ABSTRACT TULIP (TUbular LIPid binding) domain proteins (TDPs) are found in all living organisms including bacteria. They have various documented functions, some of which clearly related to their intra- or extracellular lipid transfer activities. Extracellular, BPI-related TDPs of insects (B-TDPs, also known as Takeout-related proteins), are often found in chemosensory organs, but little is known regarding their exact location or how they could contribute to olfaction or gustation. We have surveyed and updated the full set of Drosophila B-TDPs and found that roughly 50% are overexpressed in chemosensory organs. Focusing on three genes clustered on the third chromosome, we provide evidence that at least one of the encoded proteins is secreted in the lymph cavity housing the dendrites of olfactory neurons. Biochemical data give support for a putative function of B-TDPs as odorant transporters, but loss-of-function analyses also hint to a potential role as a barrier against plant-emitted terpenoids.

We studied the interaction between the invasive potato moth T. solanivora and its granulovirus PhopGV in the northern Andes. Host density was analyzed based on 1206 pheromone trap data from 106 sampled sites in Ecuador, Colombia and Venezuela. The prevalence of the virus was assessed at 15 sites in 3 regions in Ecuador and Colombia. Infection status was analyzed for spatial structure at different scales: storage bag, storage room, field, locality, country. Locality and storage bag explained 8% and 26%, respectively of the total variance in infection status in glm analysis. The field versus storeroom effect differed between localities. GLM species distribution models were optimized for bioclimatic variables for both insects and viruses. Predicted virus prevalence was not significantly correlated with predicted host density at sampled virus sites. Over the entire climatic range covered by the study, the correlation was R=-0.053. Of the total population insect in this range, 26% were expected to be infected based on the model. This basic method of using species distribution models to analyze average correlations between species densities can help investigate statistical relationships across a range of trophic models using existing non-sympatric data, with little or no additional sampling effort. It removes confounding time-lag effects and allows the use of data collected separately in the different species. The approach is correlative, and cannot be interpreted in terms of causality or outside the study area.

The genetic structure of populations depends on two parallel processes - genetic and social-ecological - providing mutual information. Models that describe species' responses within social-ecological systems are increasingly important in the context of modern environmental crises. Advances in genetic data collection now provide access to vast numbers of markers, enabling a more comprehensive understanding of species dynamics. However, current statistical inference methods do not fully integrate these processes into a single cohesive model, and rely on summary statistics derived from separate inferences and simulations. In this work, I propose a probabilistic framework based on a coalescent model to compute the likelihood of a demogenetic model represented as connected graphs. The demographic graph, linking population nodes, is characterized by a backward gene adjacency matrix, which describes the probability of gene origins and is influenced by niche and dispersal functions. The genetic graph, linking allele nodes, captures mutation probabilities. A third graph represents the coalescence process of genes within the demographic and genetic graphs. Coalescence is simulated within the demographic model, and its probability is estimated based on the genetic model, or vice versa. Likelihood estimation is performed using a Monte Carlo algorithm. This framework allow likelihood-based sampling and Bayesian inference, offering a robust approach for environmental niche and meta-population modeling. I also discuss the practical applications of this model. By combining environmental niche functions with a coalescent framework, this approach enables probabilistic reconstructions of past species distributions based on present and historical occurrence data. It can incorporate various data sources, including historical records, absence data, and genetic information. By integrating niche and dispersal processes into a unified model, this framework provides a powerful tool for improving species distribution forecasting and deepening our understanding of species' responses to environmental change as interconnected systems. ### Competing Interest Statement The authors have declared no competing interest.

