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We confirm that Cuba is home to the world’s largest European honeybee population that has naturally become Varroa-resistant, with an estimated 220,000 colonies being maintained without any form of chemical treatment for over two decades19 although some drone-trapping occurred during the early years of the transition period This is despite the presence of the K-haplotype of the mite20 and the widespread occurrence of DWV19 throughout Cuba. Hence, the Cuban honeybee population is the first major case of Varroa-resistant European bees occupying an entire country of a large size (109,884 km2). In Europe the proportion of varroa-resistant honeybee populations in each country is highly variable21,22, but they still consist of small, isolated populations within any country. For example, the second largest known area of European Varroa-resistant honeybees is in North Wales, UK where 104 beekeepers have managed around 500 honey bee colonies over an area of 2500 km2 without treatment for over a decade23.It has long been established that sub-Sharan African and Africanised honeybees are Varroa-resistant and both populations cover much larger areas than Cuba, but these honeybee races are not capable of thriving in temperate regions or are rejected by beekeepers in Northern hemispheres. However, previous studies on African/Africanised and European honeybees4,5,6,9 all appear to have evolved with the same resistance mechanism7 and Cuban honeybees follow this pattern showing high recapping behaviour, high mite removal behaviour and low mite reproduction (Figs. 1, 4, Table 1).The strongest evidence that increased recapping behaviour is a direct response to the presence of Varroa, is the very low recapping rates in Varroa-naïve colonies. This is evidenced by the recapping baseline data that has now been collected from four different Varroa-naïve (Varroa free) honeybee populations (Australia, UK [two populations] and Hawaii [this study]) all producing similar results (Fig. 1). Across the four populations, a total of 9542 worker cells from 15 colonies have been studied with an average recapping rate of 2.0% (+ SD 3.2). Interestingly, only two of the colonies had atypical recapping rates of 8.5% and 10.7%, from Australia and Kauai respectively. This may suggest increased sensitivity in these colonies as no obvious causes e.g., wax moth or dead pupa, were detected in either colony. The data summary in Fig. 1 indicates that even in Varroa-treated populations the workers are still able to detect mite infested cells, but the average consistently falls significantly below that found in resistant populations. That is, in non-infested worker cells recapping rates are significantly higher in resistant populations in comparison to susceptible populations (Fig. 1) t4, 5 = − 4.185, p = 0.0023 as well as for infested cells t4, 5 = − 6.905, p = 0.00007.The ability of Cuban honeybees to detect infested cells causes not only high recapping levels but also high removal rates of artificially mite-infested cells. A mean removal rate of 81% is among one of the highest recorded in Apis mellifera7. The average control rate of 45% is driven by three colonies that all removed more than 75% of the controls, while the average of the remaining seven colonies was 28%. During the mite-removal studies in March 2022 natural Varroa infestation was 23%, whereas in December 2021 it was only 13%. This is due to decreasing worker brood rearing, caused by a shortage of nectar during the annual dry season. During this time there is an increase in hygienic behaviour in the colonies24, which could help explain the higher-than-expected removal of control cells.The reproductive ability of Varroa to produce viable i.e., mated, female offspring (r) in infested worker cells in resistant colonies in South Africa4 (r = 0.9), Brazil4 (r = 0.8), Mexico18 (r = 0.73), Europe3 (r = 0.84) is similar to the 0.87 found in Cuba (this study). In Cuba ‘r’ reduces to 0.77 when both single and multiple infested cells are considered. This reduction in mite reproduction, relative to susceptible colonies that have values of r greater than one, is directly linked to the increased ability of resistant workers to both detect and remove, by cannibalisation, the infested pupa. Hence, this ensures the invading mite fails to reproduce7 or reduces mite fertility due to the recapping process4. Although, in this study no significant difference was found in the reproduction of Varroa in recapped or non-recapped cells, supporting the findings of two previous studies5,9. Therefore, recapping may be playing a minor role in resistance. However, recapping remains the best indicator or ‘proxy’ of resistance within the vast majority of honeybee populations since it’s easier, quicker, and it requires less skill to measure recapping rates than mite removal rates. However, recapping is a highly variable trait7, hence both many cells (200–300) per colony and many colonies ( > 10) per population ideally need to be studied to help reduce the variablity, also in temperate countries measuring recapping when mite-infestation rates peak in autumn maximises detecting infested cells since the recapping of cells is spatially associated with infested cells11.Despite the current focus on what is happening in worker cells, studies focusing on the role of recapping in drone brood are still in their infancy with. Currently, data is only available from South Africa9 (Fig. 1) and now Cuba (this study). Interestingly, both studies indicate no significant difference in recapping rates between infested and non-infested brood. This