# Contrasting patterns of spatial genetic structure in endangered southern damselfly (Coenagrion mercuriale ) populations facing habitat fragmentation and urbanisation

Agathe Lévêque, Anne Duputié, Vincent Vignon, Fabien Duez, Cécile Godé,

Cédric Vanappelghem, Jean-françois Arnaud

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► To cite this version:

Agathe Lévêque, Anne Duputié, Vincent Vignon, Fabien Duez, Cécile Godé, et al. . Contrasting patterns of spatial genetic structure in endangered southern damselfly ( Coenagrion mercuriale ) populations facing habitat fragmentation and urbanisation. Diversity and Distributions, 2024, 30 (10), 10.1111/ddi.13902. . hal-04969563

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# Contrasting patterns of spatial genetic structure in endangered southern damselfly (Coenagrion mercuriale) populations facing habitat fragmentation and urbanisation

Agathe Lévêque<fnref n="1" />,<fnref n="2" /> ID

Anne Duputié<fnref n="1" /> ID

Vincent Vignon<fnref n="2" />,<fnref n="3" />

Fabien Duez<fnref n="1" /> ID

Cécile Godé<fnref n="1" />

Cédric Vanappelghem<fnref n="4" />

Jean-François Arnaud<fnref n="1" /> ID

**Correspondence**

Jean-François Arnaud, Univ. Lille, CNRS, UMR 8198 – Evo-Eco-Paleo, Lille 59000, France.

Email: jean-francois.arnaud@univ-lille.fr

**Funding information**

Ministère de l'Enseignement Supérieur et de la Recherche; ARCOS/VINCI Autoroutes; OGE (Office de Génie Ecologique); UMR 8198 – Evo-Eco-Paleo (University of Lille/CNRS)

Editor: Henri Thomassen

**Abstract**

**Aim:** Human-induced environmental changes result in habitat loss and fragmentation, impacting wildlife population genetic structure and evolution. Urbanised and geographically peripheral areas often represent unfavourable environments, reducing connectivity among populations and causing higher population genetic differentiation and lower intra-population genetic diversity. We examined how geographic periphery and anthropogenic pressures affect genetic diversity and genetic differentiation in the protected southern damselfly (Coenagrion mercuriale, Odonata), which has low dispersal capabilities and specific habitat requirements and whose populations are declining.

**Location:** We studied two areas: one in semi-natural habitats at the periphery of the species geographic range (northern France) and the other more central to the species' range, in an urbanised area surrounding the city of Strasbourg (Alsace, eastern France).

**Methods:** We genotyped 2743 individuals from 128 populations using 11 microsatellite loci. We analysed the spatial distribution of neutral genetic diversity (allelic richness, heterozygosity, levels of inbreeding and genetic relatedness), the extent of genetic differentiation and population affiliations (sPCAs) within the two areas. We also examined fine-scale patterns of gene flow in the urbanised area of Alsace by investigating patterns of isolation by distance and estimating effective migration surfaces (EEMS) method.

**Results:** Northern peripheral populations showed lower levels of genetic diversity and higher levels of genetic differentiation than central Alsacian populations. Although located in anthropised habitats, geographically central Alsacian populations showed high levels of gene flow, with dispersal events mainly occurring overland and not restricted to watercourses. However, the highly urbanised city of Strasbourg negatively impacted nearby populations by reducing levels of genetic diversity and increasing population genetic differentiation.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Author(s). Diversity and Distributions published by John Wiley & Sons Ltd.

<footnote n="1">¹Univ. Lille, CNRS, UMR 8198 – Evo-Eco-Paleo, Lille, France</footnote>

<footnote n="2">²Office de Génie Ecologique (O.G.E.), Strasbourg, France</footnote>

<footnote n="3">³ALKIOS, Avignon, France</footnote>

<footnote n="4">⁴Conservatoire d'espaces naturels des Hauts-de-France, Dury, France</footnote>
**Main Conclusions:** These results showed the need for management action by restoring breeding sites and creating migratory corridors for peripheral southern damselfly populations. However, our results also highlighted the resilience of southern damselfly in central range populations facing strong urbanisation pressures.

KEYWORDS

central and peripheral populations, central-marginal hypothesis, conservation genetics, genetic diversity, isolation-by-distance, population genetic structure, urbanisation effects

# 1 | INTRODUCTION

In the context of major environmental changes caused by human activities, such as urbanisation, intensive agriculture or land-use changes, habitat loss and fragmentation are major threats to biodiversity (Fahrig, 2003; Wilson et al., 2016). Fragmented populations face a reduction both in the amount of favourable habitats and in gene flow among populations inhabiting the remaining habitat patches (Fahrig, 2003). This may generate both demographic and genetic effects on populations, with an increased risk of inbreeding, a loss of genetic diversity and an increase in genetic differentiation among populations, which can ultimately lead to a vortex of extinction in the long term (Ellegren & Galtier, 2016; Frankham et al., 2013).

Urbanisation also impacts the fate of wildlife populations, as described by the 'urban fragmentation' model in which habitat fragmentation and degradation magnify the extent of genetic drift and decrease gene flow events among populations (Johnson & Munshi-South, 2017; Miles et al., 2019). This process was indeed documented in numerous species of different taxa (Desender et al., 2005; Fusco et al., 2021; Miles et al., 2019; Munshi-South et al., 2016; Trumbo et al., 2019; Vandergast et al., 2006). Nonetheless, the impact of urbanisation varies greatly, depending on the species' life-history traits but also on the landscape matrix (Kimmig et al., 2020; Miles et al., 2019; Richardson et al., 2021). Urbanisation can not only reduce connectivity between populations but also create barriers or corridors that influence species' dispersal behaviours and pathways (Kimmig et al., 2020). While highly mobile species are more likely to maintain some connectivity with exurban populations, avoiding inbreeding depression and preserving high levels of genetic diversity, this might not be the case for low vagility species depending on specific habitats and facing dispersal barriers (Fusco et al., 2021; Johnson & Munshi-South, 2017; Richardson et al., 2021).

Yet, to obtain a global vision of the conservation management of a species, it is important to understand patterns of spatial genetic structure both at local scales and at the larger scale of the species' geographical range (DeWoody et al., 2021; Guo, 2012; Willi et al., 2022). The 'abundant centre' model posits that populations near the centre of a species' geographic distribution are the most abundant, and become smaller and scarcer towards the periphery of the range (Brown, 1984). Two main predictions arise from this model: (i) Peripheral populations should exhibit lower levels of genetic

diversity compared to central ones, as a result of genetic drift associated with small population size; (ii) peripheral populations are likely to exhibit higher levels of genetic differentiation than central populations, because of genetic drift and of restricted gene flow due to geographic isolation (Eckert et al., 2008; Vucetich & Waite, 2003). Nonetheless, this pattern is not systematically observed, as the geometry of the species' range, historical and current ecological factors can lead to deviations from this model (Eckert et al., 2008; Guo, 2012; Johansson et al., 2013; Pironon et al., 2017).

All these evolutionary considerations are particularly relevant to the Odonata taxa, since they are bioindicator species for freshwater ecosystems and have a biphasic life cycle, with aquatic larval and terrestrial adult stages, making them dependent on both environments for their development. While Odonata species are considered to be efficient fliers, potentially able to disperse over large spatial distances, some Zygoptera species, such as the southern damselfly (Coenagrion mercuriale, Charpentier, 1840), have low dispersal capability. Mark-recapture studies have documented rare long-distance movements of southern damselflies over more than one kilometre (Purse et al., 2003; Rouquette & Thompson, 2007; Watts, Rouquette, et al., 2004). The species is geographically restricted to western Europe, with northern range limits in the UK, France and Belgium, eastern limit in Germany and southern limit in northern Africa (Grand, 1996; Figure S1). Southern damselflies are under high conservation priority because the species has become almost extinct in seven European countries on the northern and eastern boundaries of its distribution, where many populations are threatened by the deterioration and loss of its habitat associated with changes in agricultural practices (Grand, 1996). The southern damselfly is found in lotic habitats, requiring slow-flowing small streams or ditches, open habitats with little shade and the presence of helophytes (Purse & Thompson, 2009; Rouquette & Thompson, 2005).

In this study, using a set of microsatellite loci, we aimed to examine the spatial patterns of genetic diversity and genetic differentiation within and among southern damselfly populations in two contrasting areas: (i) an urbanised area located around the city of Strasbourg in Alsace (eastern France), towards the centre of the species' geographic distribution and characterised by the occurrence of numerous populations; (ii) a second area located in northern France and Belgium, towards the northern limit of the species' distribution, where populations are scarcer and only occur in semi-natural habitats (Figure S1). We asked the following questions:
1. Do central urbanised and peripheral semi-natural populations of southern damselfly exhibit the same levels of genetic diversity and genetic differentiation? We expected populations in northern France and Belgium to show higher levels of genetic differentiation and lower levels of genetic diversity than populations in Alsace, due to their geographical isolation close to the limit of the species' distribution. However, these differences could be mitigated by a negative effect of urbanisation on Al sacian populations, even though they are more central to the species' range.

2. When focussing on the Alsace region, where populations are densely distributed, do migration pathways occur overland or through the hydrographic network? We compared the patterns of isolation-by-distance (IBD) using ecological distances measured along watercourses to those found using simple straight-line Euclidean geographic distances. We only conducted this analysis in Alsace, because of a larger occurrence of the species and, consequently, because of a larger number of sampled populations.

3. Does the urban pressure of the city of Strasbourg impact population genetic features of neighbouring southern damselfly populations? The main expectation was that the city represents a substantial barrier to the dispersal of southern damselflies. This could lead to a decrease in levels of intra-population genetic diversity for populations close to the city, according to the urban fragmentation model (Miles et al., 2019).

## 2 | METHODS

### 2.1 | Study sites and sample collection

We sampled two areas: (i) Northern France and Southern Belgium (Figure 1a-c); (ii) the Alsace region, located in eastern France (Figure 1b,c).

In Northern France and southern Belgium, southern damselfly populations are scarce (Figure S1). We thus considered this region to be at the northern limit of the species' geographic distribution. This region is characterised by intensive agriculture, and by a low occurrence of natural areas. In this region, southern damselflies are only found in semi-natural sites (Fierimonte & Vanappelchem, 2021). The 'Conservatoire des Espaces Naturels Hauts de France' (CEN) is in charge of managing biodiversity in more than 540 semi-natural sites in northern France, and surveys additional sites when required. Because southern damselfly is an endangered species in this region, its occurrence is well-recorded by biodiversity managers (Figure S2A). Over this wide region (~60,000km²), we exhaustively sampled all 24 semi-natural areas where southern damselfly was documented by the CEN (Fierimonte & Vanappelchem, 2021) who never found occurrence of the species outside these areas. These sites are located in six subregions labelled N1 to N6 (Figure 1a; N1: Belgium; N2: Oise; N3: Opal coast; N4: Scarpe; N5: Somme; N6: Vexin).

In Alsace region (and in neighbouring areas), southern damselfly populations are more densely distributed (Figures S1 and S2B). We thus considered the Alsace region to be more central to the species' geographic distribution. In this region, southern damselflies can occur not only in natural but also in more anthropised and urbanised environments. We thus surveyed an area encompassing the city of Strasbourg. This city spans 78 km² and counts about 290,000 inhabitants, with a human population density of more than 3700 people/km². Strasbourg city is located within the urban area named 'Eurométropole de Strasbourg' with about 500,000 inhabitants for 330 km² (Figure 1b). A few kilometres west are the Vosges mountains, where southern damselflies do not occur. In this region, the landscape is characterised by intensive and less intensive agriculture (mostly cereals and grapevine on the slopes). For this region of about 4000 km², we accessed to historical records of southern damselfly occurrence (http://association.imago.free.fr/; https://www.gbif.org/fr/species/1422012). We therefore surveyed all known sites where this species was reported, but we also investigated additional sites that appeared to present favourable conditions for southern damselflies, by selecting them on Google Earth, along small streams outside forest areas. In total, we surveyed 206 sites in Alsace (Figure S2B), and were able to find southern damselfly populations in 104 of them. Populations were then spatially grouped into six main subwatersheds named A1-A6 (Figure 1b; A1: Bruche/Mossig; A2: Ehn/Andlau; A3: Dreisan/Elz; A4: Ill/Rhine; A5: Souffel; A6: Zorn/Landgraben).

We collected adult individuals of southern damselfly (Coenagrion mercuriale) using an insect net in spring (May-July) from 2017 to 2022 for Northern France and from 2021 to 2022 for the Alsace sampling area. Given the limited dispersal capability of the southern damselfly (Watts et al., 2006; Watts, Rouquette, et al., 2004), we designed sampling sites following 200-metres sections located along rivers and streams, separated by at least 500 m to avoid repeated sampling of the same population (Figure S2B).

We sampled a total of 2743 adult specimens (545 in Northern France, 2198 in Alsace; 1-39 per population; mean 21.4±11.3; Figure 1 and Table S1), with a sex ratio strongly biased towards males (86.73%) due to their higher flying activities and conspicuousness. Table S1 lists the geographic coordinates and sampling sizes of each population. We collected two kinds of samples: whole individual body, or only the right middle leg of each individual, the latter method being non-lethal and not impacting the damselfly's survival (Fincke & Hadrys, 2001). All samples were stored in 100% ethanol until DNA extraction.

### 2.2 | Genotyping

For whole individuals, we removed abdomens from the rest of the body to avoid sampling intestinal microbiota, crushed the samples using five MN Beads Type D (Macherey-Nagel). We crushed leg samples with only three beads. We extracted total genomic DNA from each whole individual sample using NucleoMag® Tissue Kit
<figure><img src="image_3.png" /><figcaption>FIGURE 1 Location of 128 populations of southern damselfly (Coenagrion mercuriale) within two study areas: Northern France (a), and Alsace (b). Each circle represents one population, with circle size being proportional to the number of individuals sampled. Strasbourg city and the eurometropole of Strasbourg are represented by the striped areas. Around Strasbourg, main watercourses are represented and coloured depending on the hydrographic network they contribute to. Circle colours correspond to the main subwatershed or geographical region each population belongs to. Land cover was simplified from Corine Land Cover Edition 2018. (c) The grey zones represent the current geographic distribution of *Coenagrion mercuriale* (data obtained from IUCN SSC Odonata Specialist Group 2019. The IUCN Red List of Threatened Species. Version 2022-2).</figcaption></figure>

(Macherey-Nagel) according to the manufacturer's recommendations. For leg samples, we purified DNA with 12µL NucleoMag Beads and 12µL of pure water, and eluted in 50µL.

We genotyped all samples using 11 unlinked nuclear microsatellite loci named LIST002, LIST023, LIST034, LIST035, LIST037, LIST042, LIST062, LIST024, LIST060, LIST063 and LIST066, isolated and described in Watts, Wu, et al. (2004), Watts, Thompson, et al. (2004). Locus LIST060 was excluded from all subsequent analyses because it showed evidence for null alleles occurrence

($F_{IS}$=0.377, *p* < 10⁻³; Table S2). PCRs were performed in two complexes described in Table S2. PCRs were conducted in a volume of 10µL, using 3µL of DNA (0.5–5 ng/mL), 5µL Multiplex PCR Master Mix (QIAGEN) and a primer mix (each primer at a final concentration of 0.2µM). PCR amplifications were as follows: (i) 15 min at 95°C; (ii) 30 cycles for whole individuals, and 32 cycles for legs of 30s denaturation at 94°C, 90s annealing 55°C and 60s elongation at 72°C; (iii) 30 min at 60°C. 1.5µL of PCR products (1/10 diluted for whole individual samples) were pooled with 0.25µL of RadiantDy™
632,500 MOB size standard (Eurogentec, Seraing, Belgium) and 9.75 µL of formamide (Applied Biosystems, Foster City, CA), electro-phoresed and sized with an ABI PRISM 3130XL sequencer (Applied Biosystems) and the software GeneMapper 5.0 software (Applied Biosystems). Amplification failure was scarce (0% for Alsace and 0.14% for Northern France).

## 2.3 | Genetic diversity

Standard population genetic statistics were calculated using *FSTAT* 2.9.4 (Goudet, 2003) and R (version 4.2.1; R Core Team, 2022) libraries 'adegenet' 2.1.9 (Jombart, 2008), 'hierfstat' 0.5-11 (Goudet, 2005), 'pegas' 1.1 (Paradis, 2010) and 'demerelate' 0.9 (Kraemer & Gerlach, 2017). All statistics were weighted by population sizes (Nei, 1987; Weir & Cockerham, 1984), allowing for non-biased comparisons across populations and across regions. Moreover, to ensure statistically robust results, we estimated these standard statistics of genetic diversity only for populations with a minimal sampling size of at least eight individuals, allowing to compare relevant genetic diversity estimates between representative samples of populations (El Mousadik & Petit, 1996; Frankham et al., 2013; Hedrick, 2011). These basic statistics included the total number of alleles sampled ($A_t$), the allelic richness ($A_r$), the observed ($H_o$) and the expected ($H_e$) heterozygosity, and the mean individual kinship coefficient within populations ($F_{ij}$; Loiselle et al., 1995). Statistical differences between Alsace and Northern France in terms of mean levels of genetic diversity and of mean levels of intra-population kinship were tested by permutation tests using the R library 'perm' 1.0-0.4 (Fay & Shaw, 2010).

To investigate population genetic structure over the whole data set and among the two study areas and the different subwatersheds and subregions, we computed estimates of *F*-statistics following Weir and Cockerham (1984) and tested their statistical significance using permutation tests (10,000 permutations) implemented in *FSTAT* (Goudet, 2003). As for the calculation of basic genetic diversity estimates, *F*-statistics estimates were only used for populations with at least eight individuals.

To study the potential effect of Strasbourg city on southern damselfly populations, we considered a specific radius of 20 km around the city centre and we performed a linear regression between the distance of each population from the centre of Strasbourg city and (i) the genetic diversity estimates ($A_r$, $H_e$), (ii) the mean individual kinship coefficient within populations ($F_{ij}$) and (iii) the mean pairwise levels of $F_{ST}$ calculated according to Weir and Cockerham (1984) between each focal population and all other populations within this radius. Because other ecological factors, such as land-use, forest occurrence or the presence of the Vosges mountains, are expected to shape the genetic structure at larger scales of investigation, we restricted this analysis to a 20-km radius around the centre of Strasbourg to avoid confounding effects (Figure S3).

## 2.4 | Delimitation of population boundaries and genetic discontinuities

Genetic discontinuities and grouping of genetically related populations were assessed using spatial Principal Component Analysis (sPCA). This spatially explicit multivariate method, implemented in the 'adegenet' R library (Jombart, 2008), reveals spatial genetic patterns and detects cryptic geographical variation in allele frequencies allowing to depict population boundaries, without making assumptions with regard to linkage disequilibrium or departures from Hardy-Weinberg equilibrium (Jombart et al., 2008). We used the Delaunay triangulation graph to create the spatial network underlying the sPCA. To draw a comprehensive synthetic representation, we represented population coordinates along the first three principal components on the Red, Green and Blue colour channels as in Menozzi et al. (1978).

## 2.5 | Analysis of spatial genetic structure

We did not investigate the patterns of isolation-by-distance (IBD) in northern France, both because of the smaller number of sampled populations in this area and because of the difference in geographic scale of sampling. Indeed, because southern damselflies have limited dispersal capabilities, a migration/drift equilibrium is not expected among distant and isolated subregions. Therefore, we studied the fine-scaled spatial genetic structure by only focussing on the whole Alsace region and in the three major subwatersheds, where substantial numbers of populations and individuals ensure biologically relevant results. To search for an IBD pattern expected under migration/drift equilibrium, we considered two different estimations of geographical distances: (i) log-transformed Euclidean distances ($d_{Eucl}$); (ii) log-transformed shortest path distances along watercourses ($d_{stream}$) because southern damselflies only reproduce along watercourses. We computed Euclidean geographical distances using the R function *dist*, and geographical distances along waterways using the R package 'riverdist' 0.15.5 (Tyers, 2022). To calculate the latter distance, we used the BD TOPAGE® 2019 linear hydrology shapefile. We then assessed the spatial genetic structure using two complementary approaches.

First, to test for IBD among populations, we regressed pairwise $F_{ST}$ estimates against geographical distances and tested the significance of the Mantel statistic *rz* (Smouse et al., 1986) using the *mantel.rtest* function of the R package 'ade4' with 10,000 permutations (Dray & Dufour, 2007).

Second, to get insight into the spatial genetic structure over the Alsace area, we analysed the relationship between pairwise individual kinship coefficients $F_{ij}$ (Loiselle et al., 1995) and geographical distance using SPAGEDI 1.5 (Hardy & Vekemans, 2002). To allow comparable and relevant statistical results in terms of mean genetic relatedness among individuals, nine classes of increasing geographical distances were defined, which included nearly
identical number (mean of 268,278 per distance class) of pairwise
individual comparisons. In each distance class, we assessed 95%
upper and lower confidence intervals of F<sub>ij</sub> using 10,000 permu-
tations of individual locations. Finally, to compare the strength
of spatial genetic structure between the different geographical
distances and subwatersheds in Alsace, we used the Sp statistic
(Vekemans & Hardy, 2004).

2.6 | Estimated effective migration surface

Finally, to identify geographical regions where migration rate was
higher or lower than expected under an isolation-by-distance
null model, we used the Estimated Effective Migration Surfaces
method (EEMS; Petkova et al., 2016) in the whole Alsace region.
We ran the model for a range of deme values (400, 1000, 1500
and 2000), and parameters were optimised until the proposals
were accepted about ~20%~30% of the time, as recommended by
Petkova et al. (2016). Convergence of MCMC chains, checked using
the 'reemsplots2' R library (Petkova, 2022), was reached with the
MCMC parameters by default (numMCMCIter = 2000000, num-
BurnIter = 1000000, numThinIter = 9999). This analysis was again
restricted to the Alsace region, where populations were more
densely distributed.

3 | RESULTS

**3.1** *Genetic diversity*

Over all populations, single-locus F<sub>IS</sub> values did not significantly
differ from zero, except for locus LIST037 (F<sub>IS</sub>=0.035, p < .05;
Table S2). Single-locus F<sub>ST</sub> all significantly differed from zero, with
a mean multilocus estimate of 0.065 (p < .05; Table S2). The num-
ber of alleles per locus ranged from two to 24, the mean H<sub>e</sub> was

0.568, and the mean H₀ was 0.507 (Table S2). Within populations,
we detected no significant departures from Hardy-Weinberg
equilibrium (Table S1).

In Alsace, levels of genetic diversity were high, with a mean al-
lelic richness of 3.19 ± 0.13, a mean H<sub>e</sub> of 0.529 ± 0.021 and a mean
H<sub>o</sub> of 0.528 ± 0.030 (Table S1, Figure 2a, Figure S4). The geograph-
ical patterns of these estimates of genetic diversity are displayed
in Figure S5. The mean intra-population kinship F<sub>ij</sub> was 0.022
(Table S1, Figure 2b). Five populations showed higher mean lev-
els of genetic relatedness between individuals than other Alsacian
population (populations Gox1, S17, S21, Rlei45, Rsc1; Table S1,
locations visible on Figure S3). Gene diversity (H<sub>e</sub>) and allelic rich-
ness (A<sub>r</sub>) increased with increasing distance of populations from
the centre of Strasbourg within a radius of 20 km around the city
(Figure 3a, Figure S6A). On the contrary, mean pairwise F<sub>ST</sub> levels
and mean intra-population kinship F<sub>ij</sub> decreased with increasing
distance of populations from the centre of Strasbourg (Figure 3b,
Figure S6B). These significant relationships observed between ge-
netic diversity or genetic differentiation and the geographical dis-
tance from the city of Strasbourg disappear beyond this scale of
20 km, which suggested other ecological factors than strict urban-
isation level impacting population genetic features at larger scale
of observation (data not shown).

Populations located in Northern France exhibited significantly lower levels of genetic diversity compared to those in Alsace, with a mean allelic richness of 2.91±0.43, a mean H<sub>e</sub> of 0.43±0.070, and a mean H<sub>o</sub> of 0.48±0.070 (permutation tests, all at *p* < .001; Table S1, Figure 2a, Figure S4). The mean intra-population kinship level in Northern France was 0.136 (Table S1, Figure 2b). Within Northern France, levels of genetic diversity and intra-population relatedness considerably varied among the different subregions (Figures S5 and S7), with subregions N1, N2 and N6 showing levels similar to those observed in the Alsace subwatersheds, while the more isolated subregions N3 to N5 showed lower levels of genetic diversity (permutation tests, *p* < 10<sup>-3</sup>).

<figure><img src="image_4.png" /><figcaption>FIGURE 2 Distribution of allelic richness $A_r$ (a) and mean kinship coefficient $F_{ij}$ (b) in the southern damselfly within the Alsace and Northern France areas. \*\*\*$p<.001$.</figcaption></figure>
## 3.2 | Delimitation of population boundaries and genetic discontinuities

The sPCA showed contrasting geographic partitions of populations for the two studied areas: while a clear spatial structure appeared in Northern France, with genetic grouping corresponding to the major geographical regions, Alsacian populations were rather characterised by a gradient of population delimitation (Figure 4).

In Northern France, the first three axes of the sPCA discriminated four distinct spatial groups: (i) populations located in subregion N3 in the north-western part, (ii) the populations located in subregion N5 in the south-western part, (iii) populations located in subregions N2 and N6 in the southern part of the sampling area (iv)

<figure><img src="image_5.png" /></figure>

**FIGURE 3** Relationship between the distance of populations to the centre of Strasbourg within a radius of 20 km buffer (Figure S3) and the genetic diversity ($H_e$) (a) and the mean pairwise $F_{ST}$ calculated according to Weir and Cockerham (1984) between each population and other populations within this radius (b). Lines show the linear model. **$p$**<.01.

**FIGURE 4** sPCAs depicting population genetic affiliation in Northern France (a) and Alsace (b). Population colours reflect their coordinates on the first three axes of the sPCA, with coordinates along these three axes assigned to the Red, Green and Blue channels, respectively. Broad groupings of populations can be visualised by dashed labelled ellipses. Strasbourg city and the eurometropole of Strasbourg are represented by the striped areas. Main watercourses are also represented. Watercourses from COPERNICUS Land Monitoring Service, 2019: EU-Hydro.

and finally populations located in subregion N1. The populations in subregion N4 showed a less clear assignment (Figure 4a).

In Alsace, five spatially structured groups could be distinguished by the first three axes of the sPCA (Figure 4b). These groups gather: (i) populations north of Strasbourg, mostly in the A6 subwatershed,

<figure><img src="image_6.png" /></figure>
(ii) populations west of Strasbourg, within subwatersheds A1, A2 and A5, (iii) populations to the north of subwatershed A4, just south of Strasbourg, (iv) populations of the remainder of subwatershed A4, further south from Strasbourg (v) and populations located in subwatershed A3 and to the extreme south of subwatershed A4 (Figure 4b). As the boundaries between the different genetic groups did not show clear breaks, these groups reflected a gradient of genetic dissimilarity (Figure 4b).

## 3.3 | Levels of genetic differentiation and spatial genetic structure

Populations were highly genetically differentiated in northern France, with a mean multilocus $F_{ST}$ of 0.135 (95% confidence interval [0.113, 0.158]; Table S3, Figure S8A). Significant levels of population genetic differentiation were observed within most subregions (Table S3, Figure S8B). Accordingly, 91.9% of the 210 pairwise population $F_{ST}$ significantly differed from 0 after Bonferroni correction, most of the non-significant values of $F_{ST}$ being observed among populations belonging to the same geographical regions (Figure S9).

On the contrary, Alsacian populations were weakly, yet significantly genetically differentiated, with a mean multilocus $F_{ST}$ of 0.022 (95% confidence interval [0.020, 0.024]; Table S3, Figure S8A). Significant population differentiation was also observed within each subwatershed, except for very close populations in subwatershed A3 (Table S3, Figure S8B). Only 20.1% of the 3081 pairwise population $F_{ST}$ significantly differed from 0 after Bonferroni correction (Figure S8). Four populations (Gox1, Rlei45, S17, and S21, mapped on Figure S3) accounted for most of the significant pairwise $F_{ST}$ estimates (Figure S10).

When focussing on spatial patterns of genetic differentiation in Alsace, we observed a weak, yet statistically significant, pattern of isolation-by-distance (IBD), as shown by the positive relationship between pairwise population $F_{ST}/1-F_{ST}$ and log-transformed Euclidean geographical distances (rz = 0.238, *p* < .01, Figure 5a). However, this pattern goes along with an increase in the variance of pairwise $F_{ST}$ beyond a scale of ~8 km, which suggested a predominant effect of drift beyond this scale of observation. Consistently, genetic relatedness among individuals decreased with increasing geographical distances, with significantly positive kinship coefficients occurring up to 11 km (Figure 5c). Interestingly, when distances among populations were calculated along watercourses ($d_{stream}$), this pattern of

<figure><img src="image_7.png" /></figure>

**FIGURE 5** Spatial genetic structure in southern damselfly populations across the whole Alsace area. Relationship between levels of genetic differentiation ($F_{ST}$/(1-$F_{ST}$)) and log-transformed Euclidean distance ($d_{Eucli}$) (a) or watercourse geographical distance ($d_{stream}$) (b). Average pairwise kinship coefficients ($F_{ij}$, Loiselle et al., 1995) between individuals are plotted against increasing classes of geographical Euclidean distances (c) or of waterways distance (d). Standard errors for $F_{ij}$ for each distance class were obtained by jackknifing over loci. Dashed lines indicate the upper and lower 95% confidence intervals of non-significant spatial genetic structure, with white dots indicating significant estimates outside this confidence interval. *Sp* statistics based on the regression with ln distancer and their statistical significance are also indicated.
IBD disappeared ($rz=0.045$, $p=.136$, Figure 5b). However, positive $F_{ij}$ values still occurred for up to 20 km along streams (Figure 5d).

Within subwatersheds, IBD was stronger using Euclidean distances compared with aquatic watercourse ($d_{stream}$) distances in subwatersheds A1, A4 and A5 (respectively $rz=0.374$, 0.414 and 0.472; $p<$.0001, $p<$.05 and $p<$.0001; Figure S11). Within subwatersheds, there was a general short-distance spatial autocorrelation pattern, respectively up to 2.2, 4.6 and <1 km in watersheds A1, A4 and A5 (Figure S12). A significant correlation between pairwise population genetic differentiation estimates and $d_{stream}$ was only found in subwatersheds A1 and A4 (Figure S11D-F). Yet, pairwise individual kinship coefficients decreased with aquatic distances ($d_{stream}$), and were significantly positive for up to <500 m, 4 km and about 700 m, respectively, in subwatersheds A1, A4 and A5 (Figure S12).

## 3.4 | Estimated effective migration surface

The results with 1500 demes represented the best balance between precision and calculation time. The EEMS plot of effective migration rates identified an area of reduced migration rates covering most of

the city of Strasbourg, and extending to the South (Figure 6). This notably encompasses populations located in the north of Strasbourg city (populations named S17, S21, CCR4 and CCR2). Three additional areas showed lower migration rates than expected under an IBD at equilibrium. The first one was located downstream along subwatershed A5. The second was found in the midstream of subwatershed A1. The last area of restricted migration was centred on population $Gox1$, the population most upstream of subwatershed A2 (Figure 6).

# 4 | DISCUSSION

## 4.1 | Patterns of genetic diversity and population genetic differentiation between central and peripheral populations

Consistent with the first prediction of the 'abundant centre' hypothesis (Eckert et al., 2008; Guo, 2012), peripheral populations in northern France showed significantly lower levels of genetic diversity as compared to central Alsacian populations. In addition, the two areas displayed different spatial distributions of genetic

**FIGURE 6** Estimated effective migration rates in southern damselfly in the Alsace region, as visualised by interpolated surface of the posterior mean migration rates *m* (on a log₁₀ scale), where darker shades of blue indicate greater than expected migration (0–2), darker shades of orange indicate lower than expected migration (–2 to 0), and white indicates the null hypothesis of isolation-by-distance (0). The black circles indicate population locations. Main watercourses around Strasbourg City (blue lines) are also represented. The Strasbourg district area and the eurometropole of Strasbourg are represented by the striped areas. The locations and names of specific populations cited in the text ($Gox1$, S17, S21, CCR4, CCR2 were added to the map). Contours of the A1, A2 and A5 sub-catchments are indicated by dotted lines.

<figure><img src="image_8.png" /></figure>
diversity: While indices of intra-population genetic diversity were constantly high over all populations located in Alsace, they widely differed between the surveyed subregions in Northern France. Indeed, at a finer scale in Northern France, populations within the most peripheral, isolated and small habitat patches of the species distribution (N3, N4, N5) showed lower levels of genetic diversity than those from larger and more central patches (N1, N2, N6; Figure 1).

Also consistent with the second prediction of the 'abundant centre' (Eckert et al., 2008; Pironon et al., 2017), the mean genetic differentiation in Alsace was lower than in Northern France. Besides, the sPCA could not reveal any major disjunction across the Alsace area. Both the low levels of population differentiation and low genetic relatedness within Alsacian populations support the idea that there is a substantial amount of gene flow among Alsacian populations (Ellegren & Galtier, 2016). In contrast, we observed substantial genetic differentiation for marginal populations that may be due to larger geographical scale of population separation in Northern France. Yet, similarly high levels of population differentiation were previously observed in other peripheral regions, even at small geographical scales (Lorenzo-Carballa et al., 2015; Watts et al., 2005; Watts, Rouquette, et al., 2004). Indeed, indirect genetic approaches indicated dispersal up to 2km and small-scale population genetic structuring (Watts et al., 2006; Watts, Rouquette, et al., 2004). Overall, in Northern France, the sPCA revealed striking genetic discontinuities between four large spatial groups of populations and suggested restricted gene flow among populations even at small scales, as exemplified by high levels of genetic differentiation observed within isolated sets of populations located in subregions N3, N4, N5.

In Northern France, gene flow may be reduced over short distances, as illustrated by population Bai3 in subregion N3, which showed low levels of genetic diversity and high levels of genetic differentiation with close neighbouring populations (<5km). Our findings thus reiterate the claim of Lorenzo-Carballa et al. (2015), namely that habitat management should improve connectivity with geographically close populations to prevent further loss of genetic diversity. Interestingly, no sign of inbreeding was detected, even in isolated populations or in populations characterised by low levels of genetic diversity, a result consistent with previous reports on southern damselflies (Keller et al., 2012; Watts, Rouquette, et al., 2004).

Altogether, the spatial distribution of genetic diversity and the patterns of genetic differentiation observed in the two regions are thus in agreement with the 'abundant centre' hypothesis (Pironon et al., 2017). This pattern, observed towards the northern limit, may exist in other cardinal directions as well, because peripheral populations of the southern damselfly are declining in their northern, eastern (Grand, 1996) and southernmost limits in North Africa (Ferreira et al., 2015). It may also result from colonisation history (Johansson et al., 2013), and more extensive sampling would certainly provide a more global view of the evolution of genetic structuring in populations of the southern damselfly.

## 4.2 | Spatial genetic structure and dispersal pathways

Although the level of genetic differentiation among southern damselfly populations was weak across the Alsace area, we found an isolation-by-distance (IBD) pattern. This pattern was reflected both by an increase in genetic differentiation among populations and a decrease in pairwise individual kinship with increasing Euclidean geographical distances. This result is consistent with previous studies that observed an IBD pattern in southern damselfly populations both at a fine scale (Lorenzo-Carballa et al., 2015; Watts, Rouquette, et al., 2004; Watts & Thompson, 2012) and at a broader scale of investigation (Watts et al., 2006; Watts & Thompson, 2012).

Population genetic structure also depends on the biological attributes of a species, in particular its dispersal capabilities and habitat requirements (Ellegren & Galtier, 2016; Hutchison & Templeton, 1999; Phillipsen et al., 2015). The relationship between genetic and geographic distances across the Alsace area suggested that gene flow events among southern damselfly populations may occur in a stepping-stone pattern among patches of suitable habitats (Hutchison & Templeton, 1999; Kimura, 1953; Kimura & Weiss, 1964), consistent with the patchy distribution of habitats of this species (Conrad et al., 1999; Purse & Thompson, 2009; Rouquette & Thompson, 2005). Adults can disperse among suitable habitat patches, but since they are poor fliers (Watts, Rouquette, et al., 2004), dispersal is likely to occur mostly at short distances between neighbouring populations.

The IBD patterns were stronger at the smaller scale of subwatersheds compared with the one observed across the whole Alsace region. In addition, variance in pairwise genetic differentiation in Alsace strongly increased beyond geographical distances larger than 8km. These results suggest that at large geographical distances, gene flow among populations are influenced by ecological factors additional to the simple Euclidean distances, suggesting potential barriers to gene flow occurring over large scales over 8km (e.g. forested or elevated areas; Hutchison & Templeton, 1999; Phillipsen et al., 2015).

In this respect, the geographical distances strictly calculated along watercourses only partly explained the levels of genetic differentiation observed among populations. This suggests that dispersal events can occur overland and are not restricted to stream networks. This overland dispersal between different streams is further supported by the sPCA that suggested no clear genetic discontinuity between the different river networks. These results are in line with the results of Keller and Holderegger (2013), where different movement strategies were described in southern damselflies for short- and long-distance dispersal: dispersal along watercourses appeared to be favoured over short distances (<3km), whereas dispersal along straight lines was best supported for long distances (>3km). Altogether, a simple IBD is not likely to explain the totality of the spatial genetic structure we observed in Alsace, pinpointing the possible role of landscape features in shaping dispersal among populations (Storfer et al., 2007).
## 4.3 | Urbanisation effects on spatial genetic structure

The city of Strasbourg likely acts as a barrier to gene flow between southern damselfly populations in Alsace. Indeed, the EEMS analysis clearly showed a reduced migration rate in the vicinity of the city. This result was further supported by increasing levels of population genetic differentiation and intra-population kinship coefficients in the vicinity of the city of Strasbourg. A typical 'urban fragmentation' pattern may thus be observed; urbanised areas appeared to fragment habitats, reducing gene flow and increasing genetic differentiation among populations (Johnson & Munshi-South, 2017; Lorenzo-Carballa et al., 2015; Lourenço et al., 2017; Miles et al., 2019). However, while the city of Strasbourg represented a clearly restricted migration zone, peripheral small towns and villages did not suggest any major barriers to gene flow. Therefore, the impact of urbanisation on patterns of gene flow likely differs depending on the size and level of urbanisation (e.g. Trumbo et al., 2019). The fragmentating impact of the city of Strasbourg can extend to a large distance outside the city centre, because the urban area extends over more than 300km², up to 12km away from the city centre. Further away from the city, restricted migration among populations may be due to other landscape ecology features, such as forest patches or high-elevation areas. As a matter of fact, the significant relationship observed between levels of genetic diversity and genetic differentiation and the geographical distance from the city disappeared beyond a scale of 20km. This finding goes along with a general increase in pairwise F<sub>ST</sub> variance with increasing geographical distance, which suggested additional landscape effects other than urban areas, such as topography and land use (Hutchison & Templeton, 1999; Storfer et al., 2007).

The 'urban fragmentation' model further postulates that fragmentation and isolation of populations by unfavourable urbanised areas could increase the strength of genetic drift, which erodes neutral genetic diversity within populations (Miles et al., 2019). Levels of allelic richness and gene diversity indeed decreased in the direct neighbourhood of Strasbourg city, thus suggesting a negative relationship between urbanisation and genetic diversity, as it was described in several species (Johnson & Munshi-South, 2017; Kimmig et al., 2020; Miles et al., 2019; Munshi-South et al., 2016; Noël et al., 2007). Nonetheless, a reduction in gene flow linked to urbanisation is not systematically associated with a reduction in genetic diversity (Fusco et al., 2021; Trumbo et al., 2019): indeed, population S21 surprisingly showed high levels of genetic diversity despite a substantial genetic differentiation with close populations and its proximity to Strasbourg city. This could be explained by incoming migrant individuals from diverse sources outside our sampling area (Whitlock & McCauley, 1990).

Our findings on city impacts on population genetic features again require caution before being generalised and would need to be repeated in different urban areas in order to confirm that populations evolve in the same way in response to urbanisation (Diamond & Martin, 2021; Miles et al., 2019; Santangelo

et al., 2018). Incorporating landscape components among populations, along with the use of more resolutive genome-wide molecular markers, should be pursued to better understand the impact of urbanisation and landscape features on southern damselfly populations at a fine scale. This may also help to explain the occurrence of zones of restricted migration observed in rural areas. Furthermore, cities can exhibit considerable spatial and temporal heterogeneity at fine scale, which could influence population adaptation (Rivkin et al., 2019). It would therefore be useful to study the adaptive variation that can influence the evolution of species in urban environments (Babik et al., 2023; Diamond & Martin, 2021; Johnson & Munshi-South, 2017; Rivkin et al., 2019). Indeed, in another species of damselfly, *Coenagrion puella*, urbanisation and associated heat islands seem to select for slower development and higher survival (Tüzün et al., 2017). Likewise, in southern damselflies, development time is also affected by water temperature associated with the release of industrial cooling waters (Thelen, 1992).

## 5 | CONCLUSION

Central populations of southern damselflies located in urbanised environments in Alsace exhibited higher levels of genetic diversity and lower levels of genetic differentiation compared with northern peripheral populations, indicating that the species might follow a classical 'abundant centre' model in terms of population genetic structure (Guo, 2012; Pironon et al., 2017). Populations found in isolated suitable habitats located at high latitudes exhibited particularly low levels of genetic diversity, which is alarming with regard to their evolutionary potential and may threaten their long-term persistence. In contrast, central Alsacian populations displayed a metapopulation structure with high intra-population levels of genetic diversity and a high amount of gene flow among populations. These findings contrast with previous studies on southern damselflies that suggested very low dispersal capabilities and high levels of genetic differentiation at fine scales of investigation (Lorenzo-Carballa et al., 2015; Watts, Rouquette, et al., 2004; Watts & Thompson, 2012). The highly urbanised city of Strasbourg likely represents a substantial barrier to gene flow among southern damselfly populations. Although this barrier effect of urban areas on southern damselfly dispersal has already been suggested by previous work conducted at a smaller scale (Lorenzo-Carballa et al., 2015; Watts, Rouquette, et al., 2004), our study is the first to clearly show a progressive decrease in genetic diversity in populations near a major urban area. The impact of urbanisation may also vary according to the type and size of urban areas encountered, as smaller Alsacian villages did not seem to impede gene flow among populations. Finally, Alsacian populations matched a classical pattern of IBD population genetic structure. This suggests that individuals do not disperse exclusively along watercourses, their specific breeding habitat, but are also able to disperse over land. However, Euclidean distance alone did not
fully explain the levels of genetic differentiation and the impact of the different landscape features occurring in the Alsace region remains to be determined. A genomic approach using SNP markers coupled with a landscape analysis would provide a more detailed and comprehensive vision of the evolutionary processes influencing patterns of gene flow and levels of genetic diversity occurring in this area of the southern damselfly geographical distribution.

## ACKNOWLEDGEMENTS

We thank ARCOS/VINCI Autoroutes for financial support and the Région Hauts-de-France, the Ministère de l'Enseignement Supérieur et de la Recherche and the European Fund for Regional Economic Development for their financial support to the CPER ECRIN program. We also thank the members of the CEN and Gregory Motte from DEMNA/SPW, involved in the sampling of southern damselfly legs in the Hauts-de-France and in Belgium. We also wish to express our gratitude to Anaëlle Meunier, Hana Mohoric, Clément Gardin and Louise Laine for help with sampling in Alsace. We are grateful to Xavier Vekemans, Eric Petit, and Frédéric Austerlitz for helpful discussions and suggestions related to this project. We thank Sandrine Belingheri for administrative support and Laurence Debacker for technical support. We wish to express our gratitude to the associate Editor and two anonymous referees for very helpful comments that greatly improved the manuscript.

## FUNDING INFORMATION

This work was funded by ARCOS/VINCI Autoroutes, OGE (Office de Génie Ecologique) and the UMR 8198 – Evo-Eco-Paleo (University of Lille/CNRS).

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

## PEER REVIEW

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/ddi.13902.

## DATA AVAILABILITY STATEMENT

The data set used (southern damselflies microsatellite genotypes and their sampling locations) is available on Dryad here: https://doi.org/10.5061/dryad.2v6wwpzvq.

## ORCID

Agathe Lévêque https://orcid.org/0009-0007-4255-8116

Anne Duputié https://orcid.org/0000-0003-2213-7940

Fabien Duez https://orcid.org/0000-0003-2730-6694

Jean-François Arnaud https://orcid.org/0000-0001-5009-7144

## REFERENCES

Babik, W., Dudek, K., Marszałek, M., Palomar, G., Antunes, B., & Sniegula, S. (2023). The genomic response to urbanization in the damselfly *Ischnura elegans*. *Evolutionary Applications*, *16*, 1805–1818.

Brown, J. H. (1984). On the relationship between abundance and distribution of species. *The American Naturalist*, **124**, 255–279.

Conrad, K. F., Willson, K. H., Harvey, I. F., Thomas, C. J., & Sherratt, T. N. (1999). Dispersal characteristics of seven odonate species in an agricultural landscape. *Ecography*, **22**, 524–531.

Desender, K., Small, E., Gaublomme, E., & Verdyck, P. (2005). Rural-urban gradients and the population genetic structure of woodland ground beetles. *Conservation Genetics*, **6**, 51–62.

DeWoody, J. A., Harder, A. M., Mathur, S., & Willoughby, J. R. (2021). The long-standing significance of genetic diversity in conservation. *Molecular Ecology*, **30**, 4147–4154.

Diamond, S. E., & Martin, R. A. (2021). Evolution in cities. *Annual Review of Ecology, Evolution, and Systematics*, **52**, 519–540.

Dray, S., & Dufour, A.-B. (2007). The ade4 package: Implementing the duality diagram for ecologists. *Journal of Statistical Software*, **22**, 1–20.

Eckert, C. G., Samis, K. E., & Lougheed, S. C. (2008). Genetic variation across species' geographical ranges: The central–marginal hypothesis and beyond. *Molecular Ecology*, **17**, 1170–1188.

El Mousadik, A., & Petit, R. J. (1996). High level of genetic differentiation for allelic richness among populations of the argan tree *Argania spinosa* (L.) Skeels endemic to Morocco. *Theoretical and Applied Genetics*, **92**, 832–839.

Ellegren, H., & Galtier, N. (2016). Determinants of genetic diversity. *Nature Reviews Genetics*, **17**, 422–433.

Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. *Annual Review of Ecology, Evolution, and Systematics*, **34**, 487–515.

Fay, M., & Shaw, P. (2010). Exact and asymptotic weighted logrank tests for interval censored data: The interval R package. *Journal of Statistical Software*, **36**, 1–34.

