Philippa Kaur

Urine spraying and scraping as potential scent-markingThe urine spraying and the scraping were reported in other felids6,20,21. In this study, only half of the other excretion instances were accompanied by sniffing, whereas almost all urine spraying and scraping events were accompanied by sniffing, indicating that these are scent-markings. The sniffing was also often observed immediately before urine spraying and scraping. Given the significant association of sniffing before excretion, especially with regard to the scraping, the presence or absence of a scent on the object was thought to be a trigger.Furthermore, during the scraping, liquid secretions thought to originate from the anal glands, were released. Domestic cats have scent glands in the anal sac22. The presence of secretions from the anal sac has also been confirmed in not only tigers, lions (Panthera leo), and bobcats (Lynx rufus), but also in cheetahs1,6,23; however, this study was the first to investigate their role in excretion. Generally, secretions are considered to be caused by health problems or estrus, but in this study, none of the individuals had health problems, and all secretions were observed only in males. Therefore, it was thought that the secretion was produced by the scent glands and contributed to a stronger smell than only urine and feces.Variations based on sexUrine spraying was observed only in adult males and females, and was more frequent in males, as reported in other felids4,5,6,9,24. In wild cheetahs, although urine spraying and scraping have been observed as scent-making, the frequency of scent-marking is known to be substantially higher in territorial than in non-territorial males and in females15,16,25, and the marking locations are concentrated in the core area of the male territories16. The territories of a single male cheetah or a male group are relatively small and exclusive, whereas the relatively large home ranges of non-territorial males (also known as “floaters”) overlap with each other and with those of females15,16. A male’s home range is also larger than that of a female15,16,26,27. Male cheetahs rarely encounter other males because they communicate via marking posts28. Given these reports, the frequent urine spraying by males may help prevent encounters between males. In addition, observations of captive cheetahs have shown a significantly positive correlation between urinary spraying frequency and fecal estradiol content in female cheetahs19. Therefore, as Cornhill and Kerley24 mentioned, female urine spraying is caused by estrus, and male urine spraying is intended as a home range marker for other males or as a sign for females.The action of scraping using the hind paws has been reported to occur in both males and females in servals, lions, tigers, black-footed cats, etc.2,5,6,7,29; however, this behavior was only observed in adult males in this study. Sunquist and Sunquist3 reported that female cheetahs also perform the scraping. In this study, we only recorded observations when the cheetahs were released in the outdoor enclosures, and not when they were in the indoor facilities. In 43.6% of the scraping events, the males excreted feces. During the observation period, the females defecated in the indoor facilities, and no defecation was observed in the outdoor enclosures. It is possible that no scraping action was observed among the females because defecation was not observed in the outdoor enclosure. In indoor facilities, the cheetahs were in a completely monopolized enclosure; hence, the females defecated in their own spaces. There was a difference in the defecation sites and frequency of scraping between the males and females; this was attributed to the sex difference in scent-marking.Differences in target height for each behaviorUrine spraying was frequently done on objects approximately 170 cm or higher, such as walls or fences, standing trees, and stumps, whereas scraping was observed on low-lying objects on the ground, such as a straw pile approximately 3 cm high and a fallen tree that was 10–50 cm high. In other words, the cheetah engaged in urine spraying and scraping depending on the object nearby. This might indicate the functional role of these behaviors. This is consistent with previous findings of urine spraying by tigers being more frequent in wooded forests than in grasslands, with few prominent objects, and scraping being more common in the latter6. In addition, in a study that investigated the place where the smell of the urine of domestic cats is likely to remain, the smell persisted for a long time on rough surfaces, areas covered with moss, and overhanging slopes30. Even for cheetahs living in the savanna woodlands, where there are comparatively fewer upright objects than in the habitat of felids living in the forest, increasing the chances of transmitting information via not only urine spraying but also by the scraping might be more important. On the other hand, in their natural habitat, there are some large carnivores like lions and leopards (Panthera pardus). Wild cheetahs tend not to visit the sites where such carnivores’ scent-mark is present31, suggesting that they might confine their marking to specific sites devoid of other carnivores’ scent. Further research is needed to determine how wild cheetahs use urine spraying and scraping. In this study, scraping was frequently observed even on tall stumps and rocks if they were within the cheetahs’ reach. Scraping by wild cheetahs has been also observed on trees32. Zoos other than Zoo C had few prominent horizontal objects. Therefore, the presence of straw piles, fallen trees, stumps, and rocks may have elicited the scraping.Differences in housing conditionsIn zoos C and D, where animals shared enclosures, the frequency of both urine spraying and scraping by males was higher than in the males in the monopolized enclosures. They possibly showed a more frequent scent-marking to strengthen their home range claims when sharing the exhibition space15. Regarding the scraping, Zoo C had at least four low and horizontal objects (straw piles, fallen tree, stones, and rocks), and scraping was frequently observed. As mentioned above, the placement of objects might have elicited the scraping.In this study, the frequency of urine spraying decreased when the submissive individual (Male 17) was released in the enclosure where the dominant individual (Male 13) was previously released. Among wild cheetahs, territorial males have been reported to mark their territories more often than non-territorial males17,25. Therefore, the difference in the number of markings is considered to be related to whether or not the target individual is within the territory, and it is highly possible that the dominant/submissive relationship between males at that location has an effect on marking.Function of scraping using hind pawsOther felid studies have reported scraping in tigers, pumas, jaguars, clouded leopards, and small felids6,10,20,21,32,33; however, there are fewer studies on different types of scraping. In certain species, such as jaguars and pumas, scraping using hind paws is more frequent than urine spraying33. From this study, the use of secretions was confirmed in the scraping, and it was considered to be a significant marking of the cheetah.The possible functions of scraping include: (1) dispersing the smell of excrement, (2) placing the smell of excrement on the hindlegs, (3) smearing the objects with excrement, and (4) adding the scent of the hind paws. Domestic cats are known to cover their feces with soil34; however, in this study, the cheetahs did not cover the feces with soil and were not observed to scrap only after excretion. Therefore, scraping using hind paws was not meant for concealing urine and/or feces. The results of this study suggest that the scrapings were mostly performed during and after excretion for any of the aforementioned functions. However, 43.2% of the observed scraping events were performed before excretion, and in these cases, the functions 1–3 did not apply, since we did not observe the feces being crushed by scraping the hind paws. As for function 4, domestic cats have sweat glands on the soles of their feet that are thought to retain their smell35. Therefore, the sweat glands on the soles of the feet of the cheetahs possible retain the smell of the hind paws as well. Schaller36 reported that among tigers, scraping on the grassland was exhibited by scratches in the grass and exposure of the ground, creating a visual effect. In the case of cheetahs, scraping may have the function of creating grooves and ridges on the ground to enhance the visual effect; however, the formation of grooves and ridges were not observed in this study. In certain cases, they scraped against trees and stones. Because trees and stones are not easily deformed, it is hard to say whether the visual effect was enhanced by scraping with their hind paws.Scraping has been reported in other felids; however, the movement of the hindlimbs is not uniform. For example, in the case of bobcats, behaviors such as kicking back on the ground with no surrounding objects and scattering of soil have been observed during scrapings20. The snow leopard slowly moves its hindlimbs on the ground near the rocks, exposing the ground; in fact, Schaller29 observed a tiger scraping its hind paws to create a pile of soil [37; Kinoshita, personal communication: Online Resource 3; Scraping of snow leopard]. The movement of urine spraying also varies among species. For example, bobcats sometimes squat and urinate on the ground20, and snow leopards rub their cheeks against the target object and then spray urine9, but cheetahs do not rub their face before urine spraying. Hence, even in the same behavior of “spraying/scraping,” the actions differ. Because felids are widely distributed in various environments, such differences in movements are possibly related to differences in habitat and behavioral functions.In conclusion, urine spraying and scraping using hind paws were considered scent-markings because they were more strongly associated with sniffing than other excretion. Both behaviors were also observed only in adults; however, urine spraying was confirmed in both sexes and was more frequent in males than in females, whereas scraping was observed only in males. Also, the frequencies of both behaviors were significantly higher in males kept in shared enclosures containing other individuals than in males kept in monopolized enclosures, while there was no difference in the frequencies among females. Hence, there were sex differences in these scent-markings possibly because of the difference in the sociality between the sexes even in captivity; the frequency of scent-markings was affected by the living environment including the number of target objects; urine spraying was frequently done on tall objects such as walls or fences, whereas scraping was more commonly done on low-lying objects near the ground, such as straw piles. To our knowledge, this study is the first to confirm that during the scraping a liquid other than feces and urine was secreted, presumably from the anal glands. Taken together, the results can serve to enhance our knowledge regarding the behavior of cheetahs, help improve management of these animals in captivity as well as breeding and animal welfare ex situ conservation, and help elucidate the kind of habitat that should be preserved for the in situ conservation of cheetahs. More

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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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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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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