is caused by some colonies performing no recapping of drone brood, while some colonies do recap cells but in a non-targeted manner. Whereas there is a significant increase in the size of the recapped area between infested (3.1 mm) and non-infested (2.3 mm) worker cells (Fig. 3), this does not occur in drone brood, as it appears that the holes are entirely exploratory. However, the lack of removal of infested drone brood may be playing an important role in mite-resistance (see below).The mite infestation of worker cells currently varies between 23 and 13% in Cuba (this study), roughly 25 years after it was first detected (1996). Whereas, in Mexico and Brazil, infestation rates of worker brood have fallen from around 20% in 1996/1999 down to 4% in 2018/197. Although, Varroa was first detected in Brazil much earlier, in 197225 and the Africanised honeybees adapted to the mite and spread northward replacing the susceptible European colonies. Therefore, we predict that the worker infestation rate in Cuba will continue to fall over the next 20 years, especially if high mite-removal rates persist. Correspondingly, we would expect to see the infestation rates of the drone brood (currently at 40%) to remain high as mites potentially avoid reproduction in worker cells. This potentially is a key, but currently overlooked part, of the resistance mechanism. Since an empirical model26 indicated that negative mite population growth occurs in (resistant) Africanised honeybee colonies only when the initial drone cells are present. This is thought to arise because mites also show a tenfold preference to reproduce in drone cells (which comprises only 1–5% of all the honeybee brood) and they soon become overcrowded as the mite population increases. This leads to inter-mite competition for the limited food and space, causing an increase in mite mortality27, resulting in negative reproductive success for mites entering these overcrowded drone cells. Thus, mite population growth in drone brood cells is limited by a density-dependent mechanism. In Cuba it has been observed that strong colonies typically with drone brood do not weaken during the drought season, whereas colonies without drone brood are weak and often die during the drought (APP personal comm).Although Cuban beekeepers have been aware of their mite-resistant honeybees for 15 to 20 years’, Cuba’s situation has only recently come to light16,18. The main reason for Varroa-resistance in Cuba is due to the centralised decision to allow natural resistance to evolve, as also was done successfully in South Africa3, rather than becoming locked into using miticides, as has happened throughout the Northern hemisphere. The CIAPI and Veterinarian Services central decision to ‘not treat’ was greatly assisted by all Cuban beekeepers being professional, registered and embedded within a strong locally based beekeeping community where colony movement and exchange of queens is within each province.There is also a large feral population and due to Cuba’s sub-tropical climate, queens are replaced annually in managed colonies because of almost continuous egg-laying, similar to honeybees in Hawaii. This rapid queen turnover speeds up natural selection relative to honeybee populations in more temperate climates. Finally, Cuba’s 60-year ban on honeybee importation has helped isolate the country from been invaded by Africanised bees which has occurred in many nearby regions (eg. Mexico, Southern USA, Puerto Rico, neighbouring Dominican Republic13 and Haiti (D. Macdonald, Apiary Inspector, Min. of Agi BC, Canada, pers. Comm.). Cuba has many managed European colonies coupled with many queen rearing stations. These colonies are productive and mild mannered. Thus, Cuba is an excellent example of the power of natural selection in honeybees when they are allowed to adapt naturally to Varroa with minimal human interference. More

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Iron and carbon dependent mutualism between Chlorella sorokiniana and Ralstonia pickettii forms a synthetic phototrophic communityThe synthetic microalgal-bacterial community based on the active exchange of iron and carbon was developed by screening multiple siderophore producer bacteria and dye decolorizer algae (Fig. 1; refer to Supplementary Data S1 for detailed results). Out of seven bacterial isolates obtained from untreated textile wastewater, five showed relatively high siderophore production in CAS agar plates and broth (Fig. S1). In broth, Serratia plymuthica PW1, Serratia liquefaciens PW71, and Ralstonia pickettii PW2 produced siderophores in decreasing order of concentration, i.e., 15.26 ± 1.3  > 13.28 ± 0.9  > 10.85 ± 0.7 µMmL−1 (Table 1). Arnow’s assay confirmed that S. plymuthica PW1 (81.10 ± 9.8 µMmL−1), R. pickettii PW2 (97.43 ± 16.8 µMmL−1), and S. liquefaciens PW71 (103.1 ± 8.3 µMmL−1) produced catecholate-type siderophores. On the other hand, Csaky’s assay confirmed that Stenotrophomonas maltophilia PW5 (37.86 ± 0.4 µMmL−1) and Stenotrophomonas maltophilia PW6 (17.73 ± 0.2 µMmL−1) produced hydroxamate-type of siderophores. Out of the five algal species, only freshwater microalgae Chlorella sorokiniana and Scenedesmus sp. showed the highest dye degradation potential; therefore, they were selected for further experiments (Data S1).Fig. 1: The study design explains different stages of experiments to develop a phototrophic community of previously non-associated algae and bacteria.The stages include (A) isolation of bacterial strains from textile wastewater collected from Panipat Industrial area, Haryana (India); B cultivation of freshwater and marine algal