34 million).

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.

1. Conventional farming has been implicated in global biodiversity and pollinatordeclines and organic farming is often regarded as a more ecological alternative.However, the effects of organic farming on honeybees remain elusive, despitehoneybees’ importance as pollinators of crops and wild plants.2. Using 6 years of data from a large‐scale study with fortnightly measurements ofhoneybee colony performance traits (10 apiaries per year distributed across a435 km2‐large research site in France), we related worker brood area, number ofadult bees and honey reserves to the proportions of organic farmland in the surroundingsof the hives at two spatial scales (300 m and 1,500 m).3. We found evidence that, at the local scale, organic farming increased both workerbrood production and number of adult bees in the period of flower scarcity betweenthe blooms of oilseed rape and sunflower (hereafter ‘dearth period’). Atthe landscape scale, organic farming increased honey reserves during the dearthperiod and at the beginning of the sunflower bloom.4. The results suggest that worker brood development benefitted from organic farmingmostly through a more diverse diet due to an increase in the availability of diversepollen sources in close proximity of their hives. Reduced pesticide drift mayhave additionally improved bee survival. Honey reserves were possibly mostlyaffected by increased availability of melliferous flowers in foraging distance.5. Synthesis and applications. Organic farming increases honeybee colony performancein a period of resource scarcity, likely through a continuous supply of floral resourcesincluding weeds, cover crops and semi‐natural elements. We demonstratehow worker brood area increases in the critical dearth period (between the bloomsof oilseed rape and sunflower). This has previously been linked to winter colonysurvival, suggesting that organic farmland may mitigate repercussions of intensivefarming on colony vitality. We conclude that organic farming benefits a crucial croppollinator with potential positive implications for agriculture in the wider landscape

Most molecularly characterized plant resistance genes (R genes) belong to the nucleotide-binding-site-leucine-rich-repeat (NLR) receptor family and are prone to duplication and transposition with high sequence diversity. In this family, the Vat gene in melon is one of the few R genes known for conferring resistance to insect, i.e., Aphis gossypii, but it has been misassembled and/or mispredicted in the whole genomes of Cucurbits. We examined 14 genomic regions (about 400 kb) derived from long-read assemblies spanning Vat-related genes in Cucumis melo, Cucumis sativus, Citrullus lanatus, Benincasa hispida, Cucurbita argyrosperma, and Momordica charantia. We built the phylogeny of those genes. Investigating the paleohistory of the Vat gene cluster, we revealed a step by step process beginning from a common ancestry in cucurbits older than 50 my. We highlighted Vat exclusively in the Cucumis genera, which diverged about 20 my ago. We then focused on melon, evaluating a minimum duplication rate of Vat in 80 wild and cultivated melon lines using generalist primers; our results suggested that duplication started before melon domestication. The phylogeny of 44 Vat-CDS obtained from 21 melon lines revealed gain and loss of leucine-rich-repeat domains along diversification. Altogether, we revealed the high putative recognition scale offered in melon based on a combination of SNPs, number of leucine-rich-repeat domains within each homolog and number of homologs within each cluster that might jointly confer resistance to a large pest and pathogen spectrum. Based on our findings, we propose possible avenues for breeding programs.