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Here at the Natural History Museum at Tring, UK, I’m in our nest collection, which numbers just over 4,000. Behind me are 67 metal cabinets with nests arranged in taxonomic order. Each nest is labelled with the date and place of collection, and the collector’s name. Next to me is a 1928 mud nest from Argentina that was made by the rufous hornero (Furnarius rufus), known for its large, globular nests that shield eggs and young from predators.I’m the senior curator of birds’ eggs and nests. I ensure that specimens are stored appropriately to prevent damage and are well catalogued, so we know exactly what we have and where. Our nest and egg collections are the most comprehensive archive of information on bird breeding in the world. When I came here about 20 years ago, the nest collection was rarely used and we didn’t know how many examples of extinct and endangered species we had. I’ve spent a lot of time and effort cataloguing and understanding these particular 129 nests, 40 of which belong to extinct birds such as the Laysan crake (Zapornia palmeri) and the Aldabra brush warbler (Nesillas aldabrana).We have up to 300,000 sets of eggs. I am holding four dunlin (Calidris alpina) eggshells, collected in 1952 in Ireland. They were donated to the Wildfowl & Wetlands Trust, a UK conservation charity, which gave them to us as part of a larger collection.I have been interested in birds and natural history since childhood, and my mother used to take me to the Royal Museum of Scotland (now the National Museum of Scotland) in Edinburgh. After graduating in biological sciences from Edinburgh Napier University, I volunteered at the museum before getting my first paid museum job.When researchers want to access the collections, I check that we have specimens relevant to their research, discuss exactly what they intend to do and work with them to minimize the risk of damage. Although I want our collections to result in robust science, they must be preserved.

Nature 597, 586 (2021)
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Bat capture, handling, and tag attachment were carried out in accordance with guidelines of American Society of Mammologists33 under permit from the California Department of Fish and Wildlife (# SC-002911). Experimental methods were approved by the Institutional Animal Care and Use Committee of the USDA Forest Service (IACUC 2017-014). We captured bats using 2.6-m high mist nets in a triple-high configuration. We measured forearm length and mass and determined species, age, sex, and reproductive status for each captured individual.We used Vesper Pipistrelle on-board audio-recording devices with an accelerometer (ASD Tech, Haifa Israel) to quantify bat movement throughout the duration of attachment. We used the smallest possible battery (0.5 g) which was sufficient to allow a 3-h recording period on the first night and up to a 4-h recording period on the second night. Tags were programmed to record for 10 s once every 3 min from 23:00 to 02:00 on the first night and for 10 s once every minute from 19:00 to 23:00 on the second night. We recovered tags from bats tagged between September 28th and October 7th. Sunset was at 19:02 on September 29th and 18:47 on October 8th. Unfortunately, the timing mechanism on the tags malfunctioned some of the time, causing only some of the recordings to have synchronous audio and accelerometer data (See Results).We attached Holohil LB-2X VHF transmitter (0.27 g) to the audio tags so we could locate the device once it detached from the bats. We coated the entire tag package (except the microphone opening) with liquid silicone followed by a latex sleeve covering to provide protection from the environment. The total tag package had a mass of 2.9 g which represented 10.6–12.5% of the mass of the bat. Several studies conducted in flight tents and in the field have shown no adverse consequences of payloads up to 15% for short duration deployments16,34. The diversity of natural behaviors that we observed, including prey pursuit, conspecific interaction, and extended flight over multiple nights indicates that hoary bats are capable fliers with this payload, however we cannot rule out the possibility that tags altered the behaviors that were observed.We attached tags to the posterior dorsum of bats using latex surgical adhesive (Torbot Liquid Bonding Cement, Torbot Group Inc. Cranston, Rhode Island). We used the minimum quantity of adhesive that we estimated would be necessary for tags to remain affixed to bats for 2 nights. We recovered tags by using ground- and aircraft-based VHF telemetry to determine the