Cacao (Theobroma cacao L.) is a high-value commercial crop from which the popular chocolate is derived. Despite its importance, little is known about the associated arthropods and the factors affecting their diversity and functional dynamics. On the other hand, organic farming systems are considered a solution for biodiversity conservation. Consequently, this study aims to compare the abundance and diversity of arthropods, along with their functional roles, evaluating differences between conventional and organic systems. The study was conducted in Nilo, Cundinamarca, Colombia, comparing six conventional and organic cacao plots. Pitfall, interception, Malaise, and floral glue traps were used to recollect arthropods each day over three days in three sampling periods, each attempting to capture a phenological phase of cacao. A total of 273 arthropod families and 15,310 individuals were identified and categorized based on their ecological roles. The results indicate a significant increase in arthropod abundance in organic plots. Additionally, at the functional role level, an effect was found on the abundance of each plot. However, according to the inverse Simpson index, no differences in diversity were found between conventional and organic systems. A negative effect of temperature and precipitation on arthropod abundance was observed across several functional roles. These findings highlight the dynamics of the entomofauna in cacao and how organic management can benefit biodiversity. However, further research is needed to assess its impact on production and societal benefits.

The evolution of new variants of plant pathogens is one of the biggest challenges to controlling and managing plant diseases. Of the forces driving these evolutionary processes, global migration events are particularly important for widely distributed diseases such as potato late blight, caused by the oomycete Phytophthora infestans. However, little is known about its migration routes outside North America and Europe. This work used genotypic data from population studies to elucidate the migration history originating the Colombian P. infestans population. For this purpose, a dataset of 1,706 P. infestans genotypes was recollected, representing North and South America, Europe, and Asia. Descriptive analysis and historical records from North America and Europe were used to propose three global migration hypotheses, differing on the origin of the disease (Mexico or Peru) and the hypothesis that it returned to South America from Europe. These scenarios were tested using approximate Bayesian computation. According to this analysis, the most probable scenario (posterior probability = 0.631) was the one proposing a Peruvian origin for P. infestans, an initial migration toward Colombia and Mexico, and a later event from Mexico to the United States and then to Europe and Asia, with no return to northern South America. In Colombia, the scenario considering a single migration from Peru and posterior migrations within Colombia was the most probable, with a posterior probability of 0.640. The obtained results support the hypothesis of a Peruvian origin for P. infestans followed by rare colonization events worldwide.

AbstractPremiseThe prompt categorization of Phytophthora infestans isolates into described clonal lineages is a key tool for the management of its associated disease, potato late blight. New isolates of this pathogen are currently classified by comparing their microsatellite genotypes with characterized clonal lineages, but an automated classification tool would greatly improve this process. Here, we developed a flexible machine learning–based classifier for P. infestans genotypes.MethodsThe performance of different machine learning algorithms in classifying P. infestans genotypes into its clonal lineages was preliminarily evaluated with decreasing amounts of training data. The four best algorithms were then evaluated using all collected genotypes.ResultsmlpML, cforest, nnet, and AdaBag performed best in the preliminary test, correctly classifying almost 100% of the genotypes. AdaBag performed significantly better than the others when tested using the complete data set (Tukey HSD P < 0.001). This algorithm was then implemented in a web application for the automated classification of P. infestans genotypes, which is freely available at https://github.com/cpatarroyo/genotypeclas.DiscussionWe developed a gradient boosting–based tool to automatically classify P. infestans genotypes into its clonal lineages. This could become a valuable resource for the prompt identification of clonal lineages spreading into new regions.

Cassava Bacterial Blight (CBB) is a destructive disease widely distributed in the different areas where this crop is grown. Populations studies have been performed at local and national scales revealing a geographical genetic structure with temporal variations. A global epidemiology analysis of its causal agent Xanthomonas phaseoli pv. manihotis (Xpm) is needed to better understand the expansion of the disease for improving the monitoring of CBB. We targeted new tandem repeat (TR) loci with large repeat units, i.e. minisatellites, that we multiplexed in a scheme of Multi-Locus Variable number of TR Analysis (MLVA-8). This genotyping scheme separated 31 multilocus haplotypes in three clusters of single-locus variants and a singleton within a worldwide collection of 93 Xpm strains isolated over a period of fifty years. The major MLVA-8 cluster 1 grouped strains originating from all countries, except the unique Chinese strain. On the contrary, all the Xpm strains genotyped using the previously developed MLVA-14 microsatellite scheme were separated as unique haplotypes. We further propose an MLVA-12 scheme which takes advantage of combining TR loci with different mutation rates: the eight minisatellites and four faster evolving microsatellite markers, for global epidemiological surveillance. This MLVA-12 scheme identified 78 haplotypes and separated most of the strains in groups of double-locus variants (DLV) supporting some phylogenetic relationships. DLV groups were subdivided into closely related clusters of strains most often sharing the same geographical origin and isolated over a short period, supporting epidemiological relationships. The main MLVA-12 DLV group#1 was composed by strains from South America and all the African strains. The MLVA-12 scheme combining both minisatellite and microsatellite loci with different discriminatory power is expected to increase the accuracy of the phylogenetic signal and to minimize the homoplasy effects. Further investigation of the global epidemiology of Xpm will be helpful for a better control of CBB worldwide.

