Silas Tittes

Anne Lorant

Sean Mcginty

James B Holland

Jose De

Jesus Sánchez-González

Arun Seetharam

Maud Tenaillon

Jeffrey Ross-Ibarra

L Wang

K Crosby

A Durvasula

M B Hufford

M R Bellon

A Mastretta-Yanes

A Ponce-Mendoza

D Ortiz

Not so local: the population genetics of convergent adaptation in maize and teosinte

Keywords: Local Adaptation, Convergent Adaptation, Maize, Teosinte Consistent

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

As populations diverge and occupy new regions, they become locally adapted to the novel ecological conditions that they encounter. Decades of empirical work have carefully documented evidence for local adaptation, including the use of common garden and reciprocal transplant studies demonstrating that populations express higher fitness in their home environment [START_REF] Clausen | Experimental studies on 941 the nature of species. iii. environresponses of climatic races of achillea. 942 Experimental studies on the nature of species. III. Environresponses 943 of climatic races of Achillea[END_REF] as well as quantitative genetic approaches that show selection has acted on individual traits to make organisms better suited to their ecological conditions [START_REF] Savolainen | Ecological genomics of 1091 local adaptation[END_REF]. It is clear from these studies that local adaptation is pervasive in natural populations.

One important but understudied aspect of local adaptation is its geographic scale. Empirical studies have documented adaptation at multiple scales, from microgeographic differentiation among mesic and xeric habitats along a single hillside (Hamrick and Allard 1972) to regional [START_REF] Lowry | Ecological reproductive isolation of coast and inland races of mimulus guttatus[END_REF][START_REF] Whitehead | Functional genomics of physiological plasticity and local 1137 adaptation in killifish[END_REF] and even global scales [START_REF] Colosimo | Widespread parallel evolution in sticklebacks by repeated 946 fixation of ectodysplasin alleles[END_REF]. A key factor determining the geographic scale of local adaptation is the distribution of the biotic and abiotic challenges to which organisms are adapting, as these features place limits on the 1 For correspondence, Dept. of Evolution and Ecology, University of California, Davis, CA, USA E-mail: stittes@ucdavis.edu; rossibarra@ucdavis.edu locations over which an allele remains beneficial. Environmental 23 features overlap with each other to varying degrees (Tuanmu 24 and Jetz 2015). The degree of overlap between environmen-25 tal features may be important if mutations are pleiotropic, as 26 an allele may not be beneficial when integrating its effect over 27 multiple selective pressures (Chevin et al. 2010).

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The geographic scale of local adaptation depends, too, on 29 population structure. [START_REF] Gossmann | Genome wide analyses reveal little evidence for adaptive 990 evolution in many plant species[END_REF] showed that the cal adaptation across the entire genome, and how similar the general patterns are across taxa.

The majority of local adaptation studies are motivated by conspicuous differences in the phenotypes or environments of two or more populations. As such, many instances of local adaptation that are occurring, as well as the underlying beneficial mutations being selected, may go overlooked. This hinders our ability to draw more general conclusions about the overall frequency and impact of local adaptation on a given population's evolutionary history.

Using population genetic approaches, we can compare the observed distribution of beneficial alleles across multiple populations to get a more holistic description of the history of selection.

The patterns of selective sweeps and adaptive fixations that are exclusive to or shared among multiple populations can be used to measure a beneficial allele's geographic extent, which is influenced by the factors outlined above. If we infer multiple structured populations have fixed the same beneficial allele, this suggests that pleiotropy has not disrupted the adaptive value of the allele across environments or that the populations share a sufficiently similar set of selective pressures. Assessment of the relative frequency and geographic extent of unique and shared beneficial alleles thus allows us to quantify the scale of local adaptation. Additionally, when multiple populations do share an adaptive allele, we can infer the mode by which sharing occurred [START_REF] Lee | Distinguishing among modes of convergent adaptation using population genomic data[END_REF], providing further insights about the environmental and genetic context of each adaptation as well as the processes underlying allele sharing among populations.

Motivated to improve our understanding about the genetic basis of local adaptation and its geographic scale, we set out to use population genetic approaches to understand patterns of adaptation via selective sweeps in multiple discrete populations of domesticated maize Zea mays ssp. mays and its wild relative teosinte Zea mays ssp. parviglumis growing across their native range in Mexico. Zea mays is an annual grass, native to southern Mexico. Maize was domesticated ≈ 9, 000 years ago [START_REF] Piperno | Starch grain and phytolith evidence for early ninth millennium bp maize from the central balsas river valley, mexico[END_REF]) from its common ancestor with the extant annual grass teosinte, but traditional open-pollinated populations maintain an extremely large population size and a surprising amount of diversity (Bellon et al. 2018). Maize is also the world's most productive crop [START_REF] Ranum | Global maize production, utilization, and consumption[END_REF], and an important model system for genetics [START_REF] Nannas | Genetic and genomic toolbox of zea mays[END_REF].

Previous work in both maize and teosinte has demonstrated clear population structure at both regional [START_REF] Pyhäjärvi | Complex patterns of local adaptation in teosinte[END_REF] and fine [START_REF] Van Heerwaarden | Fine scale genetic 1123 structure in the wild ancestor of maize (zea mays ssp. parviglumis)[END_REF][START_REF] Hufford | Influence of cryptic 1006 population structure on observed mating patterns in the wild pro-1007 genitor of maize (zea mays ssp. parviglumis)[END_REF] scales, and population genetic and common garden studies in both subspecies have shown clear signatures of populations being adapted to ecological conditions across their native range [START_REF] Janzen | Demonstration of local adaptation of maize landraces by reciprocal transplantation[END_REF]. In maize this includes local adaptation to high elevation [START_REF] Fustier | Common gardens in teosintes reveal the 980 establishment of a syndrome of adaptation to altitude[END_REF][START_REF] Gates | Single-gene resolution of locally adaptive genetic variation in 984 mexican maize[END_REF], phosphorous [START_REF] Rodriguez-Zapata | Teosinte introgression modulates phosphatidyl-1084 choline levels and induces early maize flowering time[END_REF], temperature (Butler and Huybers 2013), and day length [START_REF] Swarts | Ge-1102 nomic estimation of complex traits reveals ancient maize adaptation 1103 to temperate north america[END_REF] the f4 statistic. Overall, we found little evidence of increased gene flow between sympatric pairs (Figure 1E), but Z-Scores 171 were sensitive to the specific combinations of non-focal popu-172 lations included in each test (Table S2). Specifically, we found that elevated f4 tests almost always included the maize popu-174 lation from Crucero Lagunitas (p < 2 × 10 -10 ), which was true whether or not the f4 test included its sympatric teosinte. Maize D q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q Amatlan de Canas q q q q q q q q q q q q q Maize Teosinte 0.000 0.005 0.010 0.015 Population π A q q q q q q q q q q q q q Maize Teosinte Emprical HBD length quantiles (cM) Simualted quantiles D q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q qq q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q qq q q q q q q q q q q qq q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq qq q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q qq q q q q q q q q q q q q qq q q q q q q q q q q q q q q qq q q q q q qq q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q qq q qq q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q Maize Teosinte S2).

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We independently estimated the demographic history for This indicates that the inbreeding history of our population is adequately captured by the demography. However, consistent with previous studies of teosinte [START_REF] Hufford | Genetic and ecological approaches to guide conserva-1003 tion of teosinte (Zea mays ssp. parviglumis), the wild progenitor of maize[END_REF], we do see variation in the distributions of HDB among populations. For example, the size distribution of HBD segments in San Lorenzo and Los Guajes were consistently larger than those simulated from their demographies, particularly for the smallest segments.

This likely reflects inbreeding caused by demographic changes, particularly those further in the past that may not be not as accurately captured by our demography inferences. These results are consistent with previous studies that found evidence for historical inbreeding in teosinte, particularly in individuals sampled from San Lorenzo [START_REF] Pyhäjärvi | Complex patterns of local adaptation in teosinte[END_REF]. Lastly, we estimated inbreeding coefficients (F) using ngsRelate [START_REF] Hanghøj | Fast and accurate relatedness estimation from high-throughput 1001 sequencing data in the presence of inbreeding[END_REF]. Although inbreeding coefficients were as high as 0.37, the mean value of F was 0.017 ± 0.001 (SE) and 0.033 ± 0.001 (SE) for maize and teosinte (respectively), (Figure 2E). These values are consistent with prior estimates of the rate of outcrossing of ≈ 3% in teosinte [START_REF] Hufford | Influence of cryptic 1006 population structure on observed mating patterns in the wild pro-1007 genitor of maize (zea mays ssp. parviglumis)[END_REF] and suggest there has been relatively little inbreeding in either subspecies in the recent past.

