Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T22:10:21.090Z Has data issue: false hasContentIssue false

New microsatellite loci to quantify genetic diversity of the photosymbiodeme-forming lichen Sticta canariensis

Published online by Cambridge University Press:  25 January 2024

Andrea Goss*
Affiliation:
Systematics and Ecology of Fungi and Algae, LMU Munich, 80638 Munich, Germany
Silke Werth
Affiliation:
Systematics and Ecology of Fungi and Algae, LMU Munich, 80638 Munich, Germany
*
Corresponding author: Andrea Goss; Email: [email protected]

Abstract

Sticta canariensis is a lichen which is rare in all parts of its range in Atlantic Europe and Macaronesia, where it occurs in laurisilva forests, a habitat highly threatened by global change. Thus, this species is of high priority for inclusion in conservation programmes where genetic diversity should be considered. We have established new microsatellite loci and generated a dataset that demonstrates the genetic diversity of the lichen-forming fungus S. canariensis from eight locations across its disjunct range, in Macaronesia, Norway and England. We genotyped 25 microsatellite loci for 65 specimens and detected five genetic clusters which resemble major geographical divisions, specifically among the Macaronesian archipelagos. The total number of observed alleles ranged from 2 to 22. These are the first microsatellite markers developed for S. canariensis and they will be useful for population genetic studies and for conservation assessments.

Type
Standard Paper
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (https://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of the British Lichen Society

Introduction

Due to their high polymorphism and Mendelian inheritance, microsatellites are a useful marker type for population genetic studies (Schlötterer Reference Schlötterer2000; Abdelkrim et al. Reference Abdelkrim, Robertson, Stanton and Gemmell2009) and are an inexpensive alternative to whole genome sequencing. Microsatellite markers are also a widely used tool for fungal genetic studies, specifically for plant pathogens, mycorrhizal fungi and lichens (Tenzer et al. Reference Tenzer1999; Werth Reference Werth2010; Minasiewicz et al. Reference Minasiewicz, Krawczyk, Znaniecka, Vincenot, Zheleznaya, Korybut-Orlowska, Kull and Selosse2022; Velasco-Anacona et al. Reference Velasco-Anacona, Sterling and Reyes-Bermúdez2022). In lichens they are highly species-specific and have proved useful for assessing genetic variation within species at different spatial scales (Walser et al. Reference Walser, Sperisen, Soliva and Scheidegger2003; Werth Reference Werth2010). For example, microsatellites have been used in studies of the lichen Lobaria pulmonaria (L.) Hoffm. at all levels: across 27 European countries (Widmer et al. Reference Widmer, Dal Grande, Excoffier, Holderegger, Keller, Mikryukov and Scheidegger2012), at the regional level within Europe (Scheidegger et al. Reference Scheidegger, Bilovitz, Werth, Widmer and Mayrhofer2012), between continents and from a single tree (Walser et al. Reference Walser, Sperisen, Soliva and Scheidegger2003), to name a few. There are a limited number of studies investigating the relatedness of Macaronesian lichen populations. For example, Alors et al. (Reference Alors, Dal Grande, Cubas, Crespo, Schmitt, Molina and Divakar2017) found populations of Parmelina carporrhizans (Taylor) Poelt & Vezda to be highly diverse but lacking spatial structure. Conversely, Werth et al. (Reference Werth, Meidl and Scheidegger2021) found substantial divergence between geographical regions in populations of three Lobaria species. Data on additional species are needed to better understand the genetic relationships between lichen populations on the Macaronesian islands and elsewhere in Europe.