Agroecology, defined as the ecological science of food production is also as practical approach to design food production systems based on local concerted solutions that aim to promote synergy among the diversity of human and non human food systems elements. These two facets makes agroecology a good candidate for participatory research. Information technology should help using this information for the production of structured scientific knowledge. In this respect, there is a need for information technology that is adaptive to encompass the diversity of within and between systems and that provide benefit to farmers that feed it with data. We present MiCampoApp, a webapp that aims to join participatory research and certification in agroecology, with two roles, the farmer and the administrator. The idea to join participatory research for decision support with certification for market differentiation in single system is that much of the certification data harbors information for scientific research, and this incentivize data collection for the interest of the farmer and the community. The administrator create a model for data collection in order to solve a research question of interest for a communities or to produce traceability information to feed participatory guarantee system in a community. The farmer collects information using simple icons and produces traceability pages for research or certification purposes.

A recommendation of: Sophie Mallez, Chantal Castagnone, Eric Lombaert, Philippe Castagnone-Sereno, Thomas Guillemaud Inference of the worldwide invasion routes of the pinewood nematode Bursaphelenchus xylophilus using approximate Bayesian computation analysis 10.1101/452326

The success of biological bontrol (BC) introductions can be enhanced by considering theory and knowledge of biological systems. The gregarious braconid parasitoid Cotesia sesamiae (Cameroon) is one of the best studied biological control agent from the perspective of molecular ecology. Its evolutionary adaptation to the target host involves symbiotic partners. Polydnaviruses are responsible for immune and developmental adaptations whereas Wolbachia bacteria may reinforce this local adaptation though genetic isolation mechanisms. The noctuid Busseola fusca is a major stemborer pest of maize in sub-Saharan Africa. In contrast to eastern Africa, C. sesamiae is rarely found on B. fusca in western Africa. It is however often obtained from other stemborer species feeding on wild grasses. A biological control project was launched in 2006-2007 by introducing to Cameroon seven crosses of Kenyan populations of C. sesamiae collected in different ecozones. They included populations adapted to B. fusca that develop on maize as well as populations adapted to other hosts feeding on wild plants to allow carryover between cropping seasons. Wolbachia strains responsible for cytoplasmic reproductive incompatibilities with endemic strains were included in the crosses to limit genetic exchanges between introduced and endemic C. sesamiae and preserve genetic adaptation to B. fusca of the introduced populations, while at the same time preserving their ability to survive on wild plants. Six post release surveys were carried out on maize from 2007 to 2013, and on wild grasses in 2013. A total of 393 C. sesamiae individuals, each from one cocoon mass, were genotyped for 11 microsatellite loci. Multidimensional scaling analysis, STRUCTURE and GENE CLASS analyses assigned almost all the parasitoids recovered from maize to those introduced from Kenya. The introduced strains were also recovered from wild host plants with little genetic exchanges with endemics. Each population remained strongly associated with its original Wolbachia component, suggesting that Wolbachia may contribute to genetic isolation between endemics and introduced populations in wild host plants when maize is absent, thereby conciliating biological control success and safety.

