Philippa Kaur

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The broad categories of data included in the repository are summarized in Online-only Table 2 and visualized in Fig. 2. Each category is explained in greater detail below, while full details together with accompanying notes are given in the repository (Database_structure.csv) and in Supplementary File 1. Online-only Table 2 gives an overview of data coverage per category, both across all species and for native species separately. A complete list of data sources is available in Supplementary File 2.Fig. 2Visualization of the attributes presented in the database.Full size imageGeneration of the species listTaxon names listed in the most recent and widely accepted New Flora of the British Isles’ index12 were digitized via the Optical Character Recognition Software ReadirisTM 17 (IRIS). Results from the digitization were transferred into a spreadsheet and obvious recognition errors were fixed. The resulting table contained 5,687 taxa and associated taxonomic authorities. A total of 360 unnamed hybrids were excluded, as well as species noted to have only questionable or unconfirmed records, leaving 5,038 species. Forty-one intergeneric hybrid species, 827 entries relating to (notho)subspecies, (notho)varieties, cultivars and forma were also removed along with 720 named hybrids. Species that were included by Stace12 but which he considered not to be part of the flora (i.e. listed as ‘other species’ and ‘other genera’, e.g. genus Tragus or Coreopsis verticillata) were also excluded. Seven species that were labelled ‘extinct’ in the flora were included as there were indications that the species might be in the process of reintroduction (e.g. Bromus interruptus, Bupleurum falcatum and Schoenoplectus pungens). Extinct native and archaeophyte species without any signs of reintroduction (e.g. Dryopteris remota) are also listed but no additional data are provided and they are not included in calculations of completeness of data (Online-only Table 2). The final number of extant species listed here is therefore 3,209 (comprising 1,468 natives, 1,690 aliens and 51 species with unknown status), plus 18 formally extinct species (natives and archaeophytes not seen in the study region since 1999). Species names and taxonomic authorities were revised according to the 2021 reprint of the New Flora of the British Isles, communicated to us by C.A.S. ahead of publication. Genera with less well-defined species – for example due to apomixis – contain additional information on subgenera, sections, and aggregates, as per Stace12. Since misidentifications are common in these groups, we include a column termed ‘unclear_species_marker’ that allows for these species to be quickly identified and excluded from analyses if appropriate. Such genera are often incompletely listed in our database since most microspecies are not sufficiently well defined.TaxonomyNomenclature of the list was checked by Global Names Resolver in the R package ‘taxize’20,21, using the International Plant Names Index (IPNI)22 as the data source, to remove any digitisation errors. Resolved names were used to determine accepted higher taxonomic hierarchy (family, order) again using taxize, with the National Center for Biotechnology Information (NCBI) database. Species that could not be resolved by the Global Names Resolver or did not yield matches in the NCBI database for their higher taxonomic ranks were manually checked for name matches in the World Checklist of Vascular Plants (WCVP)17. Species within the original species list that were found to be identical to a different spelling in WCVP were retained in the database. In such instances, and when slight spelling differences occurred, the columns ‘taxon_name‘ and ‘taxon_name_WCVP‘ differ. To improve clarity, each species is presented here with its unique identification number according to the WCVP (listed as ‘kew_id’) together with three additional columns (i.e. WCVP.URL, POWO.URL and IPNI.URL) which contain hyperlinks to the freely accessible taxon description websites of the (WCVP)17, Plants of the World Online (POWO)23 and (IPNI)22, respectively. Thus, while the taxon names used in the database correspond to those used by Stace12, changes in the accepted species name since publication can be traced in columns ‘taxonomic_status’ and ‘accepted_kew_id’. The family classification of WCVP follows APG IV24 for angiosperms, Christenhusz et al. (2011)25 for gymnosperms and Christenhusz & Chase (2014)26 for ferns and lycopods.Native statusWe offer three different datasets which describe the status of a species as native or non-native, and its level of establishment in BI. The first is extracted from Stace (2019)12, the second contains the status codes used in PLANTATT10 and the unpublished ALIENATT (pers. comm. author K.J.W.) dataset, and the third is extracted from Alien Plants13. The status from Stace12 and Stace & Crawley13 assigns a species to either native or alien status, with aliens subdivided into archaeophytes and neophytes at different levels of establishment (e.g. denizen, colonist etc., see Online-only Table 1). Status codes from the BSBI can be either AC (alien casual), AN (neophyte), AR (archaeophyte), N (native), NE (native endemic) or NA (native status doubtful).Functional traitsData for five ecologically relevant functional traits (i.e. seed mass, specific leaf area [SLA], leaf area, leaf dry matter content [LDMC] and vegetative height) were downloaded from public data available in the TRY database27 (for specific authors see Supplementary File 1 and Supplementary File 2). Averages were calculated using the available measurements downloaded for each species, excluding rows where the measurement was 0. In addition, the maximum vegetative height for each species is given, where available.Realized niche descriptionRealized niche descriptions based on assessments made on plants living in BI are given in the form of Ellenberg indicator values18, as published in PLANTATT10. Ellenberg indicator values place each species along an environmental gradient (e.g. light or salinity) by assigning a number on an ordinal scale, depending on the species preference for the specific gradient (Online-only Table 2). This information is often used to gain insights into environmental changes based on species occurrences28. For species listed under a previously accepted name in PLANTATT, the information was associated with the accepted synonym in Stace (2019)12. Due to the low coverage of PLANTATT for non-native species included in our list, we additionally include Ellenberg indicator values based on Central European assessments, as made available by Döring29. Each Ellenberg category is listed in a separate column, keeping the information from both data sources separate to avoid confounding of assessments based on two different regions (i.e. Britain and Ireland versus Central Europe).Life strategyTo characterize the life strategy of a species, we used the CSR scheme developed by Grime19, which classifies each species as either a competitor (C), stress tolerator (S), ruderal (R) or a combination of these (e.g. CS, SR). CSR classifications were obtained from the Electronic Comparative Plant Ecology database30. Due to the low coverage of available CSR assessments for species in our database (i.e. data available for just 460 out of 3,209 species) we imputed CSR strategies for a further 981 species using available functional trait data, following the method proposed by Pierce et al.31. The functional leaf traits required for this method – i.e. specific leaf area, leaf area, leaf dry matter content – were obtained from the TRY database27. Pre-existing30 and newly imputed CSR strategies are listed in separate columns.Growth form, succulence and life-formPlant growth form descriptions were obtained from the TRY database27 and filtered for those entries given by specific contributors (Online-only Table 2) to maintain consistent use of growth form categories. Information on whether a species was considered to be a succulent was obtained by screening the entire growth form information obtained from the TRY database for the phrase ‘succulence’ or ‘succulent’.Species life-form categories according to Raunkiaer32 were determined for each species in our dataset with regard to the typical life-form of the species as it grows in BI (pers. comm. M.J.M.C.).Associated biome and originInformation given in the Ecoflora database3 for the biome that each species is associated with was matched to the species names according to Stace12. The recognized biome categories follow Preston & Hill33 and are ‘Arctic montane’, ‘Boreal Montane’, ‘Boreo-Arctic Montane’, ‘Boreo-Temperate’, ‘Mediterranean’, ‘Mediterranean-Atlantic’, ‘Southern Temperate’, ‘Temperate’, ‘Wide Boreal’ and ‘Wide Temperate’.For non-native species, the assumed origin (i.e. the region that plants were most likely to have been introduced to BI from, rather than the full non-BI distribution of a species) was adapted from Stace12 into a brief description of their country or region of origin. In addition, these descriptions were manually allocated to the TDWG level 1 regions listed in the World Geographical Scheme for Recording Plant Distributions (WGSRPD, TDWG)34.Species distributionsDistribution metrics for each species are given as the number of 10-km square hectads in BI with records for the species in question within a specified time window. The data were derived from the BSBI Distribution Database35 and were extracted for each species, dividing the study region into Great Britain (incl. Isle of Man), Ireland and the Channel Islands, as previously partitioned for data available in PLANTATT10. The database was queried using species and hectads for grouping, showing only records ‘matching or within 2 km of county boundary’ and excluding ‘do-not-map-flagged occurrences’. The data were not corrected for sampling bias and should therefore only be used as an indication of trends.Hybrid propensityData on hybridization is provided for 641 species, obtained from the Hybrid flora of the British Isles36 which enumerates every hybrid reported in BI up until 2015 (pers. comm. M.R.B.). Each entry was transcribed manually, and then filtered to exclude (a) hybrids that have been recorded, but not formed in the British Isles, (b) triple hybrids (mainly reported for the genus Salix), (c) doubtful records, (d) hybrids between subspecific ranks, and (e) hybrids where at least one parent is not native (only archaeophytes included). This left 821 hybrid combinations for data aggregation. The metric chosen here is hybrid propensity, which is a per-species metric of how many other species a focal species hybridizes with (sensu Whitney et al., 201037). A scaled hybrid propensity metric is also given which was calculated by weighting the hybrid propensity score by the number of intrageneric combinations for a given genus, to account for the greater opportunities of hybridization in larger genera.DNA barcodesDNA barcode sequences for plant species present in BI are currently available for 1,413 species in our database. The information was derived from a dataset of rbcL, matK and ITS2 sequences compiled for the UK flora generated by the National Botanic Garden of Wales and the Royal Botanic Garden Edinburgh38,39 (pers. comm. L.J. and N.D.V.). The data are given as a hyperlink to the record’s page on the Barcode of Life Data Systems (BOLD40) which includes the DNA barcode sequences as well as scans of the herbarium specimen and information on the sample’s collection. Most species have multiple record pages associated with them, due to the sampling of more than one individual. We include a maximum of three BOLD accessions per species; the full range of individuals sampled can be accessed via the original publications38,39. DNA barcodes are almost exclusively available for native species. Future releases of our database will increase the coverage of the non-native flora significantly. Where species in the BOLD database are attributed to a species name that is considered synonymous with another name in our list, the hyperlink is matched to the latest nomenclature12. 1,421 species have at least one sequence associated with them and 935 species have sequence data for all three sequences (rbcL, matK and ITS2).Genome size and chromosome numbersGenome size data for 2,117 specimens (at least one measurement per species) were obtained from various sources. Measurements for a total of 467 species were newly estimated using plant material of known BI origin, often sourced  from the Millennium Seedbank of the Royal Botanic Gardens, Kew (RBG Kew)41. The measurements were made by flow cytometry using seeds or seedlings and following an established protocol42. Information on the extraction buffers and calibration standard species used are available in the file GS_Kew_BI.csv, along with peak CV values of the measurements as a quality control. Where more than one measurement is reported per species, the measurements were made on plant material from different populations or using different buffers. Previously published data for additional species were obtained from reports on the Czech flora43, the Dutch flora44, and prime values listed in the Plant DNA C-values database45,46. Since significant intraspecific differences in genome size between plant material from different geographical origins have previously been described, predominantly due to cytotype diversity in ploidy level47, genome size measurements from previously published sources were assessed with regard to the origin of the material. The column ‘from_BI_material’ (GS_BI.csv, BI_main.csv) allows users to filter for measurements made on material from BI to exclude a potential bias. The information was obtained from the original publication source of each measurement.Chromosome numbers for 1,410 species (at least one chromosome number per species) determined exclusively from material collected in BI were obtained from an extensive dataset compiled by R.J.G. from various published studies, unpublished theses and personal communications from trusted sources. The counts were made between 1898 and 2017, with a large proportion stemming from efforts to achieve greater coverage of the flora by a team of cytologists based at the University of Leicester and headed by R.J.G. Part of the dataset was previously incorporated into the BSBI’s data catalogue5 but has since undergone revisions to incorporate new information and changes in taxonomy. The dataset contained many measurements at subspecies level which were allocated to the species level taxon in our list. This served to include as much of the often considerable infraspecific variation as possible. Since some species for which chromosome counts have been reported elsewhere are lacking chromosome counts from British or Irish material, they are absent from this dataset. To fill such gaps, we also present chromosome numbers from reports on the Czech flora43, the Dutch flora44, and the Plant DNA C-values database45,46. More

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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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OBITUARY
10 January 2022

Edward O. Wilson (1929–2021)

Naturalist, conservationist and synthesizer who founded sociobiology.

Bert Hölldobler

0

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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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