Sticta (Schreb.) Ach. is a monophyletic fungal genus belonging to Lobarioideae (Peltigeraceae, Peltigerales, Lecanoromycetes, Ascomycota) (Miadlikowska & Lutzoni Reference Miadlikowska and Lutzoni2004; Miadlikowska et al. Reference Miadlikowska, Kauff, Högnabba, Oliver, Molnár, Fraker, Gaya, Hafellner, Hofstetter and Gueidan2014; Lumbsch & Leavitt Reference Lumbsch and Leavitt2019). Sticta species have colonized every continent except Antarctica, as well as islands throughout the world, indicating that species of this genus are capable of long-distance dispersal (LDD), a potential driver of speciation (Widhelm et al. Reference Widhelm, Bertoletti, Asztalos, Mercado-Diaz, Huang, Moncada, Lücking, Magain, Sérusiaux and Goffinet2018).

Here, we study the charismatic lichen-forming fungus Sticta canariensis (Bory) Delise, a foliose macrolichen with the unusual ability to form photosymbiodemes, that is to form symbioses with photosynthetic partners belonging to two different kingdoms (Magain & Sérusiaux Reference Magain and Sérusiaux2015). In the case of Sticta canariensis (Fig. 1), the partners are unicellular green algae (‘Symbiochloris sp. 3′, Trebouxiales) (Dal Grande et al. Reference Grande F, Beck, Cornejo, Singh, Cheenacharoen, Nelsen and Scheidegger2014; Škaloud et al. Reference Škaloud, Friedl, Hallmann, Beck and Dal2016) and cyanobacteria (Nostoc) (Magain & Sérusiaux Reference Magain and Sérusiaux2015). Association with these different partners results in dramatically different morphologies and reproductive strategies in S. canariensis, as similarly reported for other photosymbiodeme-forming lichens (Armaleo & Clerc Reference Armaleo and Clerc1991).

Figure 1. Photosymbiodemes of the lichen-forming fungus Sticta canariensis, showing dramatically different morphologies. A, chloromorph with small brown apothecia. B, cyanomorph with phyllidia (asexual propagules). Scales = 1 cm. In colour online.

The bright green chloromorph has elongate and dichotomously branched lobes, 5–15 mm in diameter, forming thalli 5–15 cm in diameter. Apothecia are brown, 1–2 mm, commonly forming both laminally and marginally. The chloromorph grows on deciduous trees (e.g. Laurus, Fraxinus, Quercus) (A. Goss and S. Werth, personal observations).

The cyanomorph is dark blue-grey, often with white mottling. Lobes, 1–2 cm in diameter, are overlapping, irregularly incised with phyllidiate margins. Phyllidia, 0.5–0.8 mm, are flattened, ascending, dichotomously and several times branched, forming dense clusters at thallus margins, becoming laminal in older individuals. Thalli, up to 10 cm diameter, often form extensive colonies. The cyanomorph also grows on deciduous trees, but additionally on the soil and rocks of cliff sides and boulders in close proximity to water sources, and in cool and shady forests (A. Goss and S. Werth, personal observations) (Moncada & Lücking Reference Moncada and Lücking2012). Composite thalli consist of cyanomorph and chloromorphs occurring together, as small bright green leaflets arising from the dark cyanomorph thalli.

Sticta canariensis is rare and threatened in all parts of its disjunct range in temperate and subtropical habitats of Macaronesia and Atlantic Europe, and is thus of great conservation concern (Moncada & Lücking Reference Moncada and Lücking2012). The cyanomorph is by far the most common morph, while the chloromorph distribution is extremely limited. The cyanomorph and composite thalli are considered of least concern (LC), but the independent chloromorph has been listed as vulnerable (VU) in England and endangered in Wales (Woods & Coppins Reference Woods and Coppins2012).

In Macaronesian sites, S. canariensis grows in laurisilva forests, an ecosystem highly threatened by global change (Fernández-Palacios et al. Reference Fernández-Palacios, de Nascimento, Otto, Delgado, García-del-Rey, Arévalo and Whittaker2011; Perry Reference Perry2011; Ritter et al. Reference Ritter, Regalado and Guerra2019; Castilla-Beltrán et al. Reference Castilla-Beltrán, de Nascimento, Fernández-Palacios, Whittaker, Willis, Edwards and Nogué2021). Our study area (Fig. 2) includes populations from across the species range: south-western England, the south-west coast of Norway, and the Macaronesian archipelagos of Azores, Madeira, and Canary Islands. In this work, our aims were twofold: 1) to develop a novel set of microsatellite loci for S. canariensis; 2) to test their polymorphism in natural populations and determine their usefulness for further studies.

