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    A deeper understanding of system interactions can explain contradictory field results on pesticide impact on honey bees

    The bee health modelThe conceptual model of the interactions of various stressors with honey bee health is described by the following system of ordinary differential equations (ODEs)$${{tau }_{{HB}}dot{x}}_{{HB}}= {-{delta }_{{HB}}x}_{{HB}}+{g}_{{TC}}left({x}_{{TC}}right)+{g}_{{VA}}left({x}_{{VA}}right)+{g}_{{VI}}left({x}_{{VI}}right) \ +{bar{f}}_{S,C}left({u}_{S},{u}_{C},{x}_{{TC}},{x}_{{VA}}right)+{bar{f}}_{P}left({u}_{P},{x}_{{TC}}right)+{underline{f}}_{{HB}}left({u}_{T}right)$$
    (1)
    $${{tau }_{{TC}}dot{x}}_{{TC}}={-{delta }_{{TC}}x}_{{TC}}+{g}_{{HB}}left({x}_{{HB}}right)$$
    (2)
    $${{tau }_{{VA}}dot{x}}_{{VA}}={-{delta }_{{VA}}x}_{{VA}}+{h}_{{VA}}left({{x}_{{HB}},x}_{{TC}},varepsilon {x}_{{VI}}right)+{underline{f}}_{{VA}}left({u}_{T}right)$$
    (3)
    $${{tau }_{{VI}}dot{x}}_{{VI}}={-{delta }_{{VI}}x}_{{VI}}+{h}_{{VI}}left({{x}_{{HB}},x}_{{TC}},{varepsilon x}_{{VI}}right)$$
    (4)
    for the state variables ({x}_{{HB}}) representing honey bee health, ({x}_{{TC}}) the stress due to toxic compounds (e.g., neonicotinoid insecticides), ({x}_{{VA}}) the stress due to parasites (e.g., V. destructor) and ({x}_{{VI}}) the stress due to pathogens (e.g., DWV). The system includes the effects of external inputs as sugar ({u}_{S}), pollen ({u}_{P}), absolute deviation from desired temperature ({u}_{T}) and sub-optimal temperature ({u}_{C}). All the inputs and possible parameters are non-negative; the coefficients (tau) denote the time constants; the coefficients (delta) denote the self-regulation parameters; (varepsilon) in the last two equations allows to account for pathogens that can ((varepsilon , > , 0)) or cannot ((varepsilon=0)) impair the immune system (through link m in Fig. 1). We assume that the functions (g) are smooth, bounded, positive, convex and decreasing to 0; the functions (bar{f}) are smooth, bounded, non-negative, concave and increasing with respect to (w.r.t.) (u) arguments (vanishing only when the first (u) argument vanishes) while convex and decreasing to 0 w.r.t. (x) arguments; the functions ({underline{f}}) are smooth, bounded, non-positive and decreasing (vanishing only when (u=0)); the functions (h) are smooth, bounded, positive, convex and decreasing to 0 w.r.t. the first argument while concave and increasing w.r.t. all the other arguments. For a detailed description of the various functions, together with a summary of the biological effects they account for and a reference to the conceptual model in Fig. 1, see Supplementary Table 3.Structural analysis of the bee health modelWe describe here the structural considerations and computations that yield the structural influence matrix for the honey bee health system.The structural influence matrix (M) is defined as follows. (M) is a symbolic matrix with entries ({M}_{{ij}}) chosen among: +,−,0,?, according to the criteria described below. Consider an equilibrium point (bar{x}) and a constant perturbation (u) applied on the (j)-th system variable (small enough not to compromise the stability of the equilibrium). The equilibrium value will be modified as (bar{x}+delta bar{x}). Consider the sign of the perturbation of the (i)-th variable, (delta bar{{x}_{i}}). Then ({M}_{{ij}}) = + if (delta bar{{x}_{i}}) always has the same sign as (u); ({M}_{{ij}}=) − if (delta bar{{x}_{i}}) always has the opposite sign as (u); ({M}_{{ij}}) = 0 if always (delta bar{{x}_{i}}=0); regardless of the system parameters. Conversely, if the sign does depend on the system parameters, we set ({M}_{{ij}}) = ?.In this section we prove that the influence matrix of the honey bee health system is structurally determined, i.e., there are no “?”‘ entries in (M).We start with the following proposition.
    Proposition 1
    Assume that a matrix
    (J)
    is Hurwitz stable (i.e., all its eigenvalues have negative real part) and has the sign pattern
    $${sign}left(Jright)=left[begin{array}{cccc}- & – & – & -\ – & – & 0 & 0\ – &+& – &+\ – &+& 0 & -end{array}right]$$
    Then, the sign pattern of
    ({adj}left(-Jright))
    , the adjoint of
    (-J)
    , is
    $${sign}left({adj}left(-Jright)right)=left[begin{array}{cccc}+& – & – & -\ – &+&+&+\ – &+&+&+\ – &+&+&+end{array}right]$$
    Proof To prove the statement, we just change the sign of the first variable, hence we change sign to the first row and column of matrix (J). The resulting matrix (M) is such that$${sign}left(Mright)=left[begin{array}{cccc}- &+&+&+\+& – & 0 & 0\+&+& – &+\+&+& 0 & -end{array}right]$$We observe that (M) is a Metzler matrix, namely, all its off-diagonal entries are non-negative. Moreover, the matrix is Hurwitz stable. Then, we can proceed as in the proof of Proposition 4 in a previous report16. Given a Metzler matrix that is Hurwitz stable, its inverse has non-positive entries; hence, the inverse of (-M) has non-negative entries: ({left(-Mright)}^{-1}ge 0) elementwise. Moreover, we observe that(,M) is an irreducible matrix, i.e., there is no variable permutation that brings the matrix in a block (either upper or lower) triangular form. This implies that the inverse of (-M) has strictly positive entries: ({left(-Mright)}^{-1} , > , 0) elementwise. Also, stability implies that the determinant of (-M) is positive: ({det }left(-Mright) , > , 0). Then, ({adj}left(-Mright)={left(-Mright)}^{-1}{det }left(-Mright) > 0), hence the adjoint of (-M) is also positive elementwise. To consider again the original sign of the variables, we change sign to the first row and column of ({adj}left(-Mright)), and we get the signature above for ({adj}left(-Jright)).The next step is the characterization of the structural influence matrix, which corresponds to the sign pattern of the adjoint of the negative Jacobian matrix in Proposition 1.To this aim, we first consider the linearized system and write it in a matrix-vector form$$dot{x}left(tright)={Jx}left(tright)+{e}_{j}u$$where (dot{x}left(tright)) is the time derivative of the four-dimensional vector (xleft(tright)) and ({e}_{k}), (k={{{{mathrm{1,2,3,4}}}}}), is an input vector, constant in time, with a single non-zero component, the (k)-th, equal to 1, while the scalar (u , > , 0) is the magnitude of the input. We wish to assess the (i)-th component of (xleft(tright)), ({x}_{i}left(tright)={e}_{i}^{T}xleft(tright)). If (J) is Hurwitz, as assumed, the steady-state value of variable ({x}_{i}left(tright)) due to the input perturbation ({e}_{k}) applied to the equation of variable ({x}_{k}left(tright)) is achieved for$$0=Jbar{x}+{e}_{k}u,$$namely$${x}_{i}=-{e}_{i}^{T}{J}^{-1}{e}_{k}u,$$which implies that the sign of the steady-state value ({bar{x}}_{i}) of variable ({x}_{i}) due to a persistent positive input acting on the (k)-th equation has the same sign as ({(-{J}^{-1})}_{{ik}}), the (left(i,kright)) entry of matrix ({left(-Jright)}^{-1}). Since we assume Hurwitz stability, we have that ({det }left(-Jright)) is positive, hence the sign pattern of the inverse ({left(-Jright)}^{-1}) corresponds to the sign pattern of the adjoint, ({adj}left(-Jright)). In fact, ({adj}left(-Jright)={left(-Jright)}^{-1}{det }left(-Jright)).We next consider the nonlinear system under investigation, which we write in the form$$dot{x}left(tright)=fleft(xleft(tright)right)$$and without restriction we assume that the zero vector is an equilibrium point: (0=fleft(0right)). This condition can be always achieved, without loss of generality, by a translation of coordinates. We also consider a stable equilibrium: we assume that the linearized system at the equilibrium is asymptotically stable, namely its Jacobian (J), which has the sign pattern considered in Proposition 1 above, is Hurwitz. We also assume that a constant input perturbation of magnitude (u) is applied to the system, affecting the (k)-th equation, i.e.,$$dot{x}left(tright)=fleft(xleft(tright)right)+{e}_{k}u,$$and that the perturbation is small enough to keep the state in the domain of attraction of the considered equilibrium. Due to this perturbation, a new steady state (bar{x}left(uright)) is reached that satisfies the condition$$0=fleft(bar{x}left(uright)right)+{e}_{k}u$$To determine the sign of the new equilibrium components (bar{x}left(uright)), we consider this new equilibrium vector as a function of (u) in a small interval (left[0,{x}_{{MAX}}right]). Adopting the implicit function theorem yields$$frac{d}{{dx}}bar{x}left(uright)=-J{left(uright)}^{-1}{e}_{k}u,$$where we have denoted by (Jleft(uright)) the Jacobian matrix computed at the perturbed equilibrium (bar{x}left(uright)). Hence, for (u) small enough, the sign of the derivatives of the entries of the new, perturbed equilibrium are, structurally, the same as those in the (k)-th column of matrix (-{J}^{-1}). Since, by construction, (xleft(0right)=0), this is also the sign of the elements of vector (bar{x}left(uright)), for (u) in the interval (left[0,{x}_{{MAX}}right]).We have therefore proved that the original nonlinear system describing honey bee health admits the following structural influence matrix:$$left[begin{array}{cccc}+& – & – & -\ – &+&+&+\ – &+&+&+\ – &+&+&+end{array}right]$$System equilibriaThe results concerning the system equilibria were obtained through a standard analytical treatment of the nonlinear equations describing the equilibrium conditions of the system of differential Eqs. (1), (2), (3), (4). A detailed description of methods is reported in Supplementary Methods.Laboratory experiments using honey beesTo confirm the bistability of the system representing honey bee health as affected by multiple stressors, we used data from several survival experiments, carried out in a laboratory environment according to the same standardized method, over a 6-year period (Source data file).All experiments involved Apis mellifera worker bees, sampled at the larval stage or before eclosion, from the hives of the experimental apiary of the University of Udine (46°04′54.2″N, 13°12′34.2″E). Previous studies indicated that the local bee population consists of hybrids between A. mellifera ligustica and A.m. carnica62,63. Ethical approval was not required for this study.We considered experiments on the effect of the following stressors: infection with 1000 DWV genome copies administered through the diet before pupation, feeding with a 50 ppm nicotine in a sugar solution at the adult stage, exposition to a sub-optimal temperature of 32 °C at the adult stage. All experiments were replicated 3 to 13 times, using, in total, the number of bees reported in Table 1.For the artificial infection with DWV, we collected with soft forceps individual L4 larvae from the brood cells of several combs. Groups of 20–30 of such larvae were placed in Petri dishes with an artificial diet made of 50% royal jelly, 37% distilled water, 6% glucose, 6% fructose, and 1% yeast. 25 DWV copies per mg of diet were added or not to the diet according to the experimental group (note that a bee larva at this stage consumes about 40 mg of larval food per day, thus the viral infection per bee was 1000 viral copies). After 24 h larvae were transferred onto a piece of filter paper to remove the residues of the diet and then into a clean Petri dish, where they were maintained until eclosion. At the day of emergence, bees were transferred to plastic cages in a thermostatic cabinet, where they were kept until death. The DWV extract was prepared according to previously described protocols64 and quantified according to standard methods.For the treatment with nicotine, 10 µL of pure nicotine were added to 200 g of the sugar solution used for the feeding of the caged bees, to reach the concentration of 50 ppm.Finally, to expose bees to a 32 °C temperature, the plastic cages with the adult bees were kept in a thermostatic cabinet whose temperature was set accordingly.To monitor the survival of the adult bees treated as above, they were maintained from eclosion until death in plastic cages in a dark incubator at 34.5 °C (or 32 °C, according to the experiment), 75% R.H.; two syringes were used to supply a sugar solution made of 2.4 mol/L of glucose and fructose (61% and 31%, respectively) and water, respectively; dead bees were counted daily.All the results of these experiments are reported in Source data file.All experiments were carried out during the summer months, from June to September for 6 consecutive years. Previous data indicated that, in this region, virus prevalence increases along the active season starting from very low levels in spring and reaching 100% of virus-infected honey bees by the end of the summer; virus abundance in infected honey bees follows a similar trend28. For this reason, it can be assumed that bees sampled early in the season are either uninfected or they bear only a very low viral infection level, whereas bees sampled later in the season are likely to be virus-infected, bearing moderate to high viral infections. To confirm this assumption and identify a method for filtering our data according to viral infection, we assessed viral infection in a sample of bees from the untreated control group of each experiment, by means of qRT-PCR. According to standard practice, we assumed that Ct values below 30 are indicative of an effective viral infection, whereas Ct above that threshold are more likely in virus negative bees. As expected, we found that virus prevalence increases from June to September (Supplementary Figure 1a), in such a way that up to mid July only the minority of bees can be considered as viral infected (Supplementary Figure 1b). Therefore, we classified as “early” all the samples collected up to mid July and assumed that viral infection in those samples was low; on the other hand, samples collected from mid July till September were classified as “late” and we assumed that viral infection in those samples was high.qRT-PCR analysis of viral infection was carried out as follows. At the beginning of every experiment (i.e., at day 0), two to five bees for each replication were sampled in liquid nitrogen and transferred in a −80 °C refrigerator. After defrosting of samples in RNA later, the gut of each honey bee was eliminated to avoid the clogging of the mini spin column used after. The whole body of sampled bees was homogenized using a TissueLyser (Qiagen®, Germany). Total RNA was extracted from each bee according to the procedure provided with the RNeasy Plus mini kit (Qiagen®, Germany). The amount of RNA in each sample was quantified with a NanoDrop® spectrophotomer (ThermoFisher™, USA). cDNA was synthetized starting from 500 ng of RNA following the manufacturer specifications (PROMEGA, Italy). Additional negative control samples containing no RT enzyme were included. DWV presence was verified by qRT-PCR considering as positive all samples with a Ct value lower than 30. The following primers were adopted: DWV (F: GGTAAGCGATGGTTGTTTG, R: CCGTGAATATAGTGTGAGG65). 10 ng of cDNA from each sample were analyzed using SYBR®green dye (Ambion®) according to the manufacturer specifications, on a BioRad CFX96 Touch™ Real time PCR Detector. Primer efficiency was calculated according to the formula (E={10}^{left(-1/{{{{{{rm{slope}}}}}}}-1right)*100}). The following thermal cycling profiles were adopted: one cycle at 95 °C for 10 min, 40 cycles at 95 °C for 15 s and 60 °C for 1 min, and one cycle at 68 °C for 7 min.Individual survival and colony stabilityTo investigate how the death rate of forager bees affects colony growth, a compartment model of honey bee colony population dynamics was proposed50. This model showed that death rates over a critical threshold led to colony failure. Here we modified this model to include premature death of bees at younger age, as predicted by our model of individual bee health in the presence of an immuno-suppressive virus. We show that the critical threshold found in the previously published model50 becomes a decreasing function of the death rate of the younger individuals, so that premature death (and, in turn, immune-suppression) favors colony collapse.In more details, we first summarize the results of the previously published model50 where two populations (F) (forager) and (H) (hive) of bees are considered and where conditions are provided on the mortality (m) of (F) under which the whole population collapses: namely, mathematically stated, the system admits the zero equilibrium only. Here we extend the model partitioning (H) in two categories, (Y) (younger hive bees) and (O) (older hive bees), asintroducing an early mortality factor (n) for the young population, showing how such a factor worsens the collapsing condition.The previously published model50 concerns the interaction between hive bees (H) and forager bees (F) and is described by the ODEs$$dot{H}=Lfrac{H+F}{w+H+F}-Hleft(alpha -sigma frac{F}{H+F}right)$$$$dot{F}=Hleft(alpha -sigma frac{F}{H+F}right)-{mF}.$$Above, (L) is the queen’s eggs laying rate, (w) is the rate at which (L) is reached as the total population (H+F) gets large, (alpha) is the maximum rate at which hive bees become forager bees in the absence of the latter, (sigma) measures the reduction of recruitment of hive bees in the presence of forager bees and, finally, (m) is the death rate of forager bees (while the death rate of hive bees is assumed to be negligible).We first summarize the main results in terms of a threshold value for (m) in view of colony collapse, as our further analysis will follow a similar approach. All the parameters are assumed to be positive.The search for the equilibria of the above ODEs leads to the unique nontrivial equilibrium (beyond the trivial one)$$bar{H}=frac{L}{{mJ}}-frac{w}{1+J}$$$$bar{F}=Jbar{H}$$for$$J=Jleft(mright):=frac{alpha -sigma -m+sqrt{{left(alpha -sigma -mright)}^{2}+4malpha }}{2m}.$$Note that (J) is alway positive (and, moreover, it is independent of (L) and (w)). It follows that (bar{F}) and (bar{H}) have the same sign, so that the existence of the nontrivial equilibrium is equivalent to (bar{F}+bar{H} , > , 0). It is not difficult to recover that$$bar{F}+bar{H}=frac{w}{m}left(lfrac{1+J}{J}-mright)$$where (l:=L/w) is introduced for brevity. Then if (alpha le l) we get$$bar{F}+bar{H}=frac{w}{m}left(lfrac{1+J}{J}-mright)ge frac{w}{m}left(alpha frac{1+J}{J}-mright)=frac{w}{m}left(sigma+{mJ}right) , > , 0,$$with the last equality following from$$alpha -sigma frac{J}{1+J}-{mJ}=0,$$which in turn comes from annihilating the right-hand side of the second ODE and from using (J=bar{F}/bar{H}) while searching for equilibria. We conclude that, independently of (m), the colony never collapses if the recruitment rate (alpha) of forager bees is sufficiently low.Hence, we assume (alpha , > , l). Observe that$$bar{F}+bar{H}iff l , > , Jleft(m-lright)$$guarantees existence whenever (m) is sufficiently small, viz. (mle l). Assume then (m , > , l), so that the above condition reads$$J , < , frac{l}{m-l}$$leading to the threshold condition$$m , < , bar{m}:=frac{l}{2}frac{alpha+sigma+sqrt{{left(alpha -sigma right)}^{2}+4sigma l}}{alpha -l}$$by using the definition of (J), see Eq. (2) the previously published model50.A standard stability analysis shows that, assuming (alpha,m , > , l), the nontrivial equilibrium is (globally) asymptotically stable whenever it exists (positive), i.e., whenever (m , < , bar{m}). Otherwise, the only (globally) attracting equilibrium is the trivial one, corresponding to colony collapse (see Fig. 5 for the previously published model50 or Fig. 4 for (n=0)). In the mathematical jargon, the disappearance of the positive equilibrium, for (m) exceeding (bar{m}), is referred to as a transcritical bifurcation43.Now, in view of the outcome of the analysis of our model of individual bee health, we introduce a mortality term for the younger bees. As forager bees are recruited from adult hive bees, we divide the class of hive bees (H) in younger (Y) and older (O), assuming that the former die at a rate (n), while the death rate of the latter remains negligible according to the previously published model50. Obviously, (H=Y+O). The original ODEs are consequently modified as$$dot{Y}=Lfrac{H+F}{w+H+F}-Y$$$$dot{O}=left(1-nright)Y-Hleft(alpha -sigma frac{F}{H+F}right)$$$$dot{F}=Hleft(alpha -sigma frac{F}{H+F}right)-{mF}.$$Note that the sum of the first two equations above gives$$dot{H}=Lfrac{H+F}{w+H+F}-Hleft(alpha -sigma frac{F}{H+F}right)-{nY}.$$The new negative mortality term for younger hive bees, (-{nY}), models the fact that only the younger hive bees die prematurely while the rest of the dynamics is unchanged with respect to the original model.The search for equilibria soon gives$$bar{Y}=Lfrac{bar{H}+bar{F}}{w+bar{H}+bar{F}}$$from the first ODE above, so that the remaining two equilibrium conditions lead to$$bar{H}=frac{{L}_{n}}{{mJ}}-frac{w}{1+J}$$$$bar{F}=Jbar{H}$$for the same (J) originally defined and ({L}_{n}:=Lleft(1-nright)) (note that (nin left({{{{mathrm{0,1}}}}}right)), and the case (n=0) brings us back to the original model). From this point on the analysis is the same as that previously summarized for the original model, but for replacing (L) with ({L}_{n}) and (l) with (l:=lleft(1-nright)). Consequently, by assuming (alpha,m , > , {l}_{n}) (which is less restrictive when (n , > , 0)), the threshold condition (m < bar{m}) becomes$$m , < , bar{m}left(nright):=frac{{l}_{n}}{2}frac{alpha+sigma+sqrt{{left(alpha -sigma right)}^{2}+4sigma {l}_{n}}}{alpha -{l}_{n}},$$which clearly returns the original threshold condition when (n=0). Since$$frac{dbar{m}}{{dn}}left(nright) , < , 0$$as it can be immediately verified, it follows that the critical value for (m), (bar{m}left(nright)), beyond which the colony system admits only the zero equilibrium, i.e., the transcritical bifurcation value, decreases with (n) (Fig. 4). We thus conclude that colony collapse is favored by the premature death of younger hive bees, possibly caused by a virus impairing the immune system as shown by the analysis of our model of individual bee health.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Genomic basis of insularity and ecological divergence in barn owls (Tyto alba) of the Canary Islands

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    Efficiency of the traditional practice of traps to stimulate black truffle production, and its ecological mechanisms

    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

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    Spatial distribution and interactions between mosquitoes (Diptera: Culicidae) and climatic factors in the Amazon, with emphasis on the tribe Mansoniini

    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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    Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models

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