You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#s1}
============

The spatial structuring of intraspecific neutral genetic diversity contains important information about both historical and current evolutionary processes. For example, extensive gene flow will constrain divergence by preventing local genetic differentiation, whereas reduced dispersal and philopatry are expected to cause genetic subdivision [@pone.0020440-Slatkin1], [@pone.0020440-Slatkin2]. Various factors can maintain neutral genetic diversity over large geographic areas, such as spatial distances between populations [@pone.0020440-Crispo1], physical barriers to gene flow [@pone.0020440-Riginos1], and habitat suitability and/or fragmentation [@pone.0020440-Riginos1], [@pone.0020440-Genner1], [@pone.0020440-Bekkevold1]. Moreover, intrinsic life history traits of the species studied (e.g. dispersal and lifespan) affect population genetic structure and hence the geographic distribution of molecular diversity [@pone.0020440-Peterson1], [@pone.0020440-Kuo1]. The relative contribution of these different factors has been difficult to estimate in the past, but recent advances in statistical and geographic genetics now makes it possible to study these factors in more detail (e.g. [@pone.0020440-Foll1], [@pone.0020440-Beaumont1]).

Many species in Europe have wide-ranging geographic distributions and several studies have demonstrated geographic signatures of within species\' genetic diversity (e.g. [@pone.0020440-Mopper1], [@pone.0020440-Keller1], [@pone.0020440-Watts1], [@pone.0020440-Abbott1]), often even over small geographic scales. Nevertheless, although a variety of factors have been put forward to explain the geographic structure of genetic diversity within species, only a few studies have explicitly tested the causal environmental factors behind these geographic patterns [@pone.0020440-Balkenhol1]. Evaluatingthe importance of different environmental factors is crucial since these factors often interact dynamically with each other, thereby confusing the spatial signatures of molecular differentiation. For example, a recent study by Kittlein and Gaggiotti[@pone.0020440-Kittlein1] found that the interactions between various environmental factors can mask expected isolation-by-distance signatures that are often found in population genetic studies (e.g. [@pone.0020440-Hoelzer1], [@pone.0020440-Mimura1]). Thus, there is a clear need to more explicitly address the underlying environmental factors producing geographic patterns in the molecular structure of species.

In this study, we investigated the genetic diversity and population structure of a common and wide-ranging insect, the blue tailed damselfly*Ischnuraelegans* (Odonata, Coenagrionidae). This small damselfly species is a well-investigated study system in evolutionary ecology, particularly in terms of mating interactions, sexual selection, female colour polymorphisms, frequency-dependent selection and sexual conflict [@pone.0020440-CorderoRivera1], [@pone.0020440-Svensson1], [@pone.0020440-Svensson2], [@pone.0020440-Cordero1], [@pone.0020440-SnchezGuilln1]. Interest in this species has also arisen due to its enigmatic mating system and the presence of a heritable colour polymorphism in females [@pone.0020440-Hinnekint1], [@pone.0020440-SnchezGuilln2] and the rapid evolutionary dynamics that has been observed in natural populations [@pone.0020440-Abbott2], [@pone.0020440-Svensson3]. To investigate the geographic pattern of intraspecific genetic diversity of *I. elegans*, we investigated the molecular structure of 22 populations over most of the western part of this species\' geographical range (spanning 12° in latitude and 38° in longitude; [Figure 1](#pone-0020440-g001){ref-type="fig"}), along with four populations of its congeneric sister species *I. graellsii*. These two sister species are similar in habitat choice and morphology [@pone.0020440-Monetti1], and hybridise in north-western Spain, where they produce fertile offspring [@pone.0020440-SnchezGuilln2], [@pone.0020440-Monetti1]. Analyses of DNA sequence variation of the mitochondrial *cytochrome b* and *coenzyme II* have shown that the genetic distance between *I. elegans*and *I. graellsii*is only 0.2%, suggesting that these two species are very closely related[@pone.0020440-SnchezGuilln2], or alternatively, that long-term on-going hybridization counteracts genetic divergence between *I. elegans* and *I. graellsii* [@pone.0020440-Monetti1], [@pone.0020440-SnchezGuilln3]. Molecular population diversity of both species was analysed with novel microsatellite markers that we specifically developed for *I. elegans.* Cross-amplification tests have revealed that these microsatellites are also polymorphic in *I. graellsii* [@pone.0020440-Wellenreuther1]. The pattern of intraspecific genetic diversity in *I. elegans* was analysed with particular attention to longitudinal and latitudinal clines. We further investigated if geographic boundaries within the sampling area have led to discontinuities in molecular population structure, since both large water masses and mountains within the sampling area present potential barriers to dispersal. We also tested if geographic distance between populations exhibits an effect on population connectivity (i.e. dispersal) and investigated if we could find evidence for a signature of past historical bottlenecks. Finally, we evaluated several different ecological scenarios by relating environmental factors and their interactions to population specific F~ST~-values of *I. elegans*, namely the role of range expansion (latitude and longitude), geographic suitability (distance to coast and altitude) and climatic suitability (mean annual temperature and precipitation).

![Map of *I. elegans* (n = 22) and *I. graellsii* (n = 4) study populations.\
The geographic range of *I. elegans* includes Europe with the exception of northern Scandinavia, Corsica, Sardinia, Sicily and Malta, and the western and southern parts of the Iberian Peninsula where it is replaced by its sister species *I. graellsii* [@pone.0020440-Askew1]. The range of *I. elegans* further extends to the Middle East, and over much of Russia and China [@pone.0020440-Askew1].](pone.0020440.g001){#pone-0020440-g001}

The results in this study suggest that this small insect has a weak genetic population structure across a major part of its geographic range, and that the genetic structure does not seem to be severely affected by large geographic barriers. Nevertheless, we found that high local precipitation rates (e.g. flooding events), which presumably reduce the local effective population size (N~e~:s), increased the degree of genetic differentiation of populations. Overall, these results confirm the emerging view that this species is a fast and efficient colonizer of disturbed habitats, and commonly undergoes population extinctions and re-colonisations [@pone.0020440-Abbott1].

Materials and Methods {#s2}
=====================

Ethics Statement {#s2a}
----------------

All procedures were conducted according to the ethical guidelines of the relevant country to ensure ethical appropriateness, and catching permits were requested from the local authorities wherever necessary.

Study populations and sample collection {#s2b}
---------------------------------------

Adults of the damselfly *I.elegans* were caught from 22 populations during the flying seasons 2005**--**2009 using hand nets. At each population, 11**--**20 (mean 17.4; median 18) damselflies were collected for subsequent genetic analysis (see [Table 1](#pone-0020440-t001){ref-type="table"} for details of each population). Sampling locations covered most of the western part of the distributional range of *I. elegans* [@pone.0020440-Askew1] and spanned from 55° in the North, to 30° in the East, to 35° in the South, to -8 in the West ([Figure 1](#pone-0020440-g001){ref-type="fig"}). In addition, four populations of the sister species *I. graellsii* (total N = 66) were sampled in Spain (Campus Lagoas-Marcosende: 42°16′68N, 8°68′54W and Córdoba: 37°46′24N, 5°32′57W), Portugal (Riveira de Cobres: 37°29′45N, 7°31′12W) and Morocco (Saidïa: 37°49′60N, 7°52′00W) and kept for molecular analysis. Captured individuals were stored in 90% ethanol in small plastic tubes until DNA extraction. Additional sampling details are given in [Table 1](#pone-0020440-t001){ref-type="table"}.

10.1371/journal.pone.0020440.t001

###### Population details.

![](pone.0020440.t001){#pone-0020440-t001-1}

  Species               Populations             Country            Region      Year   Latitude    Longitude   N     H~O~    H~E~    Alleles   Richness
  ---------------- --------------------- --------------------- -------------- ------ ----------- ----------- ---- -------- ------- --------- ----------
  *I. elegans*            Doniños             West Spain        South Europe   2007   43.29270    -8.18550    20   0.711    0.700     41       6.591
  *I. elegans*             Laxe               West Spain        South Europe   2007   43.61930    -8.11126    14   0.715    0.805     32       6.146
  *I. elegans*             Louro              West Spain        South Europe   2007   42.69088    -8.66035    15   0.712    0.729     32       5.792
  *I. elegans*             Arreo              North Spain       South Europe   2008   42.47750    -2.57870    15   0.631    0.761     50       7.784
  *I. elegans*            Baldajo            Central Spain      South Europe   2008   40.24260    -3.42060    17   0.603    0.795     48       7.673
  *I. elegans*            Alfaro              North Spain       South Europe   2008   42.19080    -1.74230    20   0.663    0.758     50       7.046
  *I. elegans*            Europa              East Spain        South Europe   2008   42.24380     3.10280    18   0.671    0.787     48       7.109
  *I. elegans*            Amposta             East Spain        South Europe   2008   40.27320     0.21560    20   0.691    0.770     51       7.156
  *I. elegans*        Marjal del Moro         East Spain        South Europe   2008   39.07270    -0.31350    20   0.671    0.751     44       5.776
  *I. elegans*           Vigueirat           South France       South Europe   2008   43.53110     4.30120    16   0.733    0.804     42       6.252
  *I. elegans*      Gran Sassod\'Italia      Central Italy      South Europe   2008   42.50150    13.43280    19   0.777    0.813     51       7.461
  *I. elegans*           Liverpool           Great Britain      North Europe   2008   53.24390    -2.58400    16   0.624    0.709     38       5.964
  *I. elegans*          Heuringhem           North France       North Europe   2008   50.42090     2.16400    19   0.729    0.781     45       7.380
  *I. elegans*        Kaiserslautern         South Germany      North Europe   2008   49.26410     7.46740    17   0.765    0.770     53       8.177
  *I. elegans*           Het Vinne              Belgium         North Europe   2007   50.83300     5.11700    18   0.682    0.795     46       7.248
  *I. elegans*           Höje Å 6               Sweden          North Europe   2005   55.70220    13.14370    20   0.653    0.717     43       7.010
  *I. elegans*            Genarp                Sweden          North Europe   2005   55.60752    13.40420    20   0.680    0.753     44       7.203
  *I. elegans*       Lublin-Zemborzyce          Poland          East Europe    2007   51.15000    22.34000    14   0.7505   0.797     60       8.081
  *I. elegans*        ZwięczycaReszów           Poland          East Europe    2007   50.01670    21.91670    11   0.668    0.827     52       7.264
  *I. elegans*           Breznica               Poland          East Europe    2007   49.96964    19.64290    18   0.712    0.796     47       6.678
  *I. elegans*         Suchoi Limon             Ukraine         East Europe    2006   46.03000    30.04700    20   0.719    0.791     45       6.537
  *I. elegans*        Enmakov Island            Ukraine         East Europe    2006   45.43500    29.52500    15   0.713    0.766     49       6.811
  *I. graellsii*          Campus              West Spain          Outgroup     1999   42.166886   -8.68542    17   0.485    0.694     31       3.249
  *I. graellsii*          Córdoba         South Central Spain     Outgroup     2005   37.883330   -4.76666    20   0.647    0.653     36       3.466
  *I. graellsii*     Riveira de Cobres         Portugal           Outgroup     2009   37.49600    -7.52000    14   0.684    0.719     31       3.713
  *I. graellsii*          Saidïa             North Morocco        Outgroup     2009   32.83000    -4.52000    13   0.490    0.677     25       3.118

Table shows the species, sampling localities, country, sampling year, latitude and longitude, the number of individuals sampled per population (N), observed (H~O~) and expected heterozygosity (H~E~), number of alleles and the allelic richness per population.

DNA extraction and microsatellite genotyping {#s2c}
--------------------------------------------

To extract DNA, the head of each damselfly was used, to avoid contamination with gut parasites and (or) sperm. Heads were subsequently dried and homogenized using a TissueLyser (Qiagen). DNA was extracted from the powder by proteinase K digestion followed by a standard phenol/chloroform-isoamylalcohol extraction [@pone.0020440-Sambrook1]. The purified DNA was re-suspended in 100 µl of sterile water. The genotypes of all damselflies were assayed at six microsatellite loci previously isolated for this species \[I-002, I-015, I-041, I-053, I-095, I-134, for details see 30\]. These loci were previously described as being polymorphic with high heterozygosity (observed range: 0.46 to 0.88), and none of them was found to deviate from Hardy--Weinberg equilibrium or be in linkage disequilibrium with each other [@pone.0020440-Wellenreuther1]. One primer of each pair was 5′-labelled with 6-FAM, HEX or NED florescent dyes. Polymerase chain reactions (PCRs) were carried out in 10 µL volumes on a GeneAmp PCR System 9700 (Applied Biosystems) and contained 4 pmol of each primer, 15 nmol MgCl~2~, 1.25 nmoldNTP, 0.5 U Ampli-taq polymerase and 10**--**20 ng template. The conditions were as follows: initial denaturation step of 94°C for 2 minutes, then 35 cycles at 94°C for 30 s, touch down from 62**--**58°C for 30 s, 72°C for 30 s followed by 72°C for 10 minutes. Multiplex primer reactions were performed for combinations of primers with matching annealing temperatures but differing size ranges and dye labels, then mixed with a labelled size standard and electrophoresis was conducted on an ABI PRISM 3730 Genetic Analyzer (Applied Biosystems). Resulting data were analyzed with GeneMapper 3.0 (Applied Biosystems) for internal standard and fragment size determination and for allelic designations. The same size standard was used on all samples analyzed for each marker.

Population genetic analyses and geographic structure {#s2d}
----------------------------------------------------

Genetic diversity within populations was assessed in terms of allele frequencies, expected heterozygosity (H~E~), observed heterozygosity (H~O~), and allelic richness for each locus, using the program FSTAT version 2.9.3 [@pone.0020440-Goudet1]. Regression analyses of genetic diversity characteristics (allelic richness, number of alleles and heterozygosity estimates) and their associations with latitude and longitude were conducted to test for possible clinal relationships. In addition, the degree of genetic differentiation over all populations, as well as for each population pair, was estimated by calculating multilocus estimates of Weir and Cockerham\'s F~ST~ (θ). F~ST~ was used rather than R~ST~ [@pone.0020440-Slatkin3], as it is considered a more reliable estimate of population differentiation when using relatively small data sets with fewer than 20 loci [@pone.0020440-Gaggiotti1]. Significance of the global F~ST~-estimate was evaluated by permuting genotypes among samples and calculating 95% CIs by bootstrapping over loci (number of permutations was set at 1000). This method assumes Hardy-Weinberg equilibrium within populations. In the pairwise tests of population differentiation, the nominal statistical significance value of 5/1000 was adjusted for multiple comparisons using the Bonferroni correction when accounting for multiple testing to minimize type I errors.

In addition to F~ST~, Jost\'sD~est~ was used as a measure of genetic differentiation between populations [@pone.0020440-Jost1] and calculated for each population pair using the web based resource SMOGDv. 1.2.5[@pone.0020440-Crawford1]. D~est~ is a relative measure of differentiation, which ranges from zero (no differentiation) to one (complete differentiation), and simulations have shown that it is an unbiased estimator of differentiation, and outperforms F~ST~, over a range of sample sizes and for markers with different numbers of alleles (including highly variable microsatellite loci)[@pone.0020440-Gerlach1]. We used 1000 bootstrap replicates and the harmonic mean of D~est~ across loci.

We used the Bayesian statistical framework provided by the program STRUCTURE version 2.2.3 [@pone.0020440-Pritchard1] to analyse the geographic structure of the 22 *I. elegans* populations and the four *I. graellsii* populations, since a NJ tree (based on F~ST~-values between population pairs) did not result in a strongly supported tree (results not shown). STRUCTURE uses a Bayesian Markov chain Monte Carlo (MCMC) method to find the number of genetic clusters (each of which is characterized by a set of allele frequencies at each locus) that, based on the likelihood of the individuals\' genotype to belong to these genetic clusters, minimizes deviations from Hardy--Weinberg equilibrium (HWE) and linkage disequilibrium (LD)[@pone.0020440-Pritchard1]. Different admixture models are implemented in STRUCTURE [@pone.0020440-Pritchard1], and because damselflies are known to be good dispersers, which would cause migration between populations, we used the 'admixture model' with 'correlated allele frequencies'[@pone.0020440-Falush1]. We did not use the sampling location of the individuals as a prior. For each model, a 'burn-in' period of 20,000 MCMC replicates and a sampling period of 100,000 replicates was used. We performed runs for a number of clusters (*K*), ranging from one to ten; and for each *K*, 20 iterations were run. In this way, multiple posterior probability values (log likelihood (lnL) values) for each *K* were generated, and the most likely *K* was evaluated by the *ΔK*-method following Evanno et al. [@pone.0020440-Evanno1]. This method compares the rate of change in lnL between successive *K*s and the corresponding variance of lnL of each *K* [@pone.0020440-Evanno1].

Clusters of **i**ndividuals were also inferred with the R-package [@pone.0020440-Ihaka1] GENELAND 3.13 [@pone.0020440-Guillot1], which uses a Bayesian MCMC algorithm to cluster samples on the basis of both genetic and geographic information. Like STRUCTURE, GENELAND finds clusters by maximising HWE and minimising LD. However, spatial information of individuals is also accounted for at the Bayesian prior level in such a way that clusters corresponding to spatially organized groups are considered more likely than those corresponding to completely random spatial patterns. The benefit of using a spatial prior is to get more accurate inferences and to explicitly infer the spatial borders between inferred clusters. Due to substantial algorithm improvement in the recent versions of GENELAND software (from version 3.0.0 onwards), we used correlated gene frequency model that allowed us to detect subtle structures in the presence of low genetic differentiation [@pone.0020440-Guillot2]. Additionally, improvements in the post-processing scheme allowed estimation of the number of clusters (*K*), as well as the assignment of individuals to the inferred clusters in a single step, treating the number of clusters as unknown. The analysis was run to identify the geographic structures among populations and barriers to dispersal using (i) all 22 *I. elegans* populations and (ii) all 22 *I. elegans* populations and the four *I. graellsii* populations. To determine the number of genetic clusters, four independent runs were implemented for each analysis using 100,000 MCMC iterations with a burn-in period of 20,000 and a thinning value of 100 and then the model with the highest posterior probability was chosen. *K* was set to K~min~ = 1, K~initial~ = 4 and a K~max~ = 22 or 26 clusters, respectively, while filtering for null alleles during the run. It should be noted that the filtering was just a precautionary option, and that the model did not change when this option was not selected. However, this option allowed us to calculate the frequency of null alleles in our dataset. Consistent with previous results [@pone.0020440-Wellenreuther1], the frequency of null alleles was very low for all loci (\<0.002). The output map of the clusters from the analysis was then compared to geographic map to identify possible barrier to gene flow, which could, for example, be caused by mountain ranges or oceans.

To examine the distribution of the genetic variance among the clusters identified by GENELAND, an analysis of molecular variance (AMOVA) was conducted using ARLEQUIN version 3.11 [@pone.0020440-Excoffier1]. Analyses of among-group variance were based on the five and six clusters identified by GENELAND, using the locus-by-locus settings for all analyses. The AMOVA program allows the hierarchical partitioning of the variance components into three components: among groups, among populations within groups, and among individuals within populations. Statistical significance was assessed by 10,000 permutations.

Role of geographic isolation and bottlenecks {#s2e}
--------------------------------------------

Isolation-by-distance, which is defined as a decrease in the genetic similarity among populations as the geographic distance between them increases \[sensu 46\], was investigated by correlating the pairwise differentiation (based on both F~ST~- and D~est~-values, but using F~ST~ /(1- F~ST~) and D~est~ /(1- D~est~), respectively [@pone.0020440-Rousset1]and geographical distances between *I. elegans* populations (i.e. logarithmic Euclidean distances between populations estimated using the GIS software ArcView 8.2, Environmental Systems Research Institute). To statistically determine if genetic and geographic distances between populations are significantly correlated, a Mantel test on the genetic and geographic matrix was performed (1,000 randomizations), using the program Isolation by Distance (Isolde) web service (<http://ibdws.sdsu.edu/~ibdws/>).

The program BOTTLENECK [@pone.0020440-Piry1] was used to identify signals of recent bottlenecks. This program generates the expected heterozygosity under mutation-drift equilibrium (HetEQ) from the number of alleles at a locus and the sample size using the Stepwise Mutation Model (SMM), Two-Phase Model (TPM), and Infinite Allele Model (IAM), the HetEQ values are then averaged across loci and compared with the observed level of heterozygosity. The SMM and TPM are most appropriate for microsatellite data [@pone.0020440-Luikart1], with the TPM providing a more realistic picture of mutational events in microsatellite loci [@pone.0020440-Piry1]. HetEQ was calculated using the SMM and the TPM, the latter allowing 95% single-step mutations and 5% multiple-step mutations (with a variance of the multiple steps of approximately 12%), following the recommendation of Piry et al. [@pone.0020440-Rousset1]. The program returns several nonparametric tests of whether heterozygosity deviates from that expected under HetEQ. The most powerful of these tests---and the one employed here---is the Wilcoxon test. This test is particularly appropriate when less than 20 loci are used [@pone.0020440-Piry1].

Range expansion, geographic suitability and climatic suitability {#s2f}
----------------------------------------------------------------

To identify the environmental factors that might determine the genetic population structure of *I. elegans*, we used the hierarchical Bayesian method of Foll and Gaggiotti[@pone.0020440-Foll1] implemented in the programme GESTE. F~ST~-values are estimated for each local population (population specific F~ST~-values) and provide information on how genetically distinct a population is relative to other populations in the sample. For example, under a model of diffusive dispersal following a single colonization event, populations furthest from the origin would have the highest F~ST~-values due to the cumulative effects of drift from repeated founder events. Population-specific F~ST~-values were related to environmental factors using a generalized linear model. We chose this approach as our primary method because it tests multiple variables simultaneously. As suggested by the authors, we used the reversible jump MCMC method, and 10 pilot runs of a length of 5,000 as burn-in prior to drawing samples from a chain of 50,000 in length, separated by a thinning interval of 50. All combinations of variables were considered and models were evaluated using estimates of posterior probability, the 95% highest probability density interval (HPDI). The output also calculates the cumulative probability for each factor individually, so that the factor importance can be compared easily. GESTE can currently be run with two factors and their interaction at a time, and we run three different scenarios.

First, we investigated if there was any signature of gradual population expansion using the factors latitude and longitude in the analysis [@pone.0020440-Foll1]. If a gradual population expansion has occurred, we can assume a fission model in which successive founder events would lead to a gradual increase in genetic differentiation between local and ancestral populations as the distance between them increases. Second, we investigated the role of geographical suitability by incorporating altitude and the distance to coast of each population as factors in the analysis. Finally, we investigated the role of local climatic factors by using the mean annual temperature and precipitation as factors in the analysis. These bioclimatic variables were extracted for each population in ARCGIS from the WorldClim climate data base (<http://www.worldclim.org/bioclim>).

Results {#s3}
=======

Population genetic analyses and geographic structure {#s3a}
----------------------------------------------------

Populations contained a substantial fraction of genetic variation, as shown by the pronounced genetic diversity at each locus ([Table 1](#pone-0020440-t001){ref-type="table"}). Estimates of observed and expected heterozygosity were similar for the *I. elegans* populations and ranged from 0.60**--**0.77 and 0.70**--**0.83, respectively ([Table 1](#pone-0020440-t001){ref-type="table"}). The total number of alleles over all loci ranged between 32 and 60 alleles among the populations studied. Estimates of allelic richness per locus were comparable between populations and ranged between 5.78**--**8.18 ([Table 1](#pone-0020440-t001){ref-type="table"}).

The European populations of *I. elegans* were significantly differentiated from each other, although the differentiation was moderate to weak (global F~ST~ = 0.063, 95% CI 0.036**--**0.099, p\<0.0001). All the investigated loci contributed to this population differentiation (each individual locus p\<0.0001). The pairwise population differentiation ranged between F~ST~ = 0.00024 to F~ST~ = 0.14. Twenty-eight comparisons of these were statistically significant after applying a Bonferroni correction for multiple comparisons (p~Bonferroni_0.05~\<0.00026; for details see [Table 2](#pone-0020440-t002){ref-type="table"}). Some populations were genetically significantly distinct from a large number of populations. Specifically, the Spanish population Doniños, the two Polish populations (Lublin-Zemborzyce and ZwiêczycaReszów) and the two Swedish populations (Genarp and Höje Å 6) showed comparatively large and statistically significant genetic differences from several other populations ([Table 2](#pone-0020440-t002){ref-type="table"}). F~ST~-values between *I. elegans* and *I. graellsii* populations ranged between 0.13 and 0.27 ([Table 2](#pone-0020440-t002){ref-type="table"}).

10.1371/journal.pone.0020440.t002

###### F~ST~-values (above diagonal) and statistical significance (below diagonal) between all study populations;the mean F~ST~value is 0.06.

![](pone.0020440.t002){#pone-0020440-t002-2}

  Group                  South Europe   North Europe   East Europe   Outgroup                                                                                                                                                                                    
  --------------------- -------------- -------------- ------------- ---------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- ------- ------
  Doniños                                   0.06          0.05         0.08      0.08      0.06      0.08      0.08      0.09      0.05      0.02      0.03      0.04      0.05      0.03      0.09      0.05      0.09      0.09      0.06      0.07     0.10    0.24
  Laxe                        X                           0.08         0.08      0.06      0.07      0.07      0.06      0.08      0.06      0.04      0.07      0.06      0.07      0.04      0.09      0.08      0.08      0.06      0.06      0.06     0.09    0.13
  Louro                       X                                        0.14      0.09      0.12      0.06      0.10      0.12      0.09      0.05      0.08      0.05      0.07      0.06      0.10      0.09      0.10      0.12      0.08      0.11     0.10    0.16
  Arreo                    0.00024           X                                   0.01      0.01      0.03      0.00      0.00      0.01      0.07      0.05      0.05      0.04      0.04      0.06      0.04      0.09      0.06      0.06      0.04     0.10    0.27
  Baldajo                  0.00024           X              X        0.10048               0.00      0.02      0.00      -0.01     0.02      0.04      0.04      0.03      0.03      0.02      0.07      0.03      0.04      0.03      0.04      0.01     0.05    0.24
  Alfaro                   0.00024           X              X        0.28452    0.16071              0.04      0.01      0.00      0.03      0.05      0.05      0.05      0.05      0.02      0.08      0.05      0.06      0.05      0.05      0.01     0.07    0.24
  Europa                   0.00429           X              X        0.15976    0.05333   0.05619              0.00      0.02      0.03      0.04      0.04      0.01      0.02      0.01      0.02      0.03      0.04      0.04      0.02      0.02     0.06    0.23
  Amposta                  0.00024           X              X        0.49881    0.45190   0.10690   0.52071              -0.01     0.00      0.06      0.05      0.02      0.01      0.02      0.03      0.03      0.07      0.05      0.04      0.03     0.09    0.24
  Marjal del Moro          0.00024           X              X        0.91810    0.89024   0.67833   0.37071   0.98810              0.03      0.06      0.06      0.04      0.04      0.03      0.07      0.05      0.06      0.05      0.05      0.03     0.08    0.25
  Vigueirat                0.00167           X              X        0.23000    0.08690   0.00357   0.28238   0.18548   0.02714              0.04      0.04      0.02      0.01      0.02      0.03      0.02      0.09      0.06      0.06      0.04     0.10    0.24
  Gran Sassod\'Italia      0.01190           X              X        0.00048    0.00405   0.00119   0.01500   0.00167   0.00857   0.03262              0.02      0.02      0.03      0.00      0.07      0.04      0.03      0.04      0.02      0.03     0.04    0.20
  Liverpool                0.00476           X              X        0.04500    0.03238   0.00048   0.01571   0.07667   0.00595   0.08333   0.06190              0.00      0.01      -0.01     0.04      0.00      0.06      0.06      0.03      0.05     0.06    0.27
  Heuringhem               0.00024           X              X        0.06452    0.02690   0.00024   0.00905   0.28619   0.02476   0.03952   0.00357   0.26167              -0.01     -0.01     0.01      0.00      0.04      0.04      0.01      0.04     0.05    0.21
  Kaiserslautern           0.00024           X              X        0.12190    0.03619   0.00024   0.01167   0.50857   0.00619   0.31286   0.00095   0.30857   0.42381              0.00      0.02      0.00      0.06      0.05      0.02      0.05     0.07    0.25
  Het Vinne                0.00024           X              X        0.13500    0.10190   0.02452   0.16238   0.20643   0.14262   0.03571   0.03738   0.55738   0.84048   0.35905              0.02      0.00      0.02      0.02      0.00      0.00     0.03    0.21
  Höje Å 6                 0.00024           X              X        0.00238    0.00381   0.00024   0.00833   0.00810   0.00119   0.00786   0.00024   0.00571   0.18190   0.00143   0.03619              0.02      0.09      0.07      0.05      0.06     0.10    0.26
  Genarp                   0.00024           X              X        0.00857    0.01357   0.00024   0.00643   0.00095   0.00143   0.03071   0.00024   0.15500   0.18452   0.55405   0.18762   0.00524              0.05      0.05      0.02      0.03     0.07    0.25
  Lublin-Zemborzyce        0.00024           X              X        0.00071    0.00452   0.00048   0.01262   0.00071   0.00786   0.00143   0.00024   0.00024   0.00024   0.00024   0.00429   0.00024   0.00024              0.00      -0.01     0.00     -0.01   0.24
  ZwięczycaReszów          0.00024           X              X        0.00071    0.03333   0.00095   0.02429   0.00190   0.03881   0.00405   0.00024   0.00095   0.00214   0.00024   0.01119   0.00048   0.00024   0.74286              0.00      0.01     0.01    0.20
  Breznica                 0.00119           X              X        0.02690    0.05786   0.01024   0.04952   0.04690   0.14095   0.01595   0.00643   0.01500   0.01714   0.00595   0.01952   0.00238   0.00048   0.50286   0.55929              0.01     0.01    0.23
  Suchoi Limon             0.00024           X              X        0.09857    0.31571   0.03214   0.01476   0.00048   0.24167   0.00786   0.00667   0.00381   0.00429   0.00119   0.11738   0.00024   0.00048   0.94571   0.69286   0.42333             0.01    0.23
  Enmakov Island           0.00048           X              X        0.00214    0.02667   0.00310   0.03167   0.00214   0.03143   0.00643   0.00667   0.00310   0.00048   0.00190   0.00548   0.00024   0.00048   0.38667   0.22286   0.03262   0.12881           0.25
  *I. graellsii*              X              X              X           X          X         X         X         X         X         X         X         X         X         X         X         X         X         X         X         X         X        X    

Adjusted nominal alpha level for multiple comparisons after Bonferroni correction was 0.00026 for table-wide significance (the populations Louro and Laxe were not included, as genotype data was missing at one locus). X denotes comparisons that were not carried out (due to missing data at one locus).

As mentioned above, we also calculated the D~est~-value for each population pair, since it represents an unbiased estimator of genetic differentiation [@pone.0020440-Jost1]. The D~est~ measures of between population differentiation ([Table 3](#pone-0020440-t003){ref-type="table"}) showed an overall similar pattern to the pairwise F~ST~-values ([Table 2](#pone-0020440-t002){ref-type="table"}); however, the D~est~-values were on average slightly higher (mean D~est~across all population pairs was 0.12, while it was 0.06 for the F~ST~-values). The pairwise population differentiation ranged between D~est~ = −0.0085 to D~est~ = 0.5412 ([Table 3](#pone-0020440-t003){ref-type="table"}). There was a high correlation between the pairwise D~est~- and F~ST~-values (Mantel test r^2^ = 0.80, p\<0.001, 1000 randomisations). The main difference between the values was that overall differences increased, in particular the interspecific differences, when using the D~est~ formula (see [Table 2](#pone-0020440-t002){ref-type="table"} and [3](#pone-0020440-t003){ref-type="table"}). This suggests that the actual genetic differentiation (D~est~) between populations is actually higher than suggested using F~ST~-comparisons alone, and highlights the need to use the more unbiased estimation of D~est~ when evaluating the degree of differentiation between population pairs [@pone.0020440-Gerlach1].

10.1371/journal.pone.0020440.t003

###### D~est~ -values between all study populations;the meanD~est~value is 0.12.

![](pone.0020440.t003){#pone-0020440-t003-3}

  Group                  South Europe   North Europe   East Europe   Outgroup                                                                                                                                  
  --------------------- -------------- -------------- ------------- ---------- ------ ------ ------ ------ ------- ------ ------ ------ ------ ------ ------- ------ ------ ------ ------ ------ ------ ------ ------
  Doniños                                   0.12          0.01         0.15     0.18   0.14   0.17   0.21   0.23    0.08   0.06   0.02   0.11   0.11   0.06    0.17   0.09   0.24   0.25   0.12   0.14   0.25   0.34
  Laxe                                                    0.14         0.21     0.20   0.18   0.23   0.20   0.30    0.24   0.13   0.23   0.23   0.22   0.14    0.27   0.24   0.28   0.19   0.22   0.17   0.31   0.12
  Louro                                                                0.24     0.24   0.26   0.19   0.30   0.27    0.25   0.07   0.15   0.12   0.21   0.14    0.20   0.25   0.27   0.42   0.21   0.25   0.28   0.31
  Arreo                                                                         0.00   0.00   0.00   0.00   0.00    0.02   0.10   0.07   0.01   0.04   0.01    0.11   0.04   0.14   0.13   0.09   0.00   0.21   0.30
  Baldajo                                                                              0.00   0.02   0.00   -0.01   0.01   0.11   0.10   0.03   0.07   0.03    0.18   0.08   0.07   0.11   0.11   0.00   0.12   0.42
  Alfaro                                                                                      0.06   0.01   0.00    0.05   0.10   0.13   0.08   0.09   0.04    0.13   0.08   0.14   0.17   0.12   0.01   0.19   0.28
  Europa                                                                                             0.00   0.00    0.04   0.07   0.10   0.02   0.06   0.01    0.06   0.08   0.05   0.13   0.06   0.02   0.13   0.39
  Amposta                                                                                                   0.00    0.00   0.12   0.12   0.00   0.00   0.00    0.06   0.06   0.11   0.13   0.08   0.02   0.23   0.33
  Marjal del Moro                                                                                                   0.01   0.06   0.12   0.03   0.03   0.03    0.12   0.10   0.09   0.11   0.10   0.01   0.13   0.37
  Vigueirat                                                                                                                0.05   0.02   0.01   0.01   0.02    0.06   0.02   0.31   0.23   0.19   0.08   0.35   0.32
  Gran Sassod\'Italia                                                                                                             0.02   0.02   0.05   0.00    0.12   0.07   0.08   0.12   0.04   0.10   0.10   0.26
  Liverpool                                                                                                                              0.00   0.02   0.00    0.04   0.01   0.17   0.17   0.10   0.12   0.19   0.44
  Heuringhem                                                                                                                                    0.00   -0.01   0.01   0.00   0.09   0.09   0.03   0.08   0.10   0.31
  Kaiserslautern                                                                                                                                       0.00    0.02   0.00   0.14   0.14   0.04   0.13   0.17   0.42
  Het Vinne                                                                                                                                                    0.01   0.00   0.06   0.05   0.01   0.01   0.09   0.28
  Höje Å 6                                                                                                                                                            0.02   0.15   0.13   0.08   0.10   0.17   0.48
  Genarp                                                                                                                                                                     0.12   0.11   0.04   0.10   0.15   0.43
  Lublin-Zemborzyce                                                                                                                                                                 0.00   0.00   0.00   0.00   0.54
  ZwięczycaReszów                                                                                                                                                                          0.00   0.02   0.01   0.41
  Breznica                                                                                                                                                                                        0.01   0.01   0.46
  Suchoi Limon                                                                                                                                                                                           0.03   0.35
  Enmakov Island                                                                                                                                                                                                0.50

The geographic pattern of genetic variation measured as the number of alleles of *I. elegans* populations revealed a significant longitudinal cline (r = 0.51, r^2^ = 0.26, p\<0.015; [Figure 2A](#pone-0020440-g002){ref-type="fig"}). There was a border-line significant relationship between longitude and expected heterozygosity (r = 0.40, r^2^ = 0.16, p = 0.069; [Figure 2B](#pone-0020440-g002){ref-type="fig"}). Regressions between longitude and observed heterozygosity and allelic richness were not significant, but both were positive in sign (r = 0.32 and r = 0.27, respectively). None of the regressions between genetic diversity and latitude were significant (p\>0.05) and are therefore not shown.

![Linear regression between longitude and allelic richness of *I. elegans* populations (n = 22, r = 0.51, p\<0.0015).](pone.0020440.g002){#pone-0020440-g002}

To further evaluate intraspecific population differentiation between *I. elegans* populations, and their genetic similarity to *I. graellsii*, we used STRUCTURE to group populations into clusters. Structure supported the presence of differentiation among the populations, and the *ΔK*-method suggested three clusters as the most likely population structure ([Figure 3A and B](#pone-0020440-g003){ref-type="fig"}). The proportion of membership of each individual to each of the three genetic clusters (*K* = 3) is given in [Figure 3C](#pone-0020440-g003){ref-type="fig"}, and the average membership of individuals in closely located populations in 10 regions is given in [Figure 3D](#pone-0020440-g003){ref-type="fig"}. The proportion of membership of each individual to each of the 1**--**10 genetic clusters (*K* = 1**--**10) is shown in [Figure S1](#pone.0020440.s001){ref-type="supplementary-material"}. The results show a single very distinct *I. graellsii* group and three relatively diffuse genetic groupings of *I. elegans*that fall into a geographic pattern that consists of (i) northern and central (Sweden, Germany, Belgium, Great Britain, North France, South France and Italy), (ii) western and southern (Spain), and (iii) eastern populations (Ukraine and Poland; [Figure 3C and D](#pone-0020440-g003){ref-type="fig"}).

![Estimated population structure of the 22 *I. elegans* populations and the four *I. graellsii* populations from Bayesian structure analysis using the program STRUCTURE 2.2.3.\
**A**). Mean likelihood (± SD) of *K* for different numbers of clusters **B**) *ΔK*-values for different *K*; suggesting *K* = 3 as the most likely structure according to Evanno et al. [@pone.0020440-Evanno1]. **C**) Individual Bayesian assignment probabilities for *K* = 3 for 22 populations of *I. elegans* and the outgroup*I. graellsii*(grouped for visualisation into ten geographically close groups). Individuals are represented by thin vertical lines, which are partitioned into *K* shaded segments representing each individual\'s estimated membership fraction. **D**) Pie charts show the mean membership fractions to each of the three genetic clusters in ten groups of populations.](pone.0020440.g003){#pone-0020440-g003}

GENELAND was employed to complement the analyses run in STRUCTURE and to add a more explicit geographic component to the tests. Two analyses were run (22 *I. elegans* populations and 22 *I. elegans* populations and four *I. graellsii* populations)and these identified fiveand six clusters, respectively, of which the first five were identicalbetween analyses ([Figure 4A and B](#pone-0020440-g004){ref-type="fig"}). The first cluster contained all populations from Poland and the Ukraine (five populations), the second cluster consisted of populations from Germany, the UK, Sweden, northern France and Belgium (six populations), the third cluster contained populations from eastern Spain and southern France (seven populations), the forth cluster was made-up of populations from western Spain (three populations), and the fifth cluster consisted of the single Italian population([Figure 4A and B](#pone-0020440-g004){ref-type="fig"}). The sixth cluster of the second analysis (22 *I. elegans* populations plus four *I. graellsii* populations) contained the four *I. graellsii* populationsin western andsouthern Spain and Morocco ([Figure 4A and B](#pone-0020440-g004){ref-type="fig"}). Finally, the finding that GENELAND identified a greater number of clusters than STRUCTURE (five/six versus three), and that the same clusters were identified by independent GENELAND runs and produced similar values of posterior probabilities, could indicate that the algorithm employed in GENELAND may be more sensitive to find weak clusters in space.

![Spatial output from GENELAND using all 22 *I. elegans* populations (A) and (B) all 22 *I. elegans* populations and the four *I. graellsii* populations.\
Black circles indicate the relative positions of the sampled populations (see [Figure 1](#pone-0020440-g001){ref-type="fig"}). Darker and lighter shading are proportional to posterior probabilities of membership in clusters, with lighter (yellow) areas showing the highest posterior probabilities of clusters.](pone.0020440.g004){#pone-0020440-g004}

The genetic variance between thefive *I. elegans*clusters was quantified using an AMOVA. The major part of molecular genetic variation was found within populations (92.60%) with 4.30% among the five groups and 2.74% among the populations within groups ([Table 4](#pone-0020440-t004){ref-type="table"}). Exact tests showed significant genetic variance on all these three levels (all three comparisons p\<0.0001). We also quantified molecular variance between the six *I. elegans*clusters and the one*I. graellsii*cluster. The molecular variance within populations then decreased to 91.20%, and was still highly significant ([Table 4](#pone-0020440-t004){ref-type="table"}). Genetic variance among groups increased to 6.17%, and the variance among populations within groups decreased slightly to 2.63% ([Table 4](#pone-0020440-t004){ref-type="table"}).

10.1371/journal.pone.0020440.t004

###### Analysis of molecular variance (AMOVA) based on six microsatellite loci.

![](pone.0020440.t004){#pone-0020440-t004-4}

  \(A\)                                                                                                     
  ----------------------------------------------------------------------- ---------------- ---------------- -----------------
  Five *I. elegans*clusters (22 populations), as identified by GENELAND    **4.30\*\*\***   **2.74\*\*\***   **92.06\*\*\***

  \(B\)                                                                                                                                     
  ------------------------------------------------------------------------------------------------------- ---------------- ---------------- -----------------
  Five *I. elegans*groups and the one *I. graellsii*cluster (26 populations), as identified by GENELAND    **6.17\*\*\***   **2.63\*\*\***   **91.20\*\*\***

Significance levels are indicated (\*\*\*: p\<0.0001).

Role of geographic isolation and bottlenecks {#s3b}
--------------------------------------------

We tested for a possible pattern of isolation-by-distance between all population pairs (n = 22) of *I. elegans*. Applying a Mantel test to statistically investigate if the pair-wise matrix of genetic differentiation (F~ST~/(1-F~ST~) and D~est~/(1-D~est~), respectively) is correlated with the matrix of geographic distances, we did indeed find that the genetic population differentiation followed an isolation-by-distance pattern (F~st~: r = 0.34, one sided Mantel test p\<0.001; D~est~: r = 0.15, p\<0.02; [Figure 5](#pone-0020440-g005){ref-type="fig"}).

![Relationship between pairwise F~ST~-values and the geographical distances for the 22 *I. elegans* populations.\
Test of isolation-by-distance: r = 0.34 and p\<0.001. B) Relationship between pairwise D~est~-values and the geographical distances for the 22 *I. elegans* populations. Test of isolation-by-distance: r = 0.15 and p\<0.020.](pone.0020440.g005){#pone-0020440-g005}

The program BOTTLENECK showed that only one of the populations examined (Laxe, p\<0.047) showed a heterozygosityexcess, while four of the populations (Europa, Höje Å 6, Kaiserslautern and Marjal del Moro) showed a heterozygositydeficiency ([Table 5](#pone-0020440-t005){ref-type="table"}). This suggests that some of the *I. elegans* populations show a weak signal of a heterozygositydeficiency, suggesting that they are not at a mutation--drift equilibrium, but that there has been a recent expansion in population size or a recent influx of rare alleles from genetically distinct immigrants. This trend is also supported by the overall lower mean of the heterozygositydeficiency compared to the heterozygosityexcess for all populations, which was 0.3 and 0.8, respectively.

10.1371/journal.pone.0020440.t005

###### Test results from the program BOTTLENECK.

![](pone.0020440.t005){#pone-0020440-t005-5}

  Populations                 1-tail, heterozygosity-deficiency        1-tail, heterozygosity-excess   2-tail, both outcomes
  --------------------- --------------------------------------------- ------------------------------- -----------------------
  Doniños                                   0.281                                  0.781                       0.563
  Laxe                                      0.969                                **0.047**                     0.094
  Louro                                     0.078                                  0.953                       0.156
  Arreo                                     0.219                                  0.922                       0.438
  Baldajo                                   0.281                                  0.781                       0.563
  Alfaro                                    0.219                                  0.922                       0.438
  Europa                 **0.040** [\*](#nt104){ref-type="table-fn"}               0.977                       0.078
  Amposta                                   0.344                                  0.719                       0.688
  Marjal del Moro        **0.008** [\*](#nt104){ref-type="table-fn"}               1.000                     **0.016**
  Vigueirat                                 0.500                                  0.578                       1.000
  Gran Sassod\'Italia                       0.422                                  0.656                       0.844
  Liverpool                                 0.055                                  0.961                       0.109
  Heuringhem                                0.055                                  0.961                       0.109
  Kaiserslautern         **0.008** [\*](#nt104){ref-type="table-fn"}               1.000                     **0.016**
  Het Vinne                                 0.922                                  0.219                       0.438
  Höje Å 6               **0.016** [\*](#nt104){ref-type="table-fn"}               0.992                     **0.031**
  Genarp                                    0.078                                  0.945                       0.156
  Lublin-Zemborzyce                         0.055                                  0.961                       0.109
  ZwięczycaReszów                           0.500                                  0.578                       1.000
  Breznica                                  0.078                                  0.945                       0.156
  Suchoi Limon                              0.344                                  0.719                       0.688
  Enmakov Island                            0.055                                  0.961                      0.10938

\*bold *P*\<0.05 (rejection of null hypothesis of mutation drift equilibrium).

Table shows the results for testing the null hypothesis for mutation drift equilibrium under the two phase model (TPM, 95% single-step mutations and 5% multiple-step mutations) using the Wilcoxon test.

Range expansion, geographic suitability and climatic suitability {#s3c}
----------------------------------------------------------------

When testing for the possible signature of a recent range expansion in GESTE, no effect of latitude or longitude on the population-specific genetic differentiation could be detected, thus rejecting a model of gradual range expansion in this species. The model including longitude and the constant had the second highest posterior probability (0.108), while the model containing latitude and the constant achieved a much lower posterior probability (0.047). The finding that longitude (east**--**west) was also more important than latitude (south**--**north) was further supported when looking at the data fit with just the factors alone, which resulted in a posterior probability of 0.117 and 0.056, respectively. Similarly, neither the distance to coast or altitude (geographic suitability) was strongly correlated to the population-specific F~ST~-values. Out of the two variables, the model including the constant term and distance performed better than the model containing the constant and altitude (0.133 and 0.058, respectively). In both of these aforementioned tests (range expansion and geographic suitability), the model that only included the constant term had the highest posterior probability (0.835 and 0.801, respectively, see [Table 6](#pone-0020440-t006){ref-type="table"}). This means that in each of the two analyses, the model excluding all variables had at least an 80% probability of being the one that best fits the genetic structure observed. When testing for the climatic suitability, however, the model including the constant term and mean annual precipitation had the highest posterior probability and lowest variance and was thus deemed the best model (0.824, modal value 0.448, 95% HPDI 0.184 and 0.769, [Table 6](#pone-0020440-t006){ref-type="table"}). The inclusion of the mean annual temperature did not improve the model fit (all models including this term had a posterior probability of \<0.05). Adding temperature to the model with the constant term only reduced the posterior probability, again suggesting that this term has much weaker influence on the local genetic differentiation than precipitation ([Table 6](#pone-0020440-t006){ref-type="table"}). All models that did not include the precipitation factors had a much lower posterior probability than models including precipitation. The regression coefficient for precipitation was positive, revealing that the population-specific F~ST~-values will be higher in areas where precipitation is high (see [Figure 6](#pone-0020440-g006){ref-type="fig"}).

![Relationship between the population specific F~ST~-values and mean annual precipitation at each population (see [**Table 5**](#pone-0020440-t005){ref-type="table"} and [Results](#s3){ref-type="sec"} for additional statistics).](pone.0020440.g006){#pone-0020440-g006}

10.1371/journal.pone.0020440.t006

###### Posterior probabilities for different models (2 factors with their interaction) under the three environmental scenarios from the GESTE analysis.

![](pone.0020440.t006){#pone-0020440-t006-6}

  Environmental Scenario                                   Factors                                  Posterior probability
  ------------------------- ---------------------------------------------------------------------- -----------------------
  Spatial range expansion                                **Constant**                                     **0.835**
                                                           Latitude                                        0.0563
                                                      Constant, Latitude                                   0.0469
                                                          Longitude                                         0.117
                                                     Constant, Longitude                                    0.108
                                                Constant, Latitude, Longitude                              0.00940
                                      Constant, Latitude, Longitude, Latitude\*Longitude                   0.00120
  Geographic Suitability                                                                           
                                                         **Constant**                                     **0.801**
                                                           Altitude                                        0.0644
                                                      Constant, Altitude                                   0.0579
                                                      Distance to Coast                                     0.140
                                                 Constant, Distance to Coast                                0.133
                                            Constant, Altitude, Distance to Coast                          0.00650
                             Constant, Altitude, Distance to Coast, Altitude \* Distance to Coast          0.00100
  Climatic Suitability                                                                             
                                                           Constant                                         0.116
                                                         Temperature                                       0.0496
                                                    Constant, Temperature                                  0.00570
                                                        Precipitation                                       0.867
                                                 **Constant, Precipitation**                              **0.824**
                                             Constant, Temperature, Precipitation                          0.0434
                              Constant, Temperature, Precipitation, Temperature \* Precipitation           0.0114

Discussion {#s4}
==========

Population genetic analyses and geographic structure {#s4a}
----------------------------------------------------

F~ST~-values between *I. elegans* populations were generally quite low (mean F~ST~ = 0.06), and the D~est~-values (meanD~est~ = 0.12) of the pairwise genetic population differentiation, albeit higher, were also low to moderate. Together these results suggest a relatively high degree of genetic connectivity across the species\' geographic range in Europe, or alternatively, a recent population expansion. Odonates (dragonflies and damselflies) are thought to be relatively good dispersers, and often leave their natal habitat after emergence in the search for new ponds and/or rivers [@pone.0020440-Conrad1], [@pone.0020440-Stettmer1]. Small-scale dispersal also occurs during the aquatic life-stage of odonates[@pone.0020440-Palmer1], but the realized amount of dispersal during this stage is challenging to reliably quantify. Several species in the genus *Ischnura* are known to be good dispersers, as their presence in remote archipelagos demonstrates [@pone.0020440-CorderoRivera2]. *Ischnuraelegans*has been described as an opportunistic damselfly species that is typically found in quite disturbed environments, such as human-made artificial ponds [@pone.0020440-Svensson3] and can, unlike many other odonates, tolerate most plants as perching substrate [@pone.0020440-Corbet1]. Given that *I. elegans* exists in environments that experience strong temporal and spatial heterogeneity, leading to strong fluctuations in local population densities, the species experiences large fluctuations in both the strength and direction of selection. This is probably partly the reason for why local populations go extinct at a high rate, i.e. there is high population turnover in this species. Some of the data in this study (e.g. the relatively low D~est~-values and the diffuse population structure across large ranges) also support the general picture that *I. elegans* is an opportunistic insect species that rapidly colonises newly created habitats [@pone.0020440-Smallshire1], but which has low local population persistence and is a weak competitor against other odonates. Presumably, other small coenagrionid damselflies have similar high dispersal potentials as *I. elegans*. *Ischnurahastata*, for example, is one interesting species in this respect, as it has been captured on nets mounted on airplanes and has also colonised the Galapagos islands [@pone.0020440-Dunkle1]. It should be mentioned that the individual sample size per population in our study ranged between 11**--**20 individuals(mean 17.3, median 17.5, including the four *I. graellsii* populations), which is lower than the recommended sample size for stable F~ST~- and D~est~-estimates. Despite this shortcoming, we would like to highlight that the strength of our study laid in the high number of populations analysed and the large geographic area covered, which allowed us to investigate large scale environmental patterns and clines.

Molecular studies on other odonate species show a higher degree of genetic differentiation.For example, a study by Keller et al. [@pone.0020440-Keller1] on the lilypad whiteface dragonfly *Leucorrhiniacaudalis* shows a slightly higher degree of microsatellite differentiation (F~ST~ = 0.130) between populations in Switzerland, and a study on the southern damselfly *Coenagrionmercuriale* by Watts et al. [@pone.0020440-Watts2] in the UK found also a higher F~ST~-value of 0.17. The two aforementioned studies covered a much smaller geographic area than the present study and are both relatively rare and threatened species, unlike *I. elegans*.The F~ST~-values for these two rarer species strengthens our conclusionsthat the more abundant and dispersive species*I. elegans*consists ofpopulations that are connected by a high degree of gene flow, even over large geographic areas, or has been recently expanding in the area. A third study by Watts et al. [@pone.0020440-Watts3]on the small red-eyed damselfly *Erythrommaviridulum*reports similarly low F~ST~-values as in our study, and this study was carried out on a large geographic scale, including samples from the UK, Germany, Netherlands, Belgium and France. Watts et al [@pone.0020440-Watts3], [@pone.0020440-Gribbin1]came to a similar conclusion to our study, namely that *E. viridulumappears* to be capable of relatively long distance dispersal, even over inhospitable habitat. *Erythrommaviridulum*is also a species that is common and expanding northwards, including recent establishment in southern Sweden, and has thus a similar ecology as *I. elegans*, compared to the aforementioned rarer species with more fragmented and localized populations.

Populations that contributed most to significant between-population differences were found at the edge of the sampling range ([Table 2](#pone-0020440-t002){ref-type="table"}). These included populations in south-western Europe (Spain: Doniños), eastern Europe (Poland: Lublin-Zemborzyce, and ZwięczycaReszów) and northern Europe (Sweden: Genarp and Höje Å 6, [Table 1](#pone-0020440-t001){ref-type="table"}). Of these, the south-western and northern populations can be defined as peripheral populations while the eastern range extends all the way to China [@pone.0020440-Askew1]. Thus, the Polish populations should not be considered as peripheral, but are rather central populations. Peripheral populations are expected to show increased inter-population differentiation due to lower effective population sizes (N~e~) and concomitant increased potential for genetic drift [@pone.0020440-Eckstein1], [@pone.0020440-Lammi1]. Such isolated populations also suffer restricted gene flow with other isolated marginal populations [@pone.0020440-Eckert1], [@pone.0020440-Bridle1]. If populations at the edge become more or less isolated from gene flow with the central area, then genetic drift and the associated loss of genetic information is expected to play an even stronger role [@pone.0020440-GarcaRamos1]. A major goal in future research would be to understand how local population dynamics in *I. elegans* affect gene flow and how this interacts with the selection regimes experienced at the edge of their range. Although microsatellite loci are not directly under selection, due to the fact that they are non-coding genes, strong local selection at range limits \[c.f. 65\] would be expected to lower the effective population sizes and hence increase the potential for genetic drift [@pone.0020440-Charlesworth1]. In addition, asymmetrical gene flow from the centre of the range can limit or prevent adaptation of populations at the periphery, even if the latter experience intense directional selection [@pone.0020440-GarcaRamos1], [@pone.0020440-Kirkpatrick1]. However, we would like to underscore that this hypothesis needs to be investigated using quantitative genetic data from adaptive traits and experiments (e.g. reciprocal transplants), and it cannot be addressed using only neutral markers [@pone.0020440-Eckert1], [@pone.0020440-Bridle1].

Genetic differentiation is thought to reflect the interplay between stochastic and selective factors that jointly influence the realised amount of population differentiation. In the case of *I. elegans*, it is likely that environmental gradients (e.g. in temperature and precipitation) together with fluctuations in population size (due to stochastic events and habitat fragmentation) are responsible for the heightened genetic differentiation of peripheral populations relative to the rest of the populations ([Table 2](#pone-0020440-t002){ref-type="table"}). Moreover, the previously documented on-going hybridization between *I. elegans* and *I. graellsii* in Spain [@pone.0020440-SnchezGuilln2], [@pone.0020440-Monetti1] could potentially affect the degree of genetic differentiation of the Spanish *I. elegans* populations versus the other *I. elegans* populations in Europe [@pone.0020440-SnchezGuilln4]. Our statistical analyses provided evidence for a significant longitudinal cline of genetic diversity between *I. elegans* populations ([Figure 2](#pone-0020440-g002){ref-type="fig"}), while we found no evidence for latitudinal clines. It should be noted, however, that the latitudinal range that was covered in the present study (central Spain to southern Sweden) spans a much smaller geographic area than the covered longitudinal range (western Spain to eastern Europe), thereby making it less likely for latitudinal clines to occur in our material. Nevertheless, we conclude that the evidence in our study for a longitudinal cline is a robust result that deserves attention in future studies investigating *I. elegans*. Longitudinal gradients in genetic diversity in Europe have been less frequently reported than latitudinal gradients, and have typically been associated with postglacial colonization processes [@pone.0020440-Hewitt1], [@pone.0020440-Hewitt2], [@pone.0020440-Hewitt3]. In our study, the longitudinal pattern of genetic diversity might indicate a post-glacial westward expansion from eastern refugia, but more data need to be collected to explicitly test this hypothesis. A postglacial westward range expansion was recently suggested for the Italian agile frog *Ranalatastei* [@pone.0020440-Garner1], whereas an eastward range expansion was suggested for the great read warbler *Acrocephalusarundinaceus* [@pone.0020440-Hansson1].

The STRUCTURE results indicated weak divisions between southern and central, northern, and eastern population clusters of *I. elegans* ([Figure 3](#pone-0020440-g003){ref-type="fig"}), and the results from the spatial clustering analyses conducted in GENELAND suggested that the GENELAND algorithm was more powerful to detect genetic clusters than STRUCTURE ([Figure 4](#pone-0020440-g004){ref-type="fig"}). This could be due to the fact that STRUCTUREonly uses individual multilocus genotype data to infer population structure, while GENELANDalso exploits the spatial positions of the individual samples as a supplemental parameter in the analyses. Using the same dataset as in STRUCTURE (22 *I. elegans* and four*I. graellsii* populations), we were able to detect six clusters ([Figure 4](#pone-0020440-g004){ref-type="fig"}) (instead of three in STRUCTURE; [Figure 3](#pone-0020440-g003){ref-type="fig"}). Comparing these geographic clusters to geographic features (such as water bodies and mountains, which would clearly constitute significant barriers to dispersal for damselflies) did not highlight any clear geographic boundaries to gene flow. Instead, the geographic location of clusters appeared to be largely independent of potential barriers to dispersal. This suggests that both large water bodies (the North and Baltic seas for instance) or mountains (such as the Carpathian mountain range in the Ukraine and Poland) are unlikely to constitute major barriers to dispersal for *I. elegans* or, alternatively, that *I. elegans* can easily use other corridors to colonise habitats that are surrounded or close to such geographic structures.

Based on the clusters identified by GENELAND, we partitioned the molecular variance within and between all*I. elegans*populations and also within and between all *I. elegans*and the *I. graellsii*populations ([Table 4](#pone-0020440-t004){ref-type="table"}). The analyses suggest a general high level of intrapopulation variation in *I. elegans*, indicating that this species is associated with large population sizes and/or frequent exchange of individuals between populations, which contrasts the pattern of reduced levels of intrapopulation genetic variation that has been found in other species that have expanded their range after the last Pleistoceneglacialmaxima (e.g. [@pone.0020440-Hewitt1], [@pone.0020440-Hewitt2], [@pone.0020440-Hewitt3], [@pone.0020440-Brunner1]).

Role of geographic isolation and bottlenecks {#s4b}
--------------------------------------------

The genetic differentiation between *I. elegans* populations in Europe showed a clear geographic signature of isolation-by-distance ([Figure 5](#pone-0020440-g005){ref-type="fig"}).Abbott et al. (2008) did not find any significant isolation-by-distance in their study of a geographically much more restricted set of *I. elegans*populations in southern Sweden (maximum distance between populations  = 20 km). The absence of any significant pattern of isolation-by-distance in their study might indicate a relatively low degree of statistic power to detect a geographic signature in their case due to the small-scaled nature of their study, possibly in combination with the fact that these northern marginal populations might not be in equilibrium [@pone.0020440-Abbott2]. The pattern of isolation-by-distance in our larger geographic study area, in combination with relatively few loci genotyped, may further explain why the Bayesian clustering approach implemented in STRUCTURE found support for few distinct clusters and a rather diffuse population structure [@pone.0020440-Pritchard1]. This problem was reduced in GENELAND ([Figure 4](#pone-0020440-g004){ref-type="fig"}), presumably because spatial geographic information was also utilised.

Analyses using BOTTLENECK did not provide strong support that any of the populations suffer from an excess or deficiency of heterozygosity. The only population to show a heterozygosity excess was the Spanish population Laxe. In another study (R. Sanchez-Guillen et al., unpublished), we have found that out of all populations examined for Spain, Laxe showed the highest degree of hybridization between *I. elegans* and *I. graellsii*, which could explain the excess of heterozygosity detected for this population. Apart from this population, there was a slight trend indicating that four populations showed a heterozygositydeficiency. Nevertheless, although the low power of this result prevents to make any strong statements, the result could point towards a situation where these populations have recently expanded in size.

The emergence of population bottlenecks is probably counteracted by the high dispersal potential in *I. elegans*, as it enables the rapid colonisation of new areas and also maintains gene flow between populations. The ability to disperse and colonise novel habitats is particularly important when the natal habitat becomes unsuitable, for instance, as a result of habitat deterioration or due to climate change [@pone.0020440-Parmesan1]. Increasing temperatures have indeed been suggested to facilitate range expansion northwards in several ectotherms and insect species (e.g. [@pone.0020440-Parmesan1], [@pone.0020440-Hellberg1], [@pone.0020440-Wellenreuther2]). For example, out of 35 butterfly species in Europe, 22 have shifted their ranges northwards by 35--240 km over the last century, whereas only two have shifted south [@pone.0020440-Parmesan2]. A recent study on odonate range expansions in the UK showed that *I. elegans* has expanded its range 168 km northwards in the last few decades, which is more than double the average distance found for other odonate species in the same study [@pone.0020440-Hickling1]. This recent range expansion of *I. elegans* in the UK further demonstrates that *I. elegans* has the ability to quickly respond to environmental changes by dispersing to new areas. This suggests that the terrestrial adult phase in odonates plays a crucial role in genetically homogenizing closely as well as quite distantly located populations.

Range expansion, geography and climatic suitability {#s4c}
---------------------------------------------------

We evaluated three different scenarios to identify environmental factors that potentially affect the genetic population structure of *I. elegans*, each of which included two factors ([Table 6](#pone-0020440-t006){ref-type="table"}). The program GESTE calculates population-specific F~ST~-values (i.e. differences between one population versus the pool containing all other populations) and correlates these differentiation values to the environmental factors. The first scenario was to test if the inclusion of latitude and/or longitude in the model would result in a higher posterior probability than when the model was run without these factors, thereby identifying any signatures of spatial population processes, such as range expansions. A recent range expansion would partly account for the relatively low levels of population differentiation that we detected in *I. elegans*, since a recent expansion from a large ancestral population andthe retention of ancestral polymorphisms would be expected to lower the overall population differentiation [@pone.0020440-Hewitt2], [@pone.0020440-Hewitt3]. However, despite the plausibility of this scenario, the model statistically rejected the possibility of a gradual range expansion (from east to west, or south to north). We were also able reject the geographic suitability model, which included altitude and distance to coast as the explanatory factors. Finally, by including two measures of climatic suitability (mean annual temperature and precipitation) we found that, although temperature did not improve the model fit, precipitation had a large and significant effect on the genetic population differentiation in *I. elegans* ([Figure 6](#pone-0020440-g006){ref-type="fig"}). The positive regression coefficient for precipitation is consistent with the expectation that F~ST~-values will be higher in areas of higher precipitation because water bodies in such areas exhibit a greater magnitude and frequency of flooding. Higher frequencies of intense flooding are likely to degrade suitable habitat for both larvae and adults, thereby causing a decreases in the effective population sizes. The finding that precipitation can have a large and negative effect on the survival of odonates is supported by a study on the damselfly *Pyrrhosomanymphula*byGribbin and Thompson [@pone.0020440-Gribbin1], which shows that the percentage mortality of this species was significantly and positively correlated with precipitation. Moreover, high rain fall during prolonged periods reduces the available time during summer to forage, mate and reproduce and could potentially contribute to local population extinctions in some years and areas (E. I. Svensson, personal observations). A negative effect on population persistence is likely to be particularly strong for a small species like *I. elegans*, which should make it particularly vulnerable to starvation. Thus, local extinctions, or a reduction in population sizes, are likely to be more frequent in areas that experience a significantly higher rate of precipitation. The influence of climate-related factors, such as precipitation, on the population structure and species diversity is of growing interest in conservation due to the possible impacts of climate change [@pone.0020440-Parmesan1], [@pone.0020440-Hassall1], [@pone.0020440-Parmesan3]. It should be noted, however, that climatic factors, such as precipitation, are likely also correlated with other environmental variables, which could have caused the positive relationship.

In conclusion, the present-day structure of *I. elegans* is likely to have been shaped by several ecological factors, including good dispersal ability and high temporal and spatial turnover of peripheral populations, making this species a good coloniser of newly established and disturbed habitats. We found that although the geographic distance affects the connectivity between populations, gene flow does not seem to be strongly affected by major geographical barriers to dispersal, such as seas and mountains. These factors are probably the main explanation for an overall weak global population structure and high degree of genetic variation within local populations. We also found a longitudinal population genetic signature, and that precipitation had a significant effect on the genetic differentiation of populations, in this species. These results suggest that longitudinal environmental gradients have resulted in genetic clines, and that the local flooding and drying sequence affects overall genetic differentiation. In recent years, *I. elegans* has significantly extended its range [@pone.0020440-Hickling1], which is consistent with a response to increasing regional temperatures in Europe [@pone.0020440-Parmesan3]. Given that many aspects of *I. elegans*\' ecology have been thoroughly investigated in recent past, this species can become an interesting model organism to understand how insects can cope with on-going climate and environmental change.

Supporting Information {#s5}
======================

###### 

**Individual Bayesian assignment probabilities for** ***K*** **1--10 using the program STRUCTURE 2.2.3 for populations of** ***I. elegans*** **and** ***I. graellsii*** **.** Individuals are represented by thin vertical lines, which are partitioned into *K* coloured segments representing each individual\'s estimated membership fraction.

(TIF)

###### 

Click here for additional data file.

We would like to thank two anonymous reviewers for helpful comments on earlier drafts of this manuscript, Keith Larson for his help with maps, IñakiMezquita in Guipúzcoa, TomásLatasa in La Rioja, Mario García-París in Madrid, BernatGarrigós and Pere Luque from Oxygastra group in Catalunya, XoaquínBaixeras in Valencia, Francisco Cano in Córdoba and Jean Pierre Boudot and Jürgen Ott in Morocco for the collection of samples. Permits to capture damselflies in Spain were issued by each Regional Government to RSG.

**Competing Interests:**The authors have declared that no competing interests exist.

**Funding:**Funding was provided by a postdoctoral scholarship from the Wenner-Gren Foundation (<http://www.wennergren.org/>), The Tryggers Foundation (<http://www.carltryggersstiftelse.se/>) and the Swedish Research Council (<http://www.vr.se>)to MW, by Spanish Ministry of Science and Innovation (<http://www.micinn.es>), grant CGL2008-02799 and CGL2008-03197-E (AC-R), and the Swedish Research Council (BH, EIS). RAS-G is supported by a grant (Formación de Personal Investigador) from the Spanish Ministry of Science and Innovation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[^1]: Conceived and designed the experiments: MW RAS-G BH. Performed the experiments: MW RAS-G BH. Analyzed the data: MW RAS-G BH. Contributed reagents/materials/analysis tools: MW RAS-G BH EIS AC-R. Wrote the paper: MW.