Figure 2. Tree model for the lichen-forming fungus Sticta canariensis. Midpoint-rooted neighbour-joining population tree from PHYLIP based on chord distance between populations, calculated with 1000 bootstrap replicates. A = Azores, E = England, N = Norway, M = Madeira, C = Canary Islands. In colour online.

Materials and Methods

Development of microsatellite markers

At the onset of this study, microsatellites were available for several species of Lobaria. However, Walser et al. (Reference Walser, Sperisen, Soliva and Scheidegger2003) showed that markers developed for one lichen species often do not work for other species within the same genus. New microsatellite markers were therefore developed for the Sticta canariensis mycobiont (fungal partner). Markers were derived from genomic data of Sticta canariensis, the generation of which is described in Resl et al. (Reference Resl, Bujold, Tagirdzhanova, Meidl, Freire Rallo, Kono, Fernández-Brime, Guðmundsson, Andrésson and Muggia2022). We used MSATCOMMANDER v. 1.0.8 (Faircloth Reference Faircloth2008) to locate a total of 147 microsatellite markers with flanking primer sequences (Supplementary Material Table S1, available online). Of these, we chose 27 with a motif size of 2–3 bp and copy number ≥ 8 (Vieira et al. Reference Vieira, Santini, Diniz and de Freitas2016). Each genomic contig sequence that returned a candidate microsatellite was first verified as fungal using BLAST (www. ncbi.nlm.nih.gov/blast), optimized for highly similar sequences (megaBLAST) and excluding uncultured environmental sequences. Primers were then designed using Primer-BLAST (www.ncbi.nlm.nih.gov/tools/primer-blast/), for a melting temperature (Tm) of 54–56 °C. The primers were checked using the OligoAnalyzer Tool (Integrated DNA Technologies; eu.idtdna.com/pages/tools/oligoanalyzer) for hairpin structure that had Tm > 10 °C lower than the primer Tm, and ≤ 4 complementary bases in self- and hetero-dimers; primers above these thresholds were excluded (Table 1).

Table 1. Primers for 25 microsatellite markers developed for the lichen-forming fungus Sticta canariensis.

* Ta refers to the annealing temperature of a given primer pair.

** Repeat motif is reported as: (motif)x with x as the number of repeats for that motif.

DNA extraction and genotyping of microsatellites

This study included 65 samples, 32 Macaronesian (eight from the Azores, 20 from Madeira and four from the Canary Islands), 11 from England and 22 from Spissøy Island, Norway. The number of chloromorphs (ch) and cyanomorphs (cy) used from each location is reported in Supplementary Material Table S2 (available online). Thallus fragments c. 1 × 1 cm were manually cleaned of foreign debris, lyophilized overnight, and pulverized with a TissueLyser II mill (Retsch) at a frequency of 30.0 s−1 for 3 min. DNA extraction was performed with the NucleoSpin Plant II Mini Kit (Macherey-Nagel) according to the manufacturer's protocol.

