Emergence Patterns of Rare Arable Plants and Conservation Implications

Article Emergence Patterns of Rare Arable Plants and Conservation Implications

Joel Torra 1,* , Frank Forcella 2, Jordi Recasens 1 and Aritz Royo-Esnal 1,*

1 Department of Hortofruticultura, Botànica i Jardineria, Agrotecnio, Universitat de Lleida. Alcalde Rovira Roure 191, 25198 Lleida, Spain; [email protected] 2 Department of Agronomy and Plant Genetics, College of Food, Agricultural and Natural Resources Sciences (CFANS), University of Minnesota, St Paul, MN 55108, USA; [email protected] * Correspondence: [email protected] (J.T.); [email protected] (A.R.-E.); Tel.: +34-9737-02318 (J.T. & A.R.-E.)

Received: 21 January 2020; Accepted: 25 February 2020; Published: 1 March 2020

Abstract: Knowledge on the emergence patterns of rare arable plants (RAP) is essential to design their conservation in Europe. This study hypothesizes that is possible to find functional groups with similar emergence patterns within RAP with the aim of establishing management strategies. Seeds of 30 different species were collected from Spanish arable fields and sown under two tillage treatments: (a) 1 cm depth without soil disturbance to simulate no-till, and (b) 1–10 cm depth with soil disturbance every autumn to simulate tillage to 10 cm depth. Two trials were established; the first trial being maintained for three seasons and the second for two seasons. Relative emergence in autumn, winter and spring was calculated each season. Afterwards, multivariate analysis was performed by K-means clustering and Principal Component Analysis to find groups of RAP species with similar emergence patterns. Four RAP groups were defined, and each was based on its main emergence season: autumn, winter, spring, or autumn-winter. Tillage treatment and the year of sowing had little effect on emergence patterns, which were mostly dependent on environmental factors, particularly temperature and rainfall. Therefore, conservation strategies could be designed for each of these RAP functional groups based on emergence patterns, rather than on a species-by-species basis.

Keywords: burial depth; Calinski–Harabasz criterion; clustering; K-means; multivariate analyses; no-tillage; Principal Component Analysis (PCA); soil disturbance; tillage

1. Introduction The ecosystem in Europe most influenced by human activity is arable land [1]. Since the advent of modern agriculture, arable plant diversity has suffered a steep decline across the continent (reviewed in Albrecht et al. [2] and Richner et al. [3]). Probably, there is no other vegetation type in Europe in which populations have been so strongly reduced in qualitative and quantitative ways [1]. A particular set of specialist plants typically found in arable land, called segetal species, have suffered the most drastic population declines in recent years, to the point of becoming rare arable plants (RAP) or even locally extinct in many countries [4,5]. Therefore, an urgent need exists in Europe to prevent the strong decline in plant diversity in arable agro-ecosystems [6]. Particularly needed is the development of conservation and management strategies focused on the threatened RAP species [7]. Several studies consider how RAP could be conserved, although data on this subject is still very scarce and insufficiently documented [2] especially in the Mediterranean countries [8]. Conservation headlands, field flora reserves, uncropped cultivated margins and wildflower strips included within the Agro-environmental schemes (AES) of the European Union are the most widespread conservation measures [2]. However, most AES projects fail often after an initially successful phase mainly due to complicated and insecure funding [9] or expansion of highly competitive weeds [10]. Other

Plants 2020, 9, 309; doi:10.3390/plants9030309 www.mdpi.com/journal/plants Plants 2020, 9, 309 2 of 19 favorable management practices to successfully establish RAP species are reduced crop densities, lower nitrogen fertilization, avoidance of herbicides, and medium sowing densities of the rare species [11]. However, there is still a lack of knowledge on best management strategies to promote establishment and abundance of RAP under real farm conditions over the long term. The timing of seedling emergence is crucial for fitness of annual plants [12], which is why tillage is one of the most important anthropogenic drivers of weed communities [13]. Arable flora, such as RAP, is highly adapted to regular soil disturbances for thousands of years [14]. For this reason, the timing of tillage and sowing are other key factors affecting the success of arable plant establishment [15]. As most threatened arable plant species are autumn-winter annuals [16], a sowing date in autumn could increase the sustainable establishment of RAP species [15]. However, most studies deal only with a few species or emergence data are not provided to support the hypothesis. The same applies to the type of soil tillage management, as almost no literature provides data on how tillage can affect RAP populations. Recent research on the effects of tillage type (no-till versus till) on a large set of RAP showed that recurrent annual tillage could be important to maintain and replenish seed banks of these species for the mid to long-term, particularly for those with secondary dormancy processes and high seed bank persistence [5]. It is no doubt that conservation measures need to be established that integrate RAP species preservation into regular farming activities. However, RAP are almost absent from many areas [17,18] and, therefore, their reintroduction provides another measure to support plant diversity. Sowing dates and soil tillage management for RAP species may play important roles for successful reintroduction, in addition to suitable site and management conditions [10,19]. Importantly, because this approach has the risk of establishing pernicious weeds, reintroduction of RAP will only gain acceptance among farmers if yield losses due to weed competition are low [20]. Delayed sowing can reduce competition by early emerging and highly competitive weeds, mostly grass species [21]. Additionally, RAP usually have relatively poor competitive abilities with arable crops [22]. Moreover, there is consistently strong evidence that maintenance of high plant diversity is not a detriment to crop yields [23]. However, there is little published information on the effects of tillage or sowing dates for RAP and their related RAP-crop interactions [20]. Moreover, there are almost no available data on RAP emergence patterns, which would allow assessment of the effects of changing sowing times so that these species can be favored compared to common problematic weeds. Plant species can be grouped into functional groups using their traits based on ecological strategies [24]. Identifying functional response groups of species with a similar response to a particular environmental factor, is a long-standing practice in weed control that is used to optimize weeding [13], although it could be also used for other objectives, such as preservation. Therefore, defining functional groups of RAP could be suitable for predicting the impact of management practices and environmental factors on this set of threatened species in arable fields and agricultural landscapes for restoration and conservation purposes. There are 193 arable plant species either on the national Red Data Lists or considered threatened in at least three of 29 European countries, and of those, 48 are critically endangered (16 are within this study) [6]. Though species-specific recommendations would be appropriate [5,7,25], such a research commitment is hardly attainable considering the numbers of RAP. Therefore, establishing functional groups to manage RAP would be highly desirable for practical recommendations to deliver maximum benefits for the widest range of species potentially present at a site. To develop methods for restoring and increasing plant agro-biodiversity, with focus on RAP, this study aimed to analyze the emergence patterns of a large set of species. This research attempts to provide new data and summarize scarce available information to identify sowing dates and soil tillage regimes to optimize conservation strategies and successful establishment of 30 RAP species [17,26]. Thus, emergence patterns of these species were studied during three consecutive seasons in a cereal field in Spain under two distinct tillage regimes (no-till versus till). Afterwards, relative emergences in autumn, winter and spring of each season were estimated for each species. With these data, a Plants 2020, 9, 309 3 of 19 multivariate analysis approach was performed to find groups of RAP with similar emergence patterns. This methodology is aimed at providing conservation strategies that can be applied to RAP of similar emergence patterns as alternatives to inefficient tactics based on species-specific recommendations.