Most endogenous viruses, an important proportion of eukaryote genomes, are doomed to slowly decay. Little is known, however, on how they evolve when they confer a benefit to their host. Bracoviruses are essential for the parasitism success of parasitoid wasps, whose genomes they integrated ~103 million years ago. Here we show, from the assembly of a parasitoid wasp genome, for the first time at a chromosomal scale, that symbiotic bracovirus genes spread to and colonized all the chromosomes. Moreover, large viral clusters are stably maintained suggesting strong evolutionary constraints. Genomic comparison with another wasps revealed that this organization was already established ~53 mya. Transcriptomic analyses highlight temporal synchronization of viral gene expression, leading to particle production. Immune genes are not induced, however, indicating the virus is not perceived as foreign by the wasp. This recognition suggests that no conflicts remain between symbiotic partners when benefits to them converge.

Dissecting the genetic basis of intraspecific variations in life history traits is essential to understand their evolution, notably for potential biocontrol agents. Such variations are observed in the endoparasitoid Cotesia typhae (Hymenoptera: Braconidae), specialized on the pest Sesamia nonagrioides (Lepidoptera: Noctuidae). Previously, we identified two strains of C. typhae that differed significantly for life history traits on an allopatric host population. To investigate the genetic basis underlying these phenotypic differences, we used a quantitative trait locus (QTL) approach based on restriction site‐associated DNA markers. The characteristic of C. typhae reproduction allowed us generating sisters sharing almost the same genetic content, named clonal sibship. Crosses between individuals from the two strains were performed to generate F2 and F8 recombinant CSS. The genotypes of 181 clonal sibships were determined as well as the phenotypes of the corresponding 4,000 females. Informative markers were then used to build a high‐quality genetic map. These 465 markers spanned a total length of 1,300 cM and were organized in 10 linkage groups which corresponded to the number of C. typhae chromosomes. Three QTLs were detected for parasitism success and two for offspring number, while none were identified for sex ratio. The QTLs explained, respectively, 27.7% and 24.5% of the phenotypic variation observed. The gene content of the genomic intervals was investigated based on the genome of C. congregata and revealed 67 interesting candidates, as potentially involved in the studied traits, including components of the venom and of the symbiotic virus (bracovirus) shown to be necessary for parasitism success in related wasps.

The population genetic structure of crop pest populations gives information about their spatial ecology, which helps in designing management strategies. In this paper, we investigated the genetic structure of the Mediterranean Corn Borer (MCB), Sesamia nonagrioides Lefèbvre (Lepidoptera: Noctuidae), one of the most important maize pests in the Mediterranean countries, using microsatellite markers for the first time in this species. Insects were collected in twenty-five locations in southwest and southeast France from cultivated and wild host plants (Zea mays, Sorghum halepense and Typha domingensis). Contrary to what has been reported so far in France, we found that MCB populations could be locally abundant on wild poales plants. Analysis was carried out at 11 polymorphic microsatellite markers. Molecular variance was significantly determined by geography, then by host plant, with 17% and 4%, respectively, when considered as a major effect, and with 14% and 1%, respectively, when considered as a marginal effect in permutational analysis. Multidimensional scaling (MDS) and GENELAND Bayesian clustering suggested that populations infecting wild plants (T. domingensis and S. halepense) were more structured locally than those affecting cultivated maize. In S. halepense, significant Isolation By Distance (IBD) indicated that this factor could explain genetic differentiation of the moth populations. In T. domingensis, local population differentiation was strong but did not depend on distance. The implication of this absence of population structure in maize and the heterogeneity of population genetics patterns in wild plants are discussed in the context of the population dynamics hypothesis and population management strategies.

Genetic samples can be used to understand and predict the behaviour of species living in a fragmented and temporally changing environment. In this regard, models of coalescence conditioned to an environment through an explicit modelling of population growth and migration have been developed in recent years, and simulators implementing these models have been developed, enabling biologists to estimate parameters of interest with Approximate Bayesian Computation techniques. However, model choice remains limited, and developing new coalescence simulators is extremely time consuming because code re-use is limited. We present Quetzal, a C++ library composed of re-usable components, which is sufficiently general to efficiently implement a wide range of spatially explicit coalescence-based environmental models of population genetics and to embed the simulation in an Approximate Bayesian Computation framework. Quetzal is not a simulation program, but a toolbox for programming simulators aimed at the community of scientific coders and research software engineers in molecular ecology and phylogeography. This new code resource is open-source and available at https://becheler.github.io/pages/quetzal.html along with other documentation resources.