Rangewide estimates of the proportion of mutations fixed by natural selection (α) are commensurate with that of individual populations

If populations are relatively isolated and adaptation occurs primarily via local selective sweeps, then we expect that most adaptive fixations will happen locally in individual populations rather than across the entire species range. This pattern should become even stronger if alleles experience negative pleiotropy, such that they are only adaptive in one environment and deleterious in others, further inhibiting the ability of such alleles to fix rangewide. If adaptation via sweeps is commonly restricted to individual populations, using a broad geographic sample to represent a population could underestimate the number of adaptive substitutions that occur [START_REF] Gossmann | Genome wide analyses reveal little evidence for adaptive 990 evolution in many plant species[END_REF]). To test this, we estimated the proportion of mutations fixed by adaptive evolution (α) [START_REF] Smith | Adaptive protein evolution in 1098 drosophila[END_REF] across all of our populations and the rangewide samples for both subspecies. We estimated α jointly among all populations by fitting a non-linear mixedeffect model based on the asymptotic McDonald-Kreitman test [START_REF] Messer | Frequent adaptation and the mcdonald-kreitman test[END_REF]. Across populations, α varied between 0.097 (teosinte San Lorenzo) and 0.282 (teosinte Palmar Chico), with more variation among teosinte populations (Figure 3). In contrast to our expectations, rangewide estimates of α were commensurate with individual populations. We additionally evaluated estimates of α for specific mutation types, which has been shown to be lower at sites mutating from A/T to G/C, due to the effects of GC-biased gene conversion in Arabidopsis (Hämälä and Tiffin 2020). While we do find some evidence that α predictions varied by mutation type (see Figure S2, Supplement III), the patterns are the opposite of that found in Arabidopsis, perhaps because of the increased level of methylation in maize and the higher mutation rate at methylated cytosines. Even after accounting for differences among mutation types, rangewide values remained commensurate with that of the populations.

Teosinte populations have a higher proportion of private sweeps

Our inferences of α are based on substitutions at nonsynonymous sites. The functional space for selection to act q q q q q q q q q q q q q q q q q q q q q q q q q q Maize Teosinte 0.05 Unique sweep proportion B q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0.0 0. 5A). Selection coefficients for sweeps neutral independent migration standing q q qq q q qqq qq qq qq q qqq q qq qqq qqq qqq qqqq qq q qqq q q q q q q q q q q q q q q q q q q q q q q q q q q q independent migration standing 1.0 1.5 2.0 20 40 50 60

1e-01 1e+01 1e+03 Index ∆ cle A q q q qq q q q q qq q q q q q q q q q q qq qq q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q independent migration standing varied among modes, with convergence via migration having the highest average estimate (Figure 5B). When migration was the mode of convergence and sweeps were shared by both subspecies, teosinte El rodeo, and maize from Crucero Lagunitas and Palmar Chico were the most frequent source populations (Figure 5D). In convergence models with migration, we tested migration rates between 10 -3 and 10 -1 . The most likely migration rate varied across sweeps, but tended to be 10 -1 for the majority of sweeps shared by the two subspecies and sweeps exclusive to maize. In contrast, the lowest migration rate (10 -3 ) was always the most likely for sweeps exclusive to teosinte (Fig- and the strong population structure we observed (Figure 1), we expected that rangewide samples would have smaller estimates of α. Instead, α for the rangewide samples of both maize and teosinte were commensurate with that of individual populations (Figure 3), a pattern that persisted even when we considered α estimated from several different mutation types (Figure S2).

This is inconsistent with the patterns we would expect fine-scale local adaptation to generate, where adaptive substitutions for a given population should not be shared by other populations experiencing their own distinct local selective pressures and antagonistic pleiotropy suppresses rangewide fixation in alternative environments. Our findings makes sense in the light of other work studying pleiotropy's impact on adaptive evolution in Arabidopsis, which found that most mutations impact few traits and that the genetic architecture was largely non-overlapping when studied across multiple environments [START_REF] Frachon | Intermediate degrees of synergistic pleiotropy drive adaptive 970 evolution in ecological time[END_REF].

Our results are consistent with models in which locally beneficial alleles are simply neutral elsewhere, a process thought to be more common when overall levels of gene flow are low [START_REF] Wadgymar | Local adaptation: Causal agents of selection and 1127 adaptive trait divergence[END_REF]) but one that could nonetheless augment the rangewide spread of such alleles.

We found a similar pattern from our analysis of shared versus unique selective sweeps, which were more often shared by at least one other allopatric population. Similar to our predictions for α, we expected that local adaptation would lead most sweeps to be exclusive to individual populations. Instead, the average proportion of sweeps exclusive to a single population was low to moderate for maize and teosinte populations, respectively (Figure 4). We also expected that maize and teosinte populations growing in close proximity would share similar local selective pressures and would therefore share more signatures of adaptation. However, no pairs showed evidence of sharing more sweeps than would be expected by chance, and overall sympatric pairs did not show increased sharing of selective sweep regions compared to allopatric pairs (Figure 4). (Figure S7). Despite the lower totals, the evidence still supports 603 this hypothesis. Firstly, we found relatively few shared sweeps exclusive to teosinte populations (Figure 5C), which is what we 605 would expect if maize populations facilitate the movement of 606 beneficial teosinte alleles. However, it is important to note that 607 there are fewer sweeps exclusive to teosinte for all modes of con-608 vergence, not just via migration, so this alone is not sufficient evi-609 dence. Thirdly, sweeps shared via migration that were exclusive to teosinte were always inferred to be the lowest migration rate

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(1 × 10 -3 ) (Figure 5F), where those shared by both subspecies 612 or exclusive to maize were primarily inferred to be the highest ). We 699 used the alignments from the two species to make minor allele frequency 700 (MAF) files using ANGSD. We used the MAF files to construct a table 701 of genotypes found at each locus. Sites with minor allele frequency esti-702 mates greater than 0.001 were treated as heterozygous. Sites that were 703 homozygous in both species were imputed onto the maize v5 reference 704 and assumed to be the ancestral allele. As there were substantially more 705 called bases in Zea luxurians than in Zea diploperennis, we also assumed 706 sites that were homozygous in luxurians and missing in diploperennis 707 were ancestral, but excluded sites that were missing from luxurians.

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Sites that were classified as heterozygous were treated as missing and 709 imputed onto the maize reference as 'N'.

Supplement II 1160

Further assessment of f4 statistic inferences 1161 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 2 4

Supplement IV

Further assessment of sweep inferences and precision

We found that only 67% and 80% of sweeps were shared between the two random subsamples for maize and teosinte populations from Palmar Chico, respectively (see Results). While our updated method to identifying sweeps improved precision over our previous one (which shared 40% and 50% [START_REF] Tittes | Not so local: the population genetics of convergent adaptation in 1113 maize and teosinte (version 1)[END_REF])), the low precision still warrants further exploration.

One explanation for the low sweep precision could be substructure within the Palmar Chico populations, leading to replicates with slightly different histories. This could create unequal power to detect sweeps if, for example, the subpopulations had different progress towards fixation of the beneficial allele. However, this explanation is unlikely as individuals were randomly assigned to subpopulations, so any substructure is likely to be distributed evenly between the two samples. This is further supported from the two samples showing relatively short branch lengths in the population phylogeny (Figure S3). The branch lengths separating the two subsamples (cophenentic distance) was 0.00835 and 0.00962 for maize and teosinte respectively, compared to the within subspecies means of 0.0170 and 0.0559. q q q q q q q q q q q q q Maize Teosinte Figure S3 Treemix phylogeny including both subsamples of Palmar Chico.

Another potential explanation for lowered sweep sharing between replicates is that sweeps vary in their detectability based on their characteristics.

Namely, sweeps that were weakly selected, incomplete, and/or ones that started at a high initial frequency prior to the onset of selection (soft sweeps) may vary in their detectability using the methods we employed. We conducted a simulation experiment to better understand the potential causes of the low shared proportion, and to measure performance to detect different kinds of sweeps more generally. We used discoal [START_REF] Kern | Discoal: flexible coalescent simulations with selection[END_REF] to simulate sweeps in a 400Kb region using the average genome-wide maize mutation and recombination rates under the inferred demographic history of the maize population from Palmar Chico (Figure 2). We simulated four distinct scenarios: classical hard sweeps, where selection acts to fix an an adaptive mutation; soft sweeps, where selection is initiated after the adaptive allele reaches a specified frequency; and incomplete sweeps, where a hard sweep simulation is stopped at a specified frequency, and neutral simulations without selection. For soft sweeps, we varied the initial by drawing from a beta distribution with shape parameters 1 and 20 . Incomplete sweeps finished when the adaptive allele reached a frequency of 0.5. For all three types of sweeps, we also varied the strength of selection using the parameter α = 4N0s to be 10, 50, or 100, where N0 is the present day effective population size and s is the selection coefficient. In addition to matching demography and other parameters, we used the same sampling scheme, simulation 50 individuals, than randomly choosing two non-overlapping subsets of 10 individuals (https://github.com/silastittes/ms_sub). From the simulations we assessed the True/False Positive/Negative Rates for each combination of sweep type and strength of selection (α), as well as the distribution of base pair overlap between sweep regions inferred in the the two random subsamples. The same sweep inference methods and parameters were used for these simulations and the empirical samples (see methods). Overall, we found that sweep characteristics we explore indeed impacted our overall power to detect them, and the about of overlap between the sweep regions. Namely, weakly selected sweeps had consistently lower True Positive Rates (Table S3). 18 Tittes et al.