PCRs were conducted on all samples with 25 mycobiont-specific primers (consisting of 14 di- and 11 trinucleotide sequences) (Table 1). The protocol was adapted from the M13 PCR protocol (Schuelke Reference Schuelke2000) utilizing 6-FAM, VIC, PET and NED fluorescent dyes bound to M13(-21) primers (Blacket et al. Reference Blacket, Robin, Good, Lee and Miller2012). PCRs were conducted in 25 μl final reaction volumes using the Type-IT Microsatellite PCR Kit (Qiagen) with 10 ng genomic DNA, 5 μl primer mixture (unique mixture for each locus containing: 0.2 μM fluorescence-labelled M13(-21) primer, 0.05 μM unlabelled forward primer including an M13(-21) tail at the 5ʹ-end, 0.2 μM reverse primer, in 1× TE buffer), 12.5 μl Type-IT Master Mix, and ultrapure H2O to bring to volume. All PCRs were performed under the following conditions: 95 °C for 5 min; followed by 30 cycles of 95 °C for 30 s, 55 °C for 45 s, and 72 °C for 45 s; then 8 cycles of 95 °C for 30 s, 53 °C for 45 s, 72 °C for 45 s; with a final extension at 72 °C for 30 min. Fragment sizes were determined with a 3730 DNA Analyzer (Applied Biosystems). Microsatellite genotyping was performed with the software Geneious v. 7.1 (Kearse et al. Reference Kearse, Moir, Wilson, Stones-Havas, Cheung, Sturrock, Buxton, Cooper, Markowitz and Duran2012).

Statistical analyses

We used GenAlEx v. 6.5 (Peakall & Smouse Reference Peakall and Smouse2006, Reference Peakall and Smouse2012) to calculate allele frequencies and genetic diversity indices, including the total number of alleles per locus (AL), the number of unique alleles per population for each locus (AU), the effective number of alleles (Ne), and Nei's unbiased gene diversity (HE) (Nei Reference Nei1978). Population pairwise fixation indices (FST) were calculated in R Statistical Software (v. 4.2.2; R Core Team 2022 using the hierfstat package v. 0.5.11 (Goudet & Jombart Reference Goudet and Jombart2022).

The ‘seqboot’ module of PHYLIP v. 3.698 (Felsenstein Reference Felsenstein2004) was used to generate 1000 bootstrapped microsatellite allele frequency datasets. The genetic relationships between the collection sites were evaluated using chord distance (DC) (Cavalli-Sforza & Edwards Reference Cavalli-Sforza and Edwards1967), as implemented in the ‘gendist’ module which is an appropriate distance for microsatellites. Neighbour-joining trees were constructed for each replicate with the ‘neighbour’ module. A majority-rule consensus tree was generated using the ‘consense’ module of PHYLIP. The consensus tree was midpoint rooted with FigTree v. 1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/).

Results

We analyzed 25 loci from 65 individuals of Sticta canariensis. All 25 mycobiont-specific primer pairs reliably amplified fragments, with less than 8% missing amplicons per locus, and 3% or less missing at each location (Supplementary Material Table S2, available online). We found a total of 175 alleles, with the total number of alleles per locus ranging from 2 (Sca1125) to 22 (Sca503) (Table 2). The highest percentage of polymorphic loci was found in the Azores (96%). Madeira had the highest number of total alleles (102), the highest number of unique genotypes (18 out of 20 individuals), and the highest number of private alleles (58). England had the lowest total allele count (29), and the fewest private alleles (1). The majority of private alleles came from only four loci (Sca1321: 16, Sca503: 14, Sca471: 15 and Sca1857: 10). Nei's unbiased gene diversity (HE) ranged from 0.000 to 0.189 in locus Scan308, and from 0.000 to 1.000 in Scan1321. (Table 2). FST values were consistently high, ranging from 0.216 to 0.743 (Supplementary Material Table S3, available online).

Table 2. Microsatellite diversity of the mycobiont Sticta canariensis, based on the analysis of 25 loci from 65 individuals.

Ns = number of successful amplifications; AT = total number of alleles per locus across all locations; AL = number of alleles per locus; AP = total number of private alleles per locus; HE = unbiased diversity by population; n = number of samples per location.

A midpoint-rooted population tree produced with PHYLIP supports genetic patterns that resemble geographical distributions (Fig. 2). A southern group includes populations from the Canary Islands and Madeira, with populations from the Azores comprising a sister group. A strongly divergent northern group comprises the closely related populations from England and Norway.