general location of the shed tag, followed by homing in on the VHF signal using ground-based telemetry. Final recovery of tags was achieved using visual searches of the ground.Microphone calibrationWe calibrated on-board microphones to determine the minimum sound pressure level (SPL) at which we could reliably detect micro calls. We did this by broadcasting a series of micro calls from an Avisoft (Glienicke/Nordbahn, Germany) Scanspeak ultrasonic speaker to the on-board tags. The series of micro calls consisted of a single high-quality micro call that was broadcast 30 times with each successive call being 3 dB lower in SPL. The absolute intensity of the broadcast was calibrated by broadcasting the same signal to a G.R.A.S (Holte, Denmark) 40DP 1/8″ microphone, which itself was calibrated with a G.R.A.S 42AB sound calibrator. For both the calibration of the sound playback and the broadcasts to the on-board microphone, the microphones were placed 10 cm from the speaker. We repeated this procedure three times for each of three microphones that had been recovered from the bats and determined the SPL of the lowest amplitude micro call that could be detected on all nine broadcasts. This SPL was used as the minimum detectable level at which our microphones could detect micro calls.Data processingDetermining whether bats are flyingWe used custom MATLAB (Natick, MA) scripts to analyze ultrasound and accelerometer recordings. We first determined whether bats were in flight for each recording. Unfortunately, we were only able to record simultaneous accelerometer and acoustic data for 364 out of 2241 recordings. For these recordings, we independently classified each file as flight or no flight using only the accelerometer data and only the audio data. Accelerometer recordings showed clear and prominent wingbeat oscillations in the dorsoventral, or Z-axis (Fig. S2A). One observer used a custom program (AccelVis) to visualize and manually classify all accelerometer files. We also quantified the magnitude of wingbeat oscillations by measuring the root-mean-square magnitude of signals after applying a high-pass filter of 4 Hz (Bats used wingbeat frequencies of approximately 8 Hz).A different observer classified all audio recordings as flight or no flight based on the presence or absence of low-frequency wind noise generated by the relative motion of the bats flying through the air (Fig. S2). The Individuals conducting the audio and acceleration analyses were blind to one another’s data. As with the accelerometer data, we analyzed all files both qualitatively and quantitatively. For the qualitative analysis, a user visualized files using a custom program (AudioBrowser; available with all data files as supplementary data) and noted presence or absence of low-frequency wind noise. We also quantified this wind noise by measuring the RMS magnitude of signals after applying a 1-Hz low pass filter. This resulted in a distinct bimodal distribution of low frequency magnitudes that corresponded to no wind and wind conditions with the two peaks being separated by approximately 30 dB. A small number of files ( 5 s). This 5 s threshold is twice the longest pulse interval recorded for echolocation calls (Supplementary Information), and therefore represents a conservative threshold for identifying silent periods.High-intensity calls could be identified by their consistently high signal levels. For recordings where no calls were initially detected, the observer made a second examination of the recording using a custom 55–90 kHz bandpass filter setting that highlights micro calls (Fig. 1D). A second observer also examined all files where either no calls or micro calls were detected by the first observer to confirm classification. Recordings were processed both by visualization of spectrograms and by listening to slowed-down recordings through headphones.Hoary bat feeding buzzes have a characteristic pattern involving a rapid increase in calling rate, and progressively decreasing call intensity (Fig. 1B)35,36. In contrast, social interactions involve prolonged (often several seconds) high-intensity echolocation calls produced at a high rate (e.g., 50–100 Hz) with a second bat also producing echolocation calls at a relatively high calling rate14. Echolocation calls of “other” bats (which could be present in any of the recordings) could be distinguished from the calls of the bat with the tag because they were typically recorded at a much lower intensity levels that increased and decreased, presumably as the other bat approached and then withdrew from the focal bat and were temporally out of phase with calling rate of the tagged bat. Calls classified as “other bat” also had lower calling rates compared to social interactions.Statistical analysisAcoustic recordings were organized by individual bat (Table 1) and by time of night (Fig. 2). To determine if bats exhibited consistent differences in the use of high-intensity echolocation, we measured the proportion of recordings including high-intensity echolocation for each bat night. Initial analysis of the data indicated that bats produced high-intensity echolocation during either most or all of the recordings (96–100%, including feeding buzzes) or at a considerably lower rate ( 96%) or low ( More

Chlorophyll plays an important part in the assimilation, transfer and conversion of light energy during photosynthesis. Its content is therefore closely related to the carbon fixation efficiency of photosynthesis and, because photosynthesis provides the energy source for metabolic responses, plays an important role in the drought resistance of plants. Chlorophyll fluorescence is often used to analyze photosynthesis efficiency under stress1. Fm is the fluorescence output when the reaction center of PSII is completely closed, and therefore reflects the maximum electron transfer through PSII40. Fv/Fm represents the energy conversion efficiency of PSII reactions, and can be used to measure the degree of external stress41.The chlorophyll content and Fm of A. squarrosum first increased and then decreased under moderate and severe drought, indicating that A. squarrosum adjusted its energy capture during the early stage of drought, and because electron transfer was relatively stable, normal photosynthesis was maintained. As stress intensified during prolonged drought, chlorophyll degradation accelerated and electron transfer through PSII slowed, which was similar to the effect of drought stress on chlorophyll of A. halodendron1. On 1 August, when the drought treatments began, the leaves of A. squarrosum in the control became noticeably yellow and slightly wilted, and the chlorophyll content and Fm were lower than those in the drought treatments. After re-watering, the chlorophyll content and Fm of A. squarrosum decreased, but they increased with increasing drought intensity. There is a limit to plant demand for water, and both too much and too little water are not conducive to plant growth. As a pioneer species during vegetation succession in sandy land, A. squarrosum is a xerophyte31. The soil moisture content in the control was higher than its requirements, and its photosynthesis was obviously adversely affected by controlling the water content at a higher level than the plants required. There was a significant positive correlation between Fv/Fm and RWC of the two plants, which indicated that water deficit was the main reason for the decrease of Fv/Fm. The chlorophyll fluorescence of A. squarrosum could maintain higher photosynthetic performance under drought stress because of its stronger water holding capacity than that of S. viridis. For A. squarrosum, Fv/Fm decreased with increasing drought duration and intensity. This is because drought reduced the electron transfer capacity of PSII and photochemical activity, leading to excessive accumulation of excitation energy, and adversely affecting photosynthesis. Fv/Fm increased after re-watering on 8 August, when the reduction of stress slowed the inhibition of photosynthesis by drought, by decreasing the inhibition of photosynthesis.For S. viridis, the chlorophyll content, Fm and Fv/Fm decreased with increasing drought duration and intensity, indicating that drought stress hindered the biosynthesis of chlorophyll, and that chlorophyll decomposition increased, leading to decreased chlorophyll content. At the same time, drought resulted in the decrease of PSII photochemical transformation efficiency and photosynthetic activity, and the damage of PSII receptor, which contributed to the damage of photosynthesis and the decrease of electron transfer ability. Fm and Fv/Fm of S. viridis increased after re-watering on 8 August, showing that rehydration relieved the drought stress. In addition, Fv/Fm increased and Fm decreased after re-watering on 14 August, suggesting that the damage to PSII was mitigated by rehydration, but the electron transfer in the PSII reaction center continued to be slower than normal. The chlorophyll content of S. viridis did not return to normal after re-watering, indicating that the leaves of S. viridis were damaged by both prolonged and severe drought stress and that chlorophyll synthesis was significantly affected1.The cell membrane is both a dynamic barrier between the cell interior and its surroundings, and a channel for the exchange of substance and information with its environment42. In particular, it controls water transport between the cell and its environment, leading to changes in RWC. RWC can be used to indicate the degree of dehydration of cells and assess the level of drought suffered by plants43. Under drought stress, the loss of water in plants is directly related to the stability of the cell membrane, and a stable cell membrane is the most basic requirement for maintaining sufficient water to support the cell’s physiological functions. ROS are produced in large quantities under stress, and this can trigger or exacerbate peroxidation of membrane lipids to produce malondialdehyde. Malondialdehyde can damage the membrane and functional molecules such as proteins and nucleic acids in cells, leading to damage or destruction of the membrane’s structure and functions. This, in turn, can increase the permeability of the membrane, leading to growth inhibition or even death. Therefore, changes in membrane permeability and the malondialdehyde content can reflect the degree of membrane lipid peroxidation and cell damage under stress1,3,32. It is consistent with our correlation analysis that RWC of the two species is negatively correlated with malondialdehyde and membrane permeability, and the correlation between RWC and membrane permeability in S. viridis is significant.In A. squarrosum, membrane permeability in the control on 1 August was significantly less than those under moderate and severe drought, but the malondialdehyde content did not differ among the treatments. The change of membrane permeability may have resulted from degreasing of membrane lipids and destruction of the membrane structure after phospholipid dissociation44. From 1 to 13 August, malondialdehyde content of A. squarrosum in the control first decreased and then increased, while membrane permeability increased continuously, indicating that membrane lipid peroxidation was significantly alleviated in wet soil after short-term drought. In contrast, the severe water deficit during the late stage of drought increased peroxidation of membrane lipids and malondialdehyde accumulation, suggesting that the cell membranes in the control had been damaged during the drought process. The malondialdehyde content and membrane permeability of A. squarrosum increased in the control after rehydration on 8 August, but decreased after rehydration on 14 August. This suggests that rehydration during the early stages of drought can exacerbate the peroxidation of membrane lipids and damage the cell membrane, but that rehydration during the late stages of drought mitigated the stress and eased the damage. Many studies showed that membrane permeability and the malondialdehyde content increased synchronously under stress1, but this contradicts our results for A. squarrosum in the control. This may be because the high soil moisture content in the control was not conducive to normal growth of this xerophyte. That is, long-term natural selection in the species’ arid sandy environment would lead to continuous adaptation to its environment, allowing A. squarrosum to become widely distributed in the mobile dunes of the Horqin sandy land45. With increasing drought duration, the malondialdehyde content and membrane permeability of A. squarrosum increased under both moderate and severe drought, indicating that the accumulation of malondialdehyde after drought stress damaged cell membrane and increased its permeability. The RWC values of A. squarrosum in the control were similar, but the membrane permeability fluctuated greatly. This can be due to more than adequate amount of irrigation.Setaria viridis is a late-successional species, and showed different responses to drought. With increasing drought duration and intensity, RWC of S. viridis decreased, while MDA and membrane permeability increased simultaneously. The results indicated that the early occurrence of water stress and membrane peroxidation in S. viridis under stress was one of the main physiological reasons for its inferior drought tolerance to A. squarrosum. Moreover, the damage degree of plants under drought stress should take into account not only the change of membrane permeability, but also the degree of membrane peroxidation and the ability of plant cell membrane to tolerate membrane lipid peroxidation. The chlorophyll content, Fm and Fv/Fm of S. viridis decreased with increasing drought duration and severity, and Fv/Fm of S. viridis was significantly negatively correlated with membrane permeability, which increased with increasing drought stress. This indicated that membrane lipid peroxidation and the accumulation of ROS under drought stress damaged the membrane and inhibited photosynthesis. Re-hydration of S. viridis increased RWC on both dates and in all drought treatments. This was accompanied by decreased malondialdehyde content, particularly after the 14 August re-watering, and by decreased membrane permeability. Rehydration reduced membrane lipid peroxidation, but it did not return to the control level, showing that drought caused a certain degree of damage that may be permanent or that may take some time to be repaired3.Stress can disrupt the balance of ROS metabolism in aerobic plants. When the concentrations of ROS are too high, peroxidation of membrane lipids and the equilibrium for exchanges of cell materials is also disrupted, resulting in a series of physiological and metabolic disorders. To counteract these disorders, plants have evolved protective enzymes during long-term evolution. The enzymes can eliminate O2-, H2O2, OH- and O- and reduce the damage they cause to the plant46. The changes in antioxidant enzyme activities of both species differed under drought stress. SOD played an active role during initial protection against membrane lipid peroxidation and its activity in A. squarrosum increased gradually during the drought. Under natural drought condition, SOD activities of the two species increased gradually, indicating that SOD activity was easily induced by drought stress. At the end of natural drought, the three enzymes of A. squarrosum maintained high level, and the combination of enzymes could resist drought stress, while only POD and SOD in S. viridis were enhanced to alleviate membrane lipid peroxidation. This transformation of the coordination of enzyme activity may be an important physiological mechanism of drought tolerance of A. squarrosum was stronger than that of S. viridis under severe drought. On 7 August, the peroxidase and catalase activities decreased in the control. Because ROS are a metabolism by-product of photorespiration, photosynthesis was inhibited by short-term drought, and the decreased accumulation of ROS caused by protective antioxidant enzymes reduced membrane lipid peroxidation by decreasing levels of malondialdehyde47. On 7 and 13 August, the activities of protective enzymes in A. squarrosum under moderate and severe drought were greater than that in the control. Drought stress led to the accumulation of ROS, and increased membrane lipid peroxidation, as reflected by the malondialdehyde content. At the same time, the accumulated ROS also stimulated the antioxidant enzyme protection system to continuously increase the activities of enzymes, so as to maintain balance of ROS48.Setaria viridis showed different responses. From 1 to 13 August, its peroxidase activity first decreased and then increased, but catalase activity showed the opposite pattern, and SOD activity increased gradually, indicating the existences of coordination among these enzymes under drought stress49. When catalase activity weakened, SOD and peroxidase activities compensated for this weakness to scavenge more ROS and mitigate cell membrane damage. The catalase activity in S. viridis remained less than 50 U g-1 DW min-1 throughout the study. After rehydration, catalase activity in the control was significantly greater than those under moderate and severe drought. There was a close relationship between Fv/Fm and catalase activity in S. viridis. It is possible that the enzyme must be contributing through ROS scavenging. Some of the antioxidant enzymes of both species did not recover after rehydration, which may be related to the possibility that in xerophytes, rehydration did not immediately improve physiological metabolism. It is possible that their antioxidant enzyme systems were so damaged that they would take longer than our study period to return to normal levels, and our samples were collected1 day after rehydration. More

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Genome-wide characterisation of SSRsWe identified and mapped a total of 428,342 microsatellites across the 47,828 scaffolds of the unpublished genome sequence draft of T. erytreae using the GMATA software35. The SSRs frequency was estimated at 765.6 SSRs/Mb, which means 1 SSR for every 1.09 Kb. In silico identified SSRs were distributed among ten types of in tandem repeated motifs (from di- to deca-nucleotides). Analysis of SSR distribution revealed that the di-nucleotide motifs (340,227) were the most abundant SSRs, with a frequency of 79.4%. Both tetra- (20,902) and tri- (61,839) nucleotide repeats comprised about 5–15% (Fig. 1A; Supplementary Data 1). The remaining motifs, from hepta- to