strains; C assessment of siderophore production in bacterial strains using Schwyn and Neilands’s universal Chrome Azurol S (CAS) assay; D assessment of dye degradation potential of algae strains using Acid Black 1 (AB1) dye; E interaction study between siderophore producing bacteria and dye degrader microalgae to identify bacterial strains that could sustain on algae-derived DOM secreted in algal exudates; F algal-bacterial co-culturability assessment to study different types of microbial interactions viz. antagonism, mutualism, or no interaction between the two organisms, and G identification of algal-bacterial model phototrophic community based on the active exchange of iron and DOM (refer to Data S1 for detailed results).Full size imageTable 1 Characterization of siderophore production in bacterial strains isolated from textile wastewater.Full size tableAfter that, the sterile exudates from C. sorokiniana and Scenedesmus sp. were used as the sole source of dissolved organic matter for bacterial growth and selection of appropriate microalgal-bacterial partners comprising the phototrophic community (Fig. 1E; Data S2). All five bacterial isolates grew well on the exudate of C. sorokiniana as a sole source of carbon. On the contrary, on exudates of Scenedesmus sp., S. plymuthica PW1 showed moderate growth in 20 h, while the growth of R. pickettii PW2 and S. liquefaciens PW71 remained insignificant. S. maltophilia PW5 and PW6 failed to grow in the exudate of Scenedesmus sp. (Fig. S2B).Finally, the compatibility between the phototrophic community of selected microalgae (C. sorokiniana/ Scenedesmus sp.) and siderophore-producer bacteria (S. plymuthica PW1/ R. pickettii PW2/ S. liquefaciens PW71) was tested by co-culturing them in iron limiting BBM media (BBM-Fe; without EDTA) (Fig. 1F). In the absence of EDTA, Fe precipitates rapidly as iron oxyhydroxides and becomes unavailable to microbes. Microalgal growth curves in co-culture assays were used to measure and compare population characteristics such as carrying capacity ‘k’, growth rate ‘r’, etc., in axenic and consortium setups. Algal growth parameters in co-culture with a bacterial partner were used to categorize their interaction as putative mutualistic, antagonistic, and neutral (Data S1, Tables S1 and S2) [42]. Under iron-limiting conditions, axenic C. sorokiniana experienced iron stress as the cell growth was 4.2 ± 0.4 × 106 cells mL−1 after 200 h incubation. On the other hand, axenic Scenedesmus sp. showed a significantly higher growth (11.3 ± 1.2 × 106 cells mL−1) than C. sorokiniana suggesting an effective iron uptake mechanism under iron-limiting conditions (k; t-test, p = 0.001) (Table S1). In contrast to the axenic microalgal culture, C. sorokiniana in co-culture with R. pickettii PW2 showed a significant increase in cell count at 200 h (6.2 ± 0.85 × 106 cells mL−1) (auc; p = 0.000). However, S. plymuthica PW1 exerted a negative effect on C. sorokiniana (Fig. 2A), as indicated by its significant increase in doubling time (p = 0.009) and reduction in auc (p = 0.001) (Fig. 3A). While S. liquefaciens PW71 remained neutral to C. sorokiniana (auc; p = 0.430) (Fig. 2A, Table 2). On the other hand, the interaction of Scenedesmus sp. with both R. pickettii PW2 and S. liquefaciens PW71 was neutral, while S. plymuthica PW1 showed a negative effect (Figs. 2A and 3A).Fig. 2: Assessment of algal and bacterial growth in co-culture experiments.A The growth curves represent the difference in the growth of C. sorokiniana when grown axenically or in co-culture with S. plymuthica PW1, R. pickettii PW2, and S. liquefaciens PW71 under iron limiting conditions. Whereas, the effect of bacteria on the growth of Scenedesmus sp. was less prominent. The difference in the CFUs of bacterial strains in axenic culture and co-culture suggests the growth-promoting effect of C. sorokiniana on S. plymuthica PW1 and R. pickettii PW2. B Anion-exchange chromatography suggests a difference in the glycosyl composition in the EPS of C. sorokiniana and Scenedesmus sp. C The area under curve (auc) of S. plymuthica PW1 and R. pickettii PW2 obtained after growth curves in different sugars. Here, ‘a’, ‘b’, etc., represent grouping after Tukey’s post hoc test.Full size imageFig. 3: Assessment of algal growth parameters in the algal-bacterial phototrophic community under iron-limiting conditions.A The confidence interval plots represent the significant difference in the growth parameters i.e., growth rate ‘r’, carrying capacity ‘k’, doubling time ‘Dt’, and area under curve ‘auc’, of C. sorokiniana (left panel) and Scenedesmus sp. (right panel) in algal-bacterial co-cultures w.r.t. to axenic culture (horizontal blue dashed line). The symbols ‘*’ and ‘**’ represent p values with statistical significance of ‘p  More

Our model results indicate that in species with a strict social dominance hierarchy where social rank is determined by nepotism, a beneficial mutation occurring in a low-ranking female is not very likely to get established. This outcome emerged despite the immense advantage of the modeled mutation, which doubled its carrier’s survival probability. Moreover, the reproductive skew in our model (see Supplementary Fig. 1) was less radical than the skew reported for the spotted hyena females21, which means that in the model, low-ranking females had a relatively higher reproductive success potential than in reality. In other words, our model may be underestimating the severity of the negative selection a low rank induces.It is reasonable to assume that a low-ranking mutant female in a female dominant society would produce very few surviving offspring