Discussion

This study is the first to report and test microsatellite markers for the lichen-forming fungus Sticta canariensis. The set of 25 microsatellite loci that we developed had varying levels of polymorphism, with four highly polymorphic loci and several with moderate polymorphism. Even though individually some of these microsatellite loci exhibited low variability, when used concurrently they allowed us to reliably characterize the genetic relationships between individuals and populations of S. canariensis across this species’ range. The only exception would be Sca1125 which had the lowest polymorphism (2), making it the least useful for analysis.

Madeira had the highest number of unique genotypes, private alleles and total alleles. This could partly be an effect of the large sample size and more diverse sampling locations, although most individuals were unique at several loci, while other locations showed less polymorphism. Additionally, we found the island populations were genetically distinct from populations in England and Norway, suggesting limited migration between these distant locations. Similarly, in their study on Macaronesian Lobaria, Werth et al. (Reference Werth, Meidl and Scheidegger2021) found high divergence between populations, high polymorphism and private alleles in populations from the Azores and Madeira, and a genetic distinction between island and mainland populations.

These novel microsatellite markers will provide a valuable tool for population genetic studies and for conservation assessments of Sticta canariensis. This is the first study to investigate the population genetic structure of S. canariensis and it provides an important starting point for understanding this rare species, which we will develop through more extensive sampling in Macaronesia and elsewhere.

Acknowledgements

We thank Christoph Scheidegger from the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) for providing lichen samples from Tenerife, Madeira and Azores that were originally collected by S. Werth together with C. Cornejo, S. Angelone and C. Scheidegger. Additionally, we thank Asbjørn Knutsen and Nicola Bacciu for providing lichen samples from Norway and England (respectively). The Sequencing Service Unit of LMU's Faculty of Biology (Genetics) ran fragment size analysis on our samples.

Author ORCIDs

Andrea Goss, 0000-0002-5699-3609; Silke Werth, 0000-0002-4981-7850.

Competing Interests

The authors declare none.

Supplementary Material

The Supplementary Material for this article can be found at https://doi.org/10.1017/S0024282923000622.

