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    Farm typology of smallholders integrated farming systems in Southern Coastal Plains of Kerala, India

    Characterization of farm typesThe principal component analysis (PCA) resulted in extraction of the first three principal components (PCs) based on eigen-value criterion (eigen-value  > 1) (Fig. 2A) explaining about 87% of the variability in surveyed farm households (Fig. 2B). The first principal component (PC 1) explained the greatest part of the variation, about 43.1% of the variability in surveyed farm households. PC 1 was more closely related to the variables describing the use of farm machinery, land area foodgrain, and income foodgrain. (Fig. 1A and Fig. 2C). The second principal component (PC 2) explained 27.1% of the variability in surveyed farm households and was strongly associated with land area fruit and vegetable, income fruit and vegetable, income on-farm, expense all farm enterprises (Fig. 1A and Fig. 2C). The third principal component (PC 3) explained 16.8% of the variability in surveyed farm households and described land area fodder, income fodder (Fig. 1B and Fig. 2C). Thus, the first three principal components explained the use of farm machinery, land use, income, and expense of farm households, giving insight into the production objective of households. The results from hierarchical clustering suggested a four-cluster cutoff point (Fig. 3A and Fig. 3B) and the non-hierarchical clustering assigned households to identified clusters (Fig. 1C and Fig. 1D). Thus households of the study area could be grouped into four farm types contrasted by their structural characteristics that describe resource endowment and functional characteristics that describe livelihood strategies. Traditionally, farm households were divided into four categories based on the size of their land holdings: marginal, small, medium, and large farmer19. The typologies created in this study are based on the possession of resources such as crops and animals, as well as decisions made by them regarding crop and livestock rearing. Based on structural factors, cropping system, livestock owned, source of income, and differences among different farm households, our study divided the farm households into four farm types. The similar type of categorization was done for smallholder’s farms in Indo‑Gangetic Plains of India20.Farm type-1. Resource constraint households with low farm income (n = 93, 46.5%): Farm type-1 was the largest cluster of sampled farm households, distinguishable from other farm types by smallest land owned by household (Table 1). The cropping system dominated by plantation crop, had fruits and vegetables. Nearly half of fruits and vegetables as sole crops and the rest are intercropped in coconut. The livestock system exhibited a low abundance of large ruminant and a high abundance of poultry, average ownership was limited to the isolated presence of cattle and 25 poultry. Egg production was highest among farm types. On-farm income were the lowest among farm types. Crop produce sales were the main source of on-farm income 76%, complemented by income from livestock 24%. Furthermore, the production cost of ₹69,000 was the lowest among farm types. Due to variables such as fluctuating commodity prices, labour shortages during peak agriculture season, farmers’ concentration shifted to adoption of few enterprises as a result of land fragmentation and economic liberalization in the 1990s21,22. These variables have had a significant impact on resource constraint farm types.Farm type-2. Resource endowed diversified households with high farm income (n = 25, 12.5%): Farm type-2 exhibited the smallest cluster of sampled farm households, mostly dominated by fruit and vegetable, plantation crop (Table 1). Nearly one-fourth of fruit and vegetable as the sole crop and the rest are intercropped in coconut in upland. Complementary and supplementary enterprises viz. apiculture, pisciculture, nutritional kitchen garden, agro-processing, and value addition generated income ₹5,010 which was substantially high in this cluster. Livestock production centered around a moderate abundance of large ruminant and moderate abundance of poultry, average ownership of 1 cattle and 17 poultry. This cluster had the highest on-farm income ₹1,25,600 among farm types. Crop produce sales provided 63% of on-farm income, complemented by income from livestock 33%. Moreover, the production cost of ₹2,02,000 was relatively high among farm types. These farm households adapted crop diversification. Diversification is a method for making better use of land, water, and other resources by growing more profitable crops. It allows farmers to choose which crops to grow on their farm in order to maximize returns, and most farmers grow multiple crops to reduce risk and uncertainty caused by climatic and biological fluctuations23. Diversification refers to switching from less profitable and non-sustainable crops to more profitable and long-term crops. It has emerged as a viable option for ensuring natural resource sustainability, ecological balance, job creation, and risk generation24.Farm type-3. Resource endowed mechanized households with low farm income (n = 43, 21.5%): Farm type-3 comprised of sampled farm households distinguishable from other farm types by the largest cropped area under foodgrain (Table 1). The foodgrain area dedicated to rice cultivation was located mostly in wetland, while the plantation crop area largely established with coconut was on paddy field bunds and in the garden land. Livestock production centered around a moderate abundance of large ruminant and low abundance of poultry, average ownership of 1 cattle and 5 poultry. This cluster had an on-farm income of ₹63,300, the main source being crop produce sales 58%, complemented by income from livestock 42%. Besides, the production cost of ₹1,79,000 was relatively high among farm types. In these farm households the farm mechanization has brought significant change in the livelihood. Especially, paddy field preparation through puddling, mechanical transplantation, and paddy combine harvester reduced the greater dependence of external labourers. The relative shortage of agricultural workers, and the comparatively high wage rate in agriculture has bought small and large scale mechanization in Kerala agricultural system21.Farm type-4. Resource endowed medium farm income households with livestock dominance (n = 39, 19.5%): A main distinguishing feature of sampled farm households in farm type-4 was the largest fodder area among farm types, established mostly in coconut garden (Table 1). A considerable number of households had a foodgrain area of in wetland, mainly dedicated to rice cultivation. The livestock system exhibited a high abundance of large ruminant and low abundance of poultry, comprised mostly of milch animal, average ownership of 2 cattle and 2 poultry. Milk production 3.84 × 103 L/year was the highest among farm types. On-farm income was ₹84,100. The main income source was livestock which constituted 65% of on-farm income, complemented by income from crop produces 35%. Production cost ₹1,54,000 was relatively high among farm types. These farmers adapted livestock has their source of livelihood and alternate means of employment especially farm women’s. The major benefit of livestock components like cattle and poultry is that they provide regular income to sustain farm family and also they provide nutritional security. Crossbred cattle adoption and crossbred milk output are important factors in increasing livestock revenue. To increase income from animal sources, a crossbreeding strategy should be implemented25.Farming system patternsDistinguishing characteristics of a farming system are highly location-specific, depend on adaptive strategies devised by farmers to cope with the adverse situations as well as take advantage of the potential opportunities for intensification and diversification of agriculture at the household level. Studies have shown that farmers come up with strategies to get along with adverse situations viz. volatile price, crop failure, flood, drought, declining soil fertility, land scarcity, climate change and also make use of potential opportunities viz. use of new technologies, value addition, which allowed for sustainable production and income10,26,27,28. These distinguishing characteristics of a farming system are discussed in relation to clustering variables grouped according to the theme, their interrelationships, and the identified farm types in the following sections.Farm household: The basic unit of social organization is the farm household where the head, typically a male lives with his nuclear family most often in a concrete roofed house. Farm households residing in traditional clay tile-roofed houses are also found occasionally. Farm households had an average size of four members (Table 1). Households were headed by the oldest male member aged 60 years. Both household size and age of household head remained unchanged across farm types. Land owned by households 0.42 ha is typically inherited (Table 1). Purchase is the less common access route to land ownership. Land owned by a household is commonly taken as a proxy for the wealth of a household as it correlates positively with livestock assets and crop production29. Results revealed variation in land owned by households across farm types with the smallest land 0.34 ha owned by the resource-constrained type-1 household. Interestingly, type-1 farmers accounted for a major proportion (46.5%) of farm households surveyed. The traditional practice of land owned by households typically fragmented into smaller parcels that are allocated to children at the time of their marriage, favors an increase in the number of small farm holdings. Eventually, the married children who had started in a household, leave the household with one’s spouse and consequently their children to build their own house and live separately in their inherited land, thus forming a new household. Small land holdings characterize Kerala agriculture. The core cause of poverty in Kerala is the tremendous fragmentation of agricultural land, and the fact that this fragmentation is only getting worse and is becoming a unique development issue. This current state of significant fragmentation, highlight the massive increase in the number of marginal farms as the area covered by large farms decreases30.Labour: A combination of family and hired wage labour was used for agricultural production in the study area. Family labour is comprised of individuals in a household who are related by blood and kinship. With all households having only one family member working on-farm on a full-time basis and the average household size being only four members, family labour availability is less (Table 1). Household size is commonly taken as a proxy for family labour availability thereby requiring the hiring of wage labour to deal with family labour shortage 11. Shortage of family labour is further exacerbated by one member in each household across farm types working non-farm on a full-time basis, either making a livelihood from overseas, running small businesses, or earning a salary from the service sector. The study area is located on the outskirts of the state capital, the educated youth in farm households have ample employment opportunities in the secondary sector namely construction, and in the tertiary sector namely health service, transportation, education, entertainment, tourism, finance, sales, and retail. Wage labourers were hence hired on a seasonal basis for labour-intensive activities such as land preparation, planting, and harvesting. The local wage rate for farm laborers in the study area were ₹650 and ₹600 per man-day for men and women respectively, which were the highest in the nation. For farmers and labourers, agriculture is not a reliable source of revenue and employment. Kerala’s labour distribution has shifted in favor of the non-agricultural sector, especially the service sector. Kerala has seen a significant increase in non-agricultural employment in both rural and urban areas, resulting in a shift in the workforce’s industrial distribution. The structure of rural employment in Kerala has transitioned from agricultural to non-agricultural enterprises as a result of these changes. The specialized agriculture practices and mono-cropping increased production cost, risk of crop failure, and lower market price31. Due to this, the small and marginal farmers migrated to neighboring cities in search of jobs and livelihood. In this scenario, IFS will be a solution to reduce the economic risk with improved employment generation. The continuous labour requirement for multiple crops and livestock systems provides an option for higher employment generation and keeps the farm families engaged in the farm activities. This holds good even during the COVID-19 pandemic for meeting the employment needs of reverse migrants (urban to rural). In IFS, farm activities are continued round the year, thus the farm family is effectively engaged in farm. The adoption of such systems avoids migration of farmers and rural youth to nearby cities and towns in search of contractual employment.Results showed increased use of farm machinery, 4.43 h/year in the type-3 household having a considerable land area under foodgrain (Table 1). Tractor-operated rotavator for puddling and combined harvester for reaping, threshing, and winnowing were extensively custom hired in the type-3 household. Mechanization in foodgrain cultivation was limited to custom hiring of tractor-operated rotavator for puddling in type-4 households resulting in the use of farm machinery1.40 h/year (Table 1). Brush cutter for trimming weeds, coconut tree climber for harvesting coconut, and plant protection sprayers were some of the machinery owned by a limited number of households across all farm types. The variables viz. use of farm machinery, land area under foodgrain, and net income from foodgrain sales were positively correlated, attributable to substitution of wage labourers with machines in agricultural enterprises having high work and maintenance requirements so that such enterprises remain economically viable (Fig. 1A, B; Table 1).Land use: Coconut plantation in upland and rice in lowland is the major land use. The two crop variables retained for principal component analysis (PCA) namely foodgrain area, fruit, and vegetable area, were negatively correlated to each other, suggesting that farms that dedicated large areas to field crops especially rice cultivation did so at the expense of fruits and vegetable crops especially banana, amaranth, cowpea and vice versa (Fig. 1A and Fig. 1B ; Table 1). Resource-constrained type-1 and resource endowed type-2 households exhibited the smallest cropped area under foodgrain (Table 1). The meager food grain area in type-1 and 2 households were under direct-seeded upland rice, cultivated as part of the latest efforts to diversify the existing cropping system in these households. Rice is the most widely consumed staple in the study area. The lower proportion of food grain in these households suggests that land resources had been preferentially allocated for production-oriented towards high-value crops especially fruit and vegetables (Table 1). This may be partially explained by copious non-farm income generated by type-1 and 2 households and apparent re-investment of that income preferentially for high-value crops especially fruit and vegetables.Results suggest that in resource-constrained type-1 and resource endowed type-2 households with ample off-farm and non-farm income having ensured access to market for foodgrain needs, land owned was preferentially allocated for production-oriented towards fruit and vegetables, to ensure nutritional security. It might have been otherwise utilized for land resource allocation in type-1 and 2 households had there been insufficient off-farm and non-farm income. A marginal shift from staple foodgrain to horticulture does not adversely affect food security at the household32.Resource endowed type-3 and 4 households, though had sufficient off-farm and non-farm income comparable with type-1 and 2 households, did not follow this pattern, with foodgrain area being more abundant among them. This suggested that farm households that dedicated large areas to field crops especially rice cultivation did so due to land topography favoring the prolonged presence of water creating wetlands. The rice crop residues were utilized to reduce the feeding cost of high-valued large ruminants especially cattle maintained in type-3 and 4 households (Table 1). In addition to the utilization of rice crop residues as feed for large ruminants, type-4 households had a higher proportion of land area dedicated to fodder, reducing even further their feeding cost.Livestock: The livestock species and their number owned represent the wealth of a farm household. Large ruminant cattle are the most valuable livestock. Small ruminant goats, though hardy and prolific, are less valued. Rearing of large and small ruminants is a crucial form of fortification against extreme shocks such as crop failure or medical emergency of household members, providing immediate cash. Results showed higher large ruminant ownership 1.08 LU in type-4 households (Table 1). Type-4 households recorded the highest milk production, followed by type-3 households, presumably due to higher fodder area in type-4 households leading to better feed quality and quantity, improved animal performance, and increased carrying capacity of cattle by maximizing stocking rate. The presence of state-owned milk marketing cooperative in the study area had played a role in the large ruminant ownership, due to the added advantage of assured steady market and stable milk price. Small ruminant ownership of 0.03 LU tended to be quite similar across farm types (Table 1).Households in all farm types had poultry flock kept in the traditional backyard poultry system, as a source of quick cash and protein-rich food (Table 1). The traditional backyard poultry system is characterized by an indigenous night shelter system, a scavenging system with scant supplementary feed, natural hatching of chicks, low productivity of birds, local marketing, and minimal health care practices24. Results indicated that the size of the poultry flock tended to increase as farm resource endowment decreased (Table 1). Resource constrained type-1 household exemplified this, as it had the highest poultry flock size of 0.25 LU and exhibited the highest income from poultry sales. Poultry flock size tended to be quite low and similar in resource endowed type-3 and 4 households. Backyard poultry system due to its least demanding nature in terms of infrastructure has been widely accepted by resource constrained households, enabling them to make a profit from the sale of poultry products11,33. Relatively high income from poultry sales in type-1 and 2 households represent a coping strategy to prop up household finances to access the local market for foodgrain needs. Farm households depending on traditional backyard poultry generally lacked access to adequate low-cost organic fertilizers especially farmyard manure, resulting in low productivity of crops, which may further exacerbate food insecurity28.Income: Shortfalls in agricultural production and thus agricultural income were common in the study area, compelling households to diversify their livelihoods. Sources of farm household income are on-farm, off-farm, and non-farm income34. On-farm income comprised of sales income from the crop, livestock, complementary, and supplementary enterprises (Table 1). Type-2 farm households recorded a high on-farm income of ₹1,25,600, as it befitted from a livelihood strategy of production of high valued fruit and vegetable in addition to plantation crops. Crop sales contributed 63% to on-farm income in type-2 farm households. Type-4 farm households recorded medium on-farm income ₹84,100, as it befitted from a livelihood strategy of production of fodder in addition to food grain and plantation crops. This resulted in increased carrying capacity and maximized stocking rate of large ruminant 1.08 LU. Livestock sales contributed 65% to on-farm income in type-4 farm households. Other farm enterprises viz. complementary and supplementary enterprises contributed 4% to on-farm income in type-2 farm households.The off-farm income included wages for working as hired casual labourers in farms of wealthier neighbors, wages for doing unskilled manual work under Kerala Rural Employment Guarantee Scheme (KREGS), and wages for manual work under women’s labour collectives. KREGS operating under the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) of the Government of India, provides 100 days of guaranteed employment in a year to every adult household member in need of wage employment and desire to do manual or unskilled work in and around the village. Works related to building and maintenance of canals, renovation of ponds, wells, and farmland, afforestation, etc. are undertaken under KREGS. Many women in the study area, who are homemakers had come together to form women’s labour collectives, locally known as ‘Thozil Koottam’, to take up agricultural activities related to the cultivation of paddy, banana, tubers, coconut palm, and land terracing. Once these women exhaust the 100 days of work under MGNREGS, they move out to the open market as a collective to seek work in private lands in neighboring areas. For the landowners, this meant labour availability in the local market at a reasonable rate, at a time when it had become difficult to find labourers to work. In converse, in some areas during peak agriculture season, the farmers are experiencing shortage of labour due to government’s schemes like KREGS and MGNREGS leading to increased labour wages and cost of production. In addition, reduced participation of youths in agricultural activity also led to increased shortage of labour in agricultural activity35.Non-farm income consisted of overseas remittances, running small businesses in the unorganized sector, and salary from the service sector. The proximity of the study area to the state capital provided educated youth in farm households with ample non-farm employment opportunities. Nevertheless, the dependence of farm households on off-farm and non-farm income was quite high since they contributed more than 65% to farm household income across all farm types (Table 1). Studies have shown that farm households are compelled to diversify their livelihood in times of shortfall in agricultural production36,37.Constraints to agricultural production identified for targeted farming systems interventionsThe typology results had identified four farm types based on resource endowment and livelihood strategy (Table 1). The target group is the households in a farm type who rely on research findings for ideas and strategies to improve the way they do agriculture. For solving agricultural production problems, identification of constraints that work as a bottleneck by hindering the problem-solving process is a vital step, so that targeted farming systems interventions based on research findings can be made, enabling the farm household to push against that constraint and overcome it. Research-for-development programs seeking to sustainably intensify agricultural production in the target communities should take into account the opportunities and constraints identified across the farm types and tailor their development strategies, interventions and policies accordingly 11. Cost-effective socially acceptable farming systems interventions were envisaged based on production constraints identified in farm households in each farm type, to optimize resource utilization in households within a farm type, and also to promote resource flow and interactions between farm types, to ensure the stability of existing farming systems (Table 2). Farm typologies are classifications based on a set of criteria, and farm types are generally uniform in terms of these criteria, with some intra-group variation. As a result, typologies are useful for bringing together farmers for discussion so that groups of farmers who manage their farms similarly, have similar basic goals, or have similar constraints and possibilities can be formed20,38. The following sections reflect on production constraints identified and targeted farming systems interventions envisaged in each farm type.Table 2 Constraints to agricultural production in farm types and farming systems interventions envisaged.Full size tableFarm household: Farm household is the centrepiece of the farming system. Improvements in the existing farming system involve intensification, diversification, and an increase in the operational area of the farm household. Crop-livestock farming systems are the backbone of small-holder agriculture in developing countries39. The largest share of surveyed farm households comprised of resource-constrained type-1 households 46.5% having limited access to land (Table 1). The rest of the households though had marginally higher land availability offers little scope for increasing agricultural production through land area expansion. Kerala with a high literacy rate of 94% has the highest overall life expectancy at birth, at 72 years for men and 78 years for women 40 (GoK, 2019). Household heads in all surveyed households were elderly males aged 60 years who are the decision-makers in the utilization of household land for agricultural activities (Table 1). Targeted farming systems interventions envisaged for intensification and diversification of existing farming system, therefore must be pragmatic and problem-solving to find acceptance among the increasingly aging household head, who tend to show reluctance towards drastic changes in the existing farming system.Dependence on off-farm and non-farm income was quite high among all surveyed households (Table 1). Only one out of four household members in each surveyed household were found working on-farm. Scarcity of household labour and the high cost of hired labour is likely to hamper efforts at diversification into supplementary enterprises having low-profit margins like a nutritional kitchen garden, except as part of increased awareness of health benefits to household members. Similarly, households are less likely to intensify existing rice-rice-fallow cropping system with legume cowpea in summer fallow and stop burning of crop residues in the field for clean cultivation, except as part of increased awareness about soil health and environmental pollution respectively (Table 2). Targeted farming systems interventions were therefore envisaged to be delivered through a capacity building and training program, to bring about a change in knowledge, attitude, and skill of the farm household for efficient farm operations.Foodgrain: Rice was the major foodgrain in the study area. Constraints of high severity in a type-3 household that had the largest area under food grain were low yield due to traditional variety, soil acidity, and imbalanced fertilization (Table 2). Crop loss due to pests was a constraint of high severity in type-4 households. The stale seedbed for weed management was the farming systems intervention envisaged to manage weeds in rice, which was a constraint of medium severity in the type-3 household. Farming systems intervention envisaged in summer rice fallow was raising cowpea utilizing the limited water available during the season. In general, the agricultural activity of Kerala is affected by limited water availability during winter rabi and summer season, poor soil fertility due to low nutrient holding capacity of the soil, inadequate crop protection, non-availability of quality seed material, and increased cost of cultivation. The farmers need to adapt soil test based fertilizer recommendation to meet the crop nutrient demand for reducing yield gap. Suitable pest and weed management are very much necessary to combat the crop loss. Adaption of climate resilient improved cultivars, bringing more area under irrigation, intercropping, crop rotation, and mulching are imperative to increase food grain production and to achieve food security of small and marginal farmers41.Horticulture: Banana, cowpea, cassava, and elephant foot yam were the widely cultivated fruit and vegetable in the study area (Table 2). Crop loss due to pests in banana and disease in cowpea were constraints of very high severity in type-1 households. The constraint in fruit and vegetable production due to traditional variety and imbalanced fertilization were of high to very high severity in type-2 households, which had a large area under fruit and vegetable. Raising cowpea is envisaged in farming systems interventions to utilize vacant interspaces of cassava and thus substantially lower the nitrogen fertilizer requirement of cassava. Cultivation of traditional poor-yielding turmeric varieties along with imbalanced fertilization were constraints of medium severity in the type-1 household (Table 2). Coconut was an important plantation crop in the study area, occupying the substantial cropped area in type-2 households (Table 2). Soil acidity and imbalanced fertilization were constraints of high severity in coconut in type-2 households. Crop loss in coconut due to pests was a constraint of high severity in type-3 and 4 households. Low green fodder availability due to poor yielding traditional fodder variety was a constraint of medium severity in type-2 and 3 households (Table 2). A multi-storeyed cropping system having cowpea, cassava, elephant foot yam, turmeric, banana, papaya, and fodder was the farming systems intervention envisaged to effectively utilize vacant interspaces of coconut. The Kerala state is major spice cultivating state and majority of the small, medium and large farmers are actively involved in the spice and plantation crops cultivation. The high value of spice and plantation crops is attracting rural youths also into horticulture sector, especially in processing of spices and their export to Gulf and European market. Kerala government is also promoting organic spice production to boost the local and international organic market for their products. In addition, Kerala’s home gardens are typical examples of low to medium-input sustainable agroecosystems. Home gardens are assemblages of plants, which may include trees, shrubs, and herbaceous plants that grow in or close to a homestead, are planted and managed by members of the household, and the products and services are primarily for household consumption. These home gardens are having great importance in meeting farm family food and nutritional security35.Livestock: Low milk yield in dairy cattle due to lack of awareness about mastitis infection was a constraint of high severity in type-2 and 3 households (Table 2). Raising awareness about hygiene to prevent mastitis and inclusion of mineral mixture in feeding schedule to increase milk fat content are the farming systems interventions envisaged for dairy cattle. Poor egg production in layer chicken due to rearing of non-descript desi chicken breed was a constraint of medium severity in the type-2 household (Table 2). Regular deworming was the farming systems intervention envisaged to improve livestock health in all households (Table 2). The dairy farmers of Kerala are experiencing several problems like high cost of veterinary service and medicine, high cost of cattle feed ,non-availability of green and dry fodder round the year, high labour cost, lack of need based training, non-availability of high yielding milch animals42. The government and Veterinary department of Kerala needs to address these issues to boost the livestock production and farmers income.Complementary enterprises: Complementary enterprises in a system support one another43. Vermicomposting and Azolla cultivation were the complementary enterprises envisaged in farming systems interventions. Crop residues interfering with field operations was a problem, with the farmer often resorting to burning crop residue in situ, causing loss of nutrients and organic matter to the soil. Lack of awareness about environmentally safe ways to manage crop residues was a constraint of low to medium severity in all households (Table 2). Promoting the use of crop residues for vermicomposting and as mulch in banana and coconut for soil moisture conservation were the farming systems interventions envisaged to discourage the burning of crop residues (Table 2). The establishment of the Azolla plot and inclusion of Azolla in the feeding schedule of livestock were envisaged in farming systems interventions to reduce feed cost (Table 2).Supplementary enterprises: Supplementary enterprises in a system utilize the otherwise unutilized resources43. Nutritional kitchen garden, agro-processing, and value addition were the supplementary enterprises envisaged in farming systems interventions. Fruits and vegetables for household consumption were found purchased from the local market due to production shortfall within the household, which was a constraint of low to high severity in all households (Table 2). The establishment of the nutritional kitchen garden and the growing of fruit trees in the backyard were the farming systems interventions envisaged ensuring nutritional security to the household. Encouraging farmers to take control of agro-processing and local marketing of primary production to capture the value that is added to it, thus fetching a better price for the produce, was the farming systems intervention envisaged for coconut, paddy, and milk, as per their recorded severity of constraints in respective farm types (Table 2).Importance of public distribution system (PDS) for food distributionThe Public Distribution System (PDS) was created as a way to manage scarcity and distribute food grains at low rates. PDS has evolved into a key component of the government’s food economy management strategy. PDS is a supplemental program that is not meant to meet a household’s or a part of society’s complete need for any of the commodities given under it. Historically, Kerala’s agricultural production has been directed toward cash crops, rather than food crops such as rice and wheat. As a result, the problem of food scarcity in Kerala has worsened. PDS is becoming more important in Kerala, where population density is high and farming patterns are mostly dependent on rains, with no consistent irrigation infrastructure, causing food supply availability to fluctuate over time, resulting in uncertainty. In order to avoid such situations and maintain the supply of required commodities, a PDS system is essential. Kerala’s below-poverty-line (BPL) households consume 40–55 percent of their rice through PDS. The PDS supplied a higher percentage of the rice requirements. It is also clear that rural areas have done marginally better than urban areas in terms of PDS system utilization. It is worth noting that in Kerala, about 80% of BPL households still have access to the PDS, even at various levels of utilization, thereby reducing the pressure on local farmland44. More

