Ecology

The impacts of climate change on host–parasite dynamics are particularly complex to predict, as they involve an interplay of both physiological and behavioural factors, from both host and parasite. For example, while warming may increase parasite developmental rates and thus increase transmission, excessive heat may instead exceed thermal limits, leading to higher parasite mortality. Transmission also relates to both the distribution and abundance of host species, which may also shift under changing climates. More

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Dataset 1: Analysis of truffle growers archivesWe selected eleven T. melanosporum orchards located across the South-West France, from Montpellier (43°44′01.4″N 3°42′13.2″E) to Jonzac (45°27′17.7″N, 0°25′26.9″W; Fig. 2). These sites were selected for (1) the quality of the records of fruitbody production and practices by truffle growers (Table S1), including the detail of inoculations since plantation (amount and frequency of added crushed sporocarps), (2) the use of truffle traps by the owners and the quality of the record from these devices, and (3) the presence of oaks (Quercus ilex, Q. pubescens and Q. suber) as the only hosts tree species. Based on the archives of truffle growers, including a systematic recording of truffle production within and outside traps, we reported at each study site the contribution of truffle traps to the annual fruitbody production of the entire truffle grounds, by using number and/or weight of collected fruitbodies within (Pin) and outside (Pout) truffle traps.Dataset 2: In situ experiment tracing the inoculation effectThree orchards located near Angoulème (45°74′35.5″N, − 0°63′78.4″W), Jonzac (45°44′09.8″N, 0°43′96.7″W), and Arles-sur-Tech (42°45′44.9″N, 2°62′89.4″W), hereafter referred to Site 1 to 3 (Fig. 2) were selected for testing both disturbance effect and inoculum effect on fruitbody production in truffle traps. These sites presented a high fruitbody production and a high Pin/Pout ratio, thus optimum conditions to test mechanisms underlying how truffle traps influence fruitbody production. Host trees were between 5 and 18 years old at the beginning of the experiment (Fig. 2). At each site, we selected three non-adjacent trees (four on Site 3) that displayed a continuous fruitbody production over the three previous years. Under each selected tree, we excavated, at two-thirds of the distance between the tree trunk and the limit of brûlé (a vegetation-poor zone that shows the extension mycelia in the soil40, eight equidistant truffle traps [20 × 20 cm large × 20 cm deep] as shown in Fig. 3a. Under each tree, two traps were filled with only a mixture of peat and vermiculite (hereafter referred as non-inoculated controls) to test for disturbance effect. The used mixture was identical to that which is currently applied in commercial orchards. In three other traps, 5 g of crushed material from a single black truffle fruitbody (including its gleba and spores) were added to the previous mixture (hereafter referred as one mating-type inoculum). In the three last traps, 5 g of crushed material from two ascocarps with gleba of opposite mating types (hereafter referred as two mating-type inoculum) were added to the previous mixture. We added the two mating-type condition to accurately test a potential contribution of the gleba (haploid and thus with a single mating type) on future production. As quoted in Introduction, maternal individuals with opposite mating types tend to exclude each other locally (spatial segregation of clusters of individuals of same mating types26. Thus, the two mating-type inoculum allows us to detect in each trap a maternal contribution by the introduced gleba, despite potential exclusion by pre-installed individuals of the locally dominant mating type in the surrounding. Moreover, it allows us to detect a paternal contribution by the introduced gleba of the mating type opposite to the locally dominant. The eight truffle traps were randomly arranged, so that two repetitions of same modality were always separated by a repetition of another modality (Fig. 3a).In March 2013, six freshly collected truffles (weighting  > 60 g) were molecularly analyzed for the mating type of their gleba as in18. On Site 1 and Site 2, the inoculum was made of fruitbodies collected at Site 1. On Site 3, fruitbodies used as inoculum originated from truffle grounds in Sarrion (Spain). In April 2013, truffles traps were installed as explained above (in all, 8 traps × 3 (or 4) trees × 3 sites) and monitored for two years by truffle growers. Harvesting was performed by trained dogs (one different dog per site) checking truffle traps and the surrounding brûlés at each visit of the orchard by truffle growers. When dogs detected truffles, a small hole was excavated to collect ascocarps without disturbing the trap further. At the end of