References

Abdelkrim, J, Robertson, BC, Stanton, J-AL and Gemmell, NJ (2009) Fast, cost-effective development of species-specific microsatellite markers by genomic sequencing. BioTechniques 46, 185192.CrossRefGoogle ScholarPubMed
Alors, D, Dal Grande, F, Cubas, P, Crespo, A, Schmitt, I, Molina, MC and Divakar, PK (2017) Panmixia and dispersal from the Mediterranean Basin to Macaronesian Islands of a macrolichen species. Scientific Reports 7, 19.CrossRefGoogle ScholarPubMed
Armaleo, D and Clerc, P (1991) Lichen chimeras: DNA analysis suggests that one fungus forms two morphotypes. Experimental Mycology 15, 110.CrossRefGoogle Scholar
Blacket, M, Robin, C, Good, R, Lee, S and Miller, A (2012) Universal primers for fluorescent labelling of PCR fragments – an efficient and cost-effective approach to genotyping by fluorescence. Molecular Ecology Resources 12, 456463.CrossRefGoogle ScholarPubMed
Castilla-Beltrán, A, de Nascimento, L, Fernández-Palacios, J-M, Whittaker, RJ, Willis, KJ, Edwards, M and Nogué, S (2021) Anthropogenic transitions from forested to human-dominated landscapes in southern Macaronesia. Proceedings of the National Academy of Sciences of the United States of America 118, e2022215118.CrossRefGoogle ScholarPubMed
Cavalli-Sforza, LL and Edwards, AW (1967) Phylogenetic analysis. Models and estimation procedures. American Journal of Human Genetics 19, 233257.Google ScholarPubMed
Grande F, Dal, Beck, A, Cornejo, C, Singh, G, Cheenacharoen, S, Nelsen, MP and Scheidegger, C (2014) Molecular phylogeny and symbiotic selectivity of the green algal genus Dictyochloropsis s.l. (Trebouxiophyceae): a polyphyletic and widespread group forming photobiont-mediated guilds in the lichen family Lobariaceae. New Phytologist 202, 455470.Google Scholar
Faircloth, BC (2008) MSATCOMMANDER: detection of microsatellite repeat arrays and automated, locus-specific primer design. Molecular Ecology Resources 8, 9294.CrossRefGoogle ScholarPubMed
Felsenstein, J (2004) PHYLIP (Phylogeny Inference Package) version 3.6. Department of Genome Sciences, University of Washington, Seattle: distributed by the author.Google Scholar
Fernández-Palacios, JM, de Nascimento, L, Otto, R, Delgado, JD, García-del-Rey, E, Arévalo, JR and Whittaker, RJ (2011) A reconstruction of Palaeo-Macaronesia, with particular reference to the long-term biogeography of the Atlantic island laurel forests. Journal of Biogeography 38, 226246.CrossRefGoogle Scholar
Goudet, J and Jombart, T (2022) Hierfstat: Estimation and Tests of Hierarchical F-Statistics. R package version 0.5-11. [WWW resource] URL https://CRAN.R-project.org/package=hierfstat.Google Scholar
Kearse, M, Moir, R, Wilson, A, Stones-Havas, S, Cheung, M, Sturrock, S, Buxton, S, Cooper, A, Markowitz, S, Duran, C, et al. (2012) Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 16471649.CrossRefGoogle ScholarPubMed
Lumbsch, HT and Leavitt, SD (2019) Introduction of subfamily names for four clades in Cladoniaceae and Peltigeraceae (Lecanoromycetes). Mycotaxon 134, 271273.CrossRefGoogle Scholar
Magain, N and Sérusiaux, E (2015) Dismantling the treasured flagship lichen Sticta fuliginosa (Peltigerales) into four species in Western Europe. Mycological Progress 14, 133.CrossRefGoogle Scholar
Miadlikowska, J and Lutzoni, F (2004) Phylogenetic classification of peltigeralean fungi (Peltigerales, Ascomycota) based on ribosomal RNA small and large subunits. American Journal of Botany 91, 449464.CrossRefGoogle ScholarPubMed
Miadlikowska, J, Kauff, F, Högnabba, F, Oliver, JC, Molnár, K, Fraker, E, Gaya, E, Hafellner, J, Hofstetter, V, Gueidan, C, et al. (2014) A multigene phylogenetic synthesis for the class Lecanoromycetes (Ascomycota): 1307 fungi representing 1139 infrageneric taxa, 317 genera and 66 families. Molecular Phylogenetics and Evolution 79, 132168.CrossRefGoogle ScholarPubMed
Minasiewicz, J, Krawczyk, E, Znaniecka, J, Vincenot, L, Zheleznaya, E, Korybut-Orlowska, J, Kull, T and Selosse, M-A (2022) Weak population spatial genetic structure and low infraspecific specificity for fungal partners in the rare mycoheterotrophic orchid Epipogium aphyllum. Journal of Plant Research 135, 275293.CrossRefGoogle ScholarPubMed