30

  estimates of the proportion of new mutations fixed by natural 31 selection across a number of plant species tended to overlap 32 with zero, suggesting there is little evidence for adaptation at 33 non-synonymous sites (though see Williamson et al. 2014; Geist 34 et al. 2019 for more recent non-zero estimates of α in two plant 35 taxa). One potential explanation raised by the authors for this 36 surprising finding was that natural populations are often struc-37 tured, such that very few adaptations would be expected to 38 be common over the entire range of the species' distribution. 39 Indeed, even when selective pressures are shared across popula-40 tions, structure can hinder a species' adaptation by limiting the 41 spread of beneficial alleles across its range (Bourne et al. 2014).

  . Similarly, studies of teosinte have documented local adaptation based on features such as the differential patterns of microbial community recruitment (O'Brien et al. 2019), elevation (Fustier et al. 2019, 2017), and temperature and phosphorous (Aguirre-Liguori et al. 2019). Studying local adaptation of maize and teosinte across the same geographic locations presents opportunities to disentangle multiple processes that interact with adaptation. For example, the effect of the domestication process in maize populations and their ongoing interaction and dependence on humans has created changes in the timing and types of selection imposed across all populations, as well as changes in demography (Wright et al. 110 2005). Based on population structure and differences in the abi-111 otic environment among populations, we anticipated that local 112 adaptation would have a small geographic scale. We predicted 113 that sweeps would be exclusive to individual populations, and 114 that adaptations shared between subspecies would be limited to 115 sympatric pairs of populations growing in similar environments 116 and with ample opportunity for genetic exchange. Because of 117 domestication and the ongoing migration facilitated by humans, 118 we expected that maize would show more shared adaptations, 119 leading to a relatively larger geographic scale of local adaptation. 120 Contrary to our predictions, our results suggest adaptations are 121 often shared across two or more populations, and commonly 122 between maize and teosinte. We also found that migration and 123 standing variation have played an important role as sources of 124 beneficial alleles, including many that are shared across the two teosinte (Zea mays subsp. parviglumis) individ-128 uals from six locations across its native range, along with a 129 nearby (sympatric) population of traditionally cultivated open-130 pollinated maize (commonly referred to as landraces) at five of 131 these locations (Figure 1C). We sampled ten individuals from 132 each population for each subspecies, with the exception of the 133 Palmar Chico populations, where we took advantage of 55 and 134 50 individuals previously sampled for maize and teosinte, re-135 spectively (Table S1, Supplement I, Chen et al. 2020). In most 136 cases, results for both Palmar Chico populations were down-137 sampled to ten randomly selected individuals to facilitate com-138 parisons to the other populations. We did, however, use a second 139 random sample of the Palmar Chico populations to estimate the 140 precision of our inference of selective sweeps (see below in Re-141 sults and Methods).142 Rangewide samples for each subspecies were constructed by 143 randomly selecting one individual from each population. All 195 144 individuals were sequenced at 20 to 25x coverage and aligned 145 to version 5 of the Zea mays B73 reference genome (Hufford et al. 146 2021; Portwood et al. 2019). Analyses were based on genotype 147 likelihoods (Korneliussen et al. 2014) except in cases where called 148 genotypes were required (see Methods). 149 Subspecies and populations are genetically distinct despite 150 evidence of gene flow. 151 To assess the relationships among our sampled populations, 152 we constructed a population-level phylogeny using Treemix 153 (Pickrell and Pritchard 2012 v.1.13). As anticipated from previous 154 work (Buckler and Holtsford 1996; Hufford et al. 2012), we found 155 clear divergence between two clades composed of maize and 156 teosinte populations, though the relationship among geographic 157 locations differed between the subspecies (Figure 1B).158 Within subspecies, populations were genetically distinct from 159 one another. Using NGSadmix (Skotte et al. 2013), there was little 160 evidence of admixture between populations of the same sub-161 species; only two of the sampled individuals revealed evidence 162 of mixed ancestry (Figure 1A and 1D).

163

  Despite the clear phylogenetic separation between the two 164 subspecies, there is evidence for gene flow between maize and 165 teosinte populations. We conducted f4 tests using Treemix (Pick-166 rell and Pritchard 2012), and found that all populations showed 167 some evidence of gene flow with various populations of the 168 other subspecies, as measured by the high absolute Z-Scores of 169 2 Tittes et al.

Figure 2

 2 Figure 2 | Inbreeding, diversity, and demography. The distribution of π (A) and Tajima's D (B) calculated in 100Kb windows for maize and teosinte populations. Dashed lines show the median values for the two subspecies. Filled white points show the median values of each statistic generated from coalescent simulations under the demographic history inferred for each population. Colors for each population are as in Figure 1 and are shown at the bottom of the figure. (C) The inferred demography for each population. (D) The quantile of observed Homozygosity By Descent (HBD) lengths (cM) versus those simulated under each population demography. Dashed lines shows the 1:1 correspondence between the axes. (E) The distribution of inbreeding coefficients in each population. Filled white points are the average values for each population.

189

  each population from their respective site frequency spectra 190 using mushi (DeWitt et al. 2021 v0.2.0). All histories estimated a 191 bottleneck that started approximately 10 thousand generations 192 ago (assuming a mutation rate of 3 × 10 -8 (Clark et al. 2005) 193 (Figure 2E). 194 Teosinte is a primarily outcrossing grass (Hufford 2010), and 195 regional maize farming practices promote outcrossing as well 196 (Bellon et al. 2018). To validate our estimated demography and 197 characterize the history of inbreeding in each population, we 198 compared the empirical quantiles of homozygosity by descent 199 (HBD) segments inferred using IBDseq (Browning and Brown-200 ing 2013) to those simulated under the demography of each 201 population. With the exception of the smallest HBD segments, 202 which are more prone to inaccurate estimation, the simulated 203 quantiles generally resemble the empirical quantiles (Figure 2D).

Figure 3

 3 Figure 3 The proportion of mutations fixed by natural selection. Estimated values of the proportion of mutations fixed by natural selection (α) by population. Vertical lines show the 95% credible interval.

Figure 4

 4 Figure 4 The distribution of shared and private selective sweeps. (A) The total number of sweeps inferred in each population. (B)The proportion of sweeps that are unique to each population. (C) The negative log10 p values for hypergeometric tests to identify maize-teosinte population pairs that shared more sweeps than expected by chance (see Methods). P values were adjusted for multiple tests using the Benjamini and Yekutieli method. Populations along the y axis are maize (order matches the legend below, with Amatlán de Cañas at the bottom), while the point color designates the teosinte population each maize population was paired with. Points with black outline highlight the sympatric population comparisons. Point size is scaled by the number of shared sweeps identified in each pair. The dotted line indicates our chosen significance level (p = 0.05). (D) Counts of shared and unique sweeps broken down by how many maize and teosinte populations they occurred in. Grey boxes show sweeps shared across the two subspecies.

Figure 5

 5 Figure 5 Modes of convergent adaptation and affiliated parameters for shared selective sweeps. (A) The difference in composite likelihood scores for the best supported mode of convergent adaptation (colors in top legend) compared to next best mode (black points), and best mode compared to the neutral model (other end of each line segment above or below black point). (B) Selection coefficients colored by the most likely mode of convergent adaptation. (C) Number of shared sweeps for both subspecies that were inferred to be from each convergent adaptation mode. (D) The most likely source population for shared sweeps that converged via migration. Bars are colored by population (bottom legend) and are outlined in black for teosinte and grey for maize. (E) Observed frequency of the inferred time in generations that each selected allele persisted prior to selection for models of convergent adaptation via standing variation. (F) Observed frequency of each inferred migration rate value for models of convergent adaptation via migration. Panels C, D, E and F are partitioned by which subspecies shared the sweep.

  ure 5F). Together, these findings indicate that many alleles are 347 adaptive in the genomic background of both maize and teosinte, 348 and that adaptive alleles are commonly shared between the two at intermediate scales 352 Gossmann et al. (2010) hypothesized that population structure 353 within a species could limit the fixation of adaptive alleles across 354 a species range, causing a reduction in the proportion of muta-355 6 Tittes et al. tions fixed by positive selection (α). Based on this hypothesis