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    West Nile virus transmission potential in Portugal

    1.Granwehr, B. P. et al. West Nile virus: Where are we now? Lancet. Infect. Dis. 4, 547–556 (2004).PubMed 

    Google Scholar 
    2.Campbell, G. L., Marfin, A. A., Lanciotti, R. S. & Gubler, D. J. West Nile virus. Lancet. Infect. Dis. 2, 519–529 (2002).PubMed 

    Google Scholar 
    3.Petersen, L. R., Brault, A. C. & Nasci, R. S. West Nile virus: Review of the literature. JAMA. 310, 308–315 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Gamino, V. & Höfle, U. Pathology and tissue tropism of natural West Nile virus infection in birds: A review. Vet. Res. 44, 39 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Bunning, M. L. et al. Experimental infection of horses with West Nile virus. Emerg. Infect. Dis. 8, 380-386 (2002).PubMed 
    PubMed Central 

    Google Scholar 
    6.Hayes, E. B. et al. Virology, pathology, and clinical manifestations of West Nile virus disease. Emerg. Infect. Dis. 11, 1174–1179 (2005).PubMed 
    PubMed Central 

    Google Scholar 
    7.Saiz, J.-C. Animal and Human Vaccines against West Nile Virus. Pathogens. 9, 1073 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    8.Rizzoli, A. et al. Parasites and wildlife in a changing world: The vector-host- pathogen interaction as a learning case. Int. J. Parasitology: Parasites. Wildl. 9, 394–401 (2019).
    Google Scholar 
    9.Wang, Y., Yim, S. H. L., Yang, Y. & Morin, C. W. The effect of urbanization and climate change on the mosquito population in the Pearl River Delta region of China. Int. J. Biometeorol. 64, 501–512 (2020).PubMed 

    Google Scholar 
    10.Braack, L., Gouveia de Almeida, A. P., Cornel, A. J., Swanepoel, R. & de Jager, C. Mosquito-borne arboviruses of African origin: Review of key viruses and vectors. Parasites. Vectors. 11, 29 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    11.Johnson, N. et al. Emerging mosquito-borne threats and the response from european and eastern mediterranean countries. Int. J. Environ. Res. Public. Health. 15, 2775 (2018).PubMed Central 

    Google Scholar 
    12.Lourenço, J. et al. Epidemiological and ecological determinants of Zika virus transmission in an urban setting. Elife. 6, e29820 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    13.Giovanetti, M. et al. Genomic and Epidemiological Surveillance of Zika Virus in the Amazon Region. Cell Rep. 30, 2275–2283.e7 (2020).CAS 
    PubMed 

    Google Scholar 
    14.Faria, N. R. et al. Genomic and epidemiological monitoring of yellow fever virus transmission potential. Science. 361, 894–899 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Wu, J. T., Peak, C. M., Leung, G. M. & Lipsitch, M. Fractional dosing of yellow fever vaccine to extend supply: a modelling study. Lancet. 388, 2904–2911 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    16.Murgue, B., Zeller, H. & Deubel, V. The ecology and epidemiology of West Nile virus in Africa, Europe, and Asia. Curr. Top. Microbiol. Immunol. 267, 195–221 (2002).CAS 
    PubMed 

    Google Scholar 
    17.Pybus, O. G. et al. Unifying the spatial epidemiology and molecular evolution of emerging epidemics. Proc. Natl Acad. Sci. USA 109, 15066–15071 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Dellicour, S. et al. Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework. Nat. Commun. 11, 1–11 (2020).
    Google Scholar 
    19.Shocket, M. S. et al. Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23 °C and 26 °C. Elife. 9, e58511 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Haussig, J. M. et al. Early start of the West Nile fever transmission season 2018 in Europe. Euro. Surveill. 23, 1800428 (2018).PubMed Central 

    Google Scholar 
    21.Riccardo, F. et al. West Nile virus in Europe: after action reviews of preparedness and response to the 2018 transmission season in Italy, Slovenia, Serbia and Greece. Glob. Health. 16, 47 (2020).
    Google Scholar 
    22.Bakonyi, T. & Haussig, J. M. West Nile virus keeps on moving up in Europe. Eurosurveillance. 25, 2001938 (2020).PubMed Central 

    Google Scholar 
    23.Vlaskamp, D. R. M. et al. First autochthonous human West Nile virus infections in the Netherlands, July to August 2020. Eurosurveillance. 25, 2001904 (2020).24.West Nile virus in Europe in 2020 – human cases compared to previous seasons, updated 8 October 2020. https://www.ecdc.europa.eu/en/publications-data/west-nile-virus-europe-2020-human-cases-compared-previous-seasons-updated-8 (2020).25.Weekly updates: 2020 West Nile virus transmission season. https://www.ecdc.europa.eu/en/west-nile-fever/surveillance-and-disease-data/disease-data-ecdc.26.Council Directive 82/894/EEC of 21 December 1982 on the notification of animal diseases within the Community. EUR-Lex https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:31982L0894.27.European Food Safety Authority. https://www.efsa.europa.eu/en.28.European Centre for Disease Prevention and Control – West Nile virus. https://www.ecdc.europa.eu/en/west-nile-virus-infection.29.REVIVE – Rede de Vigilância de Vetores. http://www2.insa.pt/sites/INSA/Portugues/AreasCientificas/DoencasInfecciosas/AreasTrabalho/EstVectDoencasInfecciosas/Paginas/Revive.aspx.30.Osório, H. C., Zé-Zé, L., Amaro, F. & Alves, M. J. Mosquito surveillance for prevention and control of emerging mosquito-borne diseases in Portugal – 2008-2014. Int. J. Environ. Res. Public. Health. 11, 11583–11596 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    31.European network for sharing data on the geographic distribution of arthropod vectors, transmitting human and animal disease agents (VectorNet). https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net.32.Filipe, A. R. Anticorpos contra virus transmitidos por artropodos-arbovirus do grupo B em animais do Sul de Portugal: inquérito serológico preliminar com o vírus West Nile, estirpe Egypt 101. Ann. Esc. Nacional de. Saúde. Pública de. Med. Tropical 1, 197–204 (1967).CAS 