Ferreira, S., Martínez-Freiría, F., Boudot, J.-P., El Haissoufi, M., Bennas, N., Alves, P. C., Watts, P. C., Thompson, D. J., & Brito, J. C. (2015). Local extinctions and range contraction of the endangered *Coenagrion mercuriale* in North Africa. *International Journal of Odonatology*, **18**, 137–152.

Fierimonte, B., & Vanappelchem, C. (2021). Plan régional d'actions Hauts-de-France en faveur des libellules menacées 2022-2031 – Agir pour la préservation des odonates menacés et de leurs habitats. Conservatoire d'espaces naturels Hauts-de-France et DREAL Hauts-de-France: 80 p.

Fincke, O. M., & Hadrys, H. (2001). Unpredictable offspring survivorship in the damselfly, *Megaloprepus coerulatus*, shapes parental behavior, constrains sexual selection, and challenges traditional fitness estimates. *Evolution*, **55**, 762–772.

Frankham, R., Briscoe, D. A., & Ballou, J. D. (2013). *Introduction to conservation genetics* (2nd ed.). Cambridge University Press.

Fusco, N. A., Pehek, E., & Munshi-South, J. (2021). Urbanization reduces gene flow but not genetic diversity of stream salamander populations in the New York City metropolitan area. *Evolutionary Applications*, **14**, 99–116.

Goudet, J. (2003). FSTAT (ver. 2.9.4), a program to estimate and test population genetics parameters. Department of Ecology & Evolution, Lausanne University.

Goudet, J. (2005). Hierfstat, a package for R to compute and test hierarchical F-statistics. *Molecular Ecology Notes*, **5**, 184–186.

Grand, D. (1996). Coenagrion mercuriale (Charpentier, 1840). In P. van Helsdingen, L. Willemse, & M. Speight (Eds.), *Background information on invertebrates of the Habitats Directive and the Bern Convention*, part II: *Mantodea, Odonata, Orthoptera, and Arachnida* (Vol. 80, pp. 245–253). Council of Europe Publishing.

Guo, Q. (2012). Incorporating latitudinal and central-marginal trends in assessing genetic variation across species ranges. *Molecular Ecology*, **21**, 5396–5403.

Hardy, O. J., & Vekemans, X. (2002). SPAGEDI: A versatile computer program to analyse spatial genetic structure at the individual or population levels. *Molecular Ecology Notes*, **2**, 618–620.
Hedrick, P. W. (2011). *Genetics of populations* (4th ed.). Jones and Bartlett
Publishers, Inc.

Hutchison, D. W., & Templeton, A. R. (1999). Correlation of pairwise genetic and geographic distance measures: Inferring the relative influences of gene flow and drift on the distribution of genetic variability. *Evolution*, *53*, 1898-1914.

Johansson, H., Stoks, R., Nilsson-Örtman, V., Ingvarsson, P. K., & Johansson, F. (2013). Large-scale patterns in genetic variation, gene flow and differentiation in five species of European Coenagrionid damselfly provide mixed support for the central-marginal hypothesis. *Ecography*, *36*, 744-755.

Johnson, M. T. J., & Munshi-South, J. (2017). Evolution of life in urban environments. *Science*, *358*, eaam8327.

Jombart, T. (2008). Adegenet: A R package for the multivariate analysis of genetic markers. *Bioinformatics*, *24*, 1403-1405.

Jombart, T., Devillard, S., Dufour, A.-B., & Pontier, D. (2008). Revealing cryptic spatial patterns in genetic variability by a new multivariate method. *Heredity*, *101*, 92-103.

Keller, D., & Holderegger, R. (2013). Damselflies use different movement strategies for short- and long-distance dispersal. *Insect Conservation and Diversity*, *6*, 590-597.

Keller, D., Van Strien, M. J., & Holderegger, R. (2012). Do landscape barriers affect functional connectivity of populations of an endangered damselfly? *Freshwater Biology*, *57*, 1373-1384.

Kimmig, S. E., Beninde, J., Brandt, M., Schleimer, A., Kramer-Schadt, S., Hofer, H., Börner, K., Schulze, C., Wittstatt, U., Heddergott, M., Halczok, T., Staubach, C., & Frantz, A. C. (2020). Beyond the landscape: Resistance modelling infers physical and behavioural gene flow barriers to a mobile carnivore across a metropolitan area. *Molecular Ecology*, *29*, 466-484.

Kimura, M. (1953). Stepping stone' model of population. *Annual Report of the National Institute of Genetics Japan*, 3, 62-63.

Kimura, M., & Weiss, G. H. (1964). The stepping stone model of population structure and the decrease. *Genetics*, *49*, 561-576.

Kraemer, P., & Gerlach, G. (2017). Demerelate: Calculating interindividual relatedness for kinship analysis based on codominant diploid genetic markers using R. *Molecular Ecology Resources*, *17*, 1371-1377.

Loiselle, B. A., Sork, V. L., Nason, J., & Graham, C. (1995). Spatial genetic structure of a tropical understory shrub, *Psychotria officinalis* (Rubiaceae). *American Journal of Botany*, *82*, 1420-1425.

Lorenzo-Carballa, M. O., Ferreira, S., Sims, A. M., Thompson, D. J., Watts, P. C., Cher, Y., Damoy, V., Evrard, A., Gelez, W., & Vanappelghem, C. (2015). Impact of landscape on spatial genetic structure and diversity of *Coenagrion mercuriale* (Zygoptera: Coenagrionidae) in northern France. *Freshwater Science*, *34*, 1065-1078.

Lourenço, A., Álvarez, D., Wang, I. J., & Velo-Antón, G. (2017). Trapped within the city: Integrating demography, time since isolation and population-specific traits to assess the genetic effects of urbanization. *Molecular Ecology*, *26*, 1498-1514.

Menozzi, P., Piazza, A., & Cavalli-Sforza, L. (1978). Synthetic maps of human gene frequencies in Europeans. *Science*, *201*, 786-792.

Miles, L. S., Rivkin, L. R., Johnson, M. T. J., Munshi-South, J., & Verrelli, B. C. (2019). Gene flow and genetic drift in urban environments. *Molecular Ecology*, *28*, 4138-4151.

Munshi-South, J., Zolnik, C. P., & Harris, S. E. (2016). Population genomics of the Anthropocene: Urbanization is negatively associated with genome-wide variation in white-footed mouse populations. *Evolutionary Applications*, *9*, 546-564.

Nei, M. (1987). *Molecular evolutionary genetics*. Columbia University Press.

Noël, S., Ouellet, M., Galois, P., & Lapointe, F.-J. (2007). Impact of urban fragmentation on the genetic structure of the eastern red-backed salamander. *Conservation Genetics*, *8*, 599-606.

Paradis, E. (2010). Pegas: An R package for population genetics with an integrated-modular approach. *Bioinformatics*, *26*, 419-420.

Petkova, D. (2022). *reemsplots2: Generate plots to inspect and visualize the results of EEMS*. R package version 0.1.0.

Petkova, D., Novembre, J., & Stephens, M. (2016). Visualizing spatial population structure with estimated effective migration surfaces. *Nature Genetics*, *48*, 94-100.

Phillipsen, I. C., Kirk, E. H., Bogan, M. T., Mims, M. C., Olden, J. D., & Lytle, D. A. (2015). Dispersal ability and habitat requirements determine landscape-level genetic patterns in desert aquatic insects. *Molecular Ecology*, *24*, 54-69.

Pironon, S., Papuga, G., Villellas, J., Angert, A. L., García, M. B., & Thompson, J. D. (2017). Geographic variation in genetic and demographic performance: New insights from an old biogeographical paradigm. *Biological Reviews*, *92*, 1877-1909.

Purse, B. V., Hopkins, G. W., Day, K. J., & Thompson, D. J. (2003). Dispersal characteristics and management of a rare amselfly. *Journal of Applied Ecology*, *40*, 716-728.

Purse, B. V., & Thompson, D. J. (2009). Oviposition site selection by *Coenagrion mercuriale* (Odonata: Coenagrionidae). *International Journal of Odonatology*, *12*, 257-273.