deca-nucleotides, comprised less than 1.5% of total SSRs identified in this study (Fig. 1A). Considering the unknown orientation of DNA strands in the Tery6 draft genome sequence of T. erytreae, a further SSRs characterization was carried out grouping the repeat motifs into pairs of complementary sequences. According to this, GA/TC (36.6%) and CT/AG (31.9%) are the most frequent motif pairs, with a total frequency of 68.5% (Fig. 1B). Grouped motif pairs GC/GC (0.05%) and CG/CG (~ 0.02%) were the least abundant di-nucleotide motifs. In decrease order, the most abundant tri-nucleotide motif pairs were ATT/AAT, ATA/TAT, ACA/TGT, TAA/TTA, AAC/GTT, TTG/CAA, and AAG/CTT, which encompassed 9.8% of all identified grouped motif pairs. Occurrence frequency of the remaining grouped motifs, including the rest of tri- and those from tetra- to deca-nucleotides (552 all together), was less than 11% of all motif pairs (Fig. 1B). Our data analysis reveals that SSR markers of 10 bp were most frequent, accounting for about 10% all SSR markers identified in this study. The overall trend of SSR length distribution in the T. erytreae genome is that the frequency of occurrence of SSRs gradually decreases as their length increases (Fig. 1C).Figure 1Frequency distribution of different classes of SSR repeat units in the Trioza erytreae genome. (A) Frequency of motif types by unit length (K-mers). (B) Frequency of grouped repeated motifs by nucleotide composition. (C) Length distribution of SSRs (total number of each type of SSR length is shown in the top of the bars).Full size imageSSR markers development for T. erytreae
Fifteen SSRs chosen from those repeated motifs identified in silico in this study (Table 1) were used as potential markers to investigate the genetic diversity, structure and phylogeography of T. erytreae individuals from populations in mainland Europe and the archipelagos of the Macaronesia. Scaffolds Tery6_s00034 (274,710 bp), Tery6_s02825 (48,689 bp) and Tery6_s07841 (26739 bp) were randomly selected based on their sequence length (long, medium, and short scaffolds, respectively). SSRs were selected on the base of their type of repeat motif (di, tri-, tetra- and penta-nucleotides), nucleotide composition and length (number of in tandem repeated motifs) (Table 1; Supplementary Data 2). For the scaffold Tery6_s00034, 11 SSR loci were chosen from the total of 106 SSRs identified in silico, three for Tery6_s07841 and one for Tery6_s02825. Selected scaffolds were further investigated to know whether SSR loci mapped into coding or non-coding regions (inter-genic or intron sequences). Although gene annotation of the T. erytreae genome draft is not yet completed, it was possible to get this information for most of the selected SSR loci (data not shown). The scaffolds Tery6_s00034, Tery06, − 07, − 13 and − 14 were found in inter-genic regions, while Tery08, − 12 and − 15 were mapped into introns. For Tery05, − 9, − 10 and − 11 was not possible to establish whether they targeted coding or non-coding regions. SSR loci Tery01, − 02 and − 03 were found in intron regions in the scaffold Tery6_s07841, and SSR locus Tery04 in an inter-genic region in the sequence corresponding to the scaffold Tery6_s02825. For amplification of SSR loci, specific PCR primers were designed on the sequence flanking the in tandem repeated motifs. Blast of the different amplicons against the T. erytreae draft genome sequence showed that PCR primers would result in the specific amplification of their specific SSR locus. Experimental validation of PCR primers was carried out on a testing panel of individuals collected in different locations in the Canary Islands and South Africa. Primers pairs for SSR loci Tery04, − 05, − 06, − 08, − 09, − 10, − 11, − 12, − 13 and − 15 yielded DNA fragments of the expected size and were chosen for carry on further population genetic analysis. These loci contain eight di-nucleotides (AC, AG, GA, CA, GT, TC, TA and TG), one tri-nucleotide (TGA), and three tetra-nucleotides (CATA, CTAC and TACC), which arranged in microsatellites of different length (from 5 to 30 in tandem repeated motifs) (Table 1). Five SSR loci (Tery01, − 02, − 03, − 07 and − 14) were not amplified efficiently and the corresponding primer pairs were discarded for further analysis.Table 1 SSR loci developed in Trioza erytreae.Full size tableThe individuals of T. erytreae collected in different geographical locations in the west coast of mainland Spain and Portugal, the Canary Islands and Madeira, as well as in South Africa and Kenya (Table 2), were analysed using the 10 selected SSR markers designed in this study. The scored allelic data for each SSR marker is summarised in the Table 3. The analysis showed that all SSR markers were polymorphic. Seventy alleles were detected over the ten selected SSR loci, and the average number of alleles per locus (Na) was seven. SSR markers Tery08 and Tery11 had the highest number of alleles (12 and 20 alleles respectively), whereas Tery13 had the lowest (only two alleles). The expected (He) and observed (Ho) heterozygosity per locus in the entire population ranged from 0.20 to 0.77 and from 0.03 to 0.84, respectively. SSRs Tery11 and Tery08 displayed the highest diversity (He of 0.77 and 0.72, respectively), and Tery09 and Tery13 (He of 0.20 and 0.22, respectively) were the least informative markers. Most of the SSR markers used in this work showed He values higher than 0.5, apart from Tery05, − 09 and − 13 (with values of 0.39, 0.20 and 0.22, respectively). With the only exception of Tery04 and Tery15, for most of the analysed SSRs He was higher than Ho. It can be also observed that the whole population displayed a deficit of average Ho (0.31) compared with the He value (0.51) under Hardy–Weinberg equilibrium. This observation agrees with the positive value of the Wright’s fixation index (Fw) estimated for all analysed SSR markers over the whole population (Fw = 0.41). The SSR markers Tery12 and Tery13 showed Fw values close to 1.0 (0.81 and 0.85, respectively), suggesting that their alleles were considerably fixed in the population.Table 2 Collection data of T. erytreae populations.Full size tableTable 3 Statistical summary of the diversity of T. erytreae SSR markers.Full size tablePopulation structure based on T. erytreae SSR dataTo assess the differentiation and genetic diversity among the local populations of T. erytreae sampled in newly invaded areas from Spain and Portugal, including Madeira and the Canary Islands, and those from the previous invaded areas in Africa (South Africa and Kenya), we used a Bayesian clustering method to analyse the SSR multi-locus genotyping data. The STRUCTURE analysis according to the method of ΔK36 showed that the overall genetic profile of all the individuals sampled could be described with two or three different hypothetically original populations corresponding to the highest ΔK values (Fig. 2). It means that the most likely values of genetic clusters (K) are 2 or 3. Nevertheless, Pritchard’s method37 showed a posterior probability of data at K = 7 (Fig. 2). The estimated likelihood distribution increased from K = 1 to K = 7, and then started to decrease. This implied that seven was the smallest value of K, which was the most likely number of inferred populations in our data set. Interestingly, the value of K at which the likelihood distribution reached its maximum coincided with a further peak value of the ΔK statistic at K = 7, suggesting a more complex hierarchical structure of the T. erytreae populations (Fig. 2). In consequence, we plotted the clustering results for K = 2, K = 3 and K = 7 (Fig. 3). Furthermore, we considered an initial structure of two populations (K = 2) as was suggested by the method of ΔK36 whereby most of the analysed individuals were classified with high probability (Q  > 0.90) in two clusters (Fig. 3). Cluster 1 (in green) was exclusively formed by individuals from newly invaded areas in Spain and Portugal, including those from the archipelagos of Madeira and the Canary Islands. On the other hand, Cluster 2 (in beige) was mainly comprised of individuals from Africa, but also included individuals from Camacha (Madeira). The exception to this pattern involved three locations in Madeira (Quebradas, Camacha and Moreno), Pretoria (South Africa), and Homa Bay (Kenya), where almost all individuals consistently had significant membership in both clusters. Looking at K = 3 plot, the Bayesian clustering analysis resolved Cluster 1 into two by reassigning some individuals to Cluster 3 (in purple). Almost of all individuals from Moreno, Poiso, and Farrobo (in Madeira and Porto Santo, respectively) were entirely reassigned to Cluster 3 along with several individuals from the Canary Islands and Galicia (Spain). In addition, individuals from Vairão (Porto) and São Vicente de Pereira Jusã (Aveiro) (both in the northwest coast of Portugal) were also assigned to Cluster 3, while those individuals sampled from southern locations up to Sobreda (Setúbal) were assigned to Cluster 1. The exceptions to this pattern were the individuals from Ribamar (Ericeira), which were assigned to Cluster 3. Most notably, samples from Kenya were genetically different from those of South Africa and grouped in Cluster 1. At K = 7 the population structure scenario was more hierarchical, but 