    Google Scholar 
    33.Filipe, A. R. & Pinto, M. R. Survey for antibodies to arboviruses in serum of animals from southern Portugal. Am. J. Trop. Med. Hyg. 18, 423–426 (1969).CAS 
    PubMed 

    Google Scholar 
    34.Filipe, A. R. & Campaniço, M. Encefalomielite equina por arbovírus. A propósito de uma epizootia presuntiva causada pelo vírus West Nile. Revista Portuguesa de Ciências Veterinárias LXVIII, (1973).35.Filipe, A. R. Isolation in Portugal of West Nile virus from Anopheles maculipennis mosquitoes. Acta Virol. 16, 361 (1972).CAS 
    PubMed 

    Google Scholar 
    36.Filipe, A. R. Anticorpos contra arbovírus na população de Portugal. Separata de O Médico. LXVII, 731–732 (1973).37.Formosinho, P. et al. O vírus West Nile em Portugal – estudos de vigilância epidemiológica. Rev. Portuguesa de. Ciências Veterinárias 101, 61–68 (2006).
    Google Scholar 
    38.Barros, S. C. et al. Serological evidence of West Nile virus circulation in Portugal. Vet. Microbiol. 152, 407–410 (2011).PubMed 

    Google Scholar 
    39.Almeida, A. P. G. et al. Potential mosquito vectors of arboviruses in Portugal: Species, distribution, abundance and West Nile infection. Trans. R. Soc. Trop. Med. Hyg. 102, 823–832 (2008).CAS 
    PubMed 

    Google Scholar 
    40.Esteves, A. et al. West Nile virus in Southern Portugal, 2004. Vector Borne Zoonotic Dis. 5, 410–413 (2005).PubMed 

    Google Scholar 
    41.Barros, S. C. et al. West Nile virus in horses during the summer and autumn seasons of 2015 and 2016, Portugal. Vet. Microbiol. 212, 75–79 (2017).PubMed 

    Google Scholar 
    42.World Organization for Animal Health (OIE) – West Nile reports. Information received on 03/09/2015 from Prof. Dr Álvaro Mendonça, Director General, Direcção Geral de Alimentação e Veterinária, Ministério da Agricultura E do Mar, Lisboa, Portugal https://www.oie.int/wahis_2/public/wahid.php/Reviewreport/Review?page_refer=MapFullEventReport&reportid=18585 (2015).43.Connell, J. et al. Two linked cases of West Nile virus (WNV) acquired by Irish tourists in the Algarve, Portugal. Weekly releases (1997–2007) 8, 2517 (2004).44.Alves, M. J. et al. Infecção por vírus West Nile [Flavivírus] em Portugal. Considerações acerca de. um. caso cl.ínico de. s.índrome febril com. exantema 8, 46–51 (2012).
    Google Scholar 
    45.Zé-Zé, L. et al. Human case of West Nile neuroinvasive disease in Portugal, summer 2015. Eurosurveillance 20, 30024 (2015).
    Google Scholar 
    46.Direcção-Geral de Veterinária (Directorate-General of Veterinary). National statistics on official number of equines in subregions of Portugal. http://srvbamid.dgv.min-agricultura.pt/portal/page/portal/DGV/genericos?actualmenu=23555&generico=33698230&cboui=33698230.47.Osório, H. C., Zé-Zé, L., Amaro, F., Nunes, A. & Alves, M. J. Sympatric occurrence of Culex pipiens (Diptera, Culicidae) biotypes pipiens, molestus and their hybrids in Portugal, Western Europe: feeding patterns and habitat determinants. Med. Vet. Entomol. 28, 103–109 (2014).PubMed 

    Google Scholar 
    48.Gottdenker, N. L., Streicker, D. G., Faust, C. L. & Carroll, C. R. Anthropogenic land use change and infectious diseases: A review of the evidence. Ecohealth. 11, 619–632 (2014).PubMed 

    Google Scholar 
    49.Paz, S. & Semenza, J. C. Environmental drivers of West Nile fever epidemiology in Europe and Western Asia–a review. Int. J. Environ. Res. Public. Health. 10, 3543–3562 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    50.Eisen, L. et al. Irrigated agriculture is an important risk factor for West Nile virus disease in the hyperendemic Larimer-Boulder-Weld area of north central Colorado. J. Med. Entomol. 47, 939–951 (2010).PubMed 

    Google Scholar 
    51.Gates, M. C. & Boston, R. C. Irrigation linked to a greater incidence of human and veterinary West Nile virus cases in the United States from 2004 to 2006. Prev. Vet. Med 89, 134–137 (2009).PubMed 

    Google Scholar 
    52.Kovach, T. J. & Kilpatrick, A. M. Increased human incidence of West Nile virus disease near rice fields in California but Not in Southern United States. Am. J. Trop. Med. Hyg. 99, 222–228 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    53.Rocheleau, J. P. et al. Characterizing environmental risk factors for West Nile virus in Quebec, Canada, using clinical data in humans and serology in pet dogs. Epidemiol. Infect. 145, 2797–2807 (2017).CAS 
    PubMed 

    Google Scholar 
    54.Lourenço, J., Thompson, R. N., Thézé, J. & Obolski, U. Characterising West Nile virus epidemiology in Israel using a transmission suitability index. Euro Surveill. 25, 1900629 (2020).PubMed Central 

    Google Scholar 
    55.Obolski, U. et al. MVSE: An R-package that estimates a climate-driven mosquito-borne viral suitability index. Methods Ecol. Evol. 10, 1357–1370 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    56.Petrone, M. E. et al. Asynchronicity of endemic and emerging mosquito-borne disease outbreaks in the Dominican Republic. Nat. Commun. 12, 151 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Hansen, B. B., Grøtan, V., Herfindal, I. & Lee, A. M. The Moran effect revisited: spatial population synchrony under global warming. Ecography 43, 1591–1602 (2020).
    Google Scholar 
    58.Arizaga, J. et al. Migratory Connectivity in European Bird Populations: Feather stable isotope values correlate with biometrics of breeding and wintering BluethroatsLuscinia svecica. Ardeola. 62, 255–267 (2015).
    Google Scholar 
    59.Pakanen, V.-M. et al. Migration strategies of the Baltic dunlin: Rapid jump migration in the autumn but slower skipping type spring migration. J. Avian Biol. 49, jav–01513 (2018).
    Google Scholar 
    60.Pardal, S. et al. Shorebird low spillover risk of mosquito-borne pathogens on Iberian wetlands. J. Ornithol. 155, 549–554 (2013).
    Google Scholar 
    61.Rizzoli, A. et al. Understanding West Nile virus ecology in Europe: Culex pipiens host feeding preference in a hotspot of virus emergence. Parasit. Vectors. 8, 1–13 (2015).
    Google Scholar 
    62.Kilpatrick, A. M., Kramer, L. D., Jones, M. J., Marra, P. P. & Daszak, P. West Nile virus epidemics in North America are driven by shifts in mosquito feeding behavior. PLoS Biol. 4, e82 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    63.Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    64.Vogels, C. B. F., Fros, J. J., Göertz, G. P., Pijlman, G. P. & Koenraadt, C. J. M. Vector competence of northern European Culex pipiens biotypes and hybrids for West Nile virus is differentially affected by temperature. Parasit. Vectors 9, 393 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    65.Chuang, T.-W., Hockett, C. W., Kightlinger, L. & Wimberly, M. C. Landscape-level spatial patterns of West Nile virus risk in the northern Great Plains. Am. J. Trop. Med. Hyg. 86, 724–731 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    66.Crowder, D. W. et al. West nile virus prevalence across landscapes is mediated by local effects of agriculture on vector and host communities. PLoS One 8, e55006 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.García-Bocanegra, I. et al. Epidemiology and spatio-temporal analysis of West Nile virus in horses in Spain between 2010 and 2016. Transbound. Emerg. Dis. 65, 567–577 (2018).PubMed 

    Google Scholar 
    68.Lourenco, J. MVSE – WNV related files for Portugal. https://doi.org/10.6084/m9.figshare.c.5281664.v1 (2021).69.Jiguet, F. et al. Bird population trends are linearly affected by climate change along species thermal ranges. Proc. Biol. Sci. 277, 3601–3608 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    70.Cator, L. J. et al. The Role of Vector Trait Variation in Vector-Borne Disease Dynamics. Front Ecol. Evol. 8, 189 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    71.Kraemer, M. U. G. et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4, e08347 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    72.Hamlet, A. et al. The seasonal influence of climate and environment on yellow fever transmission across Africa. PLoS Negl. Trop. Dis. 12, e0006284 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    73.Thézé, J. et al. Genomic Epidemiology Reconstructs the Introduction and Spread of Zika Virus in Central America and Mexico. Cell Host. Microbe. 23, 855–864.e7 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    74.Perez-Guzman, P. N. et al. Measuring Mosquito-borne Viral Suitability in Myanmar and Implications for Local Zika Virus Transmission. PLoS Curr. 10, (2018).75.Pereira Gusmão Maia, Z. et al. Return of the founder Chikungunya virus to its place of introduction into Brazil is revealed by genomic characterization of exanthematic disease cases. Emerg. Microbes Infect. 9, 53–57 (2020).PubMed 

    Google Scholar 
    76.Copernicus Climate Data Store. https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecv-for-climate-change?tab=overview.77.Lourenço, J. & Obolski, U. MVSE R-package official page. https://sourceforge.net/projects/mvse/.78.R-Forge: Circular Statistics: Project Home. https://r-forge.r-project.org/projects/circular/.79.Geraci, M. Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression. J. Stat. Softw. 57, 1–29 (2014).
    Google Scholar 
    80.Damineli, D. S. C., Portes, M. T. & Feijó, J. A. Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity, and synchronization in growing cells. J. Exp. Bot. 68, 3267–3281 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.wavelets: Functions for Computing Wavelet Filters, Wavelet Transforms and Multiresolution Analyses. https://CRAN.R-project.org/package=wavelets.82.biwavelet GitHub repository. https://github.com/tgouhier/biwavelet.83.Barros, S. C. et al. Simultaneous detection of West Nile and Japanese encephalitis virus RNA by duplex TaqMan RT-PCR. J. Virol. Methods 193, 554–557 (2013).CAS 
    PubMed 

    Google Scholar 
    84.Copernicus Climate Data Store. https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview.85.Filipe, A. R. & de Andrade, H. R. Arboviruses in the Iberian Peninsula. Acta Virol. 34, 582–591 (1990).CAS 
    PubMed 

    Google Scholar 
    86.Almeida, A. P. G. et al. Mosquito surveys and West Nile virus screening in two different areas of southern Portugal, 2004-2007. Vector. Borne. Zoonotic Dis. 10, 673–680 (2010).PubMed 

    Google Scholar 
    87.Freitas, F. B., Novo, M. T., Esteves, A. & de Almeida, A. P. Species Composition and WNV Screening of Mosquitoes from Lagoons in a Wetland Area of the Algarve, Portugal. Front. Physiol. 2, 122 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    88.Parreira, R. et al. Two distinct introductions of the West Nile virus in Portugal disclosed by phylogenetic analysis of genomic sequences. Vector. Borne. Zoonotic. Dis. 7, 344–352 (2007).CAS 
    PubMed 

    Google Scholar 
    89.Fotakis, E. A. et al. Identification and detection of a novel point mutation in the Chitin Synthase gene of Culex pipiens associated with diflubenzuron resistance. PLoS Negl. Trop. Dis. 14, e0008284 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Mixão, V. et al. Comparative morphological and molecular analysis confirms the presence of the West Nile virus mosquito vector, Culex univittatus, in the Iberian Peninsula. Parasit. Vectors. 9, 601 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    91.Osório, H. C., Zé-Zé, L. & Alves, M. J. Host-feeding patterns of Culex pipiens and other potential mosquito vectors (Diptera: Culicidae) of West Nile virus (Flaviviridae) collected in Portugal. J. Med. Entomol. 49, 717–721 (2012).PubMed 

    Google Scholar 
    92.Gomes, B. et al. The Culex pipiens complex in continental Portugal: distribution and genetic structure. J. Am. Mosq. Control. Assoc. 28, 75–80 (2012).PubMed 

    Google Scholar 
    93.Gomes, B. et al. Limited genomic divergence between intraspecific forms of Culex pipiens under different ecological pressures. BMC Evol. Biol. 15, 197 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    94.Calzolari, M. et al. Detection of mosquito-only flaviviruses in Europe. J. Gen. Virol. 93, 1215–1225 (2012).CAS 
    PubMed 

    Google Scholar 
    95.Hernández-Triana, L. M. et al. Genetic diversity and population structure of Culex modestus across Europe: does recent appearance in the United Kingdom reveal a tendency for geographical spread? Med. Vet. Entomol. 34, 86–96 (2020).PubMed 

    Google Scholar 
    96.Alves, J. M. et al. Flavivírus transmitidos por mosquitos: um risco potencial para Portugal. Investigação em ambiente e saúde – desafios e estratégias (Universidade de Aveiro) (2009).97.Conte, A. et al. Spatio-temporal identification of areas suitable for West Nile Disease in the Mediterranean Basin and Central Europe. PLoS. One. 10, e0146024 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    98.García-Carrasco, J.-M., Muñoz, A.-R., Olivero, J., Segura, M. & Real, R. Predicting the spatio-temporal spread of West Nile virus in Europe. PLoS Negl. Trop. Dis. 15, e0009022 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    99.Marini, G., Manica, M., Delucchia, L., Pugliesed, A. & Rosa, R. Spring temperature shapes West Nile virus transmission in Europe. Acta. Trop. 215, 105796 (2021).PubMed 

    Google Scholar  More

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    Edward O. Wilson (1929–2021)

    OBITUARY
    10 January 2022

    Edward O. Wilson (1929–2021)

    Naturalist, conservationist and synthesizer who founded sociobiology.

    Bert Hölldobler

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    Bert Hölldobler

    Bert Hölldobler holds the Robert A. Johnson Chair in Social Insect Research and is Regent’s Professor in the School of Life Sciences at Arizona State University, Tempe. He began working with Wilson in 1970.