R Core Team. (2022). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing.

Richardson, J. L., Michaelides, S., Combs, M., Djan, M., Bisch, L., Barrett, K., Silveira, G., Butler, J., Aye, T. T., Munshi-South, J., DiMatteo, M., Brown, C., & McGreevy, T. J. (2021). Dispersal ability predicts spatial genetic structure in native mammals persisting across an urbanization gradient. *Evolutionary Applications*, *14*, 163-177.

Rivkin, L. R., Santangelo, J. S., Alberti, M., Aronson, M. F. J., De Keyzer, C. W., Diamond, S. E., Fortin, M., Frazee, L. J., Gorton, A. J., Hendry, A. P., Liu, Y., Losos, J. B., Maclvor, J. S., Martin, R. A., McDonnell, M. J., Miles, L. S., Munshi-South, J., Ness, R. W., Newman, A. E. M., ... Johnson, M. T. J. (2019). A roadmap for urban evolutionary ecology. *Evolutionary Applications*, *12*, 384-398.

Rouquette, J. R., & Thompson, D. J. (2005). Habitat associations of the endangered damselfly, *Coenagrion mercuriale*, in a water meadow ditch system in southern England. *Biological Conservation*, *123*, 225-235.

Rouquette, J. R., & Thompson, D. J. (2007). Patterns of movement and dispersal in an endangered damselfly and the consequences for its management: Movement and dispersal in a damselfly. *Journal of Applied Ecology*, *44*, 692-701.

Santangelo, J. S., Rivkin, L. R., & Johnson, M. T. J. (2018). The evolution of city life. *Proceedings of the Royal Society B: Biological Sciences*, *285*, 20181529.

Smouse, P., Long, J., & Sokal, R. (1986). Multiple regression and correlation extensions of the mantel test of matrix correspondence. *Systematic Zoology*, *35*, 627-632.

Storfer, A., Murphy, M. A., Evans, J. S., Goldberg, C. S., Robinson, S., Spear, S. F., Dezzani, R., Delmelle, E., Vierling, L., & Waits, L. P. (2007). Putting the 'landscape' in landscape genetics. *Heredity*, *98*, 128-142.

Thelen, C. (1992). Untersuchung zum Larvenhabitat und zum Entwicklungszyklus der Helmazurjungfer (Coenagrion mercuriale, Zygoptera: Odonata) an zwei verchießen den Gewassern der Freiburger Bucht. Limnologisches Institut Konstanz: Universitat Freiburg.

Trumbo, D. R., Salerno, P. E., Logan, K. A., Alldredge, M. W., Gagne, R. B., Kozakiewicz, C. P., Kraberger, S., Fountain-Jones, N. M., Craft, M. E., Carver, S., Ernest, H. B., Crooks, K. R., VandeWoude, S., & Funk, W. C. (2019). Urbanization impacts apex predator gene flow but not genetic diversity across an urban-rural divide. *Molecular Ecology*, *28*, 4926-4940.

Tüzün, N., Op De Beeck, L., Brans, K. I., Janssens, L., & Stoks, R. (2017). Microgeographic differentiation in thermal performance curves between rural and urban populations of an aquatic insect. *Evolutionary Applications*, *10*, 1067-1075.

Tyers, M. (2022). *Riverdist: River networkd distance computation and applications*. R package version 0.15.5.
Vandergast, A. G., Bohonak, A. J., Weissman, D. B., & Fisher, R. N. (2006). Understanding the genetic effects of recent habitat fragmentation in the context of evolutionary history: Phylogeography and landscape genetics of a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus). *Molecular Ecology*, **16**, 977-992.

Vekemans, X., & Hardy, O. J. (2004). New insights from fine-scale spatial genetic structure analyses in plant populations. *Molecular Ecology*, **13**, 921-935.

Vucetich, J. A., & Waite, T. A. (2003). Spatial patterns of demography and genetic processes across the species' range: Null hypotheses for landscape conservation genetics. *Conservation Genetics*, **4**, 639-645.

Watts, P. C., Kemp, S. J., Saccheri, I. J., & Thompson, D. J. (2005). Conservation implications of genetic variation between spatially and temporally distinct colonies of the endangered damselfly *Coenagrion mercuriale*. *Ecological Entomology*, **30**, 541-547.

Watts, P. C., Rouquette, J. R., Saccheri, I. J., Kemp, S. J., & Thompson, D. J. (2004). Molecular and ecological evidence for small-scale isolation by distance in an endangered damselfly, *Coenagrion mercuriale*. *Molecular Ecology*, **13**, 2931-2945.

Watts, P. C., Saccheri, I. J., Kemp, S. J., & Thompson, D. J. (2006). Population structure and the impact of regional and local habitat isolation upon levels of genetic diversity of the endangered damselfly *Coenagrion mercuriale* (Odonata: Zygoptera). *Freshwater Biology*, **51**, 193-205.

Watts, P. C., & Thompson, D. J. (2012). Developmental plasticity as a cohesive evolutionary process between sympatric alternate-year insect cohorts. *Heredity*, **108**, 236-241.

Watts, P. C., Thompson, D. J., & Kemp, S. J. (2004). Cross-species amplification of microsatellite loci in some European zygopteran species (Odonata: Coenagrionidae). *International Journal of Odonatology*, **7**, 87-96.

Watts, P. C., Wu, J. H., Westgarth, C., Thompson, D. J., & Kemp, S. J. (2004). A panel of microsatellite loci for the southern damselfly, *Coenagrion mercuriale* (Odonata: Coenagrionidae). *Conservation Genetics*, **5**, 117-119.

Weir, B. S., & Cockerham, C. C. (1984). Estimating *F*-statistics for the analysis of population structure. *Evolution*, **38**, 1358-1370.

Whitlock, M. C., & McCauley, D. E. (1990). Some population genetic consequences of colony formation and extinction: Genetic correlations within founding groups. *Evolution*, **44**, 1717-1724.

Willi, Y., Kristensen, T. N., Sgrò, C. M., Weeks, A. R., Ørsted, M., & Hoffmann, A. (2022). Conservation genetics as a management tool: The five best-supported paradigms to assist the management of threatened species. *Proceedings of the National Academy of Sciences of the United States of America*, **119**, e2105076119.

Wilson, M. C., Chen, X.-Y., Corlett, R. T., Didham, R. K., Ding, P., Holt, R. D., Holyoak, M., Hu, G., Hughes, A. C., Jiang, L., Laurance, W. F., Liu,

J., Pimm, S. L., Robinson, S. K., Russo, S. E., Si, X., Wilcove, D. S., Wu, J., & Yu, M. (2016). Habitat fragmentation and biodiversity conservation: Key findings and future challenges. *Landscape Ecology*, **31**, 219-227.

## BIOSKETCH

This work is a part of the PhD project of Agathe Lévêque which focuses on population genetics in the southern damselfly (*Coenagrion mercuriale*). She is interested in studying the spatial genetic structure and patterns of gene flow in the context of the anthropogenic impacts of urban areas and newbuild bypass highways. Her research approach is rooted in the field of conservation biology. All authors have a diverse range of expertise in population genetics, naturalist expertise and conservation biology.

Author contributions: A.D, V.V and J-F.A conceived and designed the study; A.L, A.D, V.V, J-F.A and C.V organised the collection of specimens; A.L, A.D, F.D, C.V and J-F.A collected the samples in the field; A.L, F.D and C.G performed the molecular laboratory works; A.L. analysed the data; A.L, A.D and J-F.A wrote the first draft of the manuscript. All authors contributed to the final version of the manuscript and approved the submitted version.

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Lévêque, A., Duputié, A., Vignon, V., Duez, F., Godé, C., Vanappelghem, C., & Arnaud, J.-F. (2024). Contrasting patterns of spatial genetic structure in endangered southern damselfly (*Coenagrion mercuriale*) populations facing habitat fragmentation and urbanisation. *Diversity and Distributions*, **30**, e13902. https://doi.org/10.1111/ddi.13902