2. Results The three seasons were diﬀerent in terms of rainfall and temperature (Table1). Mean air temperatures were much higher the ﬁrst season (2012/2013) from autumn to spring every month (at least two-fold) compared to the two following seasons. The coldest autumn was the second season (2013/2014), while the coldest winter was in the third (2014/2015). In contrast, the wettest autumn was in the third season (particularly in September and November), while the driest was the second (September and October). Winter was wettest in 2013/2014; while spring was very dry in 2014/2015 (Table1).

Table 1. Monthly mean temperature (◦C) and cumulative rainfall (mm) from September to April) during three observation seasons.

Seasons 2012/2013 2013/2014 2014/2015 Mean Mean Mean Month Rainfall Rainfall Rainfall Temperature Temperature Temperature September 20.0 49 7.7 5 10.8 146 October 15.7 92 5.0 6 6.7 17 November 9.5 30 3.1 56 5.9 123 December 5.9 7 0.1 12 0.7 11 January 4.7 31 2 37 0.2 12 February 5.8 8 1.4 14 0.1 13 March 9.4 68 2.3 13 3.0 45 April 11.9 78 5.0 46 3.4 8

The emergence patterns in autumn, winter and spring, irrespective of the burial conditions (no-till at 1 cm depth vs. till at 1–10 cm depth), are shown in Figure1 for the first trial (S1) and in Figure2 for the second trial (S2). Patterns differed between trials. The first season in S1 (2012/2013) 25 of 30 RAP had at least 50% of their emergence in autumn, while the first season in S2 (2013/2014), this pattern existed only for 6 species: Agrostemma githago, Androsace maxima, Camelina microcarpa, Cerastium perfoliatum, Neslia paniculata and Silene conoidea. Results were reversed in the second season; in S1 (2013/2014) emergence was concentrated in winter and secondarily in spring (excepting Biscutella auriculata, C. microcarpa, C. perfoliatum and N. paniculata with important emergence in autumn), whereas in S2 (2014/2015) most species (28 out of 30) had at least 67% of emergence in autumn. The only exceptions were Hypecoum pendulum and Galeopsis ladanum. The latter species was the only one with a restricted spring emergence behavior since at least 98% of emergence occurred in that period irrespective of trial (S1 and S2) and season (Figures1 and2). The soil disturbance regimes, no-till vs. till, in general, did not influence appreciably the emergence patterns among the 30 RAP or between trials S1 and S2 across seasons (Figures A1–A5). In some cases, soil disturbance could delay, advance or not have any particular effect on the emergence of the 30 RAP depending on the species, trial and season. For example, in the first season in trial S1 emergence was delayed in seven species and advanced in another seven, in the second season it was delayed in 13 species and only advanced in three, while the third season emergence was delayed in six species and advanced in 15 (Figures A1–A3). Similarly, in the first season in trial S2 emergence was delayed by soil disturbance in 12 species and advanced in five, while in the second season it was delayed only in four and advanced in 12 species (Figures A4 and A5). Plants 2020, 9, 309 4 of 19

Adonis aestivalis 79 17 4 1 87 12 92 4 4 Adonis flammea 44 43 14 0 84 16 58 37 5 Agrosthemma githago 100 0 10 50 40 0 Androsace maxima 98 11 27 60 14 67 26 7 Asperula arvensis 91 6 2 0 78 22 81 6 13 Bifora radians 84 13 3 0 81 19 70 28 3 Biscutella auriculata 87 3 10 44 22 33 100 0 Bupleurum rotundifolium 85 9 6 1 73 26 58 38 3 Camelina microcarpa 92 6 2 53 47 0 52 41 8 Cerastium perfoliatum 92 7 1 53 34 13 40 34 26 Conringia orientalis 50 20 30 9 53 38 93 6 1 Consolida orientalis 88 8 4 15 71 13 46 38 16 Consolida pubescens 96 31 0 84 16 63 33 5 Delphinium gracile 91 8 1 0 32 68 83 17 Delphinium halteratum 93 5 2 0 77 23 54 43 3 Galeopsis ladanum 2 98 0 98 0 100 Hypecoum pendulum 35 46 18 1 70 30 100 0 Iberis amara 90 1 9 11 66 22 79 14 7 Lathyrus aphaca 86 10 4 0 100 0 Legousia hybrida 55 10 34 7 54 39 47 41 12 Neslia paniculata 79 21 0 46 36 17 84 15 1 Nigella gallia 51 33 15 1 57 42 48 44 8 Papaver argemone 95 2 3 0 90 10 62 35 3 Papaver dubium 98 20 0 80 20 61 36 3 Ranunculus arvensis 85 15 0 0 80 20 71 25 4 Roemeria hybrida 78 4 18 19 63 18 65 25 9 Silene conoidea 65 23 13 21 46 32 88 12 0 Thlaspi arvense 67 10 23 20 63 18 83 9 8 Turgenia latifolia 6 50 44 0 82 18 75 25 0 Vaccaria hispanica 59 23 18 3 85 12 100 0 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 season 1 (2012-13) season 2 (2013-14) season 3 (2014-15)

autumn winter spring

Figure 1. Relative Emergence patterns of 30 rare arable plants (RAP) in autumn (October through December), winter (January through February), and spring (March through April) during three consecutive seasons for seeds sown in 2012 (trial S1) in an arable ﬁeld trial in Spain. Bars represent standard error. Two burial conditions are shown averaged: 1 cm burial depth without soil disturbance and 1–10 cm depth with annual autumnal soil disturbance. See AppendixA for relative emergence for each burial condition. Plants 2020, 9Plants, 309 2020, 9, x FOR PEER REVIEW 5 of 19 5 of 20

Adonis aestivalis 3 72 25 92 3 5 Adonis flammea 0 82 18 85 2 13 Agrosthemma githago 92 8 0 100 0 Androsace maxima 57 40 3 92 5 3 Asperula arvensis 6 90 4 99 10 Bifora radians 0 96 4 87 5 8 Biscutella auriculata 26 64 10 93 7 0 Bupleurum rotundifolium 9 83 8 89 3 8 Camelina microcarpa 68 30 2 82 9 9 Cerastium perfoliatum 64 36 0 82 13 5 Conringia orientalis 22 60 19 96 13 Consolida orientalis 31 68 1 81 2 17 Consolida pubescens 8 87 5 73 23 4 Delphinium gracile 1 95 4 81 19 0 Delphinium halteratum 0 96 4 75 19 6 Galeopsis ladanum 0 99 0 100 Hypecoum pendulum 0 82 18 28 4 68 Iberis amara 2 65 33 87 3 10 Lathyrus aphaca 0 90 10 99 10 Legousia hybrida 10 70 20 67 29 5 Neslia paniculata 57 42 2 85 4 10 Nigella gallia 0 85 15 81 11 8 Papaver argemone 40 59 0 92 6 2 Papaver dubium 0 83 17 0 Ranunculus arvensis 1 95 4 92 4 5 Roemeria hybrida 20 78 3 73 13 14 Silene conoidea 59 33 8 73 17 11 Thlaspi arvense 9 86 5 92 2 6 Turgenia latifolia 0 95 5 72 2 27 Vaccaria hispanica 27 73 1 98 02