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    Harvard University Professor E.O. Wilson in his office at Harvard University in Cambridge, MA. USACredit: Rick Friedman/Corbis via Getty

    Edward (Ed) Wilson began by exploring the systematics, geographical distribution, social organization and evolution of ants. He became one of the great scholarly synthesizers, winning two Pulitzer prizes. A superb naturalist who enjoyed challenging dogma, he fought for conservation, brought ideas of biodiversity into the mainstream and set ecology on a rigorous conceptual footing. He has died aged 92.Wilson’s book Sociobiology, published in 1975, was the first to address the evolution and organization of societies in organisms ranging from colonial bacteria to primates, including humans. The final chapter, on human social interaction, ignited controversy. Wilson argued that human behaviour, although adaptable to environmental conditions, is rooted in a genetic ‘blueprint’. Opponents claimed that nothing in human behaviour is grounded in genetics, except sleeping, eating and defecation. In a letter to The New York Review of Books, a group of academics including evolutionary biologists Stephen Jay Gould and Richard Lewontin associated Wilson’s view with racism and genocide. Wilson responded with elegance and humour; in my view, most scholars now agree that he won this argument.
    Conservation: Glass half full
    Wilson was born in 1929 in Birmingham, Alabama, and grew up, as he admitted in his 2006 autobiography, Naturalist, “mostly insulated from its social problems”. After studying biology at the University of Alabama in Tuscaloosa, he did graduate studies at Harvard University in Cambridge, Massachusetts. He felt its Museum of Comparative Zoology, with the world’s largest ant collection, was his “destiny”.In 1955, he obtained his PhD on the systematics of the ant genus Lasius, which includes the widespread black garden ant. Systematic biology and the study of biodiversity remained his mission, but he made significant contributions to other fields, such as animal behaviour and chemical ecology. His early work on chemical communication in animals, particularly social insects, inspired a generation of scientists to explore a new area in behavioural physiology.In 1954, Wilson set out for Melanesia, including New Guinea, to study ant taxonomy and biogeography. On the basis of his data, he elaborated the critique that he and his Harvard colleague William Brown had previously developed on the idea of subspecies. They argued that the distinctions between species should be more clearly defined, allowing for variability within species. Equally influential was their thinking on character displacement — when similar species in the same area diverge genetically to avoid competing for resources.Through his fieldwork in Melanesia and later in the Caribbean, Wilson drafted a principle of biogeography that he called the taxon cycle. Species evolve back and forth between being able to live in marginal habitats, and thus disperse widely, and restricting their distribution to species-rich habitats in island interiors. He tested this and other original hypotheses in the Florida Keys in the 1960s, in collaboration with his former student Daniel Simberloff. With ecologist Robert MacArthur, he proposed that species maintain their populations through trade-offs between number of offspring and quality of parental care (the concept of r/K selection). Their 1967 book The Theory of Island Biogeography had far-reaching effects on studies of evolution and conservation.
    A revolution in evolution
    From early in his career, Wilson wondered about ways to understand the evolution of social organization, from primates to social insects (such as honeybees and ants). “A congenital synthesizer,” he wrote in his autobiography, “I held on to the dream of a unifying theory.” He developed a theory of adaptive demography — that certain kinds of social structure might increase reproductive fitness — and the evolution of division of labour between castes, such as insect queens and worker groups. First brought together in The Insect Societies (1971), these concepts were elaborated in Caste and Ecology in the Social Insects, with mathematical biologist George Oster, in 1978.Sociobiology was a much more far-reaching synthesis on the evolution of social systems. The furore that ensued stimulated Wilson to write an even more provocative book, On Human Nature (1978). This garnered his first Pulitzer. His highly original book Biophilia (1984) was the first to use the term to mean human empathy for the natural world. He argued that pleasure in being surrounded by diverse living organisms is a biological adaptation. These books prepared the ground for Consilience (1998), which one reviewer called a biologist’s dream of the unity of knowledge. It proposed the kind of intellectual annexation that occurs when one field can be explained in terms of a more fundamental discipline, and received a mixed response.To his and my utmost surprise, in 1990, the huge monograph The Ants, on which we worked for years, won another Pulitzer. Wilson continued to publish on human evolution and humanity’s relationship with the planet into his 90s. Half-Earth (2016) is a passionate plea to leave half of our world to nature.Ed was not a team builder. He preferred to work alone, although in a few cases he found colleagues who complemented his abilities. He thrived on controversy. In the past two decades, he had rejected the theory of inclusive fitness — the idea that the reproductive success of an individual increases when it helps to raise the offspring of its close relatives — that he once propagated. This led to heated debates, and I opposed some of his views. When we reached a compromise and submitted the manuscript of our book The Superorganism (2009), Ed’s concluding remark was: “Bert, there is one thing we agree on 100%. That is: my co-author is wrong.” One could disagree with Ed over scientific issues and remain good friends.

    Nature 601 (2022)
    doi: https://doi.org/10.1038/d41586-022-00078-7

    Competing Interests
    The author declares no competing interests.

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    Climate warming may increase the frequency of cold-adapted haplotypes in alpine plants

    Study areaAll simulations were run at a 100 × 100 m resolution for the entire European Alps, which cover ~200,000 km². Elevations reach 4,810 m above sea level at the highest peak (Mont Blanc, elevational data were obtained from ref. 44). Mean annual temperature ranges from approximately −13 up to 16 °C and annual precipitation sums reach up to ~3,600 mm (climatic conditions were obtained from WorldClim45).Species dataTrue presences/absences were derived from complete species lists of 14,040 localized plots covering subalpine and alpine non-forest vegetation of the Alps, compiled from published46 and unpublished data sources. For more information see the supplementary information in ref. 21.Data on demographic rates as well as dispersal parameters were taken from ref. 21, Supplementary Table 1. Detailed values are provided in Supplementary Table 1.Environmental variablesCurrent climate dataMaps of current climatic conditions were generated on the basis of mean, minimum and maximum monthly temperature obtained from WorldClim45 and monthly precipitation sums derived from ref. 47 at a resolutions of 0.5 arcmin and 5 km, respectively. Temperature and precipitation data were downscaled to 100 m as described in ref. 21 and using ordinary kriging with elevation as covariable. As the reference periods of these two datasets do not match (temperature values represent averages for 1950–2000, while precipitation covers 1970–2005) temperature values were adapted using the E-OBS climate grids available online (www.ecad.eu/download/ensembles/download.php). We used these spatially refined temperature and precipitation grids to derive maps of mean annual temperature and mean annual precipitation sum. We chose only two climatic variables to keep models simple and, therefore, interpretation of results more straightforward. In fact, the climatic drivers of population performance and distribution can be more complex48 and vary among species, life history stages and vital rates49. However, as correlations between different descriptors of temperature (such as coldest month or warmest month temperature, Pearson correlation of 0.84) as well as between precipitation variables are high in the European Alps, we chose mean annual temperature and mean annual precipitation sum as they give the most basic description of how climatic conditions change over geographical and elevational gradients.Future climate dataMonthly time series of mean temperature as well as precipitation sums predicted for the twenty-first century were extracted from the Cordex data portal (http://cordex.org). We chose two IPCC5 scenarios from the RCP families representing mild (RCP 2.6) and severe (RCP 8.5) climate change to consider the uncertainty in the future climate predictions. Both scenarios were generated by Météo-France/Centre National de Recherches Météorologiques using the CNRM-ALADIN53 model, fed by output from the global circulation model CNRM-CM5 (ref. 50). The RCP 2.6 scenario assumes that radiative forcing reaches nearly 3 W m−2 (equal to 490 ppm CO2 equivalent) mid-century and will decrease to 2.6 W m−2 by 2100. In the RCP 8.5 scenario, radiative forcing continues to rise throughout the twenty-first century and reaches >8.5 W m−2 (equal to 1,370 ppm CO2 equivalent) in 210024.These time series were statistically downscaled (delta method) by (1) calculating differences (deltas) between monthly temperature and precipitation values hindcasted for the current climatic conditions (mean 1970–2005) and forecasted future values at the original spatial resolution of 11′; (2) spatially interpolating these differences to a resolution of 100 × 100 m using cubic splines and (3) adding them to the downscaled current climate data separately for each climatic variable21,36. Subsequently, we calculated running means (averaged over 35 years) for every tenth year (2030, 2040 through to 2080) for each climatic variable and finally derived, on the basis of the monthly data, mean annual temperature and mean annual precipitation sums for these decadal time steps. The application of 35-yr running means ensures that we use the same time interval for parameterization and prediction. Furthermore, for long-lived species such as alpine plants, running means over long time intervals appear most appropriate to characterize climatic niches33.Soil dataIn addition to the climatic data we used a map of the percentage of calcareous substrate within a cell (5′ longitude × 3′ latitude dissolved to 100 m resolution; further referred to as soil) as described in the supplementary information of ref. 21.Environmental suitability modellingWe parameterized logistic regression models (LRMs) with a logit link function using species presence/absence data as response and the three environmental (two bioclimatic and one soil) variables as predictors. All predictor variables entered the model as second-order polynomials in agreement with the standard unimodal niche concept.From the models, we also derived a threshold value to use for translating predicted probability of occurrence (as a measure of site suitability) into predicted presence or absence of each species at a site (called occurrence threshold, OT, henceforth). The threshold was defined such that it optimized the true skills statistic (TSS), a measure of predictive accuracy derived from comparing observed and predicted presence–absence maps51.Genetic model and niche partitioningSpecies-specific suitability curves for the annual mean temperature gradient derived from the LRMs were partitioned into suitability curves of ecologically different haplotypes of a species as defined by allelic variation in up to three diploid loci (Extended Data Fig. 6). The number of alleles was varied (n = 1, 2, 3, 5 and 10 alleles) as was the proportion of the entire species’ (temperature) niche covered by each haplotype. Models with more than one locus were run with diallelic loci, as otherwise computational efforts would have increased excessively (for each haplotype the number of seeds, juveniles and adults has to be stored and all seeds have to be distributed separately). Each combination of haplotype number and allelic niche size used in a particular simulation is further referred to as setting. Species-specific suitability curves along the other two dimensions (precipitation and soil) remained unpartitioned to ease interpretation of results. The implications of relaxing this assumption by modelling niche partitioning along different environmental gradients are hard to predict. Outcomes would probably depend on the direction and strength of individual specialization along these gradients, whether they are positively or negatively correlated, as well as on how both temperature and precipitation patterns will be affected by climate change. As a consequence, the patterns we found could be re-enforced, for example when the replacement of cold-adapted haplotypes within the species elevational range is further delayed, for example, because those haplotypes adapted to warmer conditions can cope less well with higher precipitation at higher elevations. Vice versa, maladaptation to the warming temperatures might be attenuated, for example, if temperature increase is paralleled by precipitation decrease and emerging drought stress. If, in this case, haplotypes from lower elevations can better cope with both higher temperatures and less water availability than those of median elevations, they may replace the latter faster at these median elevations and hence shorten the phase of maladaptation.Allelic effects were assumed to define the temperature optimum additively. Hence, the heterozygotes’ optimum is always exactly between the optima of the two corresponding homozygotes, corresponding to a codominant genetic model. Finally, all haplotypes corresponding to one setting were assumed to have constant (temperature) niche size, defined as a proportion (ω = 50%, 75% and 100%, for one haplotype only 100%) of the entire species’ (temperature) niche. The temperature niche was computed as the difference between the upper and lower temperature values at which the LRM-derived suitability curve predicted a suitability equal to OT (with precipitation and soil set to the respective optima of the species, also derived from the LRMs). To derive the same geographic distribution under current climatic conditions for each setting, the union of the niches of all haplotypes of one set has to approximate the niche of the single-species model (Extended Data Fig. 6). Note, however, that, the aspired equality of niches is impossible, as the niches resulting from a logistic regression with quadratic terms are always elliptic in shape. Therefore, the optima of the two extreme homozygotes (that is, those carrying haplotypes adapted to the coldest or warmest margins of the entire species’ niche) are fixed at: niche limits ± 1/2 × ω × niche size and all other optima are distributed between them at equal distances (Extended Data Fig. 6 gives a schematic illustration). As a consequence, the performance of the extreme haplotypes, both coldest and warmest, is modelled to be somewhat higher towards the cold and warm margins of the temperature niche, respectively, compared with the performance modelled for the species without intraspecific niche partitioning (compare the overlap of the black with the red and blue curves in Extended Data Fig. 6a). However, as haplotype number did not affect modelled range loss (compare Table 1), this marginal mismatch does not apparently impact our results. Homozygotes were ordered from the cold-adapted A1A1 up to the warm-adapted AnAn.Finally, the suitability curves of the genotypes were assumed to have the same value at their optimum as the species-specific suitability curve at this point (Extended Data Fig. 6).Artificial landscapesArtificial landscapes were defined as a raster of 50 × 112 cells (of 100 × 100 m). These rasters were homogeneous with respect to precipitation and soil carbon conditions which were set to the values optimal for each species according to the LRMs. With respect to temperature, by contrast, we implemented a gradient across the raster running from the minimum –9.1 °C to the maximum +3.8 °C temperature for which the LRM predicts values >OT across all six species. Buffering by 1 °C at both limits was done to avoid truncating simulation results. Further 4 °C at the lower limit were added to consider simulated temperature increase (below). The final temperature range represented by the raster ran from −14.1 to +4.8 °C. Temperature increased linearly along this axis by an increment of 0.171 °C per cell, derived from a 20° slope and a temperature decrease of 0.5 °C per 100 m of elevational change. Along the 50-cell breadth of the landscape, temperature values were kept constant. On the basis of these grids, we implemented a moderate and a severe climate change scenario, characterized by temperature increases of 2 and 4 °C, respectively, over 80 yr. Therefore, temperature of each raster cell increased annually by 0.025 and 0.05 °C, respectively.Simulations of spatiotemporal range dynamicsCATS21 is a spatially and temporarily explicit model operating on a two-dimensional grid (of 100 m mesh size in this case). CATS combines simulations of local species’ demography with species’ distribution models by scaling demographic rates relative to occurrence probabilities (suitabilities) predicted for the respective site or grid cell by the latter. Dispersal among grid cells is modelled as a combination of wind, exozoochoric and endozoochoric (that is, animal dispersal via attachment to the fur or ingestion and defecation, respectively) dispersal of plant seeds. Time proceeds in annual steps. The annually changing occurrence probabilities at each site affect the demography of local populations and hence, eventually, the number of seeds that are produced in each grid cell in the respective year. As a consequence, local populations grow or decrease, become extinct or establish anew and hence the species’ distribution across the whole study area changes as a function of the changing climate.Demographic modellingClimate-dependence of local demography was modelled by linking demographic rates (seed persistence, germination, survival, flowering frequency, seed yield and clonal reproduction) and carrying capacity to occurrence probabilities predicted by LRMs by means of sigmoidal functions. Furthermore, all rates were fixed at OT at a value ensuring stable population sizes; for more information see refs. 21,33. Demographic rates were confined by zero and a species-specific maximum value (Supplementary Table 1), which was assumed to be the same for all genotypes of a species. As a corollary, the demographic rates of all genotypes of a species are the same under optimal environmental conditions but their actual values for a particular site in a particular year differ due to different temperature optima of genotypes. In addition, germination, survival and clonal reproduction were modelled as density-dependent processes to account for intraspecific competition between genotypes. In our application, for all density-dependent functions, the species abundance is defined as the sum of all adult individuals in a given cell, regardless of their genotypes. Density dependence is commonly achieved by multiplying rates with (C – N)/C, where N is the species abundance and C is the (site- and genotype-specific) carrying capacity. This correction for density dependence causes the functions to drop to zero when N approximates C. To avoid the subsequent collapse of population sizes, we defined density-dependent rates as (C – N)/C × rate() + N/C × rate(OT), which ensures stable population sizes at densely populated sites occupied by only one genotype. To account for uncertainty in parameters of demographic rates, we assigned each species two value sets representing the upper and lower end of a plausible range of values on the basis of information derived from databases and own measurements (Supplementary Table 1).The simulations allowed for cross-pollination between genotypes. We used the relative amount of flowers (genotype-specific flowering frequency as defined by the sigmoid curve for the given suitability in the given raster cell for the given year × number of adults of that genotype in the population of that cell) to derive an estimate of the haplotype frequencies in the total pollen produced by the population within a grid cell. For the multiallelic case we allowed for recombination between loci with a recombination rate of 0.1%. These frequencies were set equal to the probability that particular haplotypes are transmitted to each year’s seed yield by pollination. Spatial pollen dispersal was accounted for in the following way: in each cell, 5% of the pollen involved in producing the annual seed yield, was assumed to stem from outside the respective raster cell. The proportions of different haplotypes in this 5% were derived from the overall pollen frequencies in all cells within a 700 m radius around the target cell (following estimates in ref. 52). Subsequently, produced seeds of each genotype were divided into resulting genotypes regarding the adult’s haplotype composition and the haplotype frequencies in the cells’ entire pollen load.Dispersal modellingFor wind dispersal of plant species we parameterized the analytical WALD kernel53 on the basis of measured seed traits and wind speed data from a meteorological station in the Central Alps of Austria. Exozoochorous and endozoochorous plant kernels were parameterized on the basis of correlated random walk simulations for the most frequent mammal dispersal vector in the study area, the chamois (Rupicapra rupicapra L.). For more details, see ref. 33. To account for uncertainties in species-specific dispersal rates, the proportion of seeds dispersed by the more far-reaching zoochorous kernels was assumed either as high (1–5%) or low (0.1–0.5%), setting upper and lower boundaries of a credible range of the dispersal ability of species.Simulation set up and simulation initializationTo test for the effects of climate change on genetic diversity in 2080, we ran CATS over the period 2000 to 2080 for each of the six study species across the entire Alps under a full factorial combination of (1) three niche sizes (50%, 75% and 100%); (2) six numbers of haplotypes (equal to two, three, five and ten alleles for one locus and four and eight for the diallelic two- and three-locus models, respectively); (3) three climate scenarios (current climate, RCP 2.6 and RCP 8.5); and (4) two sets of demographic and dispersal parameters. As a ‘control’ we also ran simulations for all climate scenarios and the two demographic and dispersal parameter sets for a setting with one genotype filling the whole niche of the species. To account for stochastic elements in CATS four replications were run for each combination of ‘treatments’.For simulations in artificial landscapes we used, instead of RCP 2.6 and RCP 8.5, ‘artificial’ climatic scenarios with an assumed warming of 2 and 4 °C, respectively, and no change in precipitation.All simulation runs were started with homozygotic individuals only. As initial distribution, for each simulation run each cell predicted to be environmentally suitable to the species (that is, occurrence probability of species >OT)—and within the real distribution range of the species28 (not relevant for simulations in artificial landscapes, of course)—was assumed to be occupied by an equal number of adults of each (homozygotic) genotype, with the total sum equal to the carrying capacity of the site. To accommodate this arbitrary within-cell genetic mixture of homozygotes (and missing heterozygotes) to actual local conditions we started simulations of range dynamics with a burn-in phase of 200 yr, run under constant current climatic conditions. To have a smooth transition from the burn-in phase under current climate (corresponding to the climate of the years 1970–2005; see current climate data) to future climate projections (starting with 2030) and to derive annual climate series, climate data were linearly interpolated between these two time intervals.Statistical analysisWe evaluated the contribution of climate scenario, haplotype number and haplotype niche size to overall species’ range change as well as the change in the frequency of the warm-adapted haplotype by means of linear models. In these models, log(range size in 2080/range size in 2000) and log(percentage of warm-adapted haplotype in 2080/percentage of warm-adapted haplotype in 2000), averaged over the four replicates and the two demographic and dispersal parameter sets, were the response variables. For the analysis of the change of the warm-adapted haplotype simulation settings with 100% niche size were ignored, as in this setting all haplotypes have the same temperature optimum (that is, neutral genetic variation). Approximate normality of residuals was confirmed by visual inspection.As indicator of the ‘topographic opportunity’ remaining to the species in the real world we calculated the area colonizable at elevations higher than those already occupied at the end of the simulation period. We therefore drew a buffer of 1 km around each cell predicted to be occupied in 2080 and then summed the area within these buffers at a higher elevation than the focal, occupied cell. Overlapping buffer areas were only counted once. This calculation was done for simulations conducted with one full-niche genotype per species only.Sensitivity analysisWe interpret the simulated relative decrease of warm-adapted haplotypes mainly as an effect of (1) the unrestricted expansion of cold-adapted haplotypes at the leading edge and (2) the resistance of the locally predominating haplotype that becomes increasingly maladapted with progressive climate warming, to recruitment by better-adapted haplotypes from below that are either rare or not present at all initially. However, the results achieved, and our conclusions, may be sensitive to assumptions about particular parameter values. Parameters that control the longevity of adult plants, and indirectly the rate of recruitment of new individuals, as well as those controlling gene flow via pollen (instead of seeds) may be particularly influential in this respect. We additionally ran simulations on artificial landscapes under alternative values of these parameters. In particular, we set the maximum age of plants to 10 yr instead of 100 yr and raised the proportion of locally produced pollen assumed to be transported up to 700 m to 10%. Both of these values are thus probably set to rather extreme levels: a maximum age of 10 yr is much shorter than the 30–50 yr assumed to be the standard age of (non-clonal) alpine plants31; and a cross-pollination rate between cells of 10% is high given that among the most important alpine pollinators only bumblebees are assumed to transport pollen >100 m regularly54,55. We add that we ran these additional simulations only in combination with the demographic species parameters set to high values because a short life span combined with low-level demographic parameters did not allow for stable populations of some species, even under current climatic conditions.For individual species, adapting plant age and cross-pollination rate between cells (Extended Data Fig. 7), did change the magnitude of loss of the warm-adapted haplotype. Nevertheless, for all of them the warm-adapted haplotype still became rarer when climate warmed and this effect increased with the level of warming. We are confident that our conclusions are qualitatively insensitive to variation of these parameters within a realistic range.Finally, in the simulations where we assumed a multilocus structure of the temperature niche, the recombination rate may also affect simulation results because it determines the rate by which new haplotypes can emerge. We also tested sensitivity of our simulations to doubling the recombination rate to 0.2%. Again, we found that a higher recombination rate had little qualitative effect on the results. Quantitatively, it resulted in a slightly pronounced relative decrease of the warmth-adapted haplotype in most species (Extended Data Fig. 8).Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Biocrusts mediate a new mechanism for land degradation under a changing climate