Juvenile hormones (JHs) regulate important processes in insects, such as postembryonic development and reproduction. In the hemolymph of Lepidoptera, these lipophilic sesquiterpenic hormones are transported from their site of synthesis to target tissues by high affinity carriers, the juvenile hormone binding proteins (JHBPs). Lepidopteran JHBPs belong to a recently uncovered, yet very ancient family of proteins sharing a common lipid fold (TULIP domain) and involved in shuttling various lipid ligands. One important, but poorly understood aspect of JHs action, is the mechanism of hormone transfer to or through the plasma membranes of target cells. Since many membrane-active peptides and proteins, such as the pore-forming bacterial toxins, are activated by low pH or interaction with phospholipid membranes, we have examined the effect of these factors on JH binding by JHBPs. The affinity of Bombyx mori and Manduca sexta JHBPs for JH III was determined by the DCC assay, equilibrium dialysis, and isothermal titration calorimetry, and found to be greatly reduced at low pH, in agreement with previous observations. Loss of binding was accompanied by changes in fluorescence and near-UV CD spectra, indicating significant changes in protein structure in the environment of aromatic residues. The apparent dissociation rate constant (koff) of the JHBP-JH III complex was greater at acidic pH, suggesting that low pH favors ligand release by opening of the binding pocket. The affinity of recombinant B. mori JHBP (rBmJHBP) was also decreased in the presence of anionic phospholipid vesicles. Measurements of steady-state fluorescence anisotropy with the lipophilic probe TMA-DPH demonstrated that rBmJHBP specifically interacts with anionic membranes. These results suggest the existence of a collisional mechanism for ligand release that may be important for delivery of JHs to the target cells, and could be relevant to the function of related members of this emerging family of lipid-transport proteins.

The parasitoid lifestyle represents one of the most diversified life history strategies on earth. There are however very few studies on the variables associated with intraspecific diversity of parasitoid insects, especially regarding the relationship with spatial, biotic and abiotic ecological factors. Cotesia sesamiae is a Sub-Saharan stenophagous parasitic wasp that parasitizes several African stemborer species with variable developmental success. The different host-specialized populations are infected with different strains of Wolbachia, an endosymbiotic bacterium widespread in arthropods that is known for impacting life history traits, notably reproduction, and consequently species distribution. In this study, first we analyzed the genetic structure of C. sesamiae across Sub-Saharan Africa, using 8 microsatellite markers. We identified five major population clusters across Sub-Saharan Africa, which probably originated in the East African Rift region and expanded throughout Africa in relation to host genus and abiotic factors, such as Koppen-Geiger climate classification. Using laboratory lines, we estimated the incompatibility between the different strains of Wolbachia infecting C. sesamiae. We observed that incompatibility between Wolbachia strains was asymmetric, expressed in one direction only. Based on these results, we assessed the relationships between the direction of gene flow and Wolbachia infections in the genetic clusters. We found that host specialization was more influential on genetic structure than Wolbachia-induced reproductive incompatibility, which in turn was more influential than geography and current climatic conditions. These results are discussed in the context of African biogeography, and co-evolution between Wolbachia, virus parasitoid and host, in the perspective of improving biological control efficiency through a better knowledge of biological control agents' evolutionary ecology.

Ecological patterns result from historical contingency and deterministic processes. Taking apart these processes to extract probabilistic models of ecological dynamics is of major importance for ecological forecasting. Due to the high dimensionality of historical contingency it is usually difficult to sample history from observed patterns. In environmental population genetics, the number of possible genealogies linking genetic data and environemental data through demographic and niche models is almost infinite. In ecosystem dynamics time series, the patterns are determined as much by probabilitic model parameters, as by historical variables contingency. Aproximate bayesian computation allows to use simulations to aproximate this inference process. The rationale is to simulate data, extract summary statistics and retain in the posterior, the parameters values that produced simulations with summary statistic close to observed summary statistics. The major drawbacks of this approach is that summary statistics distance is not exhaustive regarding model likelihood and may biais the results. In the present work, I show that if we can simulate the historical contingency from observed data and probabilistic model in a backward approach, we can to use the simulations to estimate a pseudo likelihood that can be used in bayesian inference. I apply to genealogy sampling in environmental demogenetics and ecosystem dynamics modelling.

The African parasitoid wasp Cotesia sesamiae is a generalist species structured in locally adapted populations showing differences in host range. The recent discovery of Cotesia typhae , a specialist, sister species to C. sesamiae , provides a good framework to study the genetic determinants of parasitoid host range. To investigate the genomic bases of divergence between these populations and species, we used a targeted sequencing approach on 24 samples. We targeted the bracovirus genomic region encoding virulence genes involved in the interaction with the lepidopteran hosts of the wasps. High sequencing coverage was obtained for all samples, allowing the study of genetic variation between wasp populations and species. By combining population genetic estimations, such as nucleotide diversity (π), relative differentiation ( F ST ) and absolute divergence ( d xy ), with branch‐site dN/dS measures, we identified six of 98 bracovirus genes showing significant divergence and evidence of positive selection. These genes, belonging to different gene families, are potentially involved in host adaptation and in the specialization process. Fine‐scale analyses of genetic variation also revealed mutations and large deletions in certain genes inducing pseudogenization and loss of function. The image emerging from these results is that adaptation mediated by bracovirus genes happens through selection of particularly adaptive alleles and loss of nonadaptive genes. These results highlight the central role of the bracovirus in the molecular interactions between the wasps and their hosts and in the evolutionary processes of specialization.

Ecosystem dynamics forecasting is central to major problems in ecology, society, and economy. The existing models serve as decision tools but their parameters valitity are usually not confronted to real data in a formalized approach. Dynamics bayesian network inference is promissing but limited when dealing with incomplete multiple source time series with delayed time dependencies. We propose here a temporal bayesian network with time delay and aproximate inference algorithm, to learn altogether cryptic ecosystem variables, missing data, and model parameters. The novelty in the approach is that it combines simulation-based and likelihood-based aproximate bayesian inference. The advantage of simulation based is that it allows to sample hidden processes. The advantage of likelihood based is that it provides a summary statistics that is really representing the model we are interested in. The ecosystem variables and the missing data are simulated from indicator variables using the probabilistic indicator-ecosystem model. The likelihood is estimated by averaging the probability of observed-simulated data over simulations, the parameter space is sampled with Metropolis Hasting algorithm. Another innovative proposition is to parametrize the network structure in order to learn model structure within a space provided by prior distribution. We apply to plant epidemiology.

Impacts of climate change are likely to be marked in areas with steep climatic transitions. Species turnover, spread of invasive species, altered productivity, and modified processes such as fire regimes can all spread rapidly along ecotones, which challenge the current paradigms of ecosystem management. We conducted a literature review at a continental-wide scale of South-Western European forests, where the drier and warmer conditions of the Mediterranean have been widely used as examples of what is expected in more temperate areas. Results from the literature point to: (a) an expansion of slow-growing evergreen hardwood trees; (b) increased dieback and mortality episodes in forests (both natural and planted) mostly related to competition and droughts, and mainly affecting conifers; and (c) an increase in emergent diseases and pests of keystone-trees used in agroforestry zones. There is no consensus in the literature that fire regimes are directly increasing due to climate change, but available satellite data of fire intensity in the last 17 years has been lower in zones where agroforestry practices are dominant compared to unmanaged forests. In contrast, there is agreement in the literature that the current spread of fire events is probably related to land abandonment patterns. The practice of agroforestry, common in all Mediterranean countries, emerges as a frequent recommendation in the literature to cope with drought, reduce fire risk, and maintain biodiverse landscapes and rural jobs. However, it is unknown the extent to which the open vegetation resulting from agroforestry is of interest to forest managers in temperate areas used to exploiting closed forest vegetation. Hence, many transitional areas surrounding the Mediterranean Basin may be left unmanaged with potentially higher climate-change risks, which require active monitoring in order to understand and help ongoing natural adaptation processes.