0 204060801000 20406080100 season 1 (2013-14) season 2 (2014-15) autumn winter spring Figure 2. Relative Emergence patterns of 30 RAP in autumn (October through December), winter (January through February), and spring (March through April) Figure 2. Relative Emergence patterns of 30 RAP in autumn (October through December), winter (January through February), and spring (March through April) during two consecutive seasons for seeds sown in 2013 (trial S2) in an arable ﬁeld trial in Spain. Bars represent standard error. Two burial conditions are shown during two consecutive seasons for seeds sown in 2013 (trial S2) in an arable field trial in Spain. Bars represent standard error. Two burial conditions are shown averaged: 1 cm burial depth without soil disturbance and 1–10 cm depth with annual autumnal soil disturbance. See AppendixA for relative emergence for each averaged: 1 cm burial depth without soil disturbance and 1–10 cm depth with annual autumnal soil disturbance. See Appendix A for relative emergence for each burial condition. burial condition.

Plants 2020, 9, x FOR PEER REVIEW 6 of 20

Plants 2020, 9, 309 6 of 19 Multivariate Analysis MultivariateThe K-means Analysis clustering and the Calinski-Harabasz criterion (CHC) values identified only two clustersThe in K-means a first analysis clustering (Figure and the3, top Calinski-Harabasz chart). Since only criterion one species (CHC) (G. values ladanum identified) was ascribed only two to theclusters first ingroup a first and analysis the rest (Figure (29 species)3, top in chart). the seco Sincend onlygroup, one and species that the ( G. linear ladanum CHC) was plot ascribed indicated to alsothe firstthe groupsecondary and presence the rest (29 of species)four significant in the second groups group, (Figure and 3, top that chart, the linear orange CHC dot), plot a second indicated K- meansalso the analysis secondary was presenceperformed of removing four significant G. ladanum groups from (Figure the data3, set. top In chart, this second orange clustering, dot), a second three significantK-means analysis groups waswere performed identified removingaccordingG. to ladanum the CHCfrom values the data(Figure set. 3, In bottom this second chart, clustering, red dot). Speciesthree significant composition groups was were the same identified in these according three gr tooups the CHCin the values first and (Figure second3, bottom cluster chart, analysis. red Consideringdot). Species both composition K-means was cluster the same analysis in these altogether three groups four distinct in the firstgroups and of second RAP clusterwere identified analysis. accordingConsidering to their both emergence K-means cluster patterns analysis during altogether the emergence four distinctseason, as groups shown of in RAP Table were 2. identified according to their emergence patterns during the emergence season, as shown in Table2.

Figure 3. Results of the two K-means clustering applied with the Calinski–Harabasz criterion (CHC). TheFigure CHC 3. Results indicated of thatthe thetwo 30 K-means RAP could clustering be clustered applied into with two groupsthe Calinski–Harabasz (1 species in one, criterion 29 in the second)(CHC). Thein a firstCHC analysis indicated (top that chart), the also30 RAP marked could as be a red clustere pointd on into the two linear groups plot of(1 CHCspecies values in one, above 29 onin the second)right. Second in a first K-means analysis clustering (top chart), with 29also species, marked shown as a inred the point bottom on chart).the linear The plot CHC of indicated CHC values that abovethe 29 on RAP the could right. be Second clustered K-means into threeclustering groups, with (red 29 species, point in shown the linear in the plot bottom of CHC chart). values The below, CHC indicatedequivalent that tothe the first 29 RAP orange could point be inclustered CHC plot into shown three above).groups,Colors (red point within in the each linear of the plot columns of CHC of valuesthe figures below, on theequivalent left (30 aboveto the andfirst 29orange below) point are groupsin CHC of plot RAP shown in the above). K-means Colors calculations. within each of the columns of the figures on the left (30 above and 29 below) are groups of RAP in the K-means calculations.The first Principal Component (PC) of the Principal Component Analysis (PCA) explained 27.9% of the total variation, while the second PC explained 18.6%, which overall accounted for nearly 46.5%

Plants 2020, 9, x FOR PEER REVIEW 7 of 20 Plants 2020, 9, 309 7 of 19 The first Principal Component (PC) of the Principal Component Analysis (PCA) explained 27.9% of the total variation, while the second PC explained 18.6%, which overall accounted for nearly 46.5% ofof thethe total total variation variation (Figure (Figure4). 4). The The PCA PCA divided divided all studied all studied species species in two in main two groups, main groups, in one leaving in one G.leaving ladanum G. isolated,ladanum isolated, in the other in the the other rest of the the rest 29 speciesof the 29 (Figure species4). (Figure In the second 4). In the group, second an important group, an numberimportant of speciesnumber were of species close to were the centerclose to coordinates the center 0.0 coordinates (i.e., Consolida 0.0 (i.e.,or Delphinium Consolida species),or Delphinium while somespecies), species while were some located species in were the right located part, in suchthe right as A. part, githago such, A. as maxima A. githago, B., auriculataA. maxima, ,C. B. microcarpa auriculata, C.C. perfoliatummicrocarpa,, C. and perfoliatumN. paniculata, and, N. or paniculata some others, or onsome the others left part on ofthe the left PCA part biplot of the (PCAAdonis biplot ﬂammea (Adonis, H. pendulumflammea, H., Nigella pendulum gallica, Nigella, and Turgenia gallica, and latifolia Turgenia). latifolia).

FigureFigure 4.4. Biplot representation of K-meansK-means clustering using the ﬁrstfirst two PC of thethe PCAPCA forfor 3030 RAPRAP speciesspecies (three(three ﬁrstfirst lettersletters ofof thethe genusgenus andand species)species) accordingaccording toto theirtheir emergenceemergence patternspatterns inin twotwo trialstrials (S1(S1 andand S2)S2) duringduring threethree consecutiveconsecutive seasons.seasons. ThereThere were twotwo burialburial conditionsconditions consideredconsidered inin thethe analysis:analysis: 1 cm burial depth without soil disturbancedisturbance and 1–101–10 cmcm depthdepth withwith annualannual autumnalautumnal soilsoil disturbance.disturbance. PercentagesPercentages in in parenthesis parenthesis represent represent the variabilitythe variability explained explained by each by PC. each Circles, PC. ellipsesCircles, andellipses names and with names the with same the colors same include colors the include species the within species each within of the each groups of the derived groups from derived a K-means from a clusteringK-means clustering of the ordination of the ordination scores. The scores. full species The full names species are names given inare Table given2. in Table 2.