due to her low rank and ensuing lack of access to resources. Thus, this female would have only a slight chance to transmit the mutation to the next generation. If this female does reproduce successfully and produces a female which also inherits the mutation, chances of this daughter to pass on the mutation are also slim, as her rank would be even lower than that of her mother. However, if the young produced is a male and has inherited the mutation, chances of transmitting the mutation may increase depending on the male’s reproduction odds. As demonstrated by the four scenarios, the reduction in mutation establishment with decreasing mutant female’s rank became more and more prominent with increasing restrictions on male reproduction. In all four scenarios, the mutation establishment rate median was zero for the lowest ranking mutants, and in all cases but Scenario I, it was 41. Although female dominance hierarchy exists in a few of these species (e.g., Peruvian squirrel monkey41, ring-tailed lemur (Lemur catta)39,42, Verreaux’s sifaka (Propithecus verreauxi))13,25, we did not find any studies indicating female reproductive skew in any of them. Holekamp and Engh25, who reviewed the more classical female dominant species, also reported no evidence for female reproductive skew.This seemingly lack of female reproductive skew among most female dominant species is quite surprising in light of the rather common correlation between social rank and female reproductive success in male dominant species. To mention a few, considerable female reproductive skew is found in baboons (Papio spp), macaques (macaca spp.), feral horses (Equus caballus) and plains zebras (Equus burchelli)8,15,19.Holekamp and Smale28 state that “reproductive skew among female spotted hyenas appears to be greater than that documented among females of male-dominated species characterized by plural breeding”. They suggest that the key determinant of reproductive success among females in this species is rank-related priority of access to food resources. This high priority is reinforced by female dominance over males and is particularly important as this species resides in an environment in which prey availability is seasonal and scarce at times21. Our study suggests that this extreme difference in reproductive success, which, unlike in male-dominated species, is determined by nepotism rather than by physical characters, may induce a handicap on the entire population preventing the establishment of beneficial mutations. This may also hinder adaptation to a changing environment. However, our study results indicate that male equal access to females may, at least partially, counter the inhibition effect on a beneficial mutation establishment. More research is necessary in order to investigate female reproductive skew in species with a social structure similar to that of the spotted hyena, which is characterized by female dominance over males, plural breeding, and a strict female linear social hierarchy determined by nepotism.One intriguing possibility for testing this model’s validity would be an empirical study, provided that the value of some adaptive trait can be measured. In the case of the spotted hyena such a trait may refer to hunting success or physical capabilities. It is well established that adult female spotted hyenas are larger and more aggressive than adult males21, but little attention has been allocated to the study of individual physical differences among females of different ranks. Smith et al.43 studied within clan aggression in the context of the fission-fusion behavior characterizing the spotted hyena clans. Their results indicate more frequent aggression and resulting fissions occurring during times of food shortage. Rank was found to be the major correlate of an aggressive incident result. If it is possible to identify low-ranking females with some beneficial trait (independent of rank), it would be interesting to follow such females’ inclusive reproductive success along time, and even more so, the reproductive success of their sons.Another possible path around the conflict this model suggests would be through the selection of male admission into new clans. Male admission into clans is often constrained by severe aggression of resident immigrant males which may prevent or delay male admission21,26. Such behavior may in fact promote mutant male chances, at least in the case of a mutation that improves physical capabilities.One last, though not very likely possible detour around this difficulty is the occurrence of dominance rank exchanges. Such rank improvements are not very common among female dominated societies, except for in the case of aging females who may clear the way for their daughters44. However, Straus and Holekamp44 found that individuals who repeatedly form coalitions with their top allies are likely to improve their position, and, according to Strauss and Holekamp44, “facilitate revolutionary social change”. It should be kept in mind that not only are such incidents rather rare, but they are unlikely to turn a very low-ranking female into a high-ranking one, especially not when group size is large.More empirical and theoretical research should shed more light on this intriguing question of possible maladaptive evolution. Our model, in line with a few other models such as that of Holman31, suggests that evolution may not always lead to the best solution. As in every process, a local optimum may get evolution trapped and prevent further advance to better optima. More

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

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