Moncada, B and Lücking, R (2012) Ten new species of Sticta and counting: Colombia as a hot spot for unrecognized diversification in a conspicuous macrolichen genus. Phytotaxa 74, 129.CrossRefGoogle Scholar
Nei, M (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583590.CrossRefGoogle ScholarPubMed
Peakall, R and Smouse, PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6, 288295.Google Scholar
Peakall, R and Smouse, PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research – an update. Bioinformatics 28, 25372539.Google ScholarPubMed
Perry, J (2011) World Heritage hot spots: a global model identifies the 16 natural heritage properties on the World Heritage List most at risk from climate change. International Journal of Heritage Studies 17, 426441.CrossRefGoogle Scholar
R Core Team (2022) R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. [WWW resource] URL https://www.R-project.org/.Google Scholar
Resl, P, Bujold, AR, Tagirdzhanova, G, Meidl, P, Freire Rallo, S, Kono, M, Fernández-Brime, S, Guðmundsson, H, Andrésson, ÓS, Muggia, L, et al. (2022) Large differences in carbohydrate degradation and transport potential among lichen fungal symbionts. Nature Communications 13, 113.CrossRefGoogle ScholarPubMed
Ritter, A, Regalado, CM and Guerra, JC (2019) The impact of climate change on water fluxes in a Macaronesian cloud forest. Hydrological Processes 33, 28282846.CrossRefGoogle Scholar
Scheidegger, C, Bilovitz, PO, Werth, S, Widmer, I and Mayrhofer, H (2012) Hitchhiking with forests: population genetics of the epiphytic lichen Lobaria pulmonaria in primeval and managed forests in southeastern Europe. Ecology and Evolution 2, 22232240.CrossRefGoogle ScholarPubMed
Schlötterer, C (2000) Evolutionary dynamics of microsatellite DNA. Chromosoma 109, 365371.CrossRefGoogle ScholarPubMed
Schuelke, M (2000) An economic method for the fluorescent labeling of PCR fragments. Nature Biotechnology 18, 233234.CrossRefGoogle ScholarPubMed
Škaloud, P, Friedl, T, Hallmann, C, Beck, A and Dal, Grande F (2016) Taxonomic revision and species delimitation of coccoid green algae currently assigned to the genus Dictyochloropsis (Trebouxiophyceae, Chlorophyta). Journal of Phycology 52, 599617.CrossRefGoogle Scholar
Tenzer, I, degli Ivanissevich S, Morgante M and Gessler C (1999) Identification of microsatellite markers and their application to population genetics of Venturia inaequalis. Phytopathology 89, 748753.CrossRefGoogle ScholarPubMed
Velasco-Anacona, GP, Sterling, A and Reyes-Bermúdez, A (2022) Microsatellite loci reveal distinct populations with high diversity for the pathogenic fungus Pseudocercospora ulei from North-Western Amazonia. European Journal of Plant Pathology 163, 827839.CrossRefGoogle Scholar
Vieira, MLC, Santini, L, Diniz, AL and de Freitas, MC (2016) Microsatellite markers: what they mean and why they are so useful. Genetics and Molecular Biology 39, 312328.CrossRefGoogle ScholarPubMed
Walser, J-C, Sperisen, C, Soliva, M and Scheidegger, C (2003) Fungus-specific microsatellite primers of lichens: application for the assessment of genetic variation on different spatial scales in Lobaria pulmonaria. Fungal Genetics and Biology 40, 7282.CrossRefGoogle ScholarPubMed
Werth, S (2010) Population genetics of lichen-forming fungi – a review. Lichenologist 42, 499519.CrossRefGoogle Scholar
Werth, S, Meidl, P and Scheidegger, C (2021) Deep divergence between island populations in lichenized fungi. Scientific Reports 11, 113.CrossRefGoogle ScholarPubMed
Widhelm, TJ, Bertoletti, FR, Asztalos, MJ, Mercado-Diaz, JA, Huang, J-P, Moncada, B, Lücking, R, Magain, N, Sérusiaux, E, Goffinet, B, et al. (2018) Oligocene origin and drivers of diversification in the genus Sticta (Lobariaceae, Ascomycota). Molecular Phylogenetics and Evolution 126, 5873.CrossRefGoogle ScholarPubMed
Widmer, I, Dal Grande, F, Excoffier, L, Holderegger, R, Keller, C, Mikryukov, VS and Scheidegger, C (2012) European phylogeography of the epiphytic lichen fungus Lobaria pulmonaria and its green algal symbiont. Molecular Ecology 21, 58275844.CrossRefGoogle ScholarPubMed
Woods, RG and Coppins, BJ (2012) A Conservation Evaluation of British Lichens and Lichenicolous Fungi. (Species Status 13). Peterborough: Joint Nature Conservation Committee.Google Scholