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    Social Support and Network Formation in a Small-Scale Horticulturalist Population

    Human evolutionary research has historically conceptualised social support as a purely dyadic phenomenon (e.g., see Refs. 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16). That is, given some trait pertaining to two persons i and j — e.g., their genetic relatedness, history of helping each other, physical proximity, or difference in wealth — does i help j? Both elegant and tractable, this dyad-centric view of social support evokes classic theoretical models of cooperation as a “Prisoner’s Dilemma” within a void consisting only of ego (i) and alter (j)17. Yet it also belies the fact that aid relationships (i.e., who helps who) constitute complex networks of supportive social bonds that emanate throughout entire human communities.Members of such networks may, in principle, unilaterally help whomever they wish. And their decisions to help — or to not help — specific others comprise a dynamic, supra-dyadic relational context that shapes one’s plausible set of aid targets at the micro level18,19,20,21,22. Put simply, in social support networks, aid is targeted and interdependent across dyads such that the patterning of cooperation among multiple alters jointly affects whom any one network member helps. This sociocentric (i.e., whole network) view of social support is distinct from the perspective taken by evolutionary graph theorists who study the emergence of cooperation on network structure and other spatial substrates (e.g., square grids) that may be fixed or dynamic (e.g., see Refs. 23,24,25). And it is distinct from the perspective taken by analysts of egocentric (i.e., personal) networks who study how the arrangement of intimate relationships exclusively between one’s closest contacts (e.g., the extent to which one’s friends are also friends) eases access to help (e.g., see Martí, Bolíbar, and Lozares26).Differences between the dyad-centric and the sociocentric perspectives on social support are not merely cosmetic. Indeed, the dyad-centric stance of human evolutionary research has led to a situation wherein the relational context of helping behaviour is underexplored. And this has, in turn, impaired understanding of the relative importance of fundamental evolutionary mechanisms to the structuring of cooperative relationships in human communities.Specifically, human evolutionary research on helping behaviour generally takes the theories of kin selection and reciprocal altruism as lodestars. In so doing, sociometric data from subsistence societies across the globe have been used to investigate whether consanguinity (i.e., genetic kinship) and reciprocity govern aid unconditionally and in relation to multiple social and demographic factors. These include affinity (i.e., marriage-based kinship), physical proximity, relative need, homophily (e.g., based on age and gender), social closeness, friendship, religiosity, reputation, conflict, status, and anthropometric measurements such as size, height, and strength. And, on balance, evidence1,2,3,4,5,6,7,8,9,10,13,14,16,27,28,29,30,31,32,33 suggests that helping family and responding in kind when helped are the primary mechanisms by which humans informally distribute resources vital to day-to-day survival (e.g., advice, information, food, money, durables, and physical assistance).However, despite laudable exceptions2,7,15,28,29,30,31,32,33,34 and perhaps due to the influence of methodological trends in the wider behavioural ecology literature on relationships between animals (see Refs. 35,36,37), human evolutionary studies of real helping behaviour have typically relied on non-network methods — namely, monadic regression, dyadic regression, and permutation tests (e.g., see Refs. 1,2,3,5,6,8,9,10,11,12,13,14,16,27). Respectively, these techniques treat the supra-dyadic structure of social support networks as ignorable, reducible to dyads, or a nuisance to be corrected for38. Yet, sociocentric research by sociologists39,40,41,42,43,44,45,46,47,48,49 firmly establishes that humans create and maintain relationships in accordance with factors intrinsic to the supra-dyadic arrangement of network structure itself (e.g., processes of degree-reinforcement and group formation involving at least three persons). And this sociological research makes clear that network-structure-related dynamics can operate simultaneously and independently of non-network factors (e.g., age and kinship).Ultimately, reliance on methods that disregard complex interdependences between aid obscures the extent to which helping family and responding in kind when helped outrank the dynamics of the cooperative system within which decisions to assist specific individuals take place. This uncertainty represents a substantial gap in our scientific understanding of altruism. Accordingly, here I tackle a major point of interest in evolutionary anthropology and human behavioural ecology50 specifically through the lens of the sociology of social networks18,21,51, asking:RQ: How important is helping family and responding in kind when helped relative to supra-dyadic, network-structure-related constraints on the provision of aid?The Current StudyTo answer my research question, I use Koster’s27 recently-released cross-sectional data on genetic relatedness and the habitual provision of tangible aid (e.g., firewood, food, valuable items, and/or physical assistance). Re-analysed here due to their exceptional detail and measurement quality in addition to their broad relevance to the scientific community (see Methods), these data were collected in 2013 and concern a complete population. Specifically, they cover all 108 adult (18+) residents (11,556 ordered dyads) of the 32 households of Arang Dak — a remote village of 279 indigenous Mayangna and Miskito swidden (i.e., “slash-and-burn”) horticulturalists. Arang Dak sits on the Lakus River in Nicaragua’s Bosawás Biosphere Reserve, a neotropical forest in the Department of Jinotega.In total, the tangible aid network that I analyse — i.e., x(t2013)— consists of 1,485 asymmetric aid relationships between the adult residents of Arang Dak. Of the 1,485 aid relationships, 1,422 are verified by the source and the recipient of help. That is, xij(t2013) = 1 if villager i reported in 2013 that they give tangible aid to villager j at least once per month and villager j reported in 2013 that they receive tangible aid from villager i at least once per month. Still, note that Koster’s27 data document self-reported resource flows as opposed to observed transfers. Named sources and targets of aid are based on the village roster — not freely recalled from memory. See Methods for a summary of the data and details on the measurement of the network and kinship.Modelling StrategyTo analyse tangible aid in relation to supra-dyadic network structure (Fig. 1), I use generative network models following Redhead and von Rueden32 and von Rueden et al.33, amongst other human evolutionary scientists2,7,15,28,29,30,31,32,33,34. Specially, I rely on Stochastic Actor-Oriented Models (SAOMs) which are used for observational (i.e., non-causal) analyses of the temporal evolution of networks.Put simply, SAOMs are akin to multinomial logistic regression. More formally, SAOMs are simulations of individual network members’ choices between outgoing relationships with different rewards and costs. These simulations are calibrated or “tuned” to the observed network data. That is, conditional on x (i.e., the observed states of a dynamic network), SAOMs simulate network evolution between successive observations or “snapshots” of the network at (M) discrete time points — i.e., (xleft({t}_{m}right)to xleft({t}_{m+1}right)) — as a continuous-time, Markovian process of repeated, asynchronous, and sequential tie changes. The Markovian process is defined on the space of all possible directed graphs for a set of N = {1, …, n} network members40,42,44,52,53,54,55.SAOMs decompose change between successive network observations into its smallest possible unit. Specifically, “change” means creating one outgoing tie if it does not exist, dropping one outgoing tie if it does, or doing nothing (i.e., maintaining the status quo network). More formally, during a SAOM simulation, focal actors i (ego) myopically modify just one of their outgoing relationships with some alter j in the set of network members N (i.e., j ∈ N, j ≠ i). The change made by i is the change that maximises a utility or “evaluation” function. In this respect, the evaluation function captures the “attractiveness”44 of tie changes — where “attraction” means “…something like ‘sending a tie to [an actor j] with a higher probability if all other circumstances are equal.’” (Snijders and Lomi56, p. 5).The evaluation function itself is a weighted sum of parameter estimates (widehat{beta }) and their associated covariates k (i.e., SAOM “effects”44) plus a Gumbel-distributed variable used to capture random influences55. The simulated tie changes or “ministeps”44 made by i shift the network between adjacent (unobserved) states. These states differ, at most, by the presence/absence of a single tie40,42. And the probabilities of the ministeps — a large number of which are required to bring one observation of the network to the next (i.e., (xleft({t}_{m}right)to xleft({t}_{m+1}right))) — are given by a multinomial logit which uses the evaluation function as the linear predictor.Each covariate k used to specify the evaluation function summarises some structural (i.e., purely network-related) feature or non-structural feature of i’s immediate (i.e., local) network — e.g., the sum of the in-degrees of i’s alters, the number of reciprocated dyads that i is embedded in, or i’s number of outgoing ties weighted by genetic relatedness. These features correspond to theoretical mechanisms of interest (e.g., preferential attachment, reciprocal altruism, or kin selection) and generally take the form of unstandardised sums.SAOM parameter estimates (widehat{beta }) (log odds ratios) summarise the association between the covariates and the simulated tie changes or “ministeps”. Specifically, should a focal actor i have the opportunity to make a ministep in departure from some current (i.e., status-quo) network state x in transit to a new network state x±ij — i.e., the adjacent network defined by i’s addition/subtraction of the tie xij to/from x — ({widehat{beta }}_{k}) is the log odds of choosing between two different versions of x±ij in relation to some covariate k. For example, ({widehat{beta }}_{{rm{Reciprocity}}}=1.7) would indicate that the log odds of i creating and maintaining the supportive relation xij is, conditional on the other covariates k, larger by 1.7 when xij reciprocates a tie (i.e., xji) compared to when xij does not reciprocate a tie (i.e., reciprocated ties are more “attractive”). In contrast, ({widehat{beta }}_{{rm{Reciprocity}}}=-1.7) would indicate that the log odds of xij is, conditional on the other effects, smaller by −1.7 when xij reciprocates a tie compared to when xij does not reciprocate a tie (i.e., reciprocated ties are less “attractive”).Given the longitudinal nature of the model, the gain in the evaluation function for a ministep is determined by the difference Δ in the value of the statistic s for a covariate k — i.e., Δk,ij(x, x±ij) = sk,i(x±ij) − sk,i(x) — incurred through the addition/subtraction of xij to/from x (see Block et al.42 and Ripley et al.44 on “change statistics”). Accordingly, ({widehat{beta }}_{{rm{Reciprocity}}}=1.7), for example, is the value that xij positively contributes to the evaluation function when xij increases the network statistic sk,i(x) underlying the Reciprocity effect by the value of one (i.e., ΔReciprocity,ij (x, x±ij) = sReciprocity,i(x±ij) − sReciprocity,i (x) = 1 − 0 = 1).The probabilities of network members being selected for a ministep is governed by a separate “rate” function. And the baseline rate parameter λ is a kind of intercept for the amount of network change between successive observations of the analysed network. Larger baseline rates indicate that, on average, more simulated tie changes were made to bring one observation of the network to the next (i.e., (xleft({t}_{m}right)to xleft({t}_{m+1}right))).However, as the data from Nicaragua are from a single point in time (i.e., 2013), I use the cross-sectional or stationary Stochastic Actor-Oriented Model (cf. von Rueden et al.33). Accordingly, Arang Dak’s tangible aid network is assumed to be in “short-term dynamic equilibrium.” As Snijders and Steglich40 (p. 265) discuss in detail, “this ‘short-term equilibrium’ specification of the SAOM is achieved by requiring that the observed network is both the centre and the starting value of a longitudinal network evolution process in which the number of change opportunities per actor [i.e., λ] is fixed to some high (but not too high) value.”Practically speaking, this means that the cross-sectionally observed network is used as the beginning and the target state for a SAOM simulation — i.e., (xleft({t}_{2013}right)to xleft({t}_{2013}right)) — during which actors are allowed to make, on average, very many changes (i.e., λ) to their portfolio of outgoing ties. These simulated tie changes produce a distribution of synthetic networks with properties that are, on average, similar to those of the cross-sectionally observed network in a converged SAOM — where the target properties correspond to the researcher-chosen SAOM effects k. Put simply, “[cross-sectional] SAOMs assume that the network structure, although changing, is in a stochastically stable state.” (Krause, Huisman, and Snijders57, p. 36–37). Thus, the estimated parameters (widehat{beta }) continue to summarise the rules by which ministeps unfold. However, the asynchronous, sequential, simulated tie changes, in a sense, “cancel out” and thus hold the network in “short-term dynamic equilibrium”40,42. Formally, the cross-sectional SAOM is defined as a stationary distribution of a Markov Chain with transition probabilities given by the multinomial logit used to model change between adjacent network states40,42.The rate parameter λ is fixed at 36 for my analysis. The value of 36 is the maximum observed out-degree in the source-recipient-verified tangible aid network x(t2013). Accordingly, under λ = 36, all members of the tangible aid network have, on average, at least one opportunity to modify their entire portfolio of outgoing ties during the simulations. Nevertheless, to ensure the robustness of my results, I also fit a second set of models for which λ was fixed to 108 (i.e., thrice the maximum out-degree).Model SpecificationTo assess the importance of kinship and reciprocity to hypothetical decisions to help others (i.e., ministeps), I use four archetypal specifications of the SAOM’s evaluation function. These model specifications feature nested sets of covariates (i.e., the SAOM “effects”44). And, using language found in prior evolutionary studies3,5, I refer to these archetypal specifications as the “Conventional Model” (Model 1) of aid, the “Extended Model” (Model 2) of aid, the “Networked Aid Model (Limited)” (Model 3), and the “Networked Aid Model (Comprehensive)” (Model 4).The first specification (i.e., Model 1) comes from Hackman et al.3 and Kasper and Borgerhoff Mulder5 who respectively label it the “Human Behavioural Ecology” and “Conventional” model. This specification is comprised of just four dyadic covariates — one each for consanguinity (i.e., Wright’s coefficient of genetic relatedness), affinity (i.e., Wright’s coefficient of genetic relatedness between i’s spouse s and his/her blood relative j), the receipt of aid, and geographic distance. The first three covariates are used to test long-standing predictions of helping in order to reap indirect and direct fitness benefits in line with the theories of kin selection and reciprocal altruism (see Refs. 1,5,27,58,59 for primers). And the fourth covariate is used to adjust for tolerated scrounging — i.e., what Jaeggi and Gurven4 (p. 2) define as aid resulting from one’s inability to monopolise resources due to costs imposed by the resource-poor — where a covariate for distance operationalises pressure to help imposed by those who are spatially close4.The second specification (i.e., Model 2) reflects Kasper and Borgerhoff Mulder’s5 and Thomas et al.’s9 extensions to the conventional model (see also Page et al.16). Specifically, and following important work by Allen-Arave, Gurven, and Hill1, Hooper et al.14, and Nolin7, it is distinguished by nuanced tests of kin selection and reciprocal altruism via interactions between: (i) consanguinity and the receipt of aid; (ii) consanguinity and relative need; and (iii) consanguinity and geographic distance. Furthermore, Kasper and Borgerhoff Mulder’s5 and Thomas et al.’s9 extended model includes covariates for the non-network-related attributes of individuals (e.g., gender, wealth, and physical size), thus adjusting for homophily, trait-based popularity, trait-based activity, and local context (e.g., results from a gift-giving game9 or, in the present case, infidelity and discrimination based on skin-tone27).The third specification (i.e., Model 4) is my revision of the second. It is geared to make the relational context of aid explicit. This is done using nine covariates that account for the breadth of sociologists’ contemporary understanding of supra-dyadic interdependence between positive-valence (i.e., not based on disliking or aggression), asymmetric social relationships39,40,41,42,43,44,45,46,47,48,49. In keeping with the nature of the SAOM, each of these covariates summarises some structural feature of a villager’s immediate (i.e., local) network (e.g., the number of transitive triads that she is embedded in). Accordingly, each structural covariate is used to capture a form of self-organisation — i.e., network formation driven by an individual’s selection of alters in response to network structure itself (Lusher et al.49, p. 10–11 and 23–27).Specifically, the covariates added in the third specification reflect predictions derived from three fundamental sociological theories of the emergence of non-romantic relationships. The first is structural balance theory which posits that individuals create and maintain ties that move groups of three people from an intransitive to a transitive state (i.e., transitive closure), the latter of which is understood to be more psychologically harmonious or “balanced” (see Refs. 39,43,47,48,60,61,62 for primers). The second is Simmelian tie theory which posits that, once formed, individuals will maintain relationships embedded in maximally-cohesive groups of three people such that 3-cliques (i.e., fully-reciprocated triads) are resistant to dissolution (see Refs. 43,48,63 for primers). The third is social exchange theory (as it relates to structured reciprocity) which posits that individuals will unilaterally give benefits to others in response to benefits received such that indirect reciprocity (i.e., returns to generosity) and generalised reciprocity (i.e. paying-it-forward) in groups of three people encourage cyclic closure — i.e., the simplest form of chain-generalised exchange (see Refs. 19,20,43 for primers). Furthermore, the third specification reflects the broad prediction that individuals vary in their propensity to send and receive relationships based on their structural position alone (e.g., popularity-biased attachment) leading to dispersion in the distribution of in-degrees and out-degrees (see Refs. 39,44,49 for primers) — especially for ties with an inherent cost to their maintenance39,42.Last, I consider a fourth specification (i.e., Model 3) that uses a subset of the nine network-structure-related covariates included in Model 4. This limited set of structural effects typifies the specifications used in prior human evolutionary studies of empirical help that present generative models of entire networks2,7,15,28,29,30,31,32,33,34. Specifically, the fourth specification features just three network-structure-related covariates to account for structural balance theory, self-reinforcing in-degree (i.e., popularity-bias), and the interplay between in-degree and out-degree.Descriptive statistics for the relevant attributes of the 108 residents of Arang Dak appear in Table 1. Formulae used to calculate the network statistics sk,i(x) underlying each effect k used to specify my SAOMs, alongside verbal descriptions to aid reader interpretation, appear in Online-Only Table 1. See Methods for additional rationale behind the third specification.Table 1 Descriptive statistics for the monadic and dyadic attributes of the residents of Arang Dak.Full size tableModel ComparisonCompared to prior human evolutionary research on social support networks, I take two novel approaches to gauging the importance of kinship and reciprocity to help. First, I use a technique41 specifically designed to measure the relative importance of individual effects in SAOMs (see Methods). And second, I evaluate each specification’s ability to produce synthetic graphs with topologies representative of the structure of the analysed tangible aid network64.Judging model specifications using topological properties reflects one of the core purposes of methods such as the SAOM and the Exponential Random Graph Model (ERGM) — i.e., to explain the emergence of global network structure (see Refs. 40,42,46,47,49 also Refs. 18,48), not simply the state of individual dyads (i.e., is aid given or not?). Admittedly, explaining global network structure is not a stated primary aim of dyadic-centric or sociocentric studies of help by human evolutionary scientists, including those wherein authors rely on SAOMs or ERGMs2,7,15,28,29,30,31,32,33,34. Still, topological reproduction is an important, strong test of the relative quality of the four archetypal specifications as each encodes the set of rules presumed to govern network members’ decisions about whom to help.To clarify, recall that here it is assumed, a priori, that network members can, in principle, cooperate with whomever they wish, that their cooperative decisions are intertwined across multiple scales, and that their micro-level decisions ultimately give rise to macro-level patterns of supportive social bonds (see Refs. 18,19,20,21,22). The macro-level patterns generated by SAOMs and ERGMs can differ dramatically based on specification40,46,47,49,64,65. Thus, the empirical relevance of a candidate model rests with its ability to produce synthetic graphs similar to the observed structure40,42,46,47,48,49,64. Ultimately, divergence between the real and simulated graphs suggests that a candidate specification is suspect as it does not describe how some network of interest could have formed. More

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    Dust mitigation by the application of treated sewage effluent (TSE) in Iran

    Sewage and TSE quantity characteristicsThe WWT facilities have been implemented for Zabol with a capacity of 39,000 m3/day. Table 1 shows the volume of water consumption and sewage production based on the sewage coefficient in urban communities of the study area.Table 1 Water consumption, TSE volume and receiving resources in the study area—2019.Full size tableAs shown in Table 1, the total water consumption in the study area is 22.538 mcm/year while based on the development conditions. Afterward, the sewage volume was calculated to 16.194 mcm/year, considering the sewage coefficient and water consumption.Continuously, the sewage data obtained from the Water and Wastewater Organization of Zabol city, Iran, showed that the sewage entrance to the treatment plants of the study area is about 19,000 m3/day and 137 working days. Therefore, the TSE volume of the WWT plant was calculated based on the following scenarios of (1) data obtained from the Water and Wastewater Organization, Iran, and (2) based on the capacity of WWT plant. Note that the working days for both scenarios will be 137. The calculation is based on Eq. (1). The total TSE volume for scenarios 1 and 2 is 2.8 and 5.1 mcm/year, respectively.The difference between the calculation based on capacity and the existing data is due to the removal of raw sewage before entering the treatment plant, which has caused health and environmental problems in the region. Data obtained from Iran Department of Environment34 showed that 1.68 mcm/y of sewage were extracted for the farms. Previous studies in the same study area also reported the significant (P  5. Note that typical abundance of total and fecal coliforms (FC) in raw sewage are 107–109 and 106–108 100/mL, respectively, and were reduced by 1–5 orders of magnitude in treated TSE, depending on the type of treatment39,40. Classical treatments, which do not include any specific disinfection step, reduce fecal micro-organisms densities by 1–3 orders of magnitude40, but because of their high abundance in raw sewage, they are still discharged in large numbers with treated TSEs in the environment.Figure 6The results of the abundance of total coliforms (TC) and fecal coliforms (FC).Full size imageAdditionally, the results of yearly values of physicochemical factors of Zabol TSE (mg/L) including BOD5, COD, TDS, TH, and EC in the period of 2017–2019, showed in Fig. 7. The yearly results suggested that the values through the years of investigation did not show significant changes. In the following parts, the possibility of TSE evaluated considering various standards.Figure 7The results of yearly values of physicochemical factors of Zabol TSE.Full size imagePotential application of TSEComparing the quality of the TSE and sewage are based on various regulations showed in Table 3. It includes the food and agriculture organization (FAO), US environmental protection agency (USEPA), the Canadian water quality index (CWQI), and Iran’s national standards (INS), considering the irrigation and recreational application.Table 3 Guidelines for interpretations of water quality of sewage and TSE of Zabol WWT plants (average in the period of 2017–2019) compared to the standards of regulations.Full size tableAccording to the FAO Guide41 for Classifying Agricultural Water Quality, as shown in Table 3, the most crucial parameters for the application of TSE in irrigation include electrical conductivity (EC), sodium uptake ratio (SAR), chlorine, BOD, COD, and FC. However, three out of seven parameters namely BOD, COD, and FC in the TSE are largely erratic with the limits recommended in the standards.Based on USEPA42, the value of total suspended solids in TSE of Zabol WWT plant largely inconsistent with the limits recommended in the standards for TSE reuse. However, TDS, EC, and pH, met the criteria. Moreover, except TSS and pH, the other chemical parameters of sewage also meet the criteria. It is worth mentioning that EPA does not require or restrict any types of water reuse. Generally, states maintain primary regulatory authority (i.e., primacy) in allocating and developing water resources. Some US states have established programs to specifically address reuse, and some have incorporated water reuse into their existing programs. EPA, states, tribes, and local governments implement programs under the Safe Drinking Water Act and the Clean Water Act to protect the quality of drinking water source waters, community drinking water, and waterbodies like rivers and lakes.According to INS regulations for irrigation and recreation reuse of TSE33, the value parameters tested for the TSE of the Zabol WWT plant are following the limits recommended in the standards for consumption as irrigation (except chlorine) and recreation projects.Finally, the CWQI is a means to provide consistent procedures for Canadian jurisdictions to report water quality information to both management and the public. The CWQI value ranges between 1 and 100, and the result is further simplified by assigning it to a descriptive category in Table 4.Table 4 The CWQI value and descriptive.Full size tableThe results of CWQI software for analyzing the TSE of the WWT plant in the study area, as shown in Table 5 and Fig. 8, indicated its poor quality for drinking, and aquatic. While it is fair for livestock and marginal for irrigation. However, considering the purpose of this study for irrigation of the native plants, it met the criteria. Note that the input data set is based on the period of 2017–2019.Table 5 The results of TSE in various applications assessed by CWQI.Full size tableFigure 8CWQI tets results for TSE of WWT plant in the study area.Full size imageThe results of this section indicated the consideration of various parameters due to various regulations and demonstrated that the treatment technology upgrade was significantly better than those of urban miscellaneous water and agriculture water standards, indicating this system can be widely used for urban landscape hydration. Moreover, squeezing the sewage treatment process for being cost effective could be recommended considering the measurements of FC, BOD, and COD.Optimal area suggestion for project executionConsidering three steps of wind erosion which are detachment, transportation, and deposition, the sand fixation methods have to be done in the detachment area to be more effective. Hence, the most advantageous regions for project execution were selected based on the factors of (a) discovering the dust origins, and (b) vegetation cover. Regarding the first concern, it was shown that the dry sediments of the Farah river43, and the presence of dunes between the two sand movements corridors in Sistan, namely Jazinak (near Zabol city) and Tasuki corridors (shown in Fig. 9), was increased the dust concentration in Zabol city37,44 while the agricultural lands, and other infrastructures such as roads, and irrigation canals developed in the area between Zahedan and Zabol city.Figure 9Locations and names of Hamuns lake and sand movement corridors in the study area © 2022 by Springer Nature Limited is licensed under Attribution 4.0 International (created by ArcMap 10.5).Full size imageSubsequently, based on a guide that 30% of vegetation cover has a significant effect on the process of soil detachment45,46, and soil protection in the desert areas47, the regions with less than 30% vegetation cover in the study area based on field observation was investigated and showed in Fig. 10. Field observation demonstrated that most areas along with the Jazinak sand corridor and Zabol city have 1–15% and 15–30%36, which are in the priority for stabilization.Figure 10The critical dust hotspot and dust origins in the study area © 2022 by Springer Nature Limited is licensed under Attribution 4.0 International (created by ArcMap 10.5).Full size imageThe results are consistent with Abbasi et al.37, reported that the Hamun Baringak Lake plays a crucial role in the aeolian mobilization of sediments in the Sistan region because of the hydrological droughts that led to the gradual decline of the wetland vegetation cover. Notably, Jahantigh48, in the same study area, reported that the average forage yield of Aeluropus lagopoides in Hamun Hirmand lake in the condition of the water inflow and during drought, was estimated to be 8869 and 173 kg/ha, respectively. It can be explained by the effect of water presence on plant production and cover. However, the average of bare soil of Hamun lake was estimated to be 7.5% and 84.2% in the two periods of water inflow and drought, respectively48. It indicated the impact of dusty days. Therefore, the mentioned areas with the vegetation cover below 30% prioritized for stabilization techniques to dust reduction or mitigation.The detailed field investigation of the land use and vegetation cover, as shown in Fig. 12, indicated the presence of native plants such as A. lagopoides and Tamarix spp. Based on Fig. 11, among the Tamarix genus, the three species of T. aphylla, T. stricta, and T.hispida were observed in the study area. T. stricta is a native species to Iran with benefits including, traditional therapeutic uses in Persian Medicine49,50. Also, the soil EC in the habitat of T. aphylla (15.70 mhos/cm) is almost the same as the control area (15.80 mhos/cm) in the depth of 0–30 cm; while the available potassium in T. aphylla habitat (460 mg/l) was also more than the control area (180 mg/l)51. Hence, the afforestation of Tamarix spp. has caused the addition of soil amendments and increased the clods.Figure 11The most land use/cover in the study area.Full size imageConsequently, the water requirement of the plants in the desert area consisting of T.aphylla, is reported in Table 6. The water requirement of T. stricta was estimated based on Table 6 to be 580 m3/ha for 500 plants no./ha with a vegetation cover of 10–30%.Table 6 Annual water requirement of the T. aphylla for irrigation in the early stages of establishment in terms of planting density (Rad, 2018).Full size tableMoreover, Fig. 12 shows the vast (50% more) soil coverage of T. stricta in the collar area compared to T. aphylla. Therefore, it is more appropriate to cultivate T. stricta than T. aphylla for the biological restoration of the region. Note that the introduced dust mitigation technique using TSE of Zabol WWT can play a specific role in the rehabilitation of soil cover in the mentioned area due to the low water need of native plants. Consequently, it has a significant impact on dust reduction in Zabol city.Figure 12The picture of (a) T. stricta and (b) T. aphylla in the study area.Full size imageHence, based on the hotspots of dust origins in the study area, the most appropriate sites for the project executions of TSE were selected, as shown in Fig. 13. Investigations indicated that a total of 27,500 ha are suitable for the project excision. Hence, considering the water requirement of 500 m3/ha/year, TSE volume of 5.1 mcm/year, vegetation cover of below 30%, and other observations such as the soil coverage in the collar area, the native plant of T. stricta selected for the afforestation of 10,000 ha on the west part of Zabol. This region has the priority in stabilization due to companionship to the corridors with a vegetation cover of 16–30%.Figure 13Area suggested for the dust mitigation project execution by the application of TSE © 2022 by Springer Nature Limited is licensed under Attribution 4.0 International (created by ArcMap 10.5).Full size imageCost analysisFinally, due to the vast area of TSE application, the total of 27,500 ha, with the puprose of dust mitigation, the project execution costs must have been addressed. Hence, Fig. 13 shows the distance of Zabol city to Hamun Hirmand and Baringak lake for transportation calculation. Accordingly, the distance from Zabol to Hamun Hirmand and Baringak lake is 14 and 33 km, respectively. The whole area around Zabol city to Hammon Hirmand lake is cultivated lands; hence, the existing roads reduced construction costs.The two main modes of transportation are trucks and pipelines. There are various pros and cons to both methods. Truck transportation is favored for low volume and short distances, while its costs rapidly increase for large-scale transportation. On the other hand, pipeline transportation is appropriate for large volumes, and long travel distances as it has a positive impact on reducing greenhouse gas emissions. Using pipelines also reduces noise, reduces highway traffic, and improves highway safety.Based on the literature, the variable and fixed transportation cost components depend on the type of product shipped, design requirements, and other decisions related to facility planning. For the sewage sludge with a pH level of 7.0 ± 0.1; hence, a low-cost PVC pipe suggested. Moreover, for cost optimization, as the WWT facilities in the study area do not generate enough volume daily, it makes economical sense to store sewage for a few days to increase the shipped volume. However, reducing the storage to a single day condenses these investment costs drastically52.It was estimated that the total costs for a facility-owned and rented single trailer truck with a capacity of 30 m3 to be $5.6/m3 and 7.4/m3/km, respectively53. Hence, the variable unit transportation cost along a pipeline with a capacity of 480 m3/day is estimated to be $0.144/m3/km. In despite of previous studies mentioning that it is more economical to use a pipeline rather than a rented single trailer truck if the volume shipped is greater than 700 m3/day, in the study area, it is more economical to use a facility-owned single trailer truck, while the shipped volume is 1200 m3/day due to the low cost of petroleum and very close distance of the suggested area. More

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    No evidence for long-range male sex pheromones in two malaria mosquitoes

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    Gut bacteria induce oviposition preference through ovipositor recognition in fruit fly

    Insect rearingThe B. dorsalis strain collected from a carambola (Averrhoa carambola) orchard in Guangzhou, Guangdong Province, was reared under laboratory conditions (27 ± 1 °C, 12:12 h light:dark cycle, 70–80% RH). A maize-based artificial diet containing 150 g of corn flour, 150 g of banana, 0.6 g of sodium benzoate, 30 g of yeast, 30 g of sucrose, 30 g of paper towel, 1.2 mL of hydrochloric acid and 300 mL of water was used to feed the larvae. Adults were fed a solid diet (consisting of 50 g yeast and 50 g sugar) and 50 mL sterile water in a 35 cm × 35 cm × 35 cm wooden cage. For B. dorsalis, the female will start laying eggs once mated and the female will start mating 7 days after emergence. To make sure all females used in our study were gravid females, females were selected 10 day after emergence.Visualization of CF-BD with FISH and PCRFISH was carried out on dissected gut and ovary samples from B. dorsalis. The hybridization protocol for the gut and ovary was performed according to a previously described method32. Briefly, the gut and ovary were collected and immediately soaked in Carnoy’s fixative for 12 h. After sample fixation, proteinase K (2 mg/mL) treatment for 20 min at 37 °C and HCl (0.2 mol/L) treatment for 15 min at room temperature were performed successively. Then, followed by dehydration in ethanol, the samples were incubated in buffer (20 mM Tris-HCl (pH 8.0), 0.9 M NaCl, 0.01% sodium dodecyl sulfate, 30% formamide) containing 50 nM CF-BD specific probe (5′-AATGGCGTACACAAAGAG-3′) labeled with cy3 at the 5′ end for 90 min. After incubation, the samples were washed with buffer (0.1 M NaCl, 20 mM Tris/HCl (pH 8.0), 5 mM ethylenediaminetetraacetic acid (pH 8.0), 0.01% SDS) and observed under an epifluorescence microscope (Axiophot, Carl Zeiss, Shinjuku-ku, Japan).To further confirm CF-BD in rectum and ovary of mature females, rectums and ovaries of mature females were dissected and fixed in formalin fixation for 24 h. After soaking in graded alcohols and xylene, all samples were embedded in paraffin for section preparation. Samples were sliced into 4 µm each before pasting on the glass slide and then sent for FISH with the same probe (labeled with cy3 at the 5′ end) used above. Moreover, nested PCR was applied to detect CF-BD in 19 ovaries of mature females according to the method of Guo et al., 201733. Briefly, a 1149 bp region of gyrB gene of CF-BD was amplified by the specific outer primer gyrBP1-F (5′-CAGCCCACTCTGAACTGTAT-3′) and gyrBP1-R (5′-TCAGGGCGTTTTCTTCGATA-3′) under a temperature profile of 95 °C for 1 min, which was followed by 25 cycles of 95 °C for 30 s, 52 °C for 30 s, 72 °C for 90 s, and 72 °C for 5 min. Then, a 371 bp region of the gyrB gene of CF-BD was amplified by the specific inner primer gyrBP4-F (5′-ACGCTGGCTGAAGACTGCC-3′) and gyrBP4-R (5′-TGGATAGCGAGACCACGACG-3′) under a temperature profile of 95 °C for 2 min, which was followed by 35 cycles of 95 °C for 30 s, 57 °C for 30 s, 72 °C for 30 s, and 72 °C for 5 min.Influence of CF-BD on B. dorsalis ovary developmentTo evaluate the effect of CF-BD on ovary development, newly emerged B. dorsalis females were injected with streptomycin and CF-BD suspension (both dilute in sterile water). Specifically, 10 µL 25% glycerol solution containing CF-BD was added into 100 mL Luria-Bertani (LB) liquid medium and culturing for 1 day by shaking (180 rpm) in 30 °C incubator. After culturing, CF-BD was collected by centrifuging (3000 rpm, 15 min) the medium in a 50 mL centrifuge tube. Then collected CF-BD was re-suspended with 5 mL sterile water. CF-BD concentration was measured on a hemocytometer and CF-BD concentrations used in the following assays were prepared by diluting the original concentration with sterile water. A 0.5 mm inside diameter capillary needle with 1 μL streptomycin or CF-BD suspension was used for injection. The injection operation was carried out on a microinjector (Eppendorf FemtoJet), and every female was injected in the abdomen near the ovipositor. The concentrations of streptomycin used were 20 mg/mL, 10 mg/mL and 5 mg/mL, respectively. And CF-BD suspension concentrations were 3 × 107 cfu/mL, 1.5 × 107 cfu/mL and 7.5 × 106 cfu/mL, respectively. For control, the female fly was injected with 1 μL sterile water in the abdomen near the ovipositor. Then the development level of the ovary was assessed by comparing the width and length of ovary between streptomycin (or CF-BD suspension) injection flies and control. For CF-BD injected flies, developmental facilitation was observed for ovaries 2 days before the flies reached sexual maturity (flies will reach sexual maturity after 7 days). For antibiotic injected flies, ovaries were dissected after 7 days.Oviposition assaysThe method reported in previous studies was followed for the oviposition experiments17. Briefly, a 2-choice apparatus was assembled in a cage made up of wood and wire gauze (length: width: height = 60 cm: 60 cm: 60 cm) with two petri dishes (diameter: 3 cm) at the bottom of the cage (Fig. 2a). All devices were sterilized before each experiment. Fresh fruits of guava (Psidium guajava Linn.) and mango (Mangifera indica L.) were sourced from the local market in Guangzhou, China. These fruits were sterilized on the surface with ethanol and ground into puree with a sterilized grinder, and puree (2 g) was added to the sterilized Petri dishes of the cages (one dish with puree containing 100 μL CF-BD (0.8*108 cfu/mL) in sterile water, and one dish with puree containing 100 μL sterile water). Then the prepared cages were divided into two groups for different assays. Group 1: At 0 h, 50 gravid females of B. dorsalis were placed in the cages and egg numbers in the petri dishes were recorded after 2 h. Group 2: At 4 h, 50 gravid females of B. dorsalis were placed in the cages and egg numbers in the petri dishes were recorded after 2 h.To test the oviposition attraction of 3-HA, a 4-choice apparatus was assembled in a cage made up of wood and wire gauze (length: width: height = 60 cm: 60 cm: 60 cm) with four petri dishes (diameter: 3 cm) at the bottom of the cage. In the Petri dishes, 2 g puree, 2 g puree + 0.2 mg 3-HA, 2 g puree + 2 mg 3-HA and 2 g puree + 20 mg 3-HA were added. Then, the egg-laying behavior was observed31.To test the oviposition attraction of 3-HA to flies with genes knocked down, 20 females injected with dsRNA were placed into the above cage with two Petri dishes. In the Petri dishes, 2 g guava puree and 2 g guava puree + 20 mg 3-HA were added. Then, the egg-laying behavior was observed using the above method. Oviposition of normally reared females was performed as a control. The oviposition index was calculated using the following formula:Oviposition index = (O − C)/(O + C), where O is the number of eggs in the treatment and C is the number of eggs in the control.Volatile analysisThe volatile compounds in guava and mango purees were analyzed by GC–MS according to the method described in a previous study17. Briefly, 2 g puree mixed with sterile water or CF-BD was added into a 20 ml bottle, and then a 100-μm polydimethylsiloxane (PDMS) SPME fiber (Supelco) was used to extract the headspace volatiles for 30 min. GC–MS was performed with an Agilent 7890B Series GC system coupled to a quadruple-type-mass-selective detector (Agilent 5977B; transfer line 250 °C, source 230 °C, ionization potential 70 eV). The 3-HA concentrations in puree mixed with sterile water and CF-BD were measured with the standard curve drawn by the authentic standards of 3-HA. And 3-HA concentration in puree mixed with sterile water and CF-BD was compared with a paired sample Student’s t-test.Olfactometer bioassaysAn olfactometer consisting of a Y-shaped glass tube with a main arm (20 cm length*5 cm diameter) and two lateral arms (20 cm length, 5 cm diameter) was used. The lateral arms were connected to glass chambers (20 cm diameter, 45 cm height) in which the odor sources were placed. To ensure a supply of odor-free air, both arms of the olfactometer received charcoal-purified and humidified air at a rate of 1.3 L/min.To test the attraction effect of puree supplemented with CF-BD or 3-HA for females, puree mixed with CF-BD was prepared and placed in one odor glass chamber. In the control odor glass chamber, puree mixed with sterile water was placed. After 4 h, gravid females were individually released at the base of the olfactometer and allowed 5 min to show a selective response. The response was recorded when a female moved >3 cm into one arm and stayed for >1 min. Females that did not leave the base of the olfactometer were recorded as nonresponders. Only females that responded were included in the data analysis. Odor sources were randomly placed in one arm or the other at the beginning of the bioassay, and the experiment was repeated ten times. The system was washed with ethanol after every experiment. More than 100 females were selected for testing, and each female was used only once for each odor. A chi-square test was performed to compare the attraction difference between puree mixed with sterile water and CF-BD.Olfactory trap assaysThe attraction of purees supplemented with CF-BD to mature females was also tested. The test chamber was assembled with a plastic cylinder (120 × 30 cm) covered by a ventilated lid. The test chamber contained an odor-baited trap (2 g puree + 100 μL CF-BD (0.8*108 cfu/mL)) and a control trap (2 g puree + 100 μL sterile water). The traps were made of transparent plastic vials (20 × 6 cm) and were sealed with a yellow lid on which small entrances were present to let the flies in (Fig. 3a). After 0 h or 4 h of fermentation, 100 gravid females were released in the cage. The fly number in each trap bottle was recorded after 2 h. The number of flies was compared with a paired sample Student’s t-test.The attraction effect of puree supplemented with 3-HA on mature females was tested by placing four traps (2 g puree, 2 g puree + 0.2 mg 3-HA, 2 g puree + 2 mg 3-HA and 2 g puree + 20 mg 3-HA) in the test chamber. Then, the attraction effect was observed31.Video observation of egg-laying behaviorEgg-laying behavior was observed in a Petri dish. Briefly, guava puree was added to a centrifuge tube on which a hole was made. Then, one gravid female was placed into the petri dish, and the lid was closed. Above the petri dish, a camera was placed to record the behavior of the female before laying eggs.EAG analysisEAG analysis was performed to determine whether 3-HA could elicit electrogram responses in the ovipositors of gravid females and Obps knocked down gravid females. For EAG preparations, the ovipositor of a gravid female was cut off and mounted between two glass electrodes (one electrode connected with the ovipositor tip). The ovipositor tip was cut slightly to facilitate electrical contact. Dilution of 3-HA in ethanol (0.1, 1 and 10 mg/mL) was used as a stimulant. Ethanol was used as control. For each ovipositor, ethanol and 3-HA diluted in ethanol were used as stimulants. The signals from the ovipositors were analyzed with GC-EAD 2014 software (version 4.6, Syntech).Transcriptome sequencing and gene identificationTo identify the olfactory genes that contribute to B. dorsalis oviposition preference, the transcriptome sequencing results of the female ovipositors at different developmental times (0 day, 3 days, 6 days, 9 days and 12 days) were compared. For each time, 5 ovipositors were dissected for RNA extraction. In addition, five replicates were included for each time. In the next step, paired-end RNA-seq libraries were prepared by following Illumina’s library construction protocol. The libraries were sequenced on an Illumina HiSeq2000 platform (Illumina, USA). FASTQ files of raw reads were produced and sorted by barcodes for further analysis. Prior to assembly, paired-end raw reads (uploaded to National Genomics Data Center, Accession number: PRJCA004790) from each cDNA library were processed to remove adapters, low-quality sequences (Q  More

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    Selection pressure on the rhizosphere microbiome can alter nitrogen use efficiency and seed yield in Brassica rapa

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    Spatial–temporal evolution characteristics of land use and habitat quality in Shandong Province, China

    Spatial–temporal characteristics of land use changeAs shown in Fig. 2, cultivated land was the dominant land use type in Shandong Province during the past 40 years, which accounted for 69.86% (1980), 69.98% (1990), 69.25% (2000), 68.00% (2010) and 66.88% (2020) respectively. Moreover, it was found that the area of cultivated land, forest land, grassland, unused land and ocean gradually decreased, whereas the water area and URL (urban and rural industrial and mining residential land) increased obviously. In particular, grassland decreased by 7542.87 km2 in the past 40 years with a decline rate of 37.18%, which was much higher than cultivated land and forest land. This phenomenon was attributed to the fact that cultivated land and forest land were less susceptible to encroachment as their high vegetation coverage, while grassland was easily occupied by other land types. The serious occupation by other land types has led to a significant reduction in unused land with a very high decline ratio of 64.32% from 2010 to 2020. In contrast to unused land, URL increased significantly at this period (Fig. 3), which was due to the rapidly economic development.Figure 2Land use type map of Shandong Province from 1980 to 2020.Full size imageFigure 3Sankey diagram of