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The geographic area covered by the WOODIV database is the Euro-Mediterranean region, as defined by Médail et al.1. The northern Mediterranean region was selected following the definition of terrestrial ecoregions of the world by Olson et al.13. The study area covers all or part of the following countries and islands: Albania, Croatia, Cyprus, France, Greece, Italy, Malta, Montenegro, Portugal, Slovenia, Southern Macedonia, and Spain, including the Balearic archipelago, Corsica, Sardinia, Sicily, and Crete.We focused on the 245 tree taxa (210 species and 35 subspecies) identified in the Euro-Mediterranean checklist from Médail et al.1. These taxa belong to 33 families and 64 genera and include 46 endemics (as defined by Médail et al.1, i.e. range-restricted taxa in and outside of the study area).Observed occurrence dataWe collected tree occurrence data (at the species or subspecies level) from 23 sources: national databases and floras, regional databases, and publications (Table 1). Some records still unpublished were specifically provided at the grid level for this project by experts for southern Macedonia, Malta, Montenegro, and Sicily (four sources, Table 1).Table 1 Sources of the occurrence records, giving the name of the dataset (Source name; ined. if unpublished), the Type of data (records with geographic coordinates (records), records at the grid level (gridded records), or atlas-type (atlas) data), and the Countries/Islands covered by the source.Full size tableWhen considering the subspecies level, the WOODIV database lacks the occurrences of 11 sub-species among the 35 listed by Médail et al.1. When aggregated at the species level (to match the taxonomic resolution of the functional and phylogenetic data which are available at the species level only), the WOODIV database lacks only the occurrences of 3 of the 210 species from the Médail et al.1 checklist (n = 207; Table 2; Supplementary Table 2): Pyrus elaeagrifolia Pall., which occurs in Albania and Macedonia (and in northeastern Greece but outside the Mediterranean biome), P. syriaca Boiss. and Tamarix passerinoides Desv., which occur in Cyprus and in Sardinia, respectively.Table 2 Summary of the availability of data in the WOODIV database: total number of species among the 210 species from the Médail et al.1 checklist with (1) observed occurrences; (2) functional traits data, including the detail of the number of species with available data for 4 traits: adult plant height (Height), seed mass (SeedMass), specific leaf area (SLA) and wood density (SSD) (see “Functional data” section); and, (3) genetic data including the detail of the number of species with available data for 3 DNA-regions: matK, rbcL and psbA-trnH (see “Genetic data” section).Full size tableAlso, due to the taxonomic heterogeneity of the different data sources, we recommend aggregating the occurrences of certain tree taxa at the species’ group level (see sections Data Records and Usage Notes): i.e. to aggregate Pinus uncinata DC. and P. mugo Turra into P. mugo aggr., Juniperus deltoides R.P.Adams and J. oxycedrus L. into J. oxycedrus aggr. and Alnus lusitanica Vít, Douda & Mandák., A. rohlenae Vít, Douda & Mandák, and A. glutinosa (L.) Gaertn. into A. glutinosa aggr. The WOODIV database thus contains reliable occurrences of 200 species and three aggregated species (n = 203; Table 2; Supplementary Table 2).The raw dataset obtained from gathering occurrences from all sources included a total of 1,248,701 occurrence records distributed across the participating countries.The raw occurrence data were aggregated at a resolution of 10 × 10 km in line with an INSPIRE14 compliant 10 × 10 km grid (SCR 4258). This gridding procedure provided a way to standardize data from different sources. We selected this spatial grain because it was the finest resolution available for some countries of the study area (e.g. Slovenia, Croatia, Greece). Sources of occurrence data with a resolution coarser than 10 × 10 km (e.g. Atlas Florae Europaeae15) were not considered. The considered area includes 10,042 grid cells with at least one occurrence record (Fig. 1a). The occurrence dataset provided by the WOODIV database, i.e. aggregated records for species considered as native in the given grid cell using the 10 × 10 km grid (removal of duplicate species within a grid cell) includes 140,279 occurrences.Fig. 1Geographic scope of the WOODIV database, spatial distribution, and validation of trees occurrences. (a) Number of species within a 10 × 10 km grid cell based on modelled occurrence data for the 171 modelled species, with the addition of the occurrence data of the 21 small-range species; and, within grid cells of Atlas Flora Europaeae (AFE; 50x50km) (b) Number of species with presences recorded in AFE but not in the WOODIV dataset on the 104 species present both in the AFE and WOODIV data; and, (c) Number of species with presences recorded in the WOODIV dataset but not in AFE on the 104 species present both in the AFE and WOODIV data.Full size imageModelled occurrence dataThe WOODIV database provides modelled occurrences of the species from the Médail et al.1 checklist. From the 10 × 10 km gridded observed occurrence data, we modelled the distribution of each species across the Euro-Mediterranean area using Species Distribution Models (SDM). SDM statistically relate species occurrence records to environmental variables to predict the potential distribution of species16.Due to the extent of the study area, we only related species occurrence to climate gradients17. Bioclimatic variables were extracted from the CHELSA database V1.218 available at a resolution of 30 arc‐sec (http://chelsa‐climate.org/) and then averaged to a 10 × 10 km resolution. The selection of the environmental predictors for niche modeling is a source of uncertainty in model predictions that can be reduced with sound statistical methods and ecological knowledge of the target species19. We also focused on proximal predictors that directly influence species distribution and selected a low number of predictive variables to reduce the issues of model overfitting and multicollinearity20. We selected four bioclimatic variables that previous studies had reported to be relevant predictors of the distribution of plant species, especially in environments such as those that characterize the Mediterranean Basin21,22,23,24: “Minimum temperature of the coldest month” (Bio06, in °C) quantifies potentially lethal frost events and more generally, stress due to low temperatures; “Total annual precipitation” (Bio12, in mm) approximates average water availability; “Precipitation of the driest month” (Bio14, in mm) describes the extremes associated with drought events and stress due to low water availability, and “Temperature seasonality” (Bio04, no dimension) describes the variability of temperature during the year. All selected predictors showed VIF (variance inflation factor25) values below 5, indicating that a given predictor was not correlated with any linear combinations of the other predictors (VIF Bio04 = 1.68, VIF Bio06 = 2.06, VIF Bio12 = 1.53, and VIF Bio14 = 2.07).We related species occurrence to these four bioclimatic variables using the Random Forest algorithm26. As only presence data are archived in the WOODIV database, we randomly sampled a number of pseudo-absences equal to the number of observed occurrences27. This random selection of pseudo-absences was repeated 10 times for each species. When comparing the floras, occurrence data in the Italian Peninsula, Sardinia and/or Sicily were highly unrepresentative of the distribution of some species (n = 84; see Supplementary Table 3). To overcome this potential bias in the models, we did not include these regions in the model calibration step (Supplementary Table 3). The model was projected in these areas after having tested the similarity in the variables between the projection dataset (Italy, Sicily, and Sardinia) and the fitting dataset (the rest of the study area). Indeed, when model predictions are projected into regions not analyzed in the fitting data, it is necessary to measure the similarity between the new environments and those in the training sample28, as models are not so reliable when predicting outside their domain29. Similarity analyses computed using ExDet30 indicated that all covariables in the projected area are within the univariate range of the fitting area and that there is no change in correlation between covariables (NT1 and NT2 = 0).Each of these 10 datasets (per species) was then randomly split into two datasets to evaluate model performance on pseudo-independent data31: 70% of the data was used to calibrate models and the 30% remaining data was used to evaluate model performance using the True Skill Statistic (TSS32) and the Area Under the Curve (AUC) of the receiver-operating characteristic (ROC) plot33 metrics. This split-sample step was repeated 10 times resulting in 100 models per species.For each of the 171 modelled species, a mean model (from the 100 replicates) was then used to predict potential species distribution. Predicted probabilities of occurrence were finally converted into presence/absence using the threshold maximizing the TSS. We fitted all models under the R environment R Core team34 and the package biomod235,36.The WOODIV database provides modelled occurrences of each of the 171 species for each 10 × 10 km grid cell (Fig. 1a). Thirty-two species with less than 10 occurrence records were not modelled (Supplementary Table 3). Among these 32 species, 21 are small-ranged species whose distribution is limited to a few grid cells (Supplementary Table 3). The observed occurrence records for these 21 species can be considered as representative of their distribution and we therefore recommend using the non-modelled records for these species for analyses. The occurrences of the remaining 11 species should be considered unrepresentative of their distribution.Functional dataFour functional traits were considered in this project: adult plant height (Height), seed mass (SeedMass), specific leaf area (SLA), and wood density (StemSpecDens). These traits have been proposed to reflect a global spectrum of plant strategies37,38: height is a commonly measured proxy for individual size and reflects several aspects including resource acquisition, competitive ability, or dispersal capacity. SeedMass represents the trade-off between fecundity, seed survival, and dispersal. SLA (the ratio between leaf area and dry mass) is correlated to photosynthetic capacity and leaf life span and is an indirect measure of the return on investments in carbon gain compared to water loss. StemSpecDens is a key component of woody plant growth linked to the mechanical support of the stem and its growth rate.We compiled the values for these traits at the species level for the trees from the Médail et al.1 checklist, referring mostly to 2 databases: TRY9 and BROT 2.039. Supplementary values were obtained from more specific databases (Global Wood Density Database40, Kew Seed Information Database41) or from the scientific literature and atlas42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61. In total, 92% of the entries were extracted from TRY, 7% from BROT 2.0 and the remaining were retrieved from the other sources. The original ID of records from the TRY and BROT databases is provided in order to make it possible to refer to the complete observation if a user needs to have some contextual information.The WOODIV database lacks all traits data for only 6 of the 210 species from the checklist (Table 2, Supplementary Table 2): Alnus lusitanica Vít, Douda & Mandák, Alnus rohlenae Vít, Douda & Mandák, Malus dasyphylla Borkh., Quercus infectoria Olivier, Tamarix arborea Ehrenb. ex Bunge and, Tamarix passerinoides Del. ex Desf.Adult plant height and seed mass data were available for more than 75% of the 210 species (Table 2; Fig. 2a), whereas wood density and specific leaf area were available for only around 50%. The WOODIV database includes all four trait values for 41% of the 210 species (Fig. 2b; Supplementary Table 2), three trait values for 56% more species.Fig. 2Prevalence of traits and genetic data among the 210 species from Médail et al.1 checkist: (a) For each of the four considered functional traits (adult plant height (Height), seed mass (SeedMass), wood density (SSD) and specific leaf area (SLA)), percentage of the 210 species with existing data; (b) Percentage of the 210 species for which none to four functional traits data are available; (c) For each of the three considered DNA regions (matK, rbcL and psbA-trnH), percentage of the 210 species with existing data (in grey species with only one available sequence for the considered region, in black species with consensus sequence for that region); and, (d) Percentage of the 210 species for which none to three DNA regions data are available.Full size imageThe database provides an R script that can be used to estimate missing trait values using the taxonomic classification if needed.Genetic dataThree different DNA regions from the plastid genome corresponding to the most commonly used DNA barcode regions62,63,64 were considered in this project: the ribulose-bisphosphate/carboxylase Large-subunit gene (rbcL), the maturase-K gene (matK), and the psbA-trnH intergenic spacer (trnH).In a first step, we collected all sequences from GenBank (https://www.ncbi.nlm.nih.gov/genbank/) for the three DNA regions available for the species from the Médail et al.1 checklist at the species level: rbcL: n = 650 sequences for 146 species, matK: n = 644 sequences for 127 species, trnH: n = 493 sequences for 129 species). To fill the gaps, we obtained DNA from fresh samples collected in the field or gathered from herbarium specimens (Supplementary Table 4). DNA extraction and sequencing were performed at INRA-URFM, Avignon (France) and the National Research Council (IBBR-CNR), Florence (Italy) (rbcL: n = 233 for 125 species, matK: n = 162 for 91 species, trnH: n = 200 for 120 species). Methods used for DNA isolation and Sanger sequencing are described by Albassatneh et al.65. When more than one sequence was available for a given DNA region/species, a sequence alignment was performed to check data quality and a taxon-consensus sequence was generated. Consensus sequences were built using the IUPAC-IUB ambiguity66 code for a total of 119 (rbcL), 109 (matK), and 110 species (trnH), respectively (Fig. 2c). All newly created sequences were uploaded to GenBank.The WOODIV database lacks the DNA-region sequences data of only 6 of the 210 species from the Médail et al.1 checklist (Table 2, Fig. 2d): Alnus lusitanica Vít, Douda & Mandák, Cytisus aeolicus Guss., Celtis planchoniana K.I. Chr., Salix appendiculata Vill., Tamarix hampeana Boiss. & Heldr. and, Tamarix minoa J.L. Villar, Turland, Juan, Gaskin, M.A. Alonso & M.B. Crespo.PhylogenyThe WOODIV database provides a phylogram including the 204 species for which at least one piece of DNA-region sequence data was available (Supplementary Table 2) and phylograms including the 210 species from the Medail et al.1 list (Supplementary Fig. 1).Uneven taxon sampling focused on a single biogeographic area such as ours, can bias phylogenetic inferences67. Our goal here is to provide DNA sequence data that can be readily re-used to estimate, e.g. comparable phylogenetic diversity indices, not phylogenetic inferences per se. To illustrate our DNA-sequences data and to facilitate their use for future analyses (to calculate phylogenetic diversity for example), we constructed a molecular phylogeny encompassing the 204 Euro-Mediterranean tree species. Each gene was independently aligned using the MAFFT program68 and parsed using the program Gblocks69 to exclude the segments characterized by several variable positions or gaps from final alignments. An appropriate substitution model of sequence evolution was selected for each of the three plastid DNA regions using the Akaike Information Criterion (AIC) as implemented in the JModeltest 2 program70. The optimal substitution model identified was the same for all three sequences: GTR + I + G. We obtained a concatenated matrix with 1615 aligned bases. We used the Maximum Likelihood analysis71 as implemented in the RAxML V8 program72. The DNA sequence matrix of 1615 sites was analyzed using three partitions with the GTRGAMMAI model (GTR + Gamma substitution model + proportion of invariant sites). We searched for the optimal tree, running at least 20 independent maximum likelihood analyses; full analyses also consisted of 100 bootstrap replicates72.For users who would like to work on the complete pool of 210 tree species, we also built a 210 species phylogram including all Euro-Mediterranean trees. The six missing species for which no DNA-region sequence was available were added to the phylogenetic tree using the Simulation with Uncertainty for Phylogenetic Investigating (SUNPLIN) method73, with 100 replicates. The geometric median tree was computed from the set of 100 replicates with the medTree function from the R package treespace74. Both the median tree and the set of 100 replicates are provided in the WOODIV database, together with the molecular tree with 204 species. More

In this study, we gave a comprehensive description of the mating behavior of the greenhouse whitefly, T. vaporariorum. In particular, we defined the strict association between vibrational signals and behavioral steps of the pair formation process, from the male call to the final mating. We also described some social interactions between two or more individuals of both sexes, confined to a small portion of leaf, thus simulating a natural occurring aggregation. In this regard, we found that males tend to modify the quality of their vibrational signals, by changing some spectral features, according to either the social context or the behavioral step. For example, they tend to increase the fundamental frequency of their signals (i.e., chirps and PT) when in the presence of potential rivals. A possible explanation of this behavior could be associated with the male competition for food and/or mating. In fact, species that live in high population densities are subjected to strong male-male competitions and a male needs to show his quality to females but also to be clearly recognizable from the others24. The higher quality can be witnessed by the emission of specific aggressive calls which are characterized by lower frequencies, like in some anurans25 or in Chiropteran where the relative frequency of the social calls increases when more individuals compete for a food source. An example of individual recognition behavior is the change of frequency of the calling song to avoid signal overlapping thus allowing an individual to perceive the presence of more potential partners. Frequency overlapping, in general, can be noxious to animal communication, and male responsiveness can be reduced when background noise from conspecific signals obscure the species-specific temporal pattern of a female song26. In the southern green stink bug, Nezara viridula (Heteroptera, Pentatomidae), females were found to change their calling song frequency to let the males recognize them when exposed to a disturbance stimulus27. Even if small variations of the frequency pattern may potentially affect the partner responsiveness to a call28, overlapping frequencies can seriously compromise the signal reception29. In this way, the change to a different value of frequency in presence of other calling males seems to be a more desirable solution.Another signal variation that we observed in GW males, in the presence of another male (i.e., male duos trials), regarded the chirp duration. By increasing the duration of a mating signal, some species also increase the chances to elicit the female response at the earliest stage of the mating behavior30. In various acoustic insects, females prefer longer calls and males can vary their length by adding or subtracting call elements31. However, a limit of our study was that we could not associate the signal emissions to specific individuals, therefore we did not determine not only if one or both the males were actually singing but also whether this change of chirp duration involved one or both the individuals. A definitive explanation about male-male calling interactions and how males regulate their calling activities should be provided with additional experiments with the use of playbacks to stimulate single specimens.In general, we need to consider that the alteration of the signal features is a common strategy in animals with a complex mating behavior in which different stages can alternate in a non-linear sequence29,32,33. Such an intricate behavior is on the one hand, at the basis of a species-specific mate recognition system, on the other hand, is a result of the sexual selection that worked to shape signals with certain characteristics that are able to elicit the female acceptance to mate34. Despite the considerable knowledge about vibrational signal production in the family Aleyrodidae19, we still have little information about the importance of the courtship and of the female choice in driving the reproductive isolation and speciation in this family. Aleyrodid species are known to be morphologically similar and to form a species complex (i.e. Bemisia tabaci) with several biotypes35, where the characterization of the mating behavior can be an important tool to discriminate among them. For instance, variations in the courtship behavior between different B. tabaci biotypes demonstrated the presence of pre-copulation barriers36,37. Moreover, the analysis of male vibrational signals during the courtship, combined with genetic and morphological analysis, allowed to discriminate between the camellia spiny whitefly Aleurocanthus camelliae and the citrus spiny whitefly Aleurocanthus spiniferus18. In such a context, knowing the characteristics of the mating ritual may lead to distinguish, not only among different species, but also among different populations. For example, before this study, the GW mating behavior was described only from Japanese populations where the pair formation process started with the male approaching a female before emitting any vibrational signal (i.e. courtship stage)17. Instead, in our study with European populations of GW, we observed that the male, before starting the approach, emits calling signals which can elicit the female response from a certain distance. Such a difference between geographically distant GW populations seems to suggest a different strategies of mating behavior, likely associated to distinct populations or biotypes. On this regard, it would be interesting to test them with crossed mating trials (Japanese vs Europeans) to assess the effects of the observed differences on the mating success rate.In our study, we also measured a difference of male signal parameters between different behavioral phases of the pair formation process and in particular between the courtship stage and the call and alternated duet stages. We found a significant increase of signal duration, fundamental frequency and pulse repetition rate. The duration of the courtship stage was very variable in our trials, from zero (it was skipped when females replied immediately to the male signals) up to 78 min. This means, in first instance, that the role of the courtship is to elicit the female response and thus promoting her acceptance to mate. Indeed any single behavioral step is functional to elicit the female’s acceptance and in fact, whenever females showed high responsiveness since the early stage of the mating process, males could skip whole stages and even go directly from the call to the final precopula stage, the alternated duet. It also indicates that males are available to spend a remarkable amount of energy to perform the courtship38. The use of elaborated and energetic signals during the courtship is rather common in animals34. For example, the leafhopper S. titanus and the glassy-winged sharpshooter Homalodisca vitripennis have a mating strategy that reminds the GW’s, starting with a call which is followed by the location of the partner and by the courtship. While during call and location males make use of extremely simplified signals, during the courtship they emit the most elaborated (and energetically demanding) signals, through which they try to convince the female to accept the mating21,39. A study of Las (1980) demonstrated that the GW courtship persistence (i.e., duration) is an important trigger to address the female choice. A fast and prolonged male “cycling rate” (alternation of wing flicking and antennation) during the courtship is preferred by females who become even more selective after the first mating. On the other hand, in our tests, males showed a remarkable perseverance in courting the females. The ethogram showed that after a failed mating attempt, a male always restarted from the courtship. This means that the courtship phase is the key part of the mating process but also that the female choice drives the selection in favor of “stubborn” males that persist in courting the potential partner, performing a prolonged courtship, even if the first mating attempt fails. Stubbornness affects male’s survival for its energetic cost and risk of eavesdropping. Such character fits the handicap theory model, in which condition dependent and costly traits are honest indicators of male quality40,41. On the other hand, the option of an easy surrender, and the search for another available female, after investing so many energies in courting the first one, seems to be not convenient for the male in that it would mean to spend more energy in searching for/courting a new partner also risking the possibility of dealing with competitors42.In the GW, the male courtship can be considered successful when the mating moves to the overlapped duet stage in which the female emits the Female Responding Signal (FRS). The FRS is produced in synchronous with the courtship chirp and PT and, for this reason, it requires high degree of coordination between male and female. The presence of female acceptance signals synchronized with the male’s is known for the whitefly species Aleurothrixus floccosus (Maskell)43, in which the female signal can partially overlap the male’s one, but it was unknown in the GW, until now.Another signal that we found for the first time in the GW is the male rivalry signal (MRS). Males exhibit aggressiveness towards other males. A random encounter on the leaf is enough to trigger the expression of rivalry behavior in presence of a female. Such interaction has never been observed in duos, but only in groups with responsive females, thus suggesting that the presence of receptive/active females is required to trigger the MRS production and thus provoking a context of aggressiveness and competition between males. Another male rivalry behavior that we observed in the presence of a receptive female is the silent approach (satellite behavior) to intercept a female while duetting with another male44. This behavior is known in other aleyrodids like in B. tabaci. In this species, rival males interrupt the ongoing courtship of the duetting male by approaching the female from the opposite side. In response to the competitor, the first male spreads the wings and beats the rival on the head45. In GW, the rivalry behavior is associated with the continuous production of the MRS, which is the male signal at highest frequency. Such finding strengthens the hypothesis that the frequency shift has a role in competitor’s deterrence. The rivalry behavior of GW seems to be extraordinarily strong, as much to push females to abandon the interaction with both males. In our experiments, none of the females, even those that had already established a duet with a male, eventually mated. On the contrary, they left the arena before the end of the trial. Our findings are consistent with previous observations of GW behavior, in which the contended female always walked away when two males were competing15. Therefore, we can speculate that the adaptive advantage of the male rivalry behavior in GW is not immediate and the disruption of another male’s attempt could provide more chances in the future to the intruder, by leaving a receptive female unmated. Beside the effects of the male’s rivalry, we also observed females that refused to mate and rejected approaching males with the emission of specific vibrational signals. There are several reasons to refuse mating: immature females are not yet available to mate, and recently mated females must undergo to a refractory period before they accomplish other copulations15. On the other hand, a mature female can choose whether to accept or not a courting male depending on the level of his fitness which is, very likely, testified by the courtship performance. Females can evaluate the male’s quality based on the courtship persistence, so that they need to let males perform the whole ritual before choosing whether to mate or not46. In fact, we observed both females that rejected approaching males and females that rejected them at the end of the courtship performance. The latter, in particular, was associated to wing flicking and/or male’s aedeagus parrying with the legs. Similar behaviors were also observed in B. tabaci, in which the female can either walk or fly away from approaching males, flap the wings or push the male’s abdomen away with the middle pair of legs45. What seems to be a peculiar treat of GW is the use of a specific rejective signal (FRjS). The emission of FRjS seems to reinforce the motivation of the female to reject the male. However, it is not clear to us why the FRjS signal has been observed only in the group (males and females together) trials and never in pairs (one male and one female). Our hypothesis is that in case of groups, males can approach the “wrong” female, who was close the receptive one. This implies that males are not capable of precisely locating the responding female and that the emission of FRjS by an unreceptive female would help the males to not waste too much time (and energy) with them.To conclude, this study unveiled many aspects of the mating behavior of the GW that were previously overlooked and thus it contributes to fill several gaps of knowledge that will be important to start a program in the field of applied biotremology10. The question, from which originally arose this research study, was whether the use of vibrational signals could be suitable to manipulate the mating behavior of the GW. We can say that the vibrational communication is fundamental to accomplish mating and, in our trials, with pairs and groups, we never observed mating without the exchange of vibrational signals between male and female. This means that the interruption or the disruption of this communication could be potentially useful to reduce the rate of mating success. Manipulation of intraspecific communication by means of vibrational signals has been already developed for other insect species both in the lab and in the field10. For example, the male rivalry signal has been exploited for the development of a vibrational mating disruption strategy against the grapevine leafhopper Scaphoideus titanus29, while the female playback has been used to attract and trap males in the brown marmorated stink bug Halyomorpha halys47. The use of playbacks that cover the fundamental frequencies of the male and female signals could be used to mask their communication2. Another possible approach could be to use signals that mimic the natural signals of the species48. In the case of the GW, the FRS could be employed to disrupt males and induce them in courting unreceptive females. This would lead to a substantial reduction of the mating success rate but also to a considerable increase of wasted energy caused by the male persistence in courting unreceptive females. Another possible outcome could be a change of the gender balance in the population. GW females reproduce by arrhenotokous parthenogenesis in which unfertilized eggs develop into males49. Delays in mating could lead to a sex bias that could eventually mine the population structure. Another option is the use of the MRS to generate an aggressive and stressful environment. The transmission of MRS into the plant tissues in loop could eventually negatively affect the development of GW populations. All these approaches are potentially effective and could be in the future considered as tools for IPM and/or organic protection programs. Further applied research will provide a final answer to our question and will test the effectiveness of behavioural manipulation strategies for the control of the GW. Finally, considering that the GW uses a short range sexual pheromone emitted by females50 olfactory and vibratory cues could be potentially integrated to develop new pest control technologies10. More

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Experimental designThis experiment was conducted at the Science and Technology experimental site (125° 27′ 5″ N, 49° 33′ 35″ E) of the North Corporation of Sinograin, Nenjiang, Heilongjiang, China. The soil in the tested area was classified as Black soil according to Chinese Soil Taxonomy (as Mollisol according to USDA classification system) with a thick humus layer and clay texture. The area has a mid-temperate continental monsoon climate with an average annual temperature of − 1.4 to 0.8 °C, precipitation of 450 mm, a frost-free period of 115 days, and an effective accumulated temperature of 2150 °C. The basic physicochemical properties at 0–20 cm plow layer of soil were shown in Table 2. The experiment included two modes: maize straw mulching (FG) and straw returning combined with rotary tillage. In the test plot, there were six 10-m ridges per treatment, and each treatment area was 39 m2. Each treatment has three replicate. The experiment started in 2012 under the continuous planting of maize, and the straw was mechanically crushed and returned to the field in the fall. In accordance with the adjustment in the C/N of the straw, the amount of applied fertilizer was N 150 kg hm−2, P2O5 75 kg hm−2, and K2O 75 kg hm−2. Five treatments were included: (1) stubble remaining in the field, which served as the control (CK); (2) full straw mulching (FG); (3) full straw returning combined with rotary tillage (1 XG) ; (4) 1/3 of full straw returning combined with rotary tillage (1/3 XG); and (5) half of full straw returning combined with rotary tillage (1/2 XG).Table 2 Basic physicochemical properties of surface soil.Full size tableSample collectionSoil samples were collected after the maize (Demeiya 2) was harvested in November 2019. Five soil cores (diameter 5 cm) were randomly taken from 0 to 20 cm depth in each plot, mixed thoroughly, and packed into cloth bags. After the crop roots and other debris were removed, the samples were air-dried for analyzing the content and chemical structure of SOC.Determination of total soil organic carbonThe content of total SOC was measured using a TOC (total organic carbon) analyzer (NC2100, Jena, Germany,) after air-dried soil samples were passed through a 100-mesh sieve.Purification of soil organic carbonFive grams of air-dried soil was added to a 100 mL plastic centrifuge tube, followed by the addition of 50 mL of hydrogen fluoride (HF) solution (10% v/v). After the tube was capped, the solution was shaken for 1 h and centrifuged for 10 min (3000 r min−1), and the supernatant was removed. Subsequently, the residue in the tube was treated with HF solution and then followed the above shaking and centrifuging steps. A total of eight repeats (according to the conditions of the actual samples) were performed with different duration of shaking (4 × 1 h, 3 × 12 h, and 1 × 24 h). Lastly, the residue in the tube was washed with double-distilled water four times, mainly to remove the residual HF in the soil sample. The detailed steps were as follows: 50 mL of double-distilled water was added into tube, shaken for 10 min and centrifuged (3000 r min−1) for 10 min, and then the supernatant was removed. The purified samples with free-HF were dried in an oven at 40 °C, ground through a 60-mesh sieve, and stored in a Zip-lock bag for NMR measurement.Determination of the chemical structure of soil organic carbonThe 13C solid-state NMR spectrum was collected on a Bruker AV400 NMR spectrometer (Switzerland). The cross-polarization magic-angle spinning (CPMAS) technique was used, the 13C NMR frequency was 400.18 MHz, the magic angle spinning frequency was 8 kHz, the contact time was 2 ms, the delay time was 3 s, and the number of data points was 3000. The chemical shift was calibrated based on the external standard sodium 2, 2-dimethyl-2-silapentane-5-sulfonate (DSS), the integrated area was automatically given by the instrument, and the relative content of organic C in each functional group of SOC was expressed as the percentage of the integrated area of a chemical shift interval to the total integrated area. The C structures corresponding to the chemical shift of the main 13C signal of SOC (Table 3) were as follows: alkyl C region (0–45 ppm), alkoxy C region (45–110 ppm), aromatic C region (110–160 ppm), and carbonyl C region (160–220 ppm)4,19.Table 3 13C solid-state NMR determination of organic carbon functional groups and corresponding high-molecular-weight compounds.Full size tableData analysisNMR spectra (CPMAS 13C-NMR) were analyzed using MestReNova professional software. After analyzing and extracting the source data, Microsoft Office Excel 2010 and Origin 8.0 software were used for data processing and plotting. The data in the “Available Data” were plotted using Origin by overlapping the fitted curve, and SPSS 17.0 (SPSS Inc., Chicago, USA) statistical analysis software was used to test for significant differences (Duncan’s method) and for correlation analysis. More

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