LookingLooking atat thethe results fromfrom thethe K-meansK-means clustering (together with the CHC) and from the PCA, particularlyparticularly thethe biplotbiplot forfor thethe vectorsvectors (Figure(Figure5 5),), thethe nature nature of of each each group group was was clear clear regarding regarding its its emergenceemergence pattern.pattern. TheThe Y axis (PC 2) distributed thethe RAP species from those almost only germinating inin spring (group (group 1, 1, only only with with G.G. ladanum ladanum), ),from from the the rest. rest. The The X axis X axis (PC (PC1) distributed 1) distributed the species the species from fromthose those with withemergence emergence concentrated concentrated in winter in winter and andalso alsoimportant important emergence emergence in spring in spring (group (group 2, A. 2, A.flammea ﬂammea, H., H. pendulum pendulum, Legousia, Legousia hybrida hybrida, N., N. gallica gallica, and, and T.T. latifolia latifolia),), from from those those emerging mainlymainly inin autumn,autumn, withwith somesome winterwinter emergenceemergence (group (group 3, 3, mostly mostlyCariophyllaceae Cariophyllaceae, and, and other other species) species) and and group group 4 (mainly4 (mainlyRanunculaceae Ranunculaceae, and, and some someApiaceae Apiaceaeamong among others) others) that that emerged emergedboth both inin autumnautumn andand winterwinter (Table(Table2 ).2).

Plants 2020, 9, 309 8 of 19

Table 2. Groups deﬁned according to the K-means cluster analysis. Letters in parenthesis represent the Plants 2020code, 9 used, x FOR for PEER graphical REVIEW representation of the K-means clustering on a PCA biplot (Figure5). 8 of 20

TableGroups 2. Groups defined Emergence according Patterns to the K-means cluster analysis. Letters RAP Species in parenthesis represent the Groupcode used 1 for graphical representation spring of the K-means clusteringGaleopsis on ladanuma PCA biplot(Gal lad)(Figure 5). Groups Emergence Patterns Adonis ﬂammea RAP Species(Ado ﬂa), Hypecoum pendulum (Hyp GroupGroup 1 2 spring mainly winter pen),GaleopsisLegousia ladanum hybrida (Gal(Leg lad) hyb), Nigella gallica (Nig Adonis flammea (Ado fla), Hypecoumgal), Turgenia pendulum latifolia (Hyp(Tur pen), lat) Legousia Group 2 mainly winter hybrida (Leg hyb), NigellaAgrostemma gallica githago (Nig gal),(Agro Turgenia git), Androsace latifolia (Tur maxima lat) Agrostemma githago(And (Agro max), git),Biscutella Androsace auriculata maxima(Bis (And aur), max),Camelina Group 3 mainly autumnBiscutella auriculatamicrocarpa (Bis (Camaur), Camelina mic), Cerastium microcarpa perfoliatum (Cam mic),(Cer per), Group 3 mainly autumn Cerastium perfoliatumNeslia (Cer paniculata per), Neslia(Nes pan),paniculataPapaver (Nes dubium pan),(Pap Papaver dub), dubium (Pap dub), SileneSilene conoideaconoidea (Sil(Sil con)con) Adonis aestivalis Adonis(Gal lad), aestivalis Asperula(Gal arvensis lad), Asperula (Asp arv), arvensis Bifora(Asp radians arv), (Bif rad), BupleurumBifora rotundifolium radians (Bif (Bup rad), rot),Bupleurum Conringia rotundifolium orientalis (Con(Bup rin), Consolida orientalisrot), Conringia (Con ori), orientalis Consolida(Con pubescens rin), Consolida (Con orientalispub), Group 4 autumn-winter Delphinium gracile(Con (Del ori), gra),Consolida Delphinium pubescens halteratum(Con (Del pub), hal),Delphinium Iberis Group 4 autumn-winter gracile (Del gra), Delphinium halteratum (Del hal), amara (Ibe ama), Lathyrus aphaca (Lat aph), Papaver argemone (Pap Iberis amara (Ibe ama), Lathyrus aphaca (Lat aph), arg), Ranunculus arvensis (Ran arv), Roemeria hybrida (Roe hyb), Papaver argemone (Pap arg), Ranunculus arvensis (Ran Thlaspi arvensearv), Roemeria (Thl arv), hybrida Vaccaria(Roe hispanica hyb), Thlaspi (Vac his) arvense (Thl arv), Vaccaria hispanica (Vac his)

Figure 5. Biplot representation of the variables (vectors) used in a PCA of the percentage of emergence of the 30 RAP in autumn, winter or spring in two different trials under two tillage regimes. In each variable, S1 refers to the trial started in 2012, S2 for the trial started in 2013; season 1 for 2012/2013, season 2 for 2013/2014, and season 3 for 2014/2015; autumn, winter or spring, the period from which the emergence is provided; there were two burial conditions: 1 cm burial depth without soil

Plants 2020, 9, 309 9 of 19

Figure 5. Biplot representation of the variables (vectors) used in a PCA of the percentage of emergence of the 30 RAP in autumn, winter or spring in two diﬀerent trials under two tillage regimes. In each variable, S1 refers to the trial started in 2012, S2 for the trial started in 2013; season 1 for 2012/2013, season 2 for 2013/2014, and season 3 for 2014/2015; autumn, winter or spring, the period from which the emergence is provided; there were two burial conditions: 1 cm burial depth without soil disturbance (referred as no-till in the vectors) and 1–10 cm depth with annual autumnal soil disturbance (referred as till in the vectors). Vectors are shown as the eigenvectors of the covariance matrix scaled by the square root of the corresponding eigenvalue and shifted so their tails are at the mean. Relative position of the four groups of RAP by means of K-means clustering (see Table2 and Figure5) shown in circles (where 50% of observations would be found) in diﬀerent colors.

Looking at the output from PCA biplot for the vectors of each data set (Figure5), it seems that the first PC (X axis) distributed the 30 RAP species depending on an emergence pattern more concentrated in autumn or more in winter: species located on the right side of this axis had important levels of emergence in autumn, while species located on the left side had important emergence flushes in winter. On the other hand, the second PC (Y axis) distributed species in relation to emergence in spring or the rest of the season. Species with most emergence in spring were located in the bottom part of the biplot (Figure4), while species with more emergence in autumn and /or winter were located in the upper part of the graph. Finally, considering the distribution of the data vectors in the PCA (Figure5), no-till or till, burial conditions did not seem to have major effects on relative emergence patterns of the 30 RAP studied

3. Discussion According to the results presented in this research, the 30 RAP species studied could be divided into four distinct groups according to their relative emergence levels during the germinating season from autumn to spring. Looking at the results from the multivariate analyses, the nature of each group was clear regarding its emergence pattern: species almost only germinating in spring, the species with emergence concentrated in winter and also important emergence in spring, those emerging mainly in autumn with some winter emergence, and ﬁnally those that emerged both in autumn and winter. Interestingly, similar results were obtained in a previous study where late germinating species (Galeopsis sp.) were clearly separated from the rest with similar multivariate analysis [27]. The data used in this research had two soil burial conditions: 1 cm depth without soil disturbance (no-till), and 1–10 cm depth with annual autumnal soil disturbance (till). Even though these burial conditions could delay or advance emergence, there were no clear effects on the emergence patterns; for a given species one season in one trial (S1 or S2) burial could advance or delay emergence (Figures A1–A5). Seemingly,the response of emergence to tillage was species-specific and more dependent on environmental factors such as rainfall [28]. On the other hand, tillage promoted the total seedling emergence of these RAP in the same study [5] and, thus, tillage remains a relevant strategy to build RAP soil seed reserves and a viable technique for management and conservation aims [29]. Conversely, the growing no-tillage cultivation could have opposite and detrimental effects in arable farming on RAP. Weed emergence patterns are primarily influenced by temperature and secondarily by soil moisture, but also reflect species-specific temperature and moisture germination requirements [28]. Emergence patterns of all the 30 RAP the second season in trial S1 (2013/2014 in Figure1) that were more than one year old and buried seeds, were very similar to the first season in trial S2 (also 2013/2014 in Figure2), that is, seeds collected and buried that year. Almost all species (24 out of 30) had emergence concentrated ( 60%) in winter and spring that season irrespective of the group to which they belonged. Winter in ≥ 2013/2014 was the wettest and also cold (Table1). Apparently, temperature and rainfall seemed to be more important to explain the variability in emergence patterns than other factors such as seed age and time of burial, or even the species. This strong effect of seasonal weather variations on the total cumulative emergence in these experiments was already assessed and reported in previous research [5]. Plants 2020, 9, 309 10 of 19

On a species basis, this research corroborated that those of genus Galeopsis are obligate spring-germinating species and require higher temperatures [27,30]. Emergence has been studied in depth for some of the species, and for example, Thlaspi arvense, C. microcarpa, and N. paniculata mainly emerged in autumn and winter too [31–33]. Also, for species belonging to Papaveraceae, main ﬂushes were detected in autumn and winter, like Papaver argemone, which showed cooler germination requirements according to emergence distribution compared to the other species in this family [25,34]. Consolida species can germinate mainly in autumn–winter, while Delphinium species and N. gallica also germinate in spring [7]. The clustering in this research corroborated this pattern for Consolida species and N. gallica, but not for Delphinium species (Table2).

Potential Implications for Management Some management recommendations can be derived to help optimize survival in the studied RAP species in arable landscapes. Aspects of soil preparation for fields, such as the timing of cultivation and seeding, can increase the occurrence of RAP [25]. The first important decision concerns the optimal time of sowing, i.e., in relation to the sowing time of the crop or the emergence peak of competitive weeds. Results indicate that species belonging to group 3 (mainly autumn, Table2) could successfully be reintroduced only with an early sowing in autumn, both in cropped fields or non-cropped field margins. On the other hand, delayed sowing of species from groups 2 (mainly winter) and 4 (autumn-winter) in Table2 could be another management option for restoring or managing their populations. Target species of these two groups, particularly those of group 2, seemed to be well adapted to sowing delays in late autumn or early winter. Moreover, for conservation purposes, cultivation and sowing of these species would be more promising during the cold part of the year. Flexibility in the crop sowing date would help in diversifying crop rotations, for example, with less competitive crops compared to cereals [35]. Additionally, later crop sowings could reduce competition between RAP and dominant noxious weeds, which can have an emergence peak in early autumn [21]. Avoiding competition with dominant species can be crucial for the success of conservation efforts [36]. The next step will be to develop common emergence models for these four groups of RAP species established in this study according to emergence patterns. These common emergence models will aid in establishing more precise conservation and management recommendations for these species under threat across Europe. This is important as this research showed that environmental factors, mainly temperature and rainfall, play a key role, given that every season emergence patterns were similar in S1 and S2 trials. Moreover, these models will also help in understanding temperature, soil water, light, and tillage requirements for emergence for these groups of RAP. If developed, this information about emergence patterns will be critical, for example, in deciding when and which type of herbicides (contact or residual) can be applied. Chemical management can be important for controlling harmful weeds, while conserving RAP species [22]. Another step may be to purposefully test whether recommendations given in this study really work. A few distinctly contrasting species for each group could be chosen, sown at optimal and suboptimal seasonal timings, and then their fitness, particularly with respect to total seed production, measured. These results are based on a one-field study and therefore an expansive verification would be necessary for e.g., a Spain- or Europe-wide extrapolation.

4. Materials and Methods

4.1. Plant Material Seeds of 30 RAP species were harvested from winter cereal ﬁelds in Spain from late June through August in two consecutive years (2012 and 2013) as described in Torra et al. [5]. Collection sites were located in two previously surveyed areas that host high arable plant diversity: the provinces of Teruel and Lleida [5]. The details (location, coordinates and collection dates) of the 30 species studied, belonging to eight diﬀerent botanical families, are available at Torra et al. [5]. All species studied are naturally occurring as rare in the region [7,17,18,26,37]. Plants 2020, 9, 309 11 of 19

Seeds were air-dried for one week after collection, cleaned and then stored in dry conditions with silica gel at room temperature (around 21 ◦C) until experiments started. Seeds selected for the trials werePlants counted 2020, 9 either, x FOR PEER manually REVIEW or with a seed counter (Contador E 230 V; Pfeuﬀer GmbH,11 Kitzingen, of 20 Germany). Poor-quality seeds were discarded based on seed ﬁrmness and the presence of mold, thus, Kitzingen, Germany). Poor-quality seeds were discarded based on seed firmness and the presence of only high-qualitymold, thus, only seeds high-quality were used seeds [5]. were used [5].

4.2. Experimental4.2. Experimental Design Design The seedsThe seeds of the of the 30 RAP30 RAP species species were were sownsown in a fall fallowow part part of of a winter a winter cereal cereal field, field, with with a soil a soil texturetexture of 47.4% of 47.4% sand, sand, 18.4% 18.4% clay, clay, and 34.2%and 34.2% silt, pHsilt, 8.2 pH and 8.2 2.9%and 2.9% of organic of organic matter, matter, located located in Almenar in (Lleida,Almenar Spain; (Lleida, 41◦46036” Spain; N, and41°46 00′36◦32″ 0N,07” and E). The00°32 experiment′07″ E). The was experiment duplicated was in duplicated two consecutive in two trials, the firstconsecutive initiated trials, on 28 the August first initiated 2012 (assessment on 28 August started 2012 (assessment in season 2012started/2013 in season or trial 2012/2013 S1), and or the trial second on 28S1), August and the 2013 second (assessment on 28 August started 2013 in season(assessment 2013 started/2014 orin trialseason S2). 2013/2014 These dates or trial allowed S2). These seeds to dates allowed seeds to experience natural temperature and humidity fluctuations for about two experience natural temperature and humidity fluctuations for about two months until typical cereal months until typical cereal sowing dates in autumn, which corresponds to the beginning of the winter sowing dates in autumn, which corresponds to the beginning of the winter cereal season. cereal season. RAPRAP seeds seeds were were sown sown considering considering two two tillagetillage treatments:treatments: seeds seeds sown sown at 1 at cm 1 cmdepth; depth; and andseeds seeds sownsown and homogeneouslyand homogeneously distributed distributed between between 11 andand 10 cm cm depth. depth. The The 1 cm 1 cm depth depth simulated simulated no-till no-till conditionsconditions where where seeds seeds are are maintained maintained at at or or nearnear the soil soil surface surface covered covered by bya thin a thin layer layer of soil; of soil;the the contrastingcontrasting treatment treatment simulated simulated soil soil tillage tillage (e.g., (e.g., disking) disking) wherewhere seeds seeds are are evenly evenly mixed mixed between between the the 1 to 101 cm to soil10 cm depths soil usingdepths hoes. using The hoes. number The number of seeds of sown seeds in sown each in plot each varied plot accordingvaried according to availability to amongavailability species, rangingamong species, between ranging 100 and between 1000 seeds.100 and The 1000 exact seeds. number The exact of sown number seeds, of sown emerged seeds, plants and cumulativeemerged plants emergences and cumulative can be emergences found in Torra can be et found al. [5 in]. Torra et al. [5]. Each experiment (S1 and S2) was arranged in a completely randomized block design with four Each experiment (S1 and S2) was arranged in a completely randomized block design with four replications. RAP species were sown in single plots: four plots in no-till and four plots with soil replications. RAP species were sown in single plots: four plots in no-till and four plots with soil disturbance. Therefore, 240 plots were established in each trial and sown with individual species per disturbance.tillage treatment. Therefore, Plot 240 size plots was were0.5 by established 0.5 m, buffered in each with trial0.5 m and corridors, sown withand 1 individual m alleys between species per tillageblocks treatment. (Figure Plot6). On size 2 November was 0.5 by 2012, 0.5 28 m, October buffered 2013 with and 0.516 October m corridors, 2014, tilled and 1plots m alleyswere hoed between blockswith (Figure hand-tools6). On to 2simulate November a tillage 2012, operation, 28 October which 2013 are and usual 16 timings October for 2014, tillage tilled and plotssowing were hoedoperations with hand-tools in the toarea. simulate Emergence a tillage was operation, monitored which weekly are with usual destructive timings forcounts tillage from and soil sowing operationsdisturbance in the until area. May Emergence when emergence was monitored ceased. weekly S1 (started with destructivein 2012) was counts monitored from soilover disturbance three untilconsecutive May when emergenceseasons, 2012–2015, ceased. S1while (started S2 (started in 2012) in was2013) monitored was monitored over threeover two consecutive consecutive seasons, 2012–2015,seasons, while 2013–2015. S2 (started in 2013) was monitored over two consecutive seasons, 2013–2015.

Figure 6. Design of the experiment duplicated into two trials (S1 and S2), where 30 RAP were sown to Figure 6. Design of the experiment duplicated into two trials (S1 and S2), where 30 RAP were sown monitor emergence patterns during three consecutive seasons. (A) Relative locations and dimensions to monitor emergence patterns during three consecutive seasons. (A) Relative locations and of S1 and S2, season of establishment in parenthesis, and blocks distribution of one of the trials as dimensions of S1 and S2, season of establishment in parenthesis, and blocks distribution of one of the example.trials (asB) example. Randomization (B) Randomization of the 30 RAP of the (each 30 RAP number (each representsnumber represents a species) a species) for a block, for a whereblock, two burialwhere conditions two burial are shown:conditions 1 cmare burialshown: depth 1 cm burial without depth soil without disturbance soil disturbance (in white) (in and white) 1–10 and cm 1– depth with annual10 cm depth autumnal with soilannual disturbance autumnal (in soil grey). disturbance (C) Details (in ofgrey). individual (C) Details plots of arrangement individual plots and size. arrangement and size. The main weed species present in the experimental field were Papaver rhoeas, Lolium rigidum, SisymbriumThe irio main, and weedS. crassifolium species present, and in to the a lesser experimental extent some field Asteraceaewere Papaver (Anacyclus rhoeas, Lolium clavatus rigidum, Cirsium, Sisymbrium irio, and S. crassifolium, and to a lesser extent some Asteraceae2 (Anacyclus clavatus, Cirsium arvense, and Conyza sp.). Weed densities were very low (<5 plants m− ) because the field was ploughed −2 twicearvense per year, and to Conyza keep sp.). it as Weed fallow densities for endangered were very low bird (<5 species, plants m since) because it is located the field in was a protectedploughed IBA (Important Bird Area) area. The RAP species studied were not present in the field.

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4.3. Data To reduce the amount and simplify data handling, the percentage of emergence was divided into and summarized in three periods: Autumn (November through December), Winter (January through February), and Spring (March through April). For each of these three periods relative emergence of each species was calculated both with and without soil disturbance. This was done for the following series of data: (1) ﬁrst season cumulative emergence after burial was calculated as percentage of sown seeds for S1 in season 2012/2013, and for S2 in season 2013/2014; (2) second season cumulative emergence after one year of burial was calculated for S1 for 2013/2014, and for S2 in 2014/2015, subtracting previous cumulative emergence during the ﬁrst season; and (3) third season cumulative emergence after two years of burial was calculated for S1 for 2014/2015, subtracting previous cumulative emergence from initially sown seeds. Daily rainfall and temperature were obtained from a meteorological station situated 4 km away from the experimental site.

4.4. Statistical Analysis Data, namely that is relative emergence in Autumn, Winter and Spring for the series in trials S1 and S2 during three consecutive seasons (only for S1) under two tillage regimes, were analyzed using multivariate techniques. Since relative emergence each season was complementary (always summed to 1) and linearly correlated, relative emergence in spring was not included in the analyses. This same raw data, 30 rows (one per species) and 20 columns, was used in all the analyses detailed below. In order to detect groups of RAP species with similar emergence patterns inside each cluster, but clearly different from any other clusters, we used K-means clustering algorithms [38]. The K-means is an iterative, data-partitioning algorithm and requires as input a matrix of points in n dimensions and a matrix of K initial cluster exemplars in n dimensions (determined using the Euclidean distance between point and cluster). The general procedure is to search for a K-partition with locally optimal within-cluster sum of squares by moving points from one cluster to another [38]. In order to determine the optimal number of clusters, we applied the Calinski-Harabasz criterion (CHC) to the K-means clustering [29]. CHC minimizes the within-group sum of squares and maximizes the between-group sum of squares. The highest CHC value corresponds to the optimal set (of most compact clusters). The optimal set can be recognized by a peak or at least an abrupt elbow on the linear plot of CHC values. By contrast, if the line is horizontal, smooth, ascending or descending, then it is not possible to find an optimal set [39]. Besides, in order to visualize in a two-dimensional scale the RAP classification obtained according to the K-means clustering and CHC analysis, a Principal Component Analysis (PCA) was performed on the data. The first two Principal Components (PC) from the PCA were used to graphically represent the distribution of the clusters obtained. K-Means Cluster Analyses were performed using the “apcluster” software package implemented in the free statistical application, R[40], while the PCA was done with JMPro14 software (SAS Campus Drive, Cay, NC, USA).

Author Contributions: J.T. and F.F. initially conceived the study, with all authors contributing to designing the conceptual framework. J.T., J.R. and A.R.-E. established, monitored and obtained the data; J.T. conducted the analysis; J.T. led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for the publication. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Programa Estatal de Investigación, Desarrollo y Competitividad of the Ministerio de Indústria, Economía y Competitividad de España (AGL2007-60828 and AGL2010- 22084-C02-01). Joel Torra acknowledges support from the Spanish Ministry of Science, Innovation and Universities (grant Ramon y Cajal RYC2018-023866-I). Acknowledgments: Special thanks to Eva Edo-Tena, Núria Moix, Mariona Julià and Laia Mateu for their help during the experimental work. Authors want to thank Alex Juarez-Escario for his help and advice to carry out the statistical analyses. Conflicts of Interest: The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results. PlantsPlants 20202020,, 99,, 309x FOR PEER REVIEW 1314 of 1920

Appendix A Appendix A

ADO AES no-till 69 28 3 till 88 6 6 ADO FLA no-till 50 35 15 till 38 50 13 AGR GIT no-till 99 10 till 100 0 AND MAX no-till 99 11 till 98 20 ASP ARV no-till 90 6 4 till 92 7 1 BIF RAD no-till 85 14 2 till 83 13 4 BIS AUR no-till 89 0 11 till 85 6 9 BUP ROT no-till 88 6 6 till 82 12 6 CAM MIC no-till 94 3 3 till 90 9 1 CER PER no-till 97 3 0 till 88 11 1 CON ORI no-till 91 6 4 till 86 10 4 CON PUB no-till 97 3 0 till 95 3 1 CON RIN no-till 49 25 26 till 51 15 34 DEL GRA no-till 87 11 1 till 95 5 0 DEL HAL no-till 95 4 1 till 90 6 3 GAL LAD no-till 03 97 till 01 99 HYP PEN no-till 56 28 16 till 14 65 21 IBE AMA no-till 84 3 13 till 96 0 4 LAT APH no-till 81 14 5 till 90 8 3 LEG HYB no-till 42 8 49 till 68 12 20 NES PAN no-till 74 26 0 till 84 16 1 NIG GAL no-till 56 33 11 till 46 34 20 PAP ARG no-till 93 3 5 till 2 1 PAP DUB no-till 99 10 till 97 3 RAN ARV no-till 92 8 0 till 78 22 0 ROE HYB no-till 75 3 22 till 82 5 14 SIL CON no-till 63 23 14 till 66 23 11 THL ARV no-till 57 8 35 till 77 11 11 TUR LAT no-till 7 52 41 till 5 49 46 VAC HIS no-till 66 16 18 till 53 30 17

0 102030405060708090100

autumn winter spring

Figure A1. Relative Emergence patterns of 30 RAPs in autumn, winter, and spring during the ﬁrst season (2012/2013) for seeds sown in 2012 (S1) in an arable ﬁeld trial in Spain. Bars represent standard Figure A1. Relative Emergence patterns of 30 RAPs in autumn, winter, and spring during the first error. Two burial conditions are shown: 1 cm burial depth without soil disturbance (no-till) and 1–10 cm season (2012/2013) for seeds sown in 2012 (S1) in an arable field trial in Spain. Bars represent standard depth with annual autumnal soil disturbance (till). error. Two burial conditions are shown: 1 cm burial depth without soil disturbance (no-till) and 1–10 cm depth with annual autumnal soil disturbance (till).

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ADO AES no-till 0 92 8 till 1 82 17 ADO FLA no-till 0 84 16 till 0 83 17 AGR GIT no-till 17 50 33 till 0 50 50 AND MAX no-… 35 57 8 till 18 62 19 ASP ARV no-till 0 63 38 till 0 93 7 BIF RAD no-till 0 86 14 till 0 75 25 BIS AUR no-till 33 0 67 till 50 33 17 BUP ROT no-till 2 70 28 till 0 77 23 CAM MIC no-… 59 41 0 till 47 53 0 CER PER no-till 53 31 17 till 53 38 8 CON ORI no-till 20 66 15 till 11 77 12 CON PUB no-till 0 95 5 till 0 78 22 CON RIN no-till 14 33 53 till 3 73 24 DEL GRA no-till 0 38 63 till 0 29 71 DEL HAL no-till 0 79 21 till 0 76 24 GAL LAD no-till 4 0 96 till 0 100 HYP PEN no-till 2 62 36 till 0 77 23 IBE AMA no-till 22 60 18 till 0 73 27 LAT APH no-till 0 till 0 LEG HYB no-till 17 51 33 till 0 56 44 NES PAN no-till 61 20 18 till 31 52 17 NIG GAL no-till 1 60 38 till 0 54 46 PAP ARG no-till 0 89 11 till 91 9 PAP DUB no-till 0 62 38 till 0 98 2 RAN ARV no-till 0 73 27 till 0 87 13 ROE HYB no-till 27 59 14 till 11 68 21 SIL CON no-till 42 28 31 till 6 60 33 THL ARV no-till 34 42 24 till 6 84 11 TUR LAT no-till 0 83 17 till 0 81 19 VAC HIS no-till 1 81 18 till 5 89 7

0 102030405060708090100 autumn winter spring

Figure A2. Relative Emergence patterns of 30 RAPs in autumn, winter, and spring during the second season (2013/2014) for seeds sown in 2012 (S1) in an arable ﬁeld trial in Spain. Bars represent standard Figure A2. Relative Emergence patterns of 30 RAPs in autumn, winter, and spring during the second error. Two burial conditions are shown: 1 cm burial depth without soil disturbance (no-till) and 1–10 cm season (2013/2014) for seeds sown in 2012 (S1) in an arable field trial in Spain. Bars represent standard depth with annual autumnal soil disturbance (till). error. Two burial conditions are shown: 1 cm burial depth without soil disturbance (no-till) and 1–10 cm depth with annual autumnal soil disturbance (till).