land use transfer in different periods.Full size imageThe total area of land use conversion in Shandong Province was 86,909 km2 during the past 40 years, the most drastic change was observed from 2010 to 2020. On the one hand, the major project of new and old kinetic energy conversion in Shandong Province had been implemented since 2000, which led to the expansion of urban land and dramatic changes in land use patterns. On the other hand, social, economic, technological and other factors had a direct impact on land use change by influencing people’s decision-making on land use (e.g., demand for land products, investment in land, protection of land resources, etc.)45,46,47,48. Statistics showed that GDP (Gross Domestic Product) and population density of Shandong Province had increased significantly since 21st century. The GDP of 2010–2020 was about 10 times that of 1980–2000 and population density had also increased by 1.4 times (Data from: Shandong statistical yearbook, http://tjj.shandong.gov.cn/col/col6279/index.html). As the most direct reflection of human activities, land use change was obviously affected by factors such as agricultural cultivation, industrial and mining construction, and urbanization driven by population growth49,50.The most significant changes of land use type were URL (increased by 17.75%), grassland (decreased by 8.72%) and cultivated land (decreased by 7.26%) over the past forty years. URL was mostly converted from cultivated land (26,306 km2) and grassland (1684 km2), which reflected the serious situation of occupying cultivated land in the process of urbanization in Shandong Province. It was caused by tight land use scale and relatively flat terrain of grassland. Besides, the range of land use type in the four periods also exhibited great variations. The conversion of land use from 1980 to 1990 was concentrated in the Yellow River Delta, Laizhou Bay and Weishan Lake, for the same as 1990–2000. At the period of 2000 to 2010, the conversion types concentrated in Bohai Bay and Yellow River Delta. The land use conversion was violent and widely distributed from 2010 to 2020, which was different from previous periods from 1980 to 2010. The conversion of cultivated land → URL and URL → cultivated land were widely distributed in Shandong Province, while another conversion of grassland → cultivated land and forest → cultivated land were concentrated in the Central and South Shandong Mountains and Jiaodong Hills. In addition, the conversion of cultivated land → water area and URL → water area were concentrated in Bohai Bay, Yellow River Delta and Laizhou Bay. Ample water, flat terrain and fertile soils in these bays and deltas facilitates agricultural cultivation and other productive activities. Therefore, the conversion of land use types from 1980 to 2010 was mainly concentrated here (Fig. 4). Specifically, the conversion of water area → URL was 1083 km2 from 1980 to 1990, unused land → water area was 925 km2 from 1990 to 2000, cultivated land → water area was 687 km2 from 2000 to 2010. However, the pattern of land use change dominated by natural factors has been broken in the process of increasing demand for social development and continuous advancement of science and technology. The conversion of land use types has become more dispersed in spatial distribution and the types of conversion have become more diverse.Figure 4Spatial distribution map of land use conversion types in different periods.Full size imageIn fact, one issue of concern in the early exploitation of water was the ecological problems caused by over-exploitation. For example, the cut-off of the Yellow River downstream made it difficult to guarantee the water security of industrial and agricultural production and residential life in the areas along the way. At the same time, the safety of coastal ecosystems was threatened and the phenomenon of soil salinization had become more serious. To alleviate these problems, government and the public have taken a series of measures such as establishing the Yellow River Delta National Nature Reserve was established in 1992, returning farmland to lakes and wetlands, and improving the landscape pattern of rivers and lakes by carrying out ecological treatment in the coastal zone of rivers and lakes51,52. By 2020, the area of water has increased by 50% compared to 1980, while many ecological security issues have been mitigated.Spatial–temporal characteristics of habitat degradationThe spatial–temporal variation of land use types were conducted to explore the variation trend of its habitat quality in Shandong Province. The InVEST-HQ was applied to obtain layers of habitat degradation in different periods. According to the interval range of 0–0.03, 0.03–0.07 and 0.07–0.18, habitat degradation was divided into three levels: slight, moderate and high degradation35,38.As shown in Fig. 5, the habitat quality in Shandong Province was dominated by moderate degradation, with the proportion of 73.30% (1980), 73.25% (1990), 72.49% (2000), 70.45% (2010) and 64.33% (2020), respectively. The spatial pattern of habitat quality was consistent with cultivated land, indicating that cultivated land who was affected by natural and anthropogenic activities exhibited moderate degradation. The proportion of moderate degradation has decreased due to cultivated land have been encroached upon for construction in the process of development, thus habitat degradation has become more and more serious. Although some of the moderate degraded areas were also converted to slight degraded areas, the area of conversion was very small compared to its conversion to high degraded areas.Figure 5Distribution map of habitat degradation in Shandong Province from 1980 to 2020.Full size imageThe proportion of slight degradation ranges from 22.38% to 24.89%, it was concentrated in the Yellow River Delta, the Central and South of Shandong Mountains, Weishan Lake and Jiaodong Hills, which was less disturbed by human activities. Compared with 1980, the proportion of slight degraded areas increased marginally in 2020, and its change was a fluctuating process. The proportion of slight degraded areas decreased from 1980 to 1990, and its proportion slowly increased from 1990 to 2020. This dynamic change process could be verified according to the spatial distribution characteristics in the Yellow River Delta. The habitat quality of the Yellow River Delta, which originally showed slight degradation, showed high degradation in 1990, 2000 and 2010.The proportion of high degradation ranges from 4.03% to 10.78%, which was concentrated in the built-up area of the city where human activities were more intensive. The proportion of high degraded areas has been increasing, indicating that the habitat has been degraded severely and its quality has declined. As the proportion of high degraded areas raised, two patterns of their spatial distribution also emerged. First spatial pattern was concentrated in urban built-up areas because of the high degree of human exploitation of land, which led to significant habitat degradation. The second pattern was a circle structure with “slight degradation” as the center and “high degradation-moderate degradation-slight degradation” outward, which was similar to the spatial distribution structure of habitat degradation in Fujian Province studied by Li et al.40. The circle structure was formed in 2010, and the distribution range was significantly expanded in 2020. The reason for the formation was that the built-up land in the city center has been severely damaged, and the possibility of re-degradation was reduced, instead showing “slight degradation”. However, the adjacent urban areas were more threatened and severely degraded, presenting “high degradation”. With the increase of distance, habitat threat and degradation decreased gradually, displaying “slight degradation”.Spatial–temporal evolution characteristics of habitat qualityThe InVEST-HQ was used to obtain layers of habitat quality in different periods. As summarized in Table 4, habitat quality was divided into five levels by the interval range: low (0–0.2), relatively low (0.2–0.4), medium (0.4–0.6), relative high (0.6–0.8), and high (0.8–1.0)35,38.Table 4 The proportion of habitat quality level at different periods in Shandong Province.Full size tableOur study concluded that the level of habitat quality in Shandong Province declined from 1980 to 2020.The results showed