73% of all individuals (108 out from 147) could be assigned to one of the seven clusters with more than 90% probability (Q  > 0.9). The assignment of half of the remaining individuals (21 out of 39) could be done with more than 70% probability (Q  > 0.7). Among the different groups, Cluster 1 (in green) and 2 (in beige) are restricted to the populations of South Africa and Kenya, respectively, with almost no presence of individuals from any of the newly invaded areas. Clusters 3 (in purple) and 4 (in pink) are mostly exclusive to the individuals from Madeira and Portugal mainland, although with some membership in the Canary Islands and Galicia. Cluster 5 (in light blue) and Cluster 6 (in orange) are represented by individuals from Madeira, the Canary Islands and Galicia, while the individuals from Camacha (Madeira) –the only ones that were collected from Casimiroa edulis La Llave & Lex. (Rutacea: Toddalioideae)—form exclusively Cluster 7 (in dark blue). Remarkably, Q fractions corresponding to Cluster 7 are present in the individuals from Nelspruit, Tzaneen, and some in Pretoria.Figure 2Inference of the number of unique genetic clusters (K) from structure simulations derived from ten SSR markers. Diagrams of posterior probability of SSR data were obtained according to the methods of Evanno et al36 and Pritchard et al37. The likelihood of data given K (ln Pr(X|K), in open circles) and ΔK (the standardised second order rate of change of the likelihood function with respect to K, in bold circles) are plotted as functions of K. Error bars of the ln Pr(X|K) indicate standard deviations, but they are too small to be seen in the plot.Full size imageFigure 3Bayesian clustering analysis of individuals genotyped with ten SSR markers in 23 populations of T. erytreae sampled in Africa, Spain, and Portugal. The assignment of individuals to genetic clusters inferred from STRUCTURE37 simulations are based on average membership coefficient (Q). Estimated membership fractions for each individual and population are shown for K = 2, 3 and 7. Selection of the number of clusters was based both on the K value at which the likelihood distribution began to decrease and the peak values of ΔK. Each individual is represented by a single vertical bar, with the colouring of each bar represents the stacked proportion of assignment probabilities to each genetic cluster. For K = 7, clusters 1, 2, 3, 4, 5, 6 and 7 are shown in green, beige, purple, pink, light blue, orange, and dark blue, respectively. Black vertical lines separate sample sites. Labels identify T. erytreae populations from old invaded areas in Africa, and newly invaded areas in the Iberian Peninsula and the Macaronesia.Full size imageGenetic diversity analysis using T. erytreae SSR allelic dataThe genetic diversity of T. erytreae populations was also assessed by means of a distance-based clustering method. The scored SSR allelic data obtained from the ten SSR loci developed in this study were used to calculate a genetic dissimilarity matrix and to compute a Neighbor Joining (NJ) tree. A preliminary dendogram constructed using only the African populations of T. erytreae showed that the individuals from South Africa grouped together into a single cluster clearly separated from the Kenyan population. The robustness of the tree clustering was supported by the high bootstrap values obtained for nearly all branches (Fig. 4). To confirm the results obtained from the structure analysis a NJ tree under topological constraints was inferred using as initial tree the population structure of individuals from all the sampled areas with Q  > 0.7. The remaining individuals were positioned (constraint) on that previous topology. Inspection of the constrained tree topology revealed seven clusters that were in congruence with the structural population at K = 7 suggested by the STRUCTURE analysis (Fig. 5). It is noteworthy that Cluster 7 emerged as a paraphyletic group in the base of African Cluster 2. The cluster assignments of individuals with low membership coefficients (Q  0.7 according to STRUCTURE37 was used as initial tree, and the remaining individuals were positioned (constraint) on this previous topology. Spain: Aldán (A), Areeiro (AR), Gran Canaria (GC), Los Rodeos (LR), Oratava (O), Portonovo (PN), Tacoronte (T). Portugal: Areeiro-Lisbon (AR-Lis), Barreiralva (B), Camacha (C), Farrobo (F), Moreno (M), Paião (P), Poiso (PO), Quebradas (Q), Ribamar (R), Sobreda (S), São Vicente de Pereira Jusã (SV), Vairão (V). South Africa: Nelspruit (N), Pretoria (PR), Tzaneen (TZ). Kenya: Homa Bay (HB). Genetic clusters for K = 7 are indicated. Admixed individuals with Q  More