    1.Science Plan and Implementation Strategy IGBP Report No. 53/IHDP Report No. 19 (Global Land Project, 2005).2.Millennium Ecosystem Assessment—Ecosystems and Human Well-Being: Desertification Synthesis Encyclopedia of the Anthropocene vols 1–5 (MEA, 2017).3.Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2015).Article 

    Google Scholar 
    4.Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).Article 

    Google Scholar 
    5.Tilman, D. & Downing, J. A. Biodiversity and stability in grasslands. Nature 367, 363–365 (1994).Article 

    Google Scholar 
    6.Belnap, J. Surface disturbances: their role in acceleration desertification. Environ. Monit. Assess. 37, 38–57 (1995).Article 

    Google Scholar 
    7.Zhao, Y., Jia, R. L. & Wang, J. Towards stopping land degradation in drylands: water-saving techniques for cultivating biocrusts in situ. Land Degrad. Dev. 30, 2336–2346 (2019).Article 

    Google Scholar 
    8.Rodriguez-Caballero, E. et al. Dryland photoautotrophic soil surface communities endangered by global change. Nat. Geosci. 11, 185–189 (2018).CAS 
    Article 

    Google Scholar 
    9.Coe, K. K. & Sparks, J. P. Physiology-based prognostic modeling of the influence of changes in precipitation on a keystone dryland plant species. Oecologia 176, 933–942 (2014).Article 

    Google Scholar 
    10.Ferrenberg, S., Tucker, C. L. & Reed, S. C. Biological soil crusts: diminutive communities of potential global importance. Front. Ecol. Environ. 15, 160–167 (2017).Article 

    Google Scholar 
    11.Belnap, J. & Gillette, D. A. Soil surface disturbance: impacts on potential wind erodibility of sand desert soils in SE Utah, USA. Land Degrad. Dev. 8, 355–362 (1997).Article 

    Google Scholar 
    12.Rutherford, W. A. et al. Albedo feedbacks to future climate via climate change impacts on dryland biocrusts. Sci. Rep. 7, 44188 (2017).13.Duniway, M. C. et al. Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world. Ecosphere 10, e02650 (2019).14.Ferrenberg, S., Faist, A. M., Howell, A. & Reed, S. C. Biocrusts enhance soil fertility and Bromus tectorum growth, and interact with warming to influence germination. Plant Soil 429, 77–90 (2018).CAS 
    Article 

    Google Scholar 
    15.Eldridge, D. J. et al. The pervasive and multifaceted influence of biocrusts on water in the world’s drylands. Glob. Change Biol. 26, 6003–6014 (2020).16.Ferrenberg, S., Reed, S. C. & Belnap, J. Climate change and physical disturbance cause similar community shifts in biological soil crusts. Proc. Natl Acad. Sci. USA 112, 12116–12121 (2015).CAS 
    Article 

    Google Scholar 
    17.Reed, S. C. et al. Changes to dryland rainfall result in rapid moss mortality and altered soil fertility. Nat. Clim. Change 2, 752–755 (2012).CAS 
    Article 

    Google Scholar 
    18.Concostrina-Zubiri, L. et al. Biological soil crusts across disturbance-recovery scenarios: effect of grazing regime on community dynamics. Ecol. Appl. 24, 1863–1877 (2014).CAS 
    Article 

    Google Scholar 
    19.Weber, B., Bowker, M., Zhang, Y. & Belnap, J. in Biological Soil Crusts: An Organizing Principle in Drylands (eds Weber, B., Büdel, B. & Belnap, J.) 479–498 (Springer, 2016).20.Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science 316, 847–851 (2007).CAS 
    Article 

    Google Scholar 
    21.Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).CAS 
    Article 

    Google Scholar 
    22.Bestelmeyer, B. T. et al. Analysis of abrupt transitions in ecological systems. Ecosphere 2, e03360 (2011).Article 

    Google Scholar 
    23.Bestelmeyer, B. T. et al. Desertification, land use, and the transformation of global drylands. Front. Ecol. Environ. 13, 28–36 (2015).Article 

    Google Scholar 
    24.Beisner, B., Haydon, D. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Environ. 1, 376–382 (2003).25.Fukami, T. & Nakajima, M. Community assembly: alternative stable states or alternative transient states? Ecol. Lett. 14, 973–984 (2011).Article 

    Google Scholar 
    26.Belnap, J. & Büdel, B. in Biological Soil Crusts: An Organizing Principle in Drylands (eds Weber, B., Büdel, B. & Belnap, J.) 305–320 (Springer, 2016).27.Belnap, J. & Warren, S. D. Measuring restoration success: a lesson from Patton’s tank tracks. Ecol. Bull. 79, 33 (1998).28.Belnap, J. & Elderidge, D. in Biological Soil Crusts: Structure, Function and Management (eds Belnap, J. & Lange, O. L.) 363–383 (Springer, 2001).29.Turner, M. G. Disturbance and landscape dynamics in a changing world. Ecology 91, 2833–2849 (2010).Article 

    Google Scholar 
    30.Scheffer, M. & Carpenter, S. R. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol. Evol. 18, 648–656 (2003).Article 

    Google Scholar 
    31.Sala, O. E. & Lauenroth, W. K. Small rainfall events: an ecological role in semiarid regions. Oecologia 53, 301–304 (1982).32.Cayan, D. R. et al. Future dryness in the Southwest US and the hydrology of the early 21st century drought. Proc. Natl Acad. Sci. USA 107, 21271–21276 (2010).CAS 
    Article 

    Google Scholar 
    33.Christensen, N. S., Wood, A. W., Nathalie, V., Lettenmaier, D. P. & Palmer, R. N. The effects of climate change on the hydrology and water resources of the Colorado river basin. Clim. Change 62, 337 (2004).Article 

    Google Scholar 
    34.Herrick, J. et al. Field soil aggregate stability kit for soil quality and rangeland health evaluations. Catena 44, 27–35 (2001).Article 

    Google Scholar 
    35.Escolar, C., Martínez, I., Bowker, M. A. & Maestre, F. T. Warming reduces the growth and diversity of biological soil crusts in a semi-arid environment: implications for ecosystem structure and functioning. Phil. Trans. R. Soc. B 367, 3087–3099 (2012).Article 

    Google Scholar 
    36.Scheffer, M. et al. Creating a safe operating space for iconic ecosystems: manage local stressors to promote resilience to global change. Science 347, 1317–1319 (2015).CAS 
    Article 

    Google Scholar 
    37.Collins, S. L., Micheli, F. & Hartt, L. A method to determine rates and patterns of variability in ecological communities. Oikos 91, 285–293 (2000).Article 

    Google Scholar 
    38.Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).CAS 
    Article 

    Google Scholar 
    39.IPCC. Climate Change 2014: Impacts, Adaptations, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).40.Mirzabaev, A. et al. in IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) Ch. 3 (WMO, 2018).41.Torres-Cruz, T. J. et al. Species-specific nitrogenase activity in lichen-dominated biological soil crusts from the Colorado Plateau, USA. Plant Soil 429, 113–125 (2018).CAS 
    Article 

    Google Scholar 
    42.Omernik, J. M. & Griffith, G. E. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environ. Manag. 54, 1249–1266 (2014).Article 

    Google Scholar 
    43.Kuske, C. R., Yeager, C. M., Johnson, S., Ticknor, L. O. & Belnap, J. Response and resilience of soil biocrust bacterial communities to chronic physical disturbance in arid shrublands. ISME J. 6, 886–897 (2011).Article 

    Google Scholar 
    44.Tucker, C. L., Ferrenberg, S. & Reed, S. C. Climatic sensitivity of dryland soil CO2 fluxes differs dramatically with biological soil crust successional state. Ecosystems 22, 15–32 (2018). https://doi.org/10.1007/s10021-018-0250-445.Cable, J. M. & Huxman, T. E. Precipitation pulse size effects on Sonoran Desert soil microbial crusts. Oecologia 141, 317–324 (2004).Article 

    Google Scholar 
    46.Karl, T. R., Knight, R. W. & Plummer, N. Trends in high-frequency climate variability in the twentieth century. Nature 377, 217–220 (1995).CAS 
    Article 

    Google Scholar 
    47.Kunkel, K. E., Easterling, D. R., Redmond, K. & Hubbard, K. Temporal variations of extreme precipitation events in the United States: 1895–2000. Geophys. Res. Lett. 30, 1895–2000 (2003).Article 

    Google Scholar 
    48.Kim, J. A projection of the effects of the climate change induced by increased CO2 on extreme hydrologic events in the Western US. Clim. Change 68, 153–168 (2005).CAS 
    Article 

    Google Scholar 
    49.Smith, S. J. et al. Climate change impacts for the conterminous USA: an integrated assessment part 1. Scenarios and context. Clim. Change 69, 7–25 (2005). https://doi.org/10.1007/1-4020-3876-3_250.Schwinning, S., Belnap, J., Bowling, D. R. & Ehleringer, J. R. Sensitivity of the Colorado Plateau to change: climate, ecosystems, and society. Ecol. Soc. 13, 28 (2008).51.Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).Article 

    Google Scholar 
    52.Jonasson, S. The point intercept method for non-destructive estimation of biomass. Phytocoenologia 11, 385–388 (1983).Article 

    Google Scholar 
    53.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).54.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).55.Oksanen, A. J. et al. Vegan: Community Ecology Package. Rpackage version 2.5-7 https://CRAN.R-project.org/package=vegan (2020).56.Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2008).Article 

    Google Scholar 
    57.Venables, W. & Ripley, B. Modern Applied Statistics with S. (Springer, 2002).58.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Package ‘emmeans’ https://github.com/rvlenth/emmeans (2018).59.Signorell, A. DescTools: Tools for Descriptive Statistics (2021).60.Hallett, L. M. et al. codyn: an R package of community dynamics metrics. Methods Ecol. Evol. 7, 1146–1151 (2016).61.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn 1–476 (CRC/Taylor & Francis, 2017).62.Bürkner, P. C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).63.Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 547–511 (1992).
    Google Scholar 
    64.Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).Article 

    Google Scholar 
    65.Modrák, M., Barrett, M., Weber, F. & Coronado, E. bayesplot: Plotting for Bayesian Models. R package version 1.8.0 https://mc-stan.org/bayesplot/ (2021).66.Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).Article 

    Google Scholar 
    67.Phillips, M. L., Howell, A., Lauria, C. M., Belnap, J. & Reed, S. C. Data and software code from two long-term experiments (1996–2011 and 2005–2018) at three sites on the Colorado Plateau of North America (US Geological Survey, 2021); https://doi.org/10.5066/P9RUN1TP More

  • in

    Dynamic diel proteome and daytime nitrogenase activity supports buoyancy in the cyanobacterium Trichodesmium

    1.Zehr, J. P. & Capone, D. G. Changing perspectives in marine nitrogen fixation. Science 9514, 729 (2020).
    Google Scholar 
    2.Karl, D. et al. Dinitrogen fixation in the world’s oceans. Biogeochemistry 57–58, 47–98 (2002).
    Google Scholar 
    3.Dugdale, R. & Wilkerson, F. in Primary Productivity and Biogeochemical Cycles in the Sea (eds Falkowski, P. G. et al.) 107–122 (Springer, 1992).4.Carpenter, E. J. & Capone, D. G. in Nitrogen in the Marine Environment 2nd edn (eds Capone, D. G., Bronk, D. A., Mulholland, M. R. & Carpenter, E. J.) Ch. 4 (Elsevier, 2008).5.Gruber, N. & Sarmiento, J. L. Global patterns of marine nitrogen fixation and denitrification. Global Biogeochem. Cycles 11, 23–266 (1997).
    Google Scholar 
    6.Buchanan, P. J., Chase, Z., Matear, R. J., Phipps, S. J. & Bindoff, N. L. Marine nitrogen fixers mediate a low latitude pathway for atmospheric CO2 drawdown. Nat. Commun. https://doi.org/10.1038/s41467-019-12549-z (2019).7.Monteiro, F. M., Follows, M. J. & Dutkiewicz, S. Distribution of diverse nitrogen fixers in the global ocean. Global Biogeochem. Cycles 24, 1–16 (2010).
    Google Scholar 
    8.Church, M. J., Björkman, K. M., Karl, D. M., Saito, M. A. & Zehr, J. P. Regional distributions of nitrogen-fixing bacteria in the Pacific Ocean. Limnol. Oceanogr. 53, 63–77 (2008).CAS 

    Google Scholar 
    9.Monteiro, F. M., Dutkiewicz, S. & Follows, M. J. Biogeographical controls on the marine nitrogen fixers. Global Biogeochem. Cycles 25, 1–8 (2011).
    Google Scholar 
    10.Dutkiewicz, S., Ward, B. A., Monteiro, F. & Follows, M. J. Interconnection of nitrogen fixers and iron in the Pacific Ocean: theory and numerical simulations. Global Biogeochem. Cycles 26, 1–16 (2012).
    Google Scholar 
    11.Walworth, N. G. et al. Nutrient-colimited Trichodesmium as a nitrogen source or sink in a future ocean. Appl. Environ. Microbiol. 84, 1–14 (2018).CAS 

    Google Scholar 
    12.McGillicuddy, D. J. Jr. Do Trichodesmium spp. populations in the North Atlantic export most of the nitrogen they fix? Global Biogeochem. Cycles 28, 103–114 (2014).CAS 

    Google Scholar 
    13.Carpenter, E. J. & Romans, K. Major role of the cyanobacterium Trichodesmium in nutrient cycling in the North Atlantic Ocean. Science 254, 1989–1992 (1991).
    Google Scholar 
    14.Bergman, B., Sandh, G., Lin, S., Larsson, J. & Carpenter, E. J. Trichodesmium – a widespread marine cyanobacterium with unusual nitrogen fixation properties. FEMS Microbiol. Rev. 37, 286–302 (2013).CAS 
    PubMed 

    Google Scholar 
    15.Capone, D. G. Trichodesmium, a globally significant marine cyanobacterium. Science 276, 1221–1229 (1997).CAS 

    Google Scholar 
    16.Gallon, J. R. The oxygen sensitivity of nitrogenase: a problem for biochemists and micro-organisms. Trends Biochem. Sci. 6, 19–23 (1981).CAS 

    Google Scholar 
    17.Saito, M. A. et al. Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc. Natl Acad. Sci. USA 108, 2184–2189 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Dron, A. et al. Light-dark (12:12) cycle of carbon and nitrogen metabolism in Crocosphaera watsonii WH8501: relation to the cell cycle. Environ. Microbiol. 14, 967–981 (2012).CAS 
    PubMed 

    Google Scholar 
    19.Mohr, W., Intermaggio, M. P. & LaRoche, J. Diel rhythm of nitrogen and carbon metabolism in the unicellular, diazotrophic cyanobacterium Crocosphaera watsonii WH8501. Environ. Microbiol. 12, 412–421 (2010).CAS 
    PubMed 

    Google Scholar 
    20.Flores, E. & Herrero, A. Compartmentalized function through cell differentiation in filamentous cyanobacteria. Nat. Rev. Microbiol. 8, 39–50 (2010).CAS 
    PubMed 

    Google Scholar 
    21.Burnat, M., Herrero, A. & Flores, E. Compartmentalized cyanophycin metabolism in the diazotrophic filaments of a heterocyst-forming cyanobacterium. Proc. Natl Acad. Sci. USA 111, 3823–3828 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Sherman, D. M., Tucker, D. & Sherman, L. A. Heterocyst development and localization of cyanophycin in N2-fixing cultures of Anabaena sp. PCC 7120 (Cyanobacteria). J. Phycol. 941, 932–941 (2000).
    Google Scholar 
    23.Lamont, H. C., Silvester, W. B. & Torrey, J. G. Nile red fluorescence demonstrates lipid in the envelope of vesicles from N2-fixing cultures of Frankia. Can. J. Microbiol. 34, 656–660 (1988).CAS 

    Google Scholar 
    24.Saino, T. Diel variation in nitrogen fixation by a marine blue-green alga, Trichodesmium thiebautii. Deep Sea Res. 25, 1259–1263 (1978).
    Google Scholar 
    25.Saino, T. & Hattori, A. Aerobic nitrogen fixation by the marine non-heterocystous cyanobacterium Trichodesmium (Oscillatoria) spp.: its protective mechanism against oxygen. Mar. Biol. 70, 251–254 (1982).
    Google Scholar 
    26.Berman-Frank, I. et al. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium Trichodesmium. Science 294, 1534–1537 (2001).CAS 
    PubMed 

    Google Scholar 
    27.Ohki, K. & Taniuchi, Y. Detection of nitrogenase in individual cells of a natural population of Trichodesmium using immunocytochemical methods for fluorescent cells. J. Oceanogr. 65, 427–432 (2009).CAS 

    Google Scholar 
    28.Eichner, M. et al. N2 fixation in free-floating filaments of Trichodesmium is higher than in transiently suboxic colony microenvironments. New Phytol. 222, 852–863 (2019).CAS 
    PubMed 

    Google Scholar 
    29.Ohki, K. Intercellular localization of nitrogenase in a non-heterocystous cyanobacterium (cyanophyte), Trichodesmium sp. NIBB1067. J. Oceanogr. 64, 211–216 (2008).CAS 

    Google Scholar 
    30.Ohki, K., Zehr, F. & Fujita, Y. Regulation of nitrogenase activity in relation to the light-dark regime in the filamentous non-heterocystous cyanobacterium Trichodesmium sp. NIBB 1067. J. Gen. Microbiol. 138, 2679–2685 (1992).CAS 

    Google Scholar 
    31.Finzi-Hart, J. A. et al. Fixation and fate of C and N in the cyanobacterium Trichodesmium using nanometer-scale secondary ion mass spectrometry. Proc. Natl Acad. Sci. USA 106, 9931 (2009).CAS 

    Google Scholar 
    32.Sandh, G., El-Shehawy, R., Díez, B. & Bergman, B. Temporal separation of cell division and diazotrophy in the marine diazotrophic cyanobacterium Trichodesmium erythraeum IMS101. FEMS Microbiol. Lett. 295, 281–288 (2009).CAS 
    PubMed 

    Google Scholar 
    33.Küpper, H. et al. Traffic lights in Trichodesmium. Regulation of photosynthesis for nitrogen fixation studied by chlorophyll fluorescence kinetic microscopy. Plant Physiol. 135, 2120–2133 (2019).
    Google Scholar 
    34.Ohki, K. & Fujita, Y. Aerobic nitrogenase activity measured as acetylene reduction in the marine non-heterocystous cyanobacterium Trichodesmium spp. grown under artificial conditions. Mar. Biol. 98, 111–114 (1988).CAS 

    Google Scholar 
    35.Waterbury, J. B. & Willey, J. M. Isolation and growth of marine planktonic Cyanobacteria. Methods Enzymol. 167, 100–105 (1988).CAS 

    Google Scholar 
    36.Chen, Y. B., Zehr, J. P. & Mellon, M. Growth and nitrogen fixation of the diazotrophic filamentous nonheterocystous cyanobacterium Trichodesmium sp. IMS 101 in defined media: evidence for a circadian rhythm. J. Phycol. 32, 916–923 (1996).
    Google Scholar 
    37.Berman-Frank, I., Bidle, K. D., Haramaty, L. & Falkowski, P. G. The demise of the marine cyanobacterium, Trichodesmium spp., via an autocatalyzed cell death pathway. Limnol. Oceanogr. 49, 997–1005 (2004).
    Google Scholar 
    38.Bell, P. R. F. et al. Laboratory culture studies of Trichodesmium isolated from the Great Barrier Reef lagoon, Australia. Hydrobiologia 532, 9–21 (2005).
    Google Scholar 
    39.Tzubari, Y., Magnezi, L., Be’Er, A. & Berman-Frank, I. Iron and phosphorus deprivation induce sociality in the marine bloom-forming cyanobacterium Trichodesmium. ISME J. 12, 1682–1693 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Held, N. A., McIlvin, M. R., Moran, D. M., Laub, M. T. & Saito, M. A. Unique patterns and biogeochemical relevance of two-component sensing in marine bacteria. mSystems 4, 1–16 (2019).
    Google Scholar 
    41.Aryal, U. K. & Sherman, L. A. in Cyanobacteria Omics Manipulation (ed. Los, D. A.) Ch. 6 (Caister Academic Press, 2017).42.Held, N. A. et al. Co-occurrence of Fe and P stress in natural populations of the marine diazotroph Trichodesmium. Biogeosciences 17, 2537–2551 (2020).
    Google Scholar 
    43.Klugkist, J., Haaker, H., Wassink, H. & Veeger, C. The catalytic activity of nitrogenase in intact Azotobacter vinelandii cells. Eur. J. Biochem. 146, 509–515 (1985).CAS 
    PubMed 

    Google Scholar 
    44.Zehr, J. P., Wyman, M., Miller, V., Capone, D. G. & Duguay, L. Modification of the Fe protein of nitrogenase in natural populations of Trichodesmium thiebautii. Appl. Environ. Microbiol. 59, 669–676 (1993).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Rodriguez, I. B. & Ho, T.-Y. Diel nitrogen fixation pattern of Trichodesmium: the interactive control of light and Ni. Sci. Rep. 4, 4445 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    46.Eichner, M., Kranz, S. A. & Rost, B. Combined effects of different CO2 levels and N sources on the diazotrophic cyanobacterium Trichodesmium. Physiol. Plant. 152, 316–330 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Hutchins, D. A. et al. Irreversibly increased nitrogen fixation in Trichodesmium experimentally adapted to elevated carbon dioxide. Nat. Commun. 6, 1–7 (2015).
    Google Scholar 
    48.Levitan, O. et al. Combined effects of CO2 and light on the N2-fixing cyanobacterium Trichodesmium IMS101: a mechanistic view. Plant Physiol. 154, 346–356 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Villareal, T. A. & Carpenter, E. J. Buoyancy regulation and the potential for vertical migration in the oceanic cyanobacterium Trichodesmium. Microb. Ecol. 45, 1–10 (2003).CAS 
    PubMed 

    Google Scholar 
    50.Rabouille, S., Staal, M., Stal, L. J. & Soetaert, K. Modeling the dynamic regulation of nitrogen fixation in the cyanobacterium Trichodesmium sp. Appl. Environ. Microbiol. 72, 3217–3227 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Breitbarth, E., Wohlers, J., Kläs, J., LaRoche, J. & Peeken, I. Nitrogen fixation and growth rates of Trichodesmium IMS-101 as a function of light intensity. Mar. Ecol. Prog. Ser. 359, 25–36 (2008).CAS 

    Google Scholar 
    52.Chen, Y. B. et al. Circadian rhythm of nitrogenase gene expression in the diazotrophic filamentous nonheterocystous cyanobacterium Trichodesmium sp. strain IMS101. J. Bacteriol. 180, 3598–3605 (1998).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Rabouille, S., Staal, M., Stal, L. J. & Soetaert, K. Modeling the dynamic regulation of nitrogen fixation in the Cyanobacterium Trichodesmium sp. Appl. Environ. Microbiol. 72, 3217–3227 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Capone, D. G., O’Neill, J. M., Zehr, J. & Carpenter, E. J. Basis for diel variation in nitrogenase activity in the marine planktonic cyanobacterium Trichodesmium thiebautti. Appl. Environ. Microbiol. 56, 3532–3536 (1990).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Gründel, M., Scheunemann, R., Lockau, W. & Zilliges, Y. Impaired glycogen synthesis causes metabolic overflow reactions and affects stress responses in the cyanobacterium Synechocystis sp. PCC 6803. Microbiology 158, 3032–3043 (2012).PubMed 

    Google Scholar 
    56.Jackson, S. A., Eaton-Rye, J. J., Bryant, D. A., Posewitz, M. C. & Davies, F. K. Dynamics of photosynthesis in a glycogen-deficient glgC mutant of Synechococcus sp. strain PCC 7002. Appl. Environ. Microbiol. 81, 6210–6222 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Boatman, T. G., Davey, P. A., Lawson, T. & Geider, R. J. The physiological cost of diazotrophy for Trichodesmium erythraeum IMS101. PLoS ONE 13, 1–24 (2018).
    Google Scholar 
    58.Chappell, P. D., Moffett, J. W., Hynes, A. M. & Webb, E. A. Molecular evidence of iron limitation and availability in the global diazotroph Trichodesmium. ISME J. 6, 1728–1739 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Chappell, P. D. & Webb, E. A. A molecular assessment of the iron stress response in the two phylogenetic clades of Trichodesmium. Environ. Microbiol. 12, 13–27 (2010).CAS 
    PubMed 

    Google Scholar 
    60.Walsby, A. E. The properties and buoyancy-providing role of gas vacuoles in Trichodesmium Ehrenberg. Br. Phycol. J. 13, 103–116 (1978).
    Google Scholar 
    61.Villareal, T. A. & Carpenter, E. J. Diel buoyancy regulation in the marine diazotrophic cyanobacterium Trichodesmium thiebautii. Limnol. Oceanogr. 35, 1832–1837 (1990).
    Google Scholar 
    62.Romans, K. M., Carpenter, E. J. & Bergman, B. Buoyancy regulation in the colonial diazotrophic cyanobacterium Trichodesmium tenue: ultrastructure and storage of carbohydrate, polyphosphate, and nitrogen. J. Phycol. 30, 935–942 (1994).
    Google Scholar 
    63.Wang, L. et al. Molecular structure of glycogen in Escherichia coli. Biomacromolecules 20, 2821–2829 (2019).CAS 
    PubMed 

    Google Scholar 
    64.Berman-Frank, I., Cullen, J. T., Shaked, Y., Sherrell, R. M. & Falkowski, P. G. Iron availability, cellular iron quotas, and nitrogen fixation in Trichodesmium. Limnol. Oceanogr. 46, 1249–1260 (2001).CAS 

    Google Scholar 
    65.Kustka, A. B. et al. Iron requirements for dinitrogen- and ammonium-supported growth in cultures of Trichodesmium (IMS 101): comparison with nitrogen fixation rates and iron:carbon ratios of field populations. Limnol. Oceanogr. 49, 1224 (2004).CAS 

    Google Scholar 
    66.Paerl, H. W., Prufert-Bebout, I. L. E., Guo, C. & Carolina, N. Iron-stimulated N2 fixation and growth in natural and cultured populations of the planktonic marine cyanobacteria Trichodesmium spp. Appl. Environ. Microbiol. 60, 1044–1047 (1994).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Rubin, M., Berman-Frank, I. & Shaked, Y. Dust- and mineral-iron utilization by the marine dinitrogen-fixer Trichodesmium. Nat. Geosci. 4, 529–534 (2011).CAS 

    Google Scholar 
    68.Polyviou, D. et al. Desert dust as a source of iron to the globally important diazotroph Trichodesmium. Front. Microbiol. 8, 1–12 (2018).
    Google Scholar 
    69.Basu, S. & Shaked, Y. Mineral iron utilization by natural and cultured Trichodesmium and associated bacteria. Limnol. Oceanogr. 63, 2307–2320 (2018).CAS 

    Google Scholar 
    70.Held, N. A. et al. Mechanisms and heterogeneity of in situ mineral processing by the marine nitrogen fixer Trichodesmium revealed by single-colony metaproteomics. ISME Commun. 1, 35 (2021).
    Google Scholar 
    71.Basu, S., Gledhill, M., de Beer, D., Prabhu Matondkar, S. G. & Shaked, Y. Colonies of marine cyanobacteria Trichodesmium interact with associated bacteria to acquire iron from dust. Commun. Biol. 2, 1–8 (2019).CAS 