  This regional scale of local adaptation is consistent with patterns seen in maize adaptation to the highlands(Calfee et al. 2021), where sympatric maize and teosinte populations show little evidence of adaptive gene flow, and adaptive teosinte introgression appears widespread among highland maize.There are a number of considerations to make in the interpretation of our results. The two methods we used to identify signatures of adaptation, estimating α and identifying signatures of selective sweeps, are best-suited for adaptation that leads to fixation of beneficial alleles, and/or mutations of large effect.For the moderate population sizes and selection coefficients observed here, fixation of new beneficial mutations takes a considerable amount of time, on the order of 4log(2N)/s generations (Charlesworth 2020). Compared to the sojourn time of adaptive mutations, our populations may have occupied their current locations for relatively few generations. As a result, the selective sweeps underlying local adaptation to the selective pressures that populations currently face are more likely to be incomplete, so may be more difficult to detect[START_REF] Xue | Discovery of ongoing 1155 selective sweeps within anopheles mosquito populations using deep 1156 learning[END_REF][START_REF] Pritchard | The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation[END_REF]. Likewise, the adaptive sweeps that have completed may have been under selection in ancestral populations that occupied different environments than the sampled individuals, and their signatures may no longer be detectable[START_REF] Przeworski | The signature of positive selection at randomly chosen loci[END_REF], placing a limit on the temporal resolution with which we can make inference about instances of local adaptation. Another complication in detecting local adaptation relates to the size and complexity of plant genomes. Large genomes may 418 lead to more soft sweeps, where no single mutation driving 419 adaptive evolution would fix[START_REF] Mei | Adaptation in plant genomes: Bigger is different[END_REF]. Like incomplete 420 sweeps, soft sweeps are harder to identify (Schrider and Kern 421 2016; Pritchard et al. 2010), which could obscure the signatures 422 of local adaptation. Even if our populations have occupied their 423 current locations for a sufficient duration for local adaptation 424 to occur, the completion of selective sweeps may be hindered 425 by changes and fluctuations in the local biotic and abiotic con-426 ditions. Relatively rapid change in local conditions could also 427 result in fluctuating selection, such that most alleles do not re-428 main beneficial for long enough to become fixed (Rudman et al. has been on the trajectory of individual ben-431 eficial alleles, the genetic basis of many adaptive traits may be 432 highly polygenic. Allele frequency changes underlying poly-433 genic adaptation are more subtle than those assumed under 434 selective sweeps, making them harder to detect (Pritchard et al. 435 2010). Evaluating local adaptation in maize and other systems 436 will be facilitated by studying the contribution of polygenic 437 adaptation to the evolution of complex traits. However, if adap-438 tation across our studied populations were strictly polygenic, 439 and especially if it were acting on alleles with small effect sizes, 440 we would expect to find few to no shared sweeps. The fact that 441 we find many instances of sharing across populations supports 442 that a non-trival amount of local adaptation is occurring via 443 selective sweeps, or through polygenic adaptation acting on a 444 few loci with large effects that leave a signature similar to that 445 were generally similar between the two sub-449 species and among the sampled populations, there are several 450 important differences. The most obvious difference between 451 the subspecies is the ongoing interaction and dependence of 452 maize on humans via domestication and farming. Compared 453 to teosinte, maize had lower average genomewide estimates of 454 diversity (Figure 2A). These differences are consistent with the 455 previously discovered pattern that diversity tends to be lower 456 in crops compared to their wild relatives (Doebley 1989; Huf-457 ford et al. 2012), a pattern putatively driven by domestication 458 bottlenecks (Eyre-Walker et al. 1998). In line with this argument, 459 the few teosinte populations with lower diversity than those in 460 maize (El Rodeo and San Lorenzo) were inferred to have the 461 most substantial bottlenecks and historical inbreeding (Figure 2). 462 More generally, we found that π and Tajima's D were more 463 variable among teosinte populations, indicative of differences in 464 their demographic histories. 465 Our demographic inferences suggest that all populations had 466 signatures of a bottleneck, the timing of which coincides with the 467 beginning of maize domestication ≈ 9, 000 years ago (Piperno 468 et al. 2009). The severity of the bottleneck varied considerably 469 across populations, particularly for teosinte. While finding a 470 bottleneck in the maize populations is consistent with domes-471 tication, it is less clear why we found a similar bottlenecks for 472 the teosinte populations at approximately the same time. One 473 possibility is that the teosinte bottlenecks reflect land use change 474 induced by human colonization. For example, evidence from 475 Mesoamerican phytolith records in lake sediment show evidence 476 of anthropogenic burning as early as 11K years B.P. (Piperno 477 1991). The establishment and spread of human populations 478 the habitat available for teosinte and drive species-wide cen-482 sus size declines. Given the success of maize breeding and 483 domestication, we anticipated a recent expansion for maize pop-484 ulations as previously seen (Beissinger et al. 2016; Wang et al. 485 2017). However, with only 10 individuals per population, recent expansions will be difficult to detect with approaches based on 487 the site frequency spectrum (Keinan and Clark 2012; Coventry 488 et al. 2010). The demography of the rangewide samples for both 489 subspecies showed little evidence of the bottleneck inferred in in-490 dividual populations, likely due to the reduced sampled size (5 491 and 6 individuals) for the rangewide data. We additionally used 492 strong regularization penalties to avoid over-fitting (see Meth-493 ods), which limits the detection of rapid and dramatic changes 494 in population size. The near-constant size of the rangewide sam-495 ples and the lack of recent expansion in maize are both likely 496 influenced by this modeling choice. 497 Finally, demographic inferences are known to be sensitive is moderate to strong, though this estimate is likely biased as 543 strong positive selection will be easier to detect. Convergence 544 via independent mutations was by far the least frequent mode. 545 This is consistent with previous analyses of domestication (Huf-546 ford et al. 2012) and adaptation (Wang et al. 2021) in maize, and 547 unsurprising given evidence for ongoing gene flow (Figure 1E), 548 the relatively short evolutionary time scales, and the low proba-549 bility that even strongly selected new mutations can overcome 550 drift multiple times independently.551For convergent sweeps that occurred via standing variation 552 within maize or shared between maize and teosinte, the distribu-553 tion of generation times that the selected variant was standing 554 before the onset of selection tended to be bimodal, with both 555 long and short standing times. In contrast, sweeps exclusive to 556 teosinte were consistently inferred to be standing variation for 557 more generations (Figure5E). Sweeps that occurred via standing 558 variation and shared between subspecies were often found in 559 only a subset of maize populations. Many of these sweeps likely 560 reflect the presence of structure in ancestral populations, sug-561 gesting different alleles beneficial to maize were likely derived 562 from more than one teosinte population. The bimodal features 563 of sharing seem at face value surprising. How can an allele be 564 standing variation for so many generations after divergence but 565 prior to selection when the populations diverged less than ten 566 thousand generations ago? We speculate that ancestral popu-567 lation structure and limited sampling of 6 populations could 568 explain the pattern. For example, extremely long standing times 569 that predate domestication may reflect divergence between our 570 sampled teosinte populations and the populations most closely 571 related to those that gave rise to domesticated maize. This is yet 572 another area in which more comprehensive sampling could help 573 resolve patterns of local adaption. 574 The most common mode of convergent adaptation was via 575 migration, and frequently occurred between geographically dis-576 parate populations (Figures S6).This included a relatively large 577 number of shared sweeps via migration between maize and 578 teosinte (Figures 5 and S7). There is ample evidence that maize 579 and teosinte are capable of hybridizing (Wilkes et al. 1967; Ell-580 strand et al. 2007; Ross-Ibarra et al. 2009), and previous work has 581 identified gene flow between geographically disparate popula-582 tions of maize and Zea mays mexicana (Calfee et al. 2021). Further, 583 convergence via migration between geographically disparate 584 maize populations has been inferred during adaptation to high 585 elevations (Wang et al. 2021). 586 Our findings on convergence via migration point to an in-587 triguing hypothesis, namely, that some number of alleles that 588 are beneficial to teosinte may have originally arisen in another 589 teosinte and maize populations and moved between populations 590 via gene flow with maize, an idea suggested by Ross-Ibarra et al. 591 (2009) based on allele sharing at a small set of loci. Our inference 592 that migration is a frequent source of beneficial alleles in teosinte 593 populations suggests that they may be mutation limited. Con-594 sistent with this, the three teosinte populations that most often 595 shared sweeps with maize (El Rodeo, San Lorenzo, and Amatlán 596 de Cañas) (Figure S7) also showed the strongest signatures of 597 bottlenecks (Figure 2C), which would limit the rate of new and 598 beneficial mutations. It is worth noting that the total number of 599 shared sweeps is much lower than previously reported (Tittes 600 et al. 2021), and only four of fifteen instances of convergence via 601 8 Tittes et al.

613

  migration rate (1 × 10 -1 ). This indicates alleles that are only 614 beneficial in teosinte move between populations more slowly. 615 Despite the patterns of population structure in both subspecies, 616 there is evidence of gene flow based on f4 tests. In particular, f4 617 tests that included maize from Crucero Lagunitas were consis-618 tently elevated across both subspecies (Figure 1E and S1). This 619 is in accordance with our finding that Crucero Lagunitas maize 620 was one of the most commonly inferred source populations of 621 migration sweeps that were shared between maize and teosinte 622 (Figure 5D). Together, these results suggests that geographically 623 widespread varieties of maize such as Celaya (Crucero Laguni-624 tas) (Orozco-Ramírez et al. 2017) may have played a prominent 625 role as a source of and/or transport for beneficial alleles among 626 maize and teosinte populations. However, teosinte populations 627 were often the source of beneficial alleles for sweeps shared be-628 tween both subspecies (Figure 5C). As such, teosinte populations 629 may have also commonly been the source of adaptive variation 630 for both other teosinte and maize populations. from five populations of Zea mays ssp. parvig-634 lumis and four populations of Zea mays ssp. mays from plants 635 growing across the species' native range. We additionally in-636 cluded populations of parviglumis and maize from Palmar Chico 637 which were previously analyzed and reported in (Chen et al. 638 2020) for a total of 6 and 5 populations of maize and teosinte (See 639 Supplement I for accession IDs and further sample details). All 640 maize and teosinte populations from each named location were 641 less than 1km from one another, with the exception of Crucero 642 Lagunitas, which were separated by approximately 18km. 643 DNA extraction for teosinte followed (Chen et al. 2020). Ge-644 nomic DNA for landraces was extracted from leaf tissue using 645 the E.Z.N.A.® Plant DNA Kit (Omega Biotek), following manu-646 facturer's instructions. DNA was quantified using a Qubit (Life 647 Technologies) and 1ug of DNA per individual was fragmented 648 using a bioruptor (Diagenode) with 30 seconds on/off cycles. 649 DNA fragments were then prepared for Illumina sequenc-650 ing. First, DNA fragments were repaired with the End-Repair 651 enzyme mix (New England Biolabs). A deoxyadenosine triphos-652 phate was added at each 3'end with the Klenow fragment (New 653 England Biolabs). Illumina Truseq adapters (Affymetrix) were 654 then added with the Quick ligase kit (New England Biolabs). 655 Between each enzymatic step, DNA was washed with sera-mags 656 speed beads (Fisher Scientific). The libraries were sequenced to 657 an average coverage of 20 to 25x PE150 on the Xten at Novogene 658 (Sacramento, USA). 659 We additionally grew one individual of Zea diploperennis from 660 the UC Davis Botanical Conservatory as an outgroup. DNA for 661 Zea diploperennis was extracted and libraries prepared as above,662and then sequenced to 60X coverage using PE250 on 3 lanes 663 of Illumina 2000 rapid run (UC Davis Genome Center, Davis, end reads were aligned to version 5 of the maize B73 reference 669 genome[START_REF] Hufford | De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes[END_REF]) using bwa-mem (v0.7.17)[START_REF] Li | Aligning sequence reads, clone sequences and assembly contigs with bwa-mem[END_REF]. Default 670 options were used for mapping except -M to enable marking short hits 671 as secondary, -R for providing the read group, and -K 10000000, for 672 processing 10 Mb input in each batch. Sentieon (v201808.01)(Freed et al.

673 2017 )

 2017 was used to process the alignments to remove duplicates (option 674 -algo Dedup) and to calculate various alignment metrics (GC bias, MQ 675 value distribution, mean quality by cycle, and insert size metrics) to 676 ensure proper mapping of the reads. 677 All downstream analyses were based on genotype likelihoods esti-678 mated with ANGSD (v0.934) (Korneliussen et al. 2014) using the followbase nucleotide diversity (π) and Tajima's D (D) in 692 non-overlapping 1kb, 100kb and 1,000kb windows with the thetaStat util-693 ity from ANGSD, though estimates did not substantively differ between 694 window sizes. To estimate π and the unfolded site frequency spectra 695 for each population, we polarized alleles as ancestral and derived us-696 ing short-read sequence data for Zea luxurians and Zea diploperennis as 697 outgroups. Zea luxurians sequence from Tenaillon et al. (2011) was down-698 loaded from The NCBI Sequence Read Archive (study SRR088692

(

  ngsadmix[START_REF] Skotte | Estimating indi-1095 vidual admixture proportions from next generation sequencing data[END_REF] to assess population struc-712 ture within subspecies. To do so we used a SNP-calling pro-713 cedure in ANGSD with the same filters as listed above, along 714 with a SNP p-value cutoff of 1 × 10 -6 . We looked for evidence 715 of gene flow between subspecies using f4 statistics and Z-scores 716 calculated with blocked jackknifing, implemented using treemix 717[START_REF] Pickrell | Inference of population splits and mixtures from genome-wide allele frequency data[END_REF].Trees for f4 tests were always 718 of the form (Maize_X, Maize_Y; Teosinte_ f ocal, Teosinte_Z), or 719 (Maize_ f ocal, Maize_Y; Teosinte_W, Teosinte_Z); with each unique 720 combination of populations was considered to be the " f ocal" and "_X" 721 positions of the tree. We considered any tree with a Z-score greater than 722 or equal to 3 significant, indicating a departure from the allele frequency 723 differences expected if population history matched the hypothesized 724 tree. We assessed Z-scores separately based on whether the focal pop-725 ulation was maize or teosinte, and for trees that include the sympatric 726 pair of the focal population. and alpha_ridge = 1e-1. 733 We assessed homozygosity by descent (HBD) in each population 734 using IBDseq (v2.0) (Browning and Browning 2013). We compared 735 empirical results to simulations in msprime (Kelleher et al. 2016) using Inferring modes of convergent adaptation 836 For sweep regions that were overlapping in 2 to 9 of the 11 populations, 837 we used rdmc to infer the most likely mode of convergent adaptation 838 Lee and Coop 2017; Tittes 2020). To ensure a sufficient number of 839 loci were included to estimate decay in covariance across the sweep 840 regions, we added 10% of each sweep region's total length on each of 841 its ends prior to fitting the models. To reduce the computation time, 842 we exclude sites that had an allele frequency less than 1/20 across all 843 populations. As sweep regions differed in size, we subset from their 844 total number of sites to maintain approximately similar densities, with 845 a lower and upper bound of 1K and 100K SNPS, respectively. Sweep 846 regions near the ends of chromosomes for which we could not estimate 847 the number of centiMorgans for were subset to 10K SNPs. To contrast 848 allele frequency covariance in sweep regions to neutral expectations, we 849 first sampled allele frequencies at 100K random loci. When fitting rdmc, 850 we assumed the effective population size was 50K for all populations. 851 The recombination rate was approximated for each sweep region as the 852 median interpolated value based on a previously generated genetic map 853 for maize that was lifted on to the v5 reference genome (Ogut et al. 2015). 854 The rdmc function includes arguments that control the grid of parameter 855 values over which composite likelihoods were computed were: 856 n_sites = 20, 857 num_bins = 1000, 858 sels = 10^seq(-5, -1, length.out = 15), 859 times = c(1e2, 1e3, 1e4, 1e5),

Figure S1

 S1 Figure S1 f4 tests including the maize Crucero Lagunitas population are significantly elevated compared to those without.

Figure S4 Figure S5 Figure S6 Figure S7

 S4S5S6S7 Figure S4 Performance to detect simulated hard, soft, and incomplete sweeps under varying strengths of selection under the maize Palmar Chico population demography. Each panel shows a combinations of sweep type (hard, soft, or incomplete) and strength of selection (α = 4N e s = 10, 50, or 100)

Crucero Lagunitas (590) Amatlán de Cañas (880) El Rodeo (982) Los Guajes (985) San Lorenzo (982) Palmar Chico (1008)

  Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico

	176
	A
	Teosinte
	Amatlán de Cañas	Crucero Lagunitas	El Rodeo	Los Guajes	Palmar Chico	San Lorenzo
	B		C
			24°N
			22°N
			q
			20°N	q
				q
				q
			18°N
					q
			16°N			q
			105°W 102°W	99°W	96°W	93°W
	Amatlán de Cañas	Crucero Lagunitas	Los Guajes	Palmar Chico	San Lorenzo

E Figure 1 | The geographic distribution, population structure, and gene flow of maize and teosinte populations.

							Crucero Lagunitas		Los Guajes		Palmar Chico		San Lorenzo
	Absolute Z-score	0 2 4 6
		Maize	allopatric	Teosinte	allopatric	sympatric	Maize	allopatric	Teosinte	allopatric	sympatric	Maize	allopatric	Teosinte	allopatric	sympatric	Maize	allopatric	Teosinte	allopatric	sympatric	Maize	allopatric	Teosinte	allopatric	sympatric
																									(A and D)
	Admixture proportions among populations within subspecies.
	The dominant cluster in each population is colored by sam-
	pling location. (B) The unrooted tree of maize and teosinte
	populations. (C) Geographic sampling locations for the stud-
	ied maize and teosinte populations. (E) F4 tests to quantify
	evidence of gene flow between the subspecies for allopatric
	and sympatric population pairs. Each point in (E) reports the
	absolute Z-Score for an f4 test, where a given focal population
	was partnered with another population of the same subspecies
	as a sister node, and two other populations from the other sub-
	species as a sister clade (see Methods for further details). Black
	points show f4 tests that included maize from Crucero Lagu-
	nitas, otherwise points are colored by focal population. The
	dotted line corresponds to our chosen significance threshold
	(p = 0.001).

Table S2 Significant F4 tests. Each row of the table reports the number of significant f4 tests that occurred with a given focal and secondary population, where the two other tip positions were filled with each of the remaining populations for each subspecies. Rows that are left blank in the secondary column are used to report the total number of significant trees for a given focal popu- lation.

 S2

	Focal population	Secondary population	Count
	Maize Amatlan de Canas		5
	Maize Amatlan de Canas	Maize Crucero Lagunitas	5
	Maize Amatlan de Canas	Teosinte Amatlan de Canas	3
	Maize Amatlan de Canas	Teosinte El Rodeo	2
	Maize Amatlan de Canas	Teosinte Palmar Chico	2
	Maize Amatlan de Canas	Teosinte San Lorenzo	2
	Maize Amatlan de Canas	Teosinte Los Guajes	1
	Maize Crucero Lagunitas		15
	Maize Crucero Lagunitas	Teosinte Amatlan de Canas	9
	Maize Crucero Lagunitas	Teosinte Crucero Lagunitas	6
	Maize Crucero Lagunitas	Teosinte El Rodeo	6
	Maize Crucero Lagunitas	Maize Palmar Chico	5
	Maize Crucero Lagunitas	Maize Los Guajes	4
	Maize Crucero Lagunitas	Teosinte San Lorenzo	4

14

Tittes et al.

Table S3 Performance to detect simulated hard, soft, and incomplete sweeps under varying strengths of selection under the maize Palmar Chico population demography. TPR, TNR, FNR, and FPR stand for true postive, true negative, false negative, and false positive rates, respectively.

 S3

	4N e s simulation type TPR TNR FNR FPR
	10	hard	0.14	0.67	0.86 0.33
	50	hard	0.92	0.75	0.08 0.25
	100	hard	0.99	0.84	0.01 0.16
	10	incomplete	0.04	0.67	0.96 0.33
	50	incomplete	0.06	0.67	0.94 0.33
	100	incomplete	0.05	0.63	0.95 0.37
	10	soft	0.13	0.71	0.87 0.29
	50	soft	0.75	0.74	0.25 0.26
	100	soft	0.81	0.76	0.19 0.24

Acknowledgments

882

We would like to thank Andi Kur for providing the corn art, along with

Demographic and Inbreeding History

728

We inferred each population's demography using a single unfolded site 729 frequency spectrum with mushi (v0.2.0) [START_REF] Dewitt | Non-951 parametric coalescent inference of mutation spectrum history and 952 demography[END_REF]. In efforts 730 to reduce overfitting given our modest samples sizes, we increased the 731 each population's inferred demographic history. We performed ten 737 replicates of each of these simulations. Replicates were similar across all populations; only one replicate was chosen at random for visual clarity.

739

We estimated recent inbreeding using ngsrelate [START_REF] Hanghøj | Fast and accurate relatedness estimation from high-throughput 1001 sequencing data in the presence of inbreeding[END_REF] 740 with default parameters. Input files were generated using ANGSD with 741 the same filters as listed above, along with a SNP p value cutoff and maf filter of [START_REF] Messer | Frequent adaptation and the mcdonald-kreitman test[END_REF], where α is calculated at each al-750 lele frequency bin of the uSFS (from 1/n to (n -1)/n). At each allele 751 frequency bin, each α was calculated as

where d 0 and d are the number of derived fixed differences for selected 753 and putatively neutral sites, respectively, and p 0 and p are the number of 754 selected and putatively neutral polymorphic sites. We identified 0-fold and 4-fold sites using Python (https://github.com/silastittes/cds_fold).

756

We fit the Asymptotic MK extension as a nonlinear Bayesian mixed-757 model using the R package brms (Bürkner 2017(Bürkner , 2018)).

758

where α ij is the value of α calculated at the i th allele frequency bin of 759 the j th population and x ij is the corresponding allele frequency bin. The

where n S is the number of sweeps shared between the replicates,

819

and n P1 and n P2 were the number of sweeps in the first and second 820 replicates, respectively. In downstream analyses, we used the average 821 value of P for the two subspecies, although it was higher for teosinte 822 than for maize (0.67 and 0.80, respectively).

823

We evaluated the number of sweeps that we would expect popu-824 lations to share by chance using a simple statistical test based on the

where N is the total number of loci tested; n 1 and n 2 are the number of We compared composite likelihoods over four convergent adaptation 864 modes, "neutral", "independent", "standing", and "migration". We 865 assigned each sweep to the mode with the highest log composite likeli-866 hood. To assess the overall performance of the method to distinguish 867 between the four modes, we computed differences between the highest 868 composite likelihood and the next highest for each sweep.

All wrangling to prepare input data for statistical analyses was done 870 using R (R Core Team 2020) with appreciable reliance on functions 871 from the tidyverse suite [START_REF] Wickham | Welcome to the tidyverse[END_REF]

Predicting α by mutation type

Estimates of α may be effected by differences in the mutation rates of different nucleotides and genomic regions. GC biased gene conversion has been shown to reduce α by making it harder to purge slightly deleterious alleles [START_REF] Hämälä | Biased gene conversion constrains adapta-995 tion in arabidopsis thaliana[END_REF]. Likewise, the higher mutation rates observed at methylated cytosine bases increases the rate of C → T mutations [START_REF] Ossowski | The rate and molecular spectrum of spontaneous mutations in arabidopsis thaliana[END_REF], which is another mechanism that could result in variation in α by changing the ability to purging deleterious alleles, or by changing the probability of fixation of new adaptive mutations.

To study this, we used the same approach as [START_REF] Hämälä | Biased gene conversion constrains adapta-995 tion in arabidopsis thaliana[END_REF], where we separated the site frequency spectra based on mutation types according to whether the ancestral and derived nucleotides had a single (weak) or double (strong) hydrogen bond between the DNA strands. As such, we studied three mutations types: A/T → G/C mutations (WS), G/C → A/T (SW) and C/G → G/C or A/T → T/A (SS_WW).

Unlike patterns found in Arabidopsis (Hämälä and Tiffin 2020), α was highest for WS mutations, although there was considerable overlap between the credible intervals for all mutation types.

q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Teosinte All Teosinte

SS_WW


Keywords: Conservation units, Red listing, Species complex, Threatened species, 5. https://powo.science.kew.org/results?f=%2Caccepted_names%2Cspecies_f&page.size=24&q=Allium

1. The genus Allium is one of the most diverse in the world, with more than 1000 accepted species yet [POWO, 2023]. But a lot of new species are regularly described (several per year), including in the Euro-Mediterranean subgenus Allium. Within the sections Allium, Codonoprasum, Cupanioscordum and Pseudoscorodon, some old described species were recently rehabilitated and/or splitted into species complexes. This situation is not due to hazard, but to the difficult conservation of the main discriminant criteria when the plants collected were classically put in herbaria (A somewhat comparable situation is known for orchids and broomrapes for the same reasons). But the recent and prolific color photograph activities plus the more traditional comparative experimental cultivation have led several botanists to better understand the variability and therefore the taxonomy of the genus Allium.

2. During the last 10 years, redlisting activities supported by the IUCN-Med challenged us to choose a pragmatic taxonomy, not dependent to one or another backbone database but to conservation units identifiable on the field by botanists.

3. The parallel progress of both taxonomy and redlisting are commented in details for the Euro-Mediterranean A. ampeloprasum complex (sect. Allium). We can also enlarge the problematics to the Mediterranean A. cupanii / hirtovaginatum complex (sect. Cupanioscordum), the western Mediterranean A. rouyi complex (sect. Pseudoscorodon), and some Maghrebian species of the A. pallens / oleraceum / paniculatum and the A. flavum / stamineum complexes (sect. Codonoprasum).

The Mediterranean Monocots project

Main problems and limitations

What taxonomic reference to follow?

Unsolvable question…  Fluctuating taxonomy!

Species delimitation using fluctuating taxonomy (how and why)

1 st property: A taxon has to be identifiable on the field  no cryptic species (karyology, DNA, numerical data…)

Species delimitation using fluctuating taxonomy (how and why)

1. 2 . 1 .

 21 Alpha-taxonomy: old descriptions, resurrected species, new species, split, etc. » The Euro-Mediterranean sections of subgenus Allium » Sect. Allium (syn. Porrum) : 111+ species at present day… » Sect. Codonoprasum : 102+ species at present day… » Sect. Cupanioscordum : » from 2 species (1810 & 1843, stable until Garbari et al. 1979) » to 18 species (16 descriptions between 1983 and 2017) » Sect. Pseudoscorodon : » Regularly increasing until 18 species (from 1805 to 2022) Allium elaounii El Mokni [sect. Pseudoscorodon], first published in Phytotaxa 533: 207 (2022) 1.3. Study methods for the genus Allium through the ages Living specimen drawn or photographed Herbarium Linné (18th century) Brullo & Pavone 1983 (Willd., 13: 115-122) Mifsud & Mifsud 2018 (Fl. Medit., 28: 27-51) The Mediterranean biodiversity hotspot https://www.cepf.net/sites/default/files/mediterranean-basin-2017-ecosystem-profile-summary-english.pdf CEPF delimitation: The Mediterranean Basin (and biogeographically adjacent areas) More than 20 countries (from Portugal to Iraq and from Cabo Verde to Jordan) More than 30.000 vascular plant species / subspecies (≈ 50 % endemic) In 2021, 2426 vascular plants have been assessed by IUCN (< 10 %) + 1855 Bryophytes.

First

  aim : around 20% of the Mediterranean flora are Monocots (around 6000?) A previous funded project (2015-2017): 4 workshops, >70 experts, 4 project managers… A provisional checklist of 1446 (near-)endemic species was built: Out of the 1446 species listed, only 602 have been evaluated in 2021 (41,6 %)  844 drafts still needed assessment (58,4 %) Orchidaceae: 59 (assessed) / 141 (total endemics) Allium: 97 (assessed) / 210 (total endemics)

23. 1 .

 1 nd property: Have to share homogenous conservation issues  No distinct eco-evolutionary heritages (allopolyploidy, etc.) ex. Allium "ampeloprasum" s.s. versus s.l. Around 10 species (described or not, typified or not, known in their locus classicus or not…) ? ? -A French-British atlantic endemic -A possible anthropic origin (cultivated) -An allo-hexaploid plant (2n=6x) -A coastal sandy and semi-wet ecology -A plant flowering in early summer (July) -A lot of morphological criteria… -A garlic taste (not leek) ! very lumper position  our compromise  very splitter "fusca" s.l. Species delimitation using fluctuating taxonomy (how and why) 3 rd property: Avoid the never-ending controversies…  "not too splitter, not too lumper" ex. The Ophrys nightmare… (Orchidaceae): Q°: 10 // N // 400 species?! 2.5. Achieving the Mediterranean Red List… (2021's strategy) "We first try to build a strategy for achieving some objectives in order to complete the Monocots RL before to start a Dicots one in the future… -Objective 1: complete and publish the draft assessment Monocots already done on SIS (64 taxa); -Objective 2: transfer and update the data from European assessments of Mediterranean countries (576 taxa) into the global level; -Objective 3: complete all Alliums and Orchids species -Objective 4: try to complete the Poaceae family? (…)" [Véla & Numa, 3 th MPCW at Chania, 2021] -"Objective 3: complete all Alliums and Orchids species"  Objective 3a: clarify the confuse taxonomy when names exist but are lost into a false synonymy in order to assess/reassess the resurrected taxa;  Objective 3b: describe the field detected but still unnamed taxa with potential conservational issues! Allium ampeloprasum L., Sp. Pl.: 294 (1753), nom. cons.  Possibly « Near Threatened »? » « Wild » (indigenous?) populations in SW-Britain and W-France, casual elsewhere… » Mainly sandy coastal primary/secondary habitat » Also cultivated and then casually escaped or temporarily ruderal » To be confirmed as naturalised in Ireland, Spain, Portugal, Greece, etc. » Wild population severely fragmented? » AOO < 2.000 km²? » Number of mature individuals < 10.000? » Continuing decline of habitat quality? » Mainly hexaploid (wild progenitors not fully resolved) © Errol Véla 3.2. Allium multiflorum Desf., Fl. Atlant. 1: 288 (1798)  OK, but… » 3 forgotten names: are they really synonyms? » Allium mogadorense Willd. ex Schult. & Schult.f., (…) Syst. Veg., ed. 15[bis]. 7: 1004 (1830) » Allium getulum Batt. & Trab. in Bull. Soc. Bot. France 39: 75 (1892) » Allium tortifolium Batt. & Trab. in Bull. Soc. Bot. France 39: 338 (1892) » Still need more field and lab data, but possibly both distinct endemic species (Morocco and/or Algeria). © Errol Véla A. tortifolium, Algeria « Least Concern » (Véla, IUCN RLTS 2019) https://www.iucnredlist.org/species/172193/129513554 20 3.3. Allium polyanthum Schult. & Schult.f., J.J.Roemer & J.A.Schultes, Syst. Veg.Allium commutatum Guss., Enum. Pl. Inarim.: 339 (1855)  OK, but… » Delimination not achieved / Synonymy not resolved : » Diploid vs auto-tetraploid cytotypes? » Hybridogenic swarm vs hybridogenic species A. commutatum x polyanthum (Mifsud & Mifsud 2018, Fl. Medit.) » Related to the A. bourgeaui / creticum / cyclacidum complex ? https://powo.science.kew.org/taxon/urn:lsid:ipni.org:n ames:527708-1#children © Errol Véla 3.5. Allium atroviolaceum Boiss., Diagn. Pl. Orient. 7: 112 (1846)  Not better… » Described near Chiraz (Iran) » Rudero-segetal ecology » Western distribution limit not known because of numerous confusions » Relationship with A. scaberrimum still not known… Allium atroviolaceum Boiss., LECTOTYPE, G00164986 https://www.inaturalist.org/taxa/738854-Allium-atroviolaceum (Not Evaluated) 3.6. Allium leucanthum K.Koch, Linnaea 22: 240 (1849)  Probably « Data Deficient »? » Described from the Caucasus » Endemic of the Caucasus? (POWO's position) » Widespread through the Middle East? » The oldest name for the Eastern « ampeloprasum »? (both white and purple forms) Allium cf. leucanthum ? (white form and purple form), Lebanon © Errol Véla 3.7. Allium scaberrimum J.Serres, Bull. Soc. Bot. France 4: 439 (1857)  « Vulnerable » or « Near Threatened »? » Synonymy A. scaberrimum = A. pardoi recently confirmed » Recent rediscovery of the 3 historical localities in Algeria (Rebbas et al. 2019, Bull. mens. Soc. Linn. Lyon) » Continuing decline not confirmed yet… » Still needs national surveys! » + Taxonomic relationship with the Eastern A. atroviolaceum s.s. to be re-assessed? © Errol Véla Confused with A. porrum (NA) 3.8. Allium "occitanicum " J.M.Tison & Véla, nom. provis.  « Least Concern » (unpubl. data) » Nb of mature individuals unknown but very high (obviously >> 10.000) » AOO not precisely known but > 2.000 km² » EOO unknown but > 20.000 km² » SE-France, NE-Spain?, Italy? Elsewhere? » Numerous confusions under A. atroviolaceum auct. still to be checked… » The overlooked Caucasian A. leucanthum to deepen? » The unnamed Eastern A. ampeloprasum sensu Linné pro parte (excluded lectotypus) to check? » Allo-hexaploid (progenitors unknown) » Sandy coastal primary / secondary habitat (0-100m alt.) » No germaplasm collection ? (or not labelled) © Errol Véla 3.9. Allium "algarvense" J.M.Tison & Véla, nom. provis.  Possibly « Near Threatened »? (unpubl. data) » Nb of mature individuals unknown (but potentially thousands) » AOO ≈ 84 km² » EOO ≈ 40.000 km² » CW-& SW-Portugal, SW-Spain » To be searched in Morocco… » Forgotten name A. modagorene Willd. to be checked? » Allo-hexaploid (progenitors unknown) » Sandy coastal primary / secondary habitat (0-100m alt.) » No germaplasm collection ? (or not labelled) © Errol Véla Confused with A. ampeloprasum (LC) then A. porrum (NA) 3.10. Allium "debusscheorum" J.M.Tison & Véla, nom. provis.  « Critically Endangered »! (unpubl. data) » < 10 flowering individuals each year » < 50 mature individuals (potentially able to flower) » < 200 clonal juveniles (2021 survey) » AOO = 8 km² » 2 locations (sensu IUCN) » 1-site restricted » Allo-hexaploid (progenitors unknown) » Chasmophytic primary habitat » No germaplasm collection until now (few ex situ private cultivation) © Errol Véla

https://www.iucnredlist.org/species/13144620/18612715

https://www.iucnredlist.org/species/172281/19174707

https://www.iucnredlist.org/species/172241/19391362

https://www.iucnredlist.org/species/110805790/87775132


Wieslaw Kopeć

Cezary Biele

Monika Kornacka

Grzegorz Pochwatko

Anna Jaskulska

Kinga Skorupska

Julia Paluch

Piotr Gago

Barbara Karpowicz

Marcin Niewiński

Participatory Design Landscape for the Human-Machine Collaboration, Interaction and Automation at the Frontiers of HCI (PDL 2021)

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Participatory Design Landscape for the Human-Machine Collaboration, Interaction and Automation at the Frontiers of HCI (PDL 2021) Wies law Kopeć 1[0000-0001-9132-4171] , Cezary Biele 2[0000-0003-4658-5510] ,

Monika Kornacka 3[0000-0003-2737-9236] , Grzegorz Pochwatko 4[0000-0001-8548-6916] , Anna Jaskulska 5[0000-0002-2539-3934] , Kinga Skorupska 1,3[0000-0002-9005-0348] , Julia Paluch 1[0000-0002-7657-7856] , Piotr Gago 1[0000-0001-7288-4210] , Barbara Karpowicz 1[0000-0002-7478-7374] , Marcin

Niewiński 1[0000-0002-6416-3541] , and Rafa l Mas lyk 1[0000-0003-1180-2159]

1 Polish-Japanese Academy of Information Technology 2 National Information Processing Institute 3 SWPS University of Social Sciences and Humanities 4 IP Polish Academy of Sciences 5 KOBO Association

Abstract. We propose a one-day transdisciplinary creative workshop in the broad area of HCI focused on multiple opportunities of incorporating participatory design into research and industry practice. This workshop will become a venue to share experiences and novel ideas in this area. At the same time, we will brainstorm and explore frontiers of HCI related to engaging end users in design and development practices of established and emerging ICT solutions often overlooked in terms of co-design. We welcome a wide scope of contributions in HCI which explore sustainable opportunities for participatory design and development practices in the context of interconnected business, social, economic and environmental issues. The contributions ought to explore challenges and opportunities related to co-design at the frontiers of HCI -participatory design of newest and complex technologies, not easily explainable or intuitive, novel collaborative (remote or distributed) approaches to empowering users to prepare them to contribute as well as to engaging them directly in co-design. Our workshop on participatory design landscape for the human-machine collaboration, interaction and automation welcomes contributions in the broad area of Human-Computer Interaction. We focus on facilitating end-users involvement in a sustainable and ethical way in the context of the novel ICT solutions, remote co-design participation and accelerating digital transformation touching both the professional and the private spheres. This transformation presents multiple opportunities for addressing complex interdependent problems with co-designed ICT-based solutions using remote or distributed collaboration and novel empowerment approaches, to enable all users to fully participate in co-designing even complex solutions, which are not easy to relate to or intuitive.

Contributions

The contributions ought to explore challenges and opportunities related to codesign at the frontiers of HCI -participatory design of newest and complex technologies, not easily explainable or intuitive, novel collaborative (remote or distributed) approaches to empowering users to prepare them to contribute as well as to engaging them directly in co-design. Therefore, we welcome reports of interdisciplinary projects, studies and potential research initiatives which use (or would like to benefit from) various participatory approaches. These can, for example, include cases of user empowerment [START_REF] Davidson | Participatory design with older adults: an analysis of creativity in the design of mobile healthcare applications[END_REF][START_REF] Sanders | From user-centered to participatory design approaches[END_REF][START_REF] Kopeć | Guidelines towards better participation of older adults in software development processes using a new spiral method and participatory approach[END_REF][START_REF] Ladner | Design for user empowerment[END_REF] or engaging users in diverse co-design contexts, such as Living Labs [START_REF] Pallot | Living lab research landscape: From user centred design and user experience towards user cocreation[END_REF][START_REF] Kopeć | Livinglab pjait: Towards better urban participation of seniors[END_REF], design probes, and other creative co-design methods.

Topics

These studies, while necessarily focusing on participatory design practices, can touch upon various aspects of applied computer science (including data science, artificial intelligence, social informatics and human-computer interaction) alongside with current state-of-the-art in psychology, social science and economics (such as neuroscience, psychophysiology and cognitive studies). The interdisciplinary and participatory approach they ought to describe can be applied in the context of interconnected business, social, economic and environmental issues, as our focus is on the use of co-design approaches in a wide scope of novel contexts. Therefore, in the workshop we will explore the opportunities related to participatory design of both established and emerging solutions and interfaces like Voice Assistants (VA), Virtual (VR), Augmented (AR) and Mixed Reality alongside with major underlying trends like Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) or Complex Event Processing (CEP) in the context of multiple areas of interest to science and industry where co-design can be particularly beneficial. One example of such area of interest is related to creating health solutions using technologies that are new to end users -patients -but that have already been proven to be effective at improving the efficiency of mental health therapy, such as VR or AR. Another opportunity is related to the co-development of self-help mobile applications or games [START_REF] Jessen | Creating gameful design in mhealth: A participatory co-design approach[END_REF] for health (including mental health) [START_REF] Jessen | Mystrengths, a strengths-focused mobile health tool: Participatory design and development[END_REF] and of wearable monitoring solutions. The development and increased access to wearable, mobile and robotic technology brings novel opportunities for using IT-based solutions in everyday life, including personal spaces and workplaces. Such solutions make it possible to measure psychological processes and to provide treatment in ecological conditions of patients' everyday life. Yet, research and practice show that functionality and ergonomics of the proposed solutions are crucial for acceptability and compliance. Thus, participatory design may be an adequate way to address those challenges. Especially the design of social robots, which heavily rely on expectations based on social interaction norms, stands to greatly benefit from participatory design [START_REF] Piçarra | Designing social robots for interaction at work: Socio-cognitive factors underlying intention to work with social robots[END_REF].

Organizers

We are a multidisciplinary group of scientists, researchers, IT professionals and activists whose key focus is on multiple positive applications of ICT through engaging in participatory practices 6 , especially focused on engaging the users excluded from the technological discourse [START_REF] Kopeć | Guidelines towards better participation of older adults in software development processes using a new spiral method and participatory approach[END_REF] (for example, with games7 or hackathons 8 or through inviting stakeholders to take part in educational and capacity building projects 9 ).

Key

Objectives

In organizing this workshop we have three key objectives:

1. Exchanging experiences, ideas, best practices and guidelines for co-designing at the transdisciplinary frontiers of HCI. 2. Outlining the participants' aspirations and opportunities for breaking new ground in the area of HCI and exploring ideas for innovative project proposals and practices involving participatory design. 3. Establishing common ground and voice for the discussion of the challenges and opportunities related to the future of participatory practices at the frontiers of HCI.

Target Audience

We invite researchers and practitioners in the broad area of HCI, who want to explore the opportunities of incorporating participatory practices in their work, including design, collaborative ML, RPA, and crowdsourcing in an ethical and sustainable way. At the same time we want to find new areas where co-design of ICT-based solutions may work towards improving social cohesion and addressing complex business, social, economic and environmental issues.

Methodology

We propose a one-day exploratory workshop combining sharing best practices as interactive presentations and discussions with a creative flair of a collaborative design-inspired toolbox geared towards exploring opportunities at the frontiers of co-design and HCI. In the course of the workshop we want to use creative tools to aid free thinking, instead of stifling it with over-formalized "creative" methodology focused on structured results. We believe that allowing for a degree of ambiguity at the beginning of the creative process is a must, while the outlines of our combined expertise are sufficient to work as bounds that limit the scope of our imagination. In this vein, the workshop will be led in a friendly atmosphere, without formalized introductions or strict presentation formats. Our workshop facilitators have diverse creative tools and practices at their disposal, which they can adjust accordingly to the number and needs of the participants and their levels of energy.

Expected Outcomes

As our key motivation is to explore the opportunities related to participatory design at the frontiers of HCI, while grounding these firmly in established and emerging practices, the most important outcome will be a post-workshop multiauthor publication. Its format will be the result of issues discussed during the workshop, but the idea for it is inspired by Kittur et al. [START_REF] Kittur | The future of crowd work[END_REF] whose paper "The Future of Crowd Work" clearly outlined the aspects, trends and opportunities related to crowdsourcing. We feel the same is needed in the area of participatory design and human factors at the frontiers of HCI, as such design ought to take into account ethics, preferences and motivations as well as the broad user experience and visual design. Especially now, that more remote tools and approaches are needed to engage users, and novel forms of empowerment ought to emerge to let co-designers really understand the context of the complex co-designed solutions. Additionally, we would be happy to invite our participants to look for opportunities to publish a collection of extended papers from the workshop.

Keywords:

  Participatory design • Human-computer interaction • Robotic process automation • Collaborative interactive machine learning • Business process management systems • Crowdsourcing • Natural language processing. Supported by Human Aspects in Software Engineering (HASE) Research Initiative 1 Theme and Topics 1.1 Theme

Fig. 1 .

 1 Fig. 1. Example topics for this workshop in relation to its key co-design theme.

  Paper Co-Chairs Kinga Skorupska, XR Lab PJAIT, (Google Scholar Profile) and Julia Paluch, XR Lab PJAIT (ResearchGate Profile).

	Technical Chairs Rafa l Mas lyk, XR Lab PJAIT; Barbara Karpowicz, XR Lab
	PJAIT; Maciej Krzywicki, XR Lab PJAIT; Piotr Gago, XR Lab PJAIT.
	-Monika Kornacka, PhD, (Google Scholar Profile)
	Head of Emotion Cognition Lab, Institute of Psychlogy,
	SWPS University of Social Sciences and Humanities (SWPS)
	-Grzegorz Pochwatko, PhD, (Google Scholar Profile)
	Head of Virtual Reality and Psychophysiology Lab, Institute of Psychology,
	Polish Academy of Sciences (IP PAS)
	Program Committee Jakub Możaryn, PhD, Warsaw University of Technol-
	ogy (Google Scholar Profile); Jacek Lebiedź, PhD, Gdansk University of Technol-
	ogy (University Profile); Marzena Wojciechowska, PhD, PJAIT Resarch Center
	(ResearchGate Profile); Dominika Tkaczyk, PhD, Crossref (Google Scholar Pro-
	file); Sebastian Zagrodzki, Google (LinkedIn Profile) Lukasz Czarnecki, Amazon
	(LinkedIn Profile); Anna Jaskulska, Kobo Association and Living Lab (Kobo
	Profile); Ewa Makowska, Business and System Analyst and IT Project Manager
	(LinkedIn Profile).

Organizers -Wies law Kopeć, PhD, MBA, (Google Scholar Profile) Head of XR Lab, Computer Science Department, Polish-Japanese Academy of Information Technology (PJAIT) -Cezary Biele, PhD, (Google Scholar Profile) Head of Laboratory of Interactive Technologies, National Information Processing Institute (NIPI)

Participatory Design Workshops

Location-Based Game

Hackathon