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ADO AES no-till 100 0 till 83 8 8 ADO FLA no-till 0 100 0 till 72 21 7 AGR GIT no-till 0 till 0 AND MAX no-till 83 0 17 till 58 39 2 ASP ARV no-till 50 0 50 till 92 8 0 BIF RAD no-till 100 0 till 50 46 4 BIS AUR no-till 100 0 till 100 0 BUP ROT no-till 33 67 0 till 77 17 6 CAM MIC no-till 67 23 10 till 37 59 5 CER PER no-till 11 44 44 till 68 25 7 CON ORI no-till 33 33 33 till 49 39 12 CON PUB no-till 40 40 20 till 70 30 0 CON RIN no-till 93 6 1 till 92 6 1 DEL GRA no-till 94 6 till 74 26 0 DEL HAL no-till 52 43 5 till 56 43 2 GAL LAD no-till 0 100 till 0 100 HYP PEN no-till 100 0 till 100 0 IBE AMA no-till 50 33 17 till 100 0 LAT APH no-till 100 0 till 100 0 LEG HYB no-till 33 54 13 till 61 29 11 NES PAN no-till 75 25 0 till 91 8 1 NIG GAL no-till 29 71 0 till 57 13 PAP ARG no-till 55 44 1 till 28 5 PAP DUB no-till 78 22 0 till 45 50 6 RAN ARV no-till 50 50 0 till 92 0 8 ROE HYB no-till 40 53 7 till 84 5 11 SIL CON no-till 73 27 till 96 4 THL ARV no-till 75 13 13 till 91 5 4 TUR LAT no-till 0 100 0 till 100 0 VAC HIS no-till 100 0 till 100 0

0 20406080100

autumn winter spring

Figure A3. Relative Emergence patterns of 30 RAPs in autumn, winter, and spring during the third seasonFigure (2014A3. Relative/2015) for Emergence seeds sown patterns in 2012 of (S1) 30 inRAPs an arable in au ﬁeldtumn, trial winter, in Spain. and Barsspring represent during standardthe third error.season Two (2014/2015) burial conditions for seeds are sown shown: in 2012 1 cm (S1) burial in an depth arable without field trial soil in disturbance Spain. Bars (no-till) represent and standard 1–10 cm deptherror. Two with burial annual conditions autumnal are soil shown: disturbance 1 cm (till).burial depth without soil disturbance (no-till) and 1–10 cm depth with annual autumnal soil disturbance (till).

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ADO AES no-till 5 52 43 till 1 92 8 ADO FLA no-till 0 94 6 till 0 66 34 AGR GIT no-till 97 3 0 till 88 12 0 AND MAX no-till 64 35 1 till 49 46 5 ASP ARV no-till 7 90 3 till 5 90 5 BIF RAD no-till 0 98 2 till 0 94 6 BIS AUR no-till 36 53 11 till 17 75 8 BUP ROT no-till 10 86 4 till 8 79 13 CAM MIC no-till 76 21 3 till 60 40 1 CER PER no-till 58 42 0 till 69 30 1 CON ORI no-till 38 61 0 till 25 74 1 CON PUB no-till 13 87 0 till 3 87 10 CON RIN no-till 26 60 14 till 17 59 24 DEL GRA no-till 2 98 till 0 92 8 DEL HAL no-till 0 99 0 till 0 92 8 GAL LAD no-till 20 98 till 0 100 HYP PEN no-till 0 82 18 till 0 81 19 IBE AMA no-till 4 30 67 till 0 100 0 LAT APH no-till 0 90 10 till 0 91 9 LEG HYB no-till 0 100 0 till 17 50 33 NES PAN no-till 40 59 1 till 73 24 3 NIG GAL no-till 0 95 5 till 0 76 24 PAP ARG no-till 46 0 till 27 73 0 PAP DUB no-till 0 till 0 RAN ARV no-till 1 97 2 till 0 94 6 ROE HYB no-till 28 68 4 till 11 88 1 SIL CON no-till 62 32 6 till 56 33 11 THL ARV no-till 10 84 5 till 8 88 4 TUR LAT no-till 0 97 3 till 1 92 7 VAC HIS no-till 34 66 0

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autumn winter spring

Figure A4. Relative Emergence patterns of 30 RAPs in autumn, winter, and spring during the ﬁrst Figureseason A4. (2013 Relative/2014) forEmergence seeds sown patterns in 2013 of (S2) 30 inRAPs an arable in autumn, ﬁeld trial winter, in Spain. and spring Bars represent during standardthe first seasonerror. Two(2013/2014) burial conditions for seeds sown are shown: in 2013 1 cm(S2) burial in an deptharable withoutfield trial soil in disturbanceSpain. Bars (no-till)represent and standard 1–10 cm error.depth Two with burial annual conditions autumnal are soil shown: disturbance 1 cm buri (till).al depth without soil disturbance (no-till) and 1–10 cm depth with annual autumnal soil disturbance (till).

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ADO AES no-till 96 1 3 till 88 5 8 ADO FLA no-till 85 1 14 till 85 4 11 AGR GIT no-till 100 0 till 100 0 AND MAX no-till 92 4 4 till 92 5 3 ASP ARV no-till 97 2 1 till 100 0 BIF RAD no-till 73 11 16 till 98 02 BIS AUR no-till 83 17 0 till 100 0 BUP ROT no-till 88 1 11 till 91 4 5 CAM MIC no-till 78 11 11 till 87 7 6 CER PER no-till 75 20 5 till 89 5 6 CON ORI no-till 75 0 25 till 86 3 10 CON PUB no-till 77 15 8 till 68 32 0 CON RIN no-till 97 0 3 till 95 3 2 DEL GRA no-till 61 39 0 till 100 0 DEL HAL no-till 66 25 9 till 84 12 3 GAL LAD no-till 0 100 till 0 100 HYP PEN no-till 33 0 67 till 23 7 70 IBE AMA no-till 75 6 19 till 99 01 LAT APH no-till 99 10 till 99 10 LEG HYB no-till 100 0 till 42 50 8 NES PAN no-till 72 9 20 till 99 01 NIG GAL no-till 92 8 0 till 71 13 15 PAP ARG no-till 94 3 3 till 9 1 PAP DUB no-till 67 33 0 till 100 0 RAN ARV no-till 89 7 5 till 95 1 4 ROE HYB no-till 63 25 13 till 84 2 15 SIL CON no-till 54 25 21 till 91 9 0 THL ARV no-till 91 0 9 till 93 3 3 TUR LAT no-till 88 0 13 till 56 3 41 VAC HIS no-till 100 0 till 96 0 4

0 102030405060708090100 autumn winter spring

Figure A5. Relative Emergence patterns of 30 RAPs in autumn, winter, and spring during the second seasonFigure (2014A5. Relative/2015) for Emergence seeds sown patterns in 2013 of (S2) 30 inRAPs an arablein autumn, ﬁeld trialwinter, in Spain. and spring Bars represent during the standard second error.season Two (2014/2015) burial conditions for seeds are sown shown: in 2013 1 cm (S2) burial in an depth arable without field trial soil in disturbance Spain. Bars (no-till) represent and standard 1–10 cm deptherror. withTwo annualburial conditions autumnal are soil shown: disturbance 1 cm (till).burial depth without soil disturbance (no-till) and 1–10 cm depth with annual autumnal soil disturbance (till).

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