    Google Scholar 
    72.Tyrrell, T. et al. Large-scale latitudinal distribution of Trichodesmium spp. in the Atlantic Ocean. J. Plankton Res. 25, 405–416 (2003).CAS 

    Google Scholar 
    73.Robson, R. L. & Postgate, J. R. Oxygen and hydrogen in biological nitrogen fixation. Annu. Rev. Microbiol. 34, 183–207 (1980).74.Zehr, J. P. Nitrogen fixation by marine cyanobacteria. Trends Microbiol. 19, 162–173 (2011).CAS 
    PubMed 

    Google Scholar 
    75.Bergman, B. & Carpenter, E. J. Nitrogenase confined to randomly distributed trichomes in the marine cyanobacterium Trichodesmium thiebautii. J. Phycol. 27, 158–165 (1991).CAS 

    Google Scholar 
    76.Inomura, K., Wilson, S. T. & Deutsch, C. Mechanistic model for the coexistence of nitrogen fixation and photosynthesis in marine Trichodesmium. mSystems 4, 1–13 (2019).
    Google Scholar 
    77.Janson, S., Matveyev, A. & Bergman, B. The presence and expression of hetR in the non-heterocystous cyanobacterium Symploca PCC 8002. FEMS Microbiol. Lett. 168, 173–179 (1998).CAS 
    PubMed 

    Google Scholar 
    78.Zhang, J. Y., Chen, W. L. & Zhang, C. C. hetR and patS, two genes necessary for heterocyst pattern formation, are widespread in filamentous nonheterocyst-forming cyanobacteria. Microbiology 155, 1418–1426 (2009).CAS 
    PubMed 

    Google Scholar 
    79.Moore, J. K., Doney, S. C., Glover, D. M. & Fung, I. Y. Iron cycling and nutrient-limitation patterns in surface waters of the world ocean. Deep Sea Res. 2 Top. Stud. Oceanogr. 49, 463–507 (2001).
    Google Scholar 
    80.Chisholm, S. W. in Primary Productivity and Biogeochemical Cycles in the Sea (eds Falkowski, P. G. et al.) 213–237 (Springer, 1992).https://doi.org/10.1007/978-1-4899-0762-2_1281.Young, K. D. The selective value of bacterial shape. Microbiol. Mol. Biol. Rev. 70, 660–703 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    82.Lu, X. & Zhu, H. Tube-gel digestion: a novel proteomic approach for high-throughput analysis of membrane proteins. Mol. Cell Proteom. 4, 1948–1958 (2005).CAS 

    Google Scholar 
    83.Saito, M. A. et al. Multiple nutrient stresses at intersecting Pacific Ocean biomes detected by protein biomarkers. Science 345, 1173–1177 (2014).CAS 
    PubMed 

    Google Scholar 
    84.McIlvin, M. R. & Saito, M. A. Online nanoflow two-dimension comprehensive active modulation reversed phase-reversed phase liquid chromatography high-resolution mass spectrometry for metaproteomics of environmental and microbiome samples. J. Proteome Res. 20, 4589–4597 (2021).CAS 
    PubMed 

    Google Scholar 
    85.Lee, M. D. et al. Transcriptional activities of the microbial consortium living with the marine nitrogen-fixing cyanobacterium Trichodesmium reveal potential roles in community-level nitrogen cycling. Appl. Environ. Microbiol. 84, AEM.02026-17 (2017).
    Google Scholar 
    86.Zhang, Y., Wen, Z., Washburn, M. P. & Florens, L. Refinements to label-free proteome quantitation: how to deal with peptides shared by multiple proteins. Anal. Chem. 82, 2272–2281 (2010).CAS 
    PubMed 

    Google Scholar 
    87.Gallien, S., Bourmaud, A., Kim, S. Y. & Domon, B. Technical considerations for large-scale parallel reaction monitoring analysis. J. Proteom. 100, 147–159 (2014).CAS 

    Google Scholar 
    88.Pino, L. K. et al. The skyline ecosystem: informatics for quantitative mass spectrometry proteomics. Mass Spectrom. Rev. 176, 139–148 (2019).
    Google Scholar 
    89.Held, N. A. et al. Mechanisms and heterogeneity of in situ mineral processing by the marine nitrogen fixer Trichodesmium revealed by single-colony metaproteomics. ISME Commun. https://doi.org/10.1038/s43705-021-00034-y (2021).90.White, A. E., Spitz, Y. H. & Letelier, R. M. Modeling carbohydrate ballasting by Trichodesmium spp. Mar. Ecol. Prog. Ser. 323, 35–45 (2006).
    Google Scholar 
    91.Morrison, F. A. An Introduction to Fluid Mechanics (Cambridge Univ. Press, 2013).92.Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
    Google Scholar 
    93.Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    94.Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using NetworkX. In 7th Python Scientific Conference (SciPy 2008) 11–15 (2008). More

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    Neuroanatomy of the nodosaurid Struthiosaurus austriacus (Dinosauria: Thyreophora) supports potential ecological differentiations within Ankylosauria

    1.Thompson, R. S., Parish, J. C., Maidment, S. C. R. & Barrett, P. M. Phylogeny of the ankylosaurian dinosaurs (Ornithischia: Thyreophora). J. Syst. Palaeontol. 10(2), 301–312 (2012).Article 

    Google Scholar 
    2.Arbour, V. M. & Currie, P. J. Ankylosaurid dinosaur tail clubs evolved through stepwise acquisition of key features. J. Anat. 227, 514–523. https://doi.org/10.1111/joa.12363 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Brown, C. M. An exceptionally preserved armored dinosaur reveals the morphology and allometry of osteoderms and their horny epidermal coverings. PeerJ 5, e4066. https://doi.org/10.7717/peerj.4066 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Butler, R. J. & Barrett, P. M. Palaeoenvironmental controls on the distribution of Cretaceous herbivorous dinosaurs. Sci. Nat. 95, 1027–1032 (2008).CAS 
    Article 

    Google Scholar 
    5.Brown, C. M. et al. Dietary palaeoecology of an Early Cretaceous armoured dinosaur (Ornithischia; Nodosauridae) based on floral analysis of stomach contents. R. Soc. Open Sci. 7, 200305. https://doi.org/10.1098/rsos.200305 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Bourke, J. M., Porter, W. R. & Witmer, L. M. Convoluted nasal passages function as efficient heat exchangers in ankylosaurs (Dinosauria: Ornithischia: Thyreophora). PLoS ONE 13, e0207381. https://doi.org/10.1371/journal.pone.0207381 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Mallon, J. C. & Anderson, J. S. The functional and palaeoecological implications of tooth morphology and wear for the megaherbivorous dinosaurs from the dinosaur park formation (Upper Campanian) of Alberta, Canada. PLoS ONE 9(6), e98605. https://doi.org/10.1371/journal.pone.0098605 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Ősi, A., Prondvai, E., Mallon, J. & Bodor, E. R. Diversity and convergences in the evolution of feeding adaptations in ankylosaurs (Dinosauria: Ornithischia). Hist. Biol. 29(4), 539–570. https://doi.org/10.1080/08912963.2016.1208194 (2017).Article 

    Google Scholar 
    9.Hayashi, S., Carpenter, K., Scheyer, T. M., Watanabe, M. & Suzuki, D. Function and evolution of ankylosaur dermal armor. Acta Palaeontol. Pol. 55(2), 213–228 (2010).Article 

    Google Scholar 
    10.Norman, D. B. Scelidosaurus harrisonii from the Early Jurassic of Dorset, England: Cranial anatomy. Zool. J. Linn. Soc. 188(1), 1–81. https://doi.org/10.1093/zoolinnean/zlz074 (2019).Article 

    Google Scholar 
    11.Galton, P. M. Endocranial casts of the plated dinosaur Stegosaurus (Upper Jurassic, Western USA): A complete undistorted cast and the original specimens of Othniel Charles Marsh. In The Armored Dinosaurs (ed. Carpenter, K.) 103–129 (Indiana University Press, 2001).
    Google Scholar 
    12.Galton, P. M. Skull bones and endocranial casts of stegosaurian dinosaur Kentrosaurus Hennig, 1915 from Upper Jurassic of Tanzania, East Africa. Geol. Palaeontol. 22, 123–143 (1988).
    Google Scholar 
    13.Kuzmin, I. et al. The braincase of Bissektipelta archibaldi—new insights into endocranial osteology, vasculature, and paleoneurobiology of ankylosaurian dinosaurs. Biol. Commun. 65(2), 85–156. https://doi.org/10.21638/spbu03.2020.201 (2020).Article 

    Google Scholar 
    14.Leahey, L. G., Molnar, R. E., Carpenter, K., Witmer, L. M. & Salisbury, S. W. Cranial osteology of the ankylosaurian dinosaur formerly known as Minmi sp. (Ornithischia: Thyreophora) from the Lower Cretaceous Allaru Mudstone of Richmond, Queensland, Australia. PeerJ 3, e1475. https://doi.org/10.7717/peerj.1475 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Paulina-Carabajal, A., Lee, Y. N., Kobayashi, Y., Lee, H. J. & Currie, P. J. Neuroanatomy of the ankylosaurid dinosaurs Tarchia teresae and Talarurus plicatospineus from the Upper Cretaceous of Mongolia, with comments on endocranial variability among ankylosaurs. Palaeogeogr. Palaeoclimatol. Palaeoecol. 494, 135–146. https://doi.org/10.1016/j.palaeo.2017.11.030 (2018).Article 

    Google Scholar 
    16.Pereda-Suberbiola, X. & Galton, P. M. On the taxonomic status of the dinosaur Struthiosaurus austriacus Bunzel from the Late Cretaceous of Austria. C. R. Acad. Sci. Paris II 315, 1275–1280 (1992).
    Google Scholar 
    17.Pereda-Suberbiola, X. & Galton, P. M. Revision of the cranial features of the dinosaur Struthiosaurus austriacus Bunzel (Ornithischia: Ankylosauria) from the Late Cretaceous of Europe. Neues Jahrb. Geol. Palaontol. Abh. 191, 173–200 (1994).
    Google Scholar 
    18.Pereda-Suberbiola, X. & Galton, P. M. Reappraisal of the nodosaurid ankylosaur Struthiosaurus austriacus Bunzel from the Upper Cretaceous Gosau Beds of Austria. In The Armored Dinosaurs (ed. Carpenter, K.) 173–210 (Indiana University Press, 2001).
    Google Scholar 
    19.Pereda-Suberbiola, X. A revised census of European Late Cretaceous nodosaurids (Ornithischia: Ankylosauria): Last occurrence and possible extinction scenarios. Terra Nova 4, 641–648 (1992).ADS 
    Article 

    Google Scholar 
    20.Pereda-Suberbiola, X. Ankylosaurian dinosaur remains from the Upper Cretaceous of Laño (Iberian Peninsula). Est. Mus. Cienc. Nat. de Álava 14(Número especial 1), 273–288 (1999).
    Google Scholar 
    21.Pereda-Suberbiola, X., Astibia, H. & Buffetaut, E. New remains of the armoured dinosaur Struthiosaurus from the Late Cretaceous of the Iberian peninsula (Lafio locality, Basque-Cantabric Basin). Bull. Soc. Géol. Fr. 166, 207–211 (1995).
    Google Scholar 
    22.Garcia, G. & Pereda-Suberbiola, X. A new species of Struthiosaurus (Dinosauria: Ankylosauria) from the upper cretaceous of Villeveyrac (Southern France). J. Vertbr. Paleontol 23(1), 156–165 (2003).Article 

    Google Scholar 
    23.Nopcsa, F. Dinosaurierreste aus Siebenbürgen V. Geologica Hungarica, Series Palaeontologica 4, 1–76 (1929).
    Google Scholar 
    24.Codrea, V. et al. More than just Nopcsa’s Transylvanian dinosaurs: A look outside the Haţeg Basin. Palaeogeogr. Palaeoclimatol. Palaeoecol. 293, 391–405. https://doi.org/10.1016/j.palaeo.2009.10.027 (2010).Article 

    Google Scholar 
    25.Ősi, A. & Prondvai, E. Sympatry of two ankylosaurs (Hungarosaurus and cf. Struthiosaurus) in the Santonian of Hungary. Cretac. Res. 44, 30–38. https://doi.org/10.1016/j.cretres.2013.03.006 (2013).Article 

    Google Scholar 
    26.Bunzel, E. Die Reptilfauna der Gosaformation in der Neuen Welt bei Wiener-Neustadt. Abhandlungen der Kaiserlich-Königlichen Geologischen Reichsanstalt. 5, 1–18 (1871).
    Google Scholar 
    27.Franzosa, J. & Rowe, T. Cranial endocast of the Cretaceous theropod dinosaur Acrocanthosaurus atokensis. J. Vertebr. Paleontol. 25, 859–864 (2005).Article 

    Google Scholar 
    28.Witmer, L. M. & Ridgely, R. C. New insights into the brain, braincase, and ear region of tyrannosaurs (Dinosauria, Theropoda), with implications for sensory organization and behavior. Anat. Rec. 292(9), 1266–1296. https://doi.org/10.1002/ar.20983 (2009).Article 

    Google Scholar 
    29.Schade, M., Rauhut, O. W. M. & Evers, S. W. Neuroanatomy of the spinosaurid Irritator challengeri (Dinosauria: Theropoda) indicates potential adaptations for piscivory. Sci. Rep. 10(9259), 1613–1616. https://doi.org/10.1038/s41598-020-66261 (2020).Article 

    Google Scholar 
    30.Witmer, L. M., Ridgely, R. C., Dufeau, D. L. & Semones, M. C. Using CT to peer into the past: 3D visualization of the brain and ear regions of birds, crocodiles, and nonavian dinosaurs. In Anatomical imaging: towards a new morphology (eds Endo, H. & Frey, R.) 67–87 (Springer, 2008).Chapter 

    Google Scholar 
    31.Evers, S. W. et al. Neurovascular anatomy of the protostegid turtle Rhinochelys pulchriceps and comparisons of membranous and endosseous labyrinth shape in an extant turtle. Zool. J. Linn. Soc. 187, 800–828. https://doi.org/10.1093/zoolinnean/zlz063 (2019).Article 

    Google Scholar 
    32.Ősi, A., Pereda Suberbiola, X. & Földes, T. Partial skull and endocranial cast of the ankylosaurian dinosaur Hungarosaurus from the Late Cretaceous of Hungary: Implications for locomotion. Palaeontol. Electro. 17(1), 18p (2014).
    Google Scholar 
    33.Sampson, S. D. & Witmer, L. M. Craniofacial anatomy of Majungasaurus crenatissimus (Theropoda: Abelisauridae) from the Late Cretaceous of Madagascar. J. Vertebr. Paleontol. 27, 32–104. https://doi.org/10.1671/0272-4634(2007)27[32:CAOMCT]2.0.CO;2 (2007).Article 

    Google Scholar 
    34.Walsh, S. A., Barrett, P. M., Milner, A. C., Manley, G. & Witmer, L. M. Inner ear anatomy is a proxy for deducing auditory capability and behaviour in reptiles and birds. Proc. R. Soc. B 276, 1355–1360 (2009).Article 

    Google Scholar 
    35.Porter, W. R. & Witmer, L. M. Vascular patterns in the heads of dinosaurs: Evidence for blood vessels, sites of thermal exchange, and their role in physiological thermoregulatory strategies. Anat. Rec. 303, 1075–1103. https://doi.org/10.1002/ar.24234 (2019).Article 

    Google Scholar 
    36.Benson, R. B. J., Starmer-Jones, E., Close, R. A. & Walsh, S. A. Comparative analysis of vestibular ecomorphology in birds. J. Anat. 231, 990–1018. https://doi.org/10.1111/joa.12726 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Bronzati, M. et al. Deep evolutionary diversification of semicircular canals in archosaurs. Curr. Biol. 31(12), 2520-2529.e6. https://doi.org/10.1016/j.cub.2021.03.086 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    38.Hanson, M., Hoffman, E. A., Norell, M. A. & Bhullar, B. S. The early origin of a birdlike inner ear and the evolution of dinosaurian movement and vocalization. Science 372(6542), 601–609. https://doi.org/10.1126/science.abb4305 (2021).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Miyashita, T., Arbour, V. M., Witmer, L. M. & Currie, P. J. The internal cranial morphology of an armoured dinosaur Euoplocephalus corroborated by X-ray computed tomographic reconstruction. J. Anat. 219, 661–675. https://doi.org/10.1111/j.1469-7580.2011.01427.x (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Paulina-Carabajal, A., Lee, Y. N. & Jacobs, L. L. Endocranial morphology of the primitive nodosaurid dinosaur Pawpawsaurus campbelli from the Early Cretaceous of North America. PLoS ONE 11, e0150845. https://doi.org/10.1371/journal.pone.0150845 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Spoor, F. & Thewissen, J. G. M. Comparative and functional anatomy of balance in aquatic mammals. In Sensory Evolution on the Threshold, Adaptations in Secondarily Aquatic Vertebrates (eds Thewissen, J. G. M. & Nummela, S.) 65–81 (University of California Press, 2008).
    Google Scholar 
    42.Sobral, G. & Müller, J. Archosaurs and their kin: The ruling reptiles. In Evolution of the Vertebrate Ear (eds Clack, J. A. et al.) 285–326 (Springer, 2016).Chapter 

    Google Scholar 
    43.Arbour, V. M. Estimating impact forces of tail club strikes by ankylosaurid dinosaurs. PLoS ONE 4(8), e6738. https://doi.org/10.1371/journal.pone.0006738 (2009).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Park, J. Y. et al. A new ankylosaurid skeleton from the Upper Cretaceous Baruungoyot Formation of Mongolia: Its implications for ankylosaurid postcranial evolution. Sci. Rep. 11, 4101. https://doi.org/10.1038/s41598-021-83568-4 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Witmer, L. M., Chatterjee, S., Franzosa, J. & Rowe, T. Neuroanatomy of flying reptiles and implications for flight, posture and behaviour. Nature 425, 950–953. https://doi.org/10.1038/nature02048 (2003).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    46.Walsh, S. A. et al. Avian cerebellar floccular fossa size is not a proxy for flying ability in birds. PLoS ONE 8(6), e67176. https://doi.org/10.1371/journal.pone.0067176 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Ferreira-Cardoso, S. et al. Floccular fossa size is not a reliable proxy of ecology and behaviour in vertebrates. Sci. Rep. 7(1), 2017. https://doi.org/10.1038/s41598-017-01981-0 (2005).CAS 
    Article 

    Google Scholar 
    48.Ezcurra, M. D. et al. Enigmatic dinosaur precursors bridge the gap to the origin of Pterosauria. Nature 588(7838), 445–449. https://doi.org/10.1038/s41586-020-3011-4 (2020) (Epub 2020 Dec 9).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    49.Carpenter, K., Sanders, F., McWhinney, L. A. & Wood, L. Evidence for predator–prey relationships: examples for Allosaurus and Stegosaurus. In The Carnivorous Dinosaurs (ed. Carpenter, K.) 325–350 (Indiana University Press, 2005).
    Google Scholar 
    50.Mallison, H. CAD assessment of the posture and range of motion of Kentrosaurus aethiopicus HENNIG 1915. Swiss. J. Geosci. 103, 211–233. https://doi.org/10.1007/s00015-010-0024-2 (2010).Article 

    Google Scholar 
    51.Arbour, V. M., Lech-Hernes, N. L., Guldberg, T. E., Hurum, J. H. & Currie, P. J. An ankylosaurid dinosaur from Mongolia with in situ armour and keratinous scale impressions. Acta Palaeontol. Pol. 58(1), 55–64. https://doi.org/10.4202/app.2011.0081 (2013).Article 

    Google Scholar 
    52.Scheyer, T. M. & Sander, P. M. Histology of ankylosaur osteoderms: Implications for systematics and function. J. Vertebr. Paleontol. 24, 874–893 (2004).Article 

    Google Scholar 
    53.Carpenter, K. Ankylosauria. In The Complete Dinosaur 2nd edn (eds Brett-Surman, M. K. et al.) 505–526 (Indiana University Press, 2012).
    Google Scholar 
    54.Jerison, H. J. Evolution of the brain and intelligence 482 (Academic Press, 1973).
    Google Scholar 
    55.Marugan-Lobon, J., Chiappe, L. M. & Farke, A. A. The variability of inner ear orientation in saurischian dinosaurs: Testing the use of semicircular canals as a reference system for comparative anatomy. PeerJ 1, e124 (2013).Article 

    Google Scholar 
    56.Benoit, J. et al. A test of the lateral semicircular canal correlation to head posture, diet and other biological traits in “ungulate” mammals. Sci. Rep. 10, 19602. https://doi.org/10.1038/s41598-020-76757-0 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Coombs, W. P. Jr. Forelimb muscles of the Ankylosauria (Reptilia, Ornithischia). J. Paleontol. 52, 642–657 (1978).
    Google Scholar 
    58.Maidment, S. C. R. & Barrett, P. M. Osteological correlates for quadrupedality in ornithischian dinosaurs. Acta Palaeontol. Pol. 59(1), 53–70 (2014).
    Google Scholar  More

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    Multiple bacterial partners in symbiosis with the nudibranch mollusk Rostanga alisae

    Symbiont diversity and distributionThe present study provides the first evidence of symbiosis in R. alisae, a species of nudibranchs. This is the most multiple symbiosis that have ever been recorded for marine invertebrates. While many organisms establish an exclusively one-on-one relationship with a single microbial species or microbes belonging to the same functional group5,12, there are also organisms that harbor multiple microbial species, in which symbiont–symbiont and host–symbiont interactions occur. Six phylotypes of chemoautotrophic bacteria were reported for mussel Idas sp. from a cold seep area11 and five extracellular symbionts for the gutless oligochaete worm Olavius algarvensis34. However, in these cases, symbioses involving bacteria and marine invertebrates are either endosymbiotic microbes co-occurring inside the host bacteriocytes5,11 or ectosymbiotic microbes associated with the external surfaces of the animals3,4,9,15,34, with the exception of scaly-foot snail from hydrothermal vents having partnerships simultaneously with epi- and endosymbiontic microbes35.Bacterial symbionts in R. alisae have appeared to be more diverse than was previously known for marine invertebrates. It is evident that the detected symbiont phylotypes differ greatly from all other known symbionts found in marine invertebrates. Labrenzia (Rodobacteriales) and Maritalea (Rhizobiales) have not been recorded as forming symbiotic associations with invertebrates or plants so far, although other members of the families Rodobacteriales and Rhizobiales are well known symbionts14. Strains of Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas are reported as symbionts of plants, interacting with a vast majority of nodulating legume species and efficient in biological nitrogen fixation36. This may be important when considering the nature of these symbionts in the nudibranch. Symbioses between cyanobacteria and marine organisms are commonly found among marine plants, fungi, sponges, ascidians, corals, and protists37,38. Synechococcus, identified as dominant symbiont clones of R. alisae (Table S2), is a unicellular cyanobacterium common in the marine environment, providing a range of beneficial functions including photosynthesis, nitrogen fixation, UV protection, and production of defensive toxins8,9,37. Symbiotic interactions between actinobacteria and their host have been observed in insects, human, animals, and plants, where the bacteria provide the host with protection against pathogens and produce essential nutrients39. However, none of the members of the clade Actinobacteria recorded in R. alisae are known to live symbiotically.Arrangement of symbiotic associationDespite the high diversity of bacteria, they are well organized in the host. Dense clusters of rod-shaped bacteria, Labrenzia, Maritalea, Bradyrhizobium, Burcholderia, Achromobacter, and Stenotrophomonas, were found within host-derived vacuoles, referred to as bacteriocytes, inside epithelial cells of R. alisae (Fig. 3). Although such arrangement differs from that typical of bacteriocytes, which are usually considered as specialized cells of the hosts for harboring bacteria, it resembles that reported for scaly-food snail from hydrothermal vents, which harbor symbionts in the esophageal gland35. Bacteriocytes in the gastropod Lurifax vitreus found near hydrothermal vents also constitute a portion of the mantle epithelium; they have large vacuoles containing many live and dividing bacteria40. Each bacteriocyte was densely packed with certain symbionts, and the bacteriocytes were randomly distributed within the epithelium cells. A distinctly regular distribution pattern was observed in the gill epithelium of the mussel Bathymodiolus sp.: the thiotrophic symbionts occupy the apical region, and the methanotrophic symbionts are more abundant in the basal region of bacteriocytes4. In the mussel Idas sp., however, there is no spatial pattern of the six distinct bacterial phylotypes, and the symbionts are mixed within bacteriocytes11.Synechococcus dominated the cytoplasm of intestinal epithelium and, more rarely, epidermis cells, mainly as specialized cell type referred to as nitrogen-fixing heterocysts. They are visually similar to cyanobacteria from corals and sponges8,37.The phylogenetic diversity and the spatial organization of the symbiotic community in R. alisae were determined by the 16S rRNA analysis, which was consistent with the results of FISH and TEM. Unlike most symbioses of marine invertebrates when bacteria house specialized host cells5,11 or cover epidermis7,15, symbiotic association of R. alisae exhibited spatial partitioning between symbionts, which were unevenly distributed between the tissues (Table S2). It has been established that different members of the microbial community can complement each other in acquisition of various restrictive nutrients, confirming the importance of the functional diversity of symbionts41. Thus, Stenotrophomonas rhizophila and Bradyrhizobium build a beneficial association in the rhizosphere and can act synergistically on promoting growth and nutrient uptake of soybean36. Cyanobacteria can interact synergistically with beneficial members from the endophytic microbiome of rice seedlings42. The location of bacterium in the organism of R. alisae may, in fact, depend on the specific metabolic and ecological roles that the symbionts play, and also on the interaction with bacterium belonging to different physiological groups.Nature of symbiosisSymbiotic associations between microbes and invertebrates are acquired mainly in a nutrient-depleted environment where symbionts usually provide their hosts with essential nutrients and high-energy compounds1. In contrast to known symbioses between microbes and gutless invertebrates, which obtain nutrients exclusively from the bacteria, R. alisae, like most nudibranch species, is a sponge-eating predator. However, due to the lack of adipose tissue, sponges are distinguished by a low lipid content (0.4 to 1.5% of wet weight)43 and also by specific proteinaceous spongin fibers and chitin, a polysaccharide similar to cellulose that can be indigestible for some predators, which together indicate their low nutritional value. Furthermore, R. alisae feeds exclusively on the sponge O. pennata; therefore, in habitats with low prey availability, this nudibranch has to survive starvation while searching for sponge assemblages. We suppose that symbiotic bacteria of R. alisae contribute to the utilization of low-quality food, similarly to symbiotic bacteria from the genera Rhodobacter, Burkholderia, and Aeromonas associated with the detritivorous isopod Asellus aquaticus44.A fatty acid analysis, as a useful approach to clarifying the nature of symbiosis5,20,32, has confirmed the trophic interaction between symbionts and the nudibranch host (Table S2). Among the fatty acids of symbiotic bacteria in R. alisae, OBFA are a major acyl constituent of membranes in Stenotrophomonas45 and also in Actinobacteria, Arthrobacter, Iamia, Ilumatobacter, and Kocuria46. Cis-vaccenic acid is a major component of Maritalea30. Omega-cyclohexyl tridecanoic acid (cyclo19:0) is specific to Bradyrhizobium47, Burkholderia, and Achromobacter48. Linoleic acid is produced by cyanobacteria including marine species of Synecoccocus33; in nudibranch, it obviously serves as a precursor in the synthesis of arachidonic acid (20:4n-6), thus, providing additional evidence for the transfer of fatty acids from symbionts to the host. Mollusks are capable of converting linoleic acid to arachidonic acid, since they have enzymes required for its synthesis21. The presence of these bacteria-specific markers and the abundance of arachidonic acid confirm the metabolic role of symbionts in the nudibranch host.Among nutrients, biologically available nitrogen can be considered a restrictive nutrient for the sponge-eating R. alisae, which can be acquired with the help of nitrogen-fixing symbionts, also referred to as diazotrophs. R. alisae harbors Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas that are efficient in biological nitrogen fixation previously found to be associated with nodulating legume species36. Symbiotic nitrogen fixers are known to be associated with a variety of marine invertebrates such as wood-boring bivalves, corals, sponges, sea urchins, tunicates, and polychaetes7,8,37. Moreover, the protection of the enzyme nitrogenase that catalyzes N2 fixation against oxygen is an important physiological requirement in bacteria such as symbiotic Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas that are located in bacteriocytes and provide this protection. Synechococcus is known as a nitrogen-fixer37,49. It performs N2 fixation in heterocysts where nitrogenase is restricted under oxic conditions. Indeed, heterocysts of Synechococcus are abundant in the intestine cells of R. alisae (Fig. 5B–D).Nitrate assimilation is one of the major processes of nitrogen acquisition by many heterotrophic bacteria and cyanobacteria50,51. Symbionts of R. alisae can play an important role in the process of nitrate utilization through denitrification, dissimilatory nitrate reduction, and assimilatory nitrate reduction as a nitrogen source and synthesize it into organic nitrogen. The nitrate reducers, Labrenzia52, Stenotrophomonas53, Maritalea30, and Rhodobacteraceae29 are widely represented in R. alisae. Synechococcus also utilizes nitrate, nitrite, or ammonium for growth50. Thus, symbiotic bacteria may play a significant role in the N-budget of the nudibranch mollusk.The symbiotic bacteria of R. alisae, including Bradyrhizobium, Maritalea, Labrenzia, Burkholderia, Achromobacter, Stenotrophomonas, Arthrobacter, Iamia, Ilumatobacter, and Kocuria, are known as carboxydotrophic or carbon monoxide (CO) oxidizers54,55. Despite the toxicity of CO for multicellular organisms, numerous aerobic and anaerobic microorganisms can use CO as a source of energy and/or carbon for cell growth56. The marine worm Olavius algarvensis establishes symbiosis with chemosynthetic bacteria using CO, a substrate previously not known to play a role in symbiotic associations with marine invertebrates, as an energy source57. We do not rule out that the R. alisae symbionts also might exploit CO as carbon and energy source. Despite this, assumption may seem impossible taking in account the CO toxicity, but, since many invertebrates (mollusks, tube worm, etc.) use toxic sulfate, thiosulfate, and methane as an energy source1,15, this hypothesis is worth to be addressed.An important component of skeleton in marine sponges of the family Microcionidae, including O. pennata, is the structural polysaccharide chitin58. Some bacteria are capable of hydrolyzing chitin via the activity of chitinolytic enzymes and can utilize chitin as a source of carbon, nitrogen, and/or energy59. Chitinase activity was documented for strains of Labrenzia60, Burkholderia61, Arthrobacter62, Achromobacter63, Stenotrophomonas64, Alcaligenes65, and actinobacteria59 associated with R. alisae. Thus, these bacteria can work synergistically to digest chitin and spongin, contributing to feeding success of the host nudibranch which depends solely on low-quality, nitrogen- and carbon-deficient food available.Furthermore, direct evidence has confirmed that many bioactive compounds from invertebrates are produced by symbiotic microorganisms66,67. Many biologically active compounds including toxic and deterrent secretions have been identified in nudibranchs of the family Discodorididae68. Symbiotic bacteria may exhibit toxic activity to provide the host nudibranch with chemical defense against predators and environment. Bacteria, especially actinobacteria, living in a symbiotic relationship with R. alisae may help the host in defense, since nudibranch lack a shell, and secondary metabolites of bacteria can provide them with chemical defense against predators and environment, as has been reported for some marine invertebrates2,9,10.In complex associations, the integration and coexistence of symbionts depend on supplementary partnerships and mutual contribution to the host’s metabolism41. The most intensively studied cases are highly specialized associations, where both partners can only exist in close relationship with one another. The relatively high diversity of microbes in R. alisae complicates understanding the complex pattern of molecular and cellular interactions between the host and its symbionts. More