January, 2015, all truffle traps were completely excavated, remnant truffles overlooked by dogs were systematically collected (Fig. 3b). Three soil aliquots were collected within all traps and pooled. All truffles and soil aliquots were frozen for subsequent DNA analysis.Molecular and genetic analysesDNA extractions, mating typing and genotyping were done as in18. Briefly, DNA was extracted from the gleba and from spores of each fruitbody to get access to the maternal and zygotic DNA, respectively. Simple sequence repeat (SSRs) genotyping was performed using 12 polymorphic markers and the mating-type locus as in18. Gleba extracts displaying apparent heterozygous genotypes, likely due to contamination by spore DNA were systematically discarded from further analyses. For each fruitbody, the haploid paternal genotype was then deduced by subtracting the haploid maternal genotype from the zygotic diploid genotype. This data set was used for relatedness estimations. We discarded from all further analysis the marker me11, which displayed more than 39% missing data, as well as all samples with missing data for at any locus.Multilocus genotypes comparisonsBased on the 11 remaining SSRs and the mating-type (Table S5 and Figure S2), MLGs were identified on all maternal and paternal haploid genomes using GenClone v.2.041, and the probability that MLGs represented more than once resulted from independent events of sexual reproduction was calculated (PSex41,42). On each site, clonal diversity was measured as R = (G − 1)/(N − 1) according to43, where N is the number of fruitbodies and G the number of MLGs. For testing whether the gleba of the inoculated fruitbody contributed, either paternally (H1) or maternally (H2) to the harvested fruitbodies (Fig. 1c), the inoculated maternal MLG was compared to the paternal and maternal MLG of the harvested fruitbodies.Relatedness estimationFor testing whether the spores of the inoculum, which carry many distinct haploid MLGs due to meiosis, had paternal or maternal contribution(s) to the harvested fruitbodies (H3; Fig. 1c), we used relatedness estimation.For testing whether spores of the inoculum had a paternal contribution, an individual relatedness estimate to the spore inoculum was computed for each paternal genome detected in truffle traps. Relatedness r here describes the expected frequency E[p_offpat] of each allele in a given genome, E[p_offpat] = p_pop + r * (p_inoc − p_pop), where p_pop is the allele frequency in the local population (here estimated from the glebas of other truffles collected under the focal tree), and p_inoc is the frequency of the allele in the inoculum. Thus, p_offpat takes values 0 or 1, and p_inoc takes values 0, 0.5 or 1, except when two fruitbodies were used as inoculum (two gleba mating types traps). Thus r = (p_offpat − p_pop)/(p_inoc − p_pop). An individual relatedness estimate for each genome is then obtained by summing over alleles and loci the observed values of the numerator and denominator in this expression. A population-level estimate is further obtained by summing numerators and denominators over the paternity events in each population.To test whether such estimates are compatible with the hypothesis that the paternal individuals are not from the inocula, we obtained the distribution of population-level relatedness estimates by simulating samples under this hypothesis: paternal genotypes were randomly simulated according to alleles frequencies in the local population. For each population, 10,000 samples were simulated, and p-values were estimated as the proportion of simulations with higher population-level relatedness with inocula than the observed one. Confidence intervals for these p-values were computed from the binomial distribution for 10,000 draws, and Bonferroni-corrected over the three populations.For testing whether spores of the inoculum had a maternal contribution (H4, Fig. 1c), we estimated the relatedness of the locally used spore inoculum to each maternal genome detected in truffle traps (deduced from the gleba), and we confronted it to simulated samples as previously but with one modification: if the focal fruitbody was harvested in a trap inoculated with the inoculum A1, all genomes of truffles from traps inoculated with the same inoculum (A1 or A1 + A2 + A3, see Fig. 3c.) were discarded from the estimation of p_pop.Assessment of T. melanosporum mycelium concentration in truffle trapsOn Sites 1, 2 and 3, soil samples were collected in all traps and in the surrounding brûlés at harvesting date (January, 2015). In collected soils, total DNA was extracted and quantified as in19. Briefly, after sieving and homogenizing soil collected in each trap and from out of the brûlés, aliquots (10 g) were analyzed as follows. After extraction with the kit Power Soil (MoBio Laboratories, Carlsbad, CA, USA), the extra-radical mycelium of T. melanosporum was quantified using quantitative Taqman™ PCR (qPCR) with the primers and probe described in44. Triplicate real-time PCR were performed on each sample using the same concentration of primer and the same thermocycling program as in19. Standards were prepared using fresh immature T. melanosporum ascocarp, and a standard curve was generated for each site by plotting serial tenfold dilutions against corresponding initial amount of ascocarp. Absolute quantification of mycelium biomass of T. melanosporum was expressed in mg of mycelium per g of soil.Statistical analysesStatistics were done using R version 4.0.445.Effect of truffle traps on fruitbody production—The contribution of truffle traps to the overall production of orchards was assessed by (1) data mining of truffle growers’ archives (Dataset 1) and (2) comparing the density of truffles harvested in traps (expressed in number of truffles per m2 per orchard; for each sampled tree, traps correspond to an investigated soil surface of s = 8 × 0.2 x 0.2 = 0.32 m2) with the density measured within surrounding brûlés (Dataset 1). On Dataset2, at each site, the area occupied by brûlés was evaluated by measuring in the field the surface of soil devoid of vegetation consecutively to spontaneous T. melanosporum brûlé.Fruitbody production under different conditions (i.e. non-inoculated controls versus one gleba mating type traps versus two gleba mating type traps) were compared using generalized linear mixed models with negative binominal family and log link (R, spam package46). The full model included the logarithm of the sampled area as offset to account for variations in this sampled area, interactions of trap-modality effects with site effect. Formal likelihood ratio tests are based on one-step deletions from this full model, applied to subsets of the data relevant for each hypothesis tested. Additional bootstrap tests (1000 iterations) were run to correct any bias in small sample likelihood ratio tests.Concentrations of T. melanosporum mycelium in soil—Similarly as above, the inoculum effect on mycelium concentrations was compared using generalized linear mixed models with Gamma log family.Plant materialThe use of plants in the present study complies with international, national and/or institutional guidelines. All permissions to collect T. melanosporum fruitbodies in truffle orchards were obtained. The formal identification of biological material used in the study (T. melanosporum fruitbodies) was undertaken by F. Richard and E. Taschen. Voucher specimens of all collected fruitbodies have been deposited in the Centre d’Ecologie Fonctionnelle et Evolutive herbarium in Montpellier (France).Ethical approvalAll co-authors approve the ethical statement regarding the submitted manuscript.Consent to participateAll co-authors consent to participate to the research and agree with the content of the submitted manuscript. All authors reviewed and submitted manuscript. More

Changes in temperature and extreme environmental conditions can affect the dynamics of vector-borne pathogens. These include leishmaniasis, transmitted by phlebotomine sandflies, as well as mosquitoes that spread arboviruses like dengue, encephalitis, yellow fever, West Nile fever, and lymphatic filariasis19,20,21.The CCA analysis showed that maximum temperature significantly influenced the abundance of mosquito populations in the study area. In addition, the NMDS showed two different groupings that consisted of samples collected during the rainy and dry seasons. Accordingly, Refs.22,23 report that changes in temperature and relative humidity determine the abundance of mosquitoes, which can disappear entirely during the dry season. Moreover, Refs.22,24,25 note that certain species of mosquitoes increase proportionally with the regional rainfall regime. This is consistent with Ref.10, who find alternating patterns in tropical and temperate climates in some Brazilian regions.As shown by the geometric regression, there is a positive correlation between cumulative rainfall in the days before collection and the number of species found in the study period. Likewise, Ref.26 reported that under the conditions observed in the Serra do Mar State Park, climate variables directly influenced the abundance of Cq. chrysonotum and Cq. venezuelensis, favoring the occurrence of culicids during the more warm, wet, and rainy months.The current climate scenario and future projections about climate, environmental, demographic, and meteorological factors directly influence the distribution and abundance of mosquito vectors and/or diseases27,28,29,30. Environmental temperature alters mosquito population dynamics, thereby affecting the development of immature stages as well as reproduction31. While temperature has an important effect on population dynamics, rainfall and drought also affect the density and dispersal of mosquitoes in temperate and tropical regions32.To be sure, environmental changes other than climate can modify the behavior of vector insects and, subsequently, the mechanism of transmission of parasites20. Specifically, human impacts on the environment can result in drastically different disease transmission cycles in and around inhabited areas33.A previous study34 reported that changes in land use influence the mosquito communities with potential implications for the emergence of arboviruses. Another study35 noted that environmental changes negatively affect natural ecosystems with accelerated biodiversity loss. This is due to the modification and loss of natural habitat and unsustainable land use, which leads to the spread of pathogens and disease vectors.Hence, understanding the relationship between humans and the environment becomes increasingly critical, given the way in which climate changes can lead to alterations in the epidemiology of diseases such as dengue in areas considered free of the disease, as well as in endemic areas36.We found that the abundance and diversity of Mansoniini were directly influenced by the effect of the rainy season and other climatic factors. The rainfall regime has been shown to affect the development of immature forms12,37; explaining the greater frequency of these specimens in the warmer and wetter months38,39,40. According to Ref.41, stable ecosystems such as forests contain great species diversity. On the other hand, diversity tends to be reduced in biotic communities suffering from stress.Studies of insect populations in natural areas are important because they allow a direct analysis of how environmental factors influence phenomena such as the choice of breeding sites by females for oviposition, hematophagous behavior, and the distribution of species along a vegetation gradient12,26,42,43.Throughout the experimental period of the present study, we observed that Shannon light traps are an effective method for catching mosquitoes from the Mansoniini tribe. Interestingly, Ref.44 reported a species richness pattern strongly influenced by Coquillettidia fasciolata (Lynch Arribálzaga, 1891) on mosquito samples from different capture points by using CDC and Shannon light traps as sampling methods. In contrast to the results of Ref.44, where the highest population density of mosquitoes was captured with CDC traps, we observed that these traps were not effective at capturing specimens of Mansoniini in spite of being used in large numbers in the present study. Moreover, Ref.45 conducted another study on faunal diversity in an Atlantic Forest remnant of the state of Rio de Janeiro and observed the highest abundance of Cq. chrysonotum (Peryassú, 1922) and Cq. venezuelensis by using Shannon light traps, while the numbers of captures of Ma. titillans were very similar using CDC and Shannon traps.The results of this study indicate that the makeup of culicid fauna remains quite similar throughout the year, despite seasonal variations in abundance, though there was a lower variability of fauna in the dry season. Therefore, although the seasonality did not affect the temporal variation of the faunal composition in a generalized way, it was possible to detect a partial effect of the seasonality on fauna abundance.
Reference46 report that the incidence peaks of mosquitoes in the warmer and wetter months, as well as mosquito populations remaining between tolerance limits for most of the year, indicate the sensitivity of some species to the local climate.The elevated abundance and diversity of species of Mansoniini in the study area were influenced by the favorable maintenance of breeding sites, including specific water accumulations with emerging vegetation that remain present throughout the year and the well-defined rainy season in the region. In addition, the representatives of Mansoniini, which prefer breeding sites containing macrophytes, made up nearly all of the species collected7.Besides providing a greater awareness of mosquito populations’ ecological and biological aspects, research carried out in wild areas also provides information on the relationship between species diversity and the area in which they are found. Considering that wild insects may become potential vectors of diseases, research in wild areas also provides helpful information for understanding relevant epidemiological aspects. These studies facilitate the identification, monitoring, and control of mosquito populations following environmental changes caused by direct human action, which can lead to major epidemics26.We observed considerable heterogeneity among Mansoniini fauna, and the months with the highest rainfall directly influence the structure of the communities and contribute to the increase in mosquito diversity and abundance, possibly due to variations in the availability of habitat for their immature forms. More

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