an overall decline of 4.75% in Shandong Province. Among them, the most significant rate of decline was observed in 2010–2020 (1.86%), which was similar to the phase change characteristics of land use types. At this period, the “Development Plan of Yellow River Delta Efficient Ecological Economic Zone” and the “Development Plan of Shandong Peninsula Blue Economic Zone” have become national development strategies. The demonstration area of “Bohai granary” and the restructuring of steel industry were carried out simultaneously. Meanwhile, the Beijing-Shanghai high-speed railway (Shandong section), Qingdao Jiaozhou Bay Bridge, Jiaozhou Bay Tunnel have strengthened the connection between Shandong Province and the outside world. As a result, rapid development has led to a rapid decline in the quality of its habitat. The rate of decline in 1980–1990 (1.43%) and 2000–2010 (1.42%) was comparable and the rate of decline in 1990–2000 was the lowest at 0.12%, which was significantly related to the development level of cities in each period. The period of 1980–1990 and 2000–2010 were in the initial and rapid promotion stages of reform and opening-up respectively. The initial stage was led by rural reform, and urban reform was launched on a pilot basis. The rapid advancement stage was led by urban reform, and economic development entered a healthy track of steady progress. Therefore, the proportion of habitat quality changes in the two periods was comparable. The period of 1990–2000 was in the exploration and transition stage of reform and opening-up, whose development process was relatively stable, resulting in the lowest rate of change in habitat quality.The average value of habitat quality in Shandong Province was 1980 (0.5091), 1990 (0.5018), 2000 (0.5012), 2010 (0.4941) and 2020 (0.4849), which decreased during the entire period. Habitat quality was dominated by medium-level throughout the whole period, with the proportion in 1980 (68.95%), 1990 (68.54%), 2000 (67.74%), 2010 (66.37%) and 2020 (65.47%). The land type in this category was mainly cultivated land (Fig. 6), which was continuous encroachment during the study period, resulting in a decrease in the percentage of medium-level habitat quality. From 1980 to 2020, the percentage of low-level habitat quality increased from 12.67% to 17.44%, and the relatively low-level decreased from 0.46% to 0.23%. The main reason was the continuous increasing of construction land and the degree of habitat threat led to the decreasing of habitat suitability. Therefore, the area of low-level habitat quality showed an increasing trend. Low and relative low-level habitat quality areas were concentrated in the urban areas of coastal and inland cities, and the Yellow River Delta. Urban areas, with a large scale of industry, commerce and population, also have a high level of urbanization. The original natural habitat has been modified during the development process, which resulted low-level habitat quality. The habitat quality of the Yellow River Delta was dynamic. The low-level pattern formed by early over-exploitation was improved in later conservation and development. The proportion of high-level habitat quality increased from 11.64% to 12.98%, and the relatively high-level decreased from 6.28% to 3.88%. In terms of spatial distribution, it was concentrated in the Central and South Shandong Mountains, Jiaodong Hills, the Yellow River Delta (2020), Weishan Lake and Wulian Mountain. These areas were dominated by mountains and well-protected water, which had high habitat suitability and were less stressed by surrounding construction land, thus maintaining high-level habitat quality. The increase of high-level habitat quality was due to the influence of water with high habitat suitability, which expanded a lot in the past 40 years, leading to the spread of high-level regional habitat quality, especially in the Yellow River Delta.Figure 6Distribution map of habitat quality in Shandong Province from 1980 to 2020.Full size imageThe value of Moran’s I was 0.3935 (1980), 0.3852 (1990), 0.4031 (2000), 0.4186 (2010) and 0.4644 (2020), respectively, which revealed that the spatial agglomeration of habitat quality in Shandong Province was characterized by agglomeration, and the trend of agglomeration increased obviously after 2000.As shown in Fig. 7, the habitat quality in Shandong Province exhibited obvious spatial heterogeneity, and spatial distribution of cold and hot spot was consistent with the topographic features. Hot spot (high-value area of habitat quality) presented “two primary and two secondary + Yellow River Delta”. Two primary hot spots distributed in the Central and South Shandong Mountains and the Jiaodong Hills, the two secondary hot spots located in Weishan Lake and Wulian Mountain. The formation of above hot spot was mainly due to high altitudes or steep slopes conferred favorable habitat quality, which was associated with the accessibility of human activities. Human accessibility at high altitudes or steep slopes was limited, so it was unlikely to cause major interference with the original environment53,54. However, the formation of other hot spot in Yellow River Delta was due to protective human activities. Cold spot (low-value area of habitat quality) was scattered in the northwestern Plain of Shandong Province, provincial capital metropolitan area and peninsula urban agglomeration which was dominated by cultivated land and built-up land in the cities that was affected by agricultural cultivation and industrial activities.Figure 7Distribution map of hot and cold spots of habitat quality in Shandong Province from 1980 to 2020.Full size imageOverall, the spatial distribution pattern of habitat quality in Shandong Province was relatively stable and affected by many factors, among which land use change was the most important one9,40,55. The most dominant land type in Shandong Province was cultivated land, which was concentrated in the northwest plain. Influenced by agricultural farming, the habitat quality of cultivated land presented medium-level category. At the same time, the habitat quality of some cultivated land has decreased due to the influence of construction land intrusion. The high vegetation coverage and rich species diversity of mountains and hills make their natural habitat quality superior. With the development of urban economy, the scale of construction land in coastal lowlands as well as inland urban areas continued to expand. The increase in population density as well as the intensity of land use activities has led to the expansion of regional dehabitatization. In addition, the dynamic changes in the habitat quality of the Yellow River Delta indicated that differences in the degree of land use change led to a variety of impacts on habitat quality. Therefore, habitat quality improvement and ecological protection should be based on local regional resource endowments and follow the concept of comprehensive, coordinated and sustainable development. Administration should formulate differentiated ecological protection strategies. For urban land development, authorities should increase the intensive utilization of construction land, limit the development boundaries of urban land and increase the greening rate inside urban land, such as equipped with urban green space park and other ecological land. In order to ensure the efficiency of agricultural production in Shandong Province, authorities should pay special attention to the conservation of cultivated land and to the development of ecological agriculture56. For natural ecosystems such as forest and grassland, authorities should improve the natural reserve system57. The vegetation ecological restoration project should be carried out according to local conditions. Drawing on the effective experience of ecological changes in the Yellow River Delta, we would take it as a typical example in future development and adopt corresponding administrative methods to coordinate the relationship between economy and habitat quality and change the dilemma of low-level habitat quality areas. Therefore, it is necessary to implement reasonable and effective territorial space planning to achieve regional sustainable development. More

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    Exploring plant volatile-mediated interactions between native and introduced plants and insects

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