Predicting species invasiveness with genomic data: Is genomic offset related to establishment probability?

The B. tryoni data have already been published and have permissions appropriate for fully public release. The code necessary to reproduce the simulations, compute simulations GO, and use the optimized version of Gradient Forest package is available at https://forgemia.inra.fr/simon.boitard/popgenomicprediction.International audiencePredicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species but have still rarely been used in this context. Here, we evaluated the relationship between GO and the establishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which theoretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public empirical data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between “source” populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the establishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g., propagule pressure or admixture) will need further investigation

Unveiling the hypotheses of endemic richness: A study case in the Southwestern Alps

International audienceAreas where range-restricted species are concentrated are of importance for conservation. However, most of the studies aim at identifying areas rich in endemics for conservation planning, while few studies aim at understanding the causal factors of endemic richness. Here, our goal is to identify the determinants of endemic richness within a centre of endemism, the Southwestern European Alps, by testing four non-mutually exclusive hypotheses that have been proposed to explain patterns of endemic richness. In particular, we examined to what extent temporal and spatial climatic stability and environmental heterogeneity are related to endemic richness. Almost all hypotheses partially support the observed patterns of plant endemics richness within the SW Alps. In general, most of the relationships between environmental variables and endemic richness are statistically significant. However, the highest effect in explaining endemic richness is found for climate change velocity and standard deviation of slope, two factors affecting the possibility of species to disperse. This is in line with the idea that endemics are strongly limited by dispersal and not only by climate. Our results suggest that in regions where the effects of past climate changes were less dramatic endemic richness results from the interaction of species dispersal with regional and specific historical factors

Unveiling the impact of human urine fertilization on soil bacterial communities: A path toward sustainable fertilization

Raw sequencing data has been deposited in NCBI's Sequence Read Archive (SRA) under the BioProject PRJNA1081346 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1081346). Other datasets are available from the corresponding author on reasonable request.International audienceHighlights: • After storage, urine microbiome was depleted but still had few common urine bacteria. • Urine fertilization did not affect the overall soil bacterial community structure. • Urine fertilization increased the abundance of nitrifying and denitrifying groups. • No bacterial salt stress was induced despite high urine salt concentration.Abstract: Using human urine as a crop fertilizer has sparked interest due to its potential benefits, but its application requires an understanding of how urine can affect soil functions and microbial communities. This study aims at elucidating the response of soil bacterial communities to fertilization with human urine. To this end, a spinach crop was fertilized with 2 different doses of a source-separated and stored human urine (170 kg N ha−1 + 8.5 kg P ha−1 and 510 kg N ha−1 + 25.5 kg P ha−1) and compared with a synthetic fertilizer treatment (170 kg N ha−1 + 8.5 kg P ha−1) and a water treatment without fertilization. The experiment was conducted in four soil tanks in greenhouse conditions, according to a randomized block scheme. We assessed urine and soil bacterial composition at the beginning and the end of the experiment that we compared to soil and plant properties to understand the drivers in bacterial composition changes. After 12 months of storage, urine had a depleted microbiome but still contained few common strains of urine or faeces. Overall, soil bacterial communities were resistant to urine fertilization with only 3 % of the taxa impacted. However, urine fertilization increased the relative abundance of nitrifying and denitrifying groups compared to the synthetic fertilizer implying that more N2O and NO could be emitted when fertilizing with urine. The urine's high salt concentration had little discernible effect on the bacterial community. In a broader context, this experiment provides evidence that one-year-stored urine can be applied to a plant-soil system without negatively impacting soil bacterial communities in the short term

Unravelling life cycle impacts of coffee: Why do results differ so much among studies?

International audienceCoffee beans are a major agricultural product and coffee is one of the most widely traded commodities and consumed beverages globally. Supply chains and cropping systems are very diverse, with contrasted potentials and performance, as well as environmental impacts. Life Cycle Assessment (LCA) studies are needed to inform on reduction in impacts, but there is a lack of comprehensive understanding of the variability of existing LCA results and impacts of the cropping systems and their trade-offs along the supply chains. In an attempt to address this knowledge gap, the paper presents a systematic literature review of coffee LCA, considering a total of 34 studies covering 234 coffee systems. Global warming potential (GWP) was the impact category most reported in the literature, but the results varied greatly at both the farm and drink levels. For the former, the GWP values ranged from 0.15 to 14.5 (median: 3.6) kg CO 2 eq./kg green coffee beans and for the latter the values ranged from 2 to 23 (median: 8.8) kg CO 2 eq./kg consumed coffee in drinks. Main contributors to the GWP of production of green coffee beans were land use change (LUC), fertilisers and wet processing. However, there were great inconsistencies across studies in terms of LUC accounting, field emissions and wet process modelling. Green coffee beans production was also the main contributor to the GWP of coffee consumed, followed by brewing and coffee cup washing. Some studies covered other impacts, in addition to GWP. At both the farm and drink levels, fertilisers and pesticides were the main contributors to eutrophication and acidification, and to ecotoxicity, respectively. Brewing was the second main contributor at the drink level, in some cases the top contributor for energy -related indicators. Assumptions on packaging, cup washing and waste disposal were highly variable across studies. Water impact indicators were hardly comparable due to the system variability and method inconsistencies. Given the large diversity of coffee cropping systems worldwide, but also the diversity of possible coffee drinks, we recommend that LCA studies be standardised with respect to the definition of the functional unit, including consistent quality aspects for both green coffee beans (moisture) and coffee drinks (organoleptic properties). They should also be more thorough in detailing processes at all stages. More attention should be paid to the farming system complexity and a mass balance should be ensured when assessing biomass flows concerning LUC, co -products and residue emissions. Finally, more primary data would be needed to decipher the cropping system diversity, as well as to characterise emissions from all inputs to the field and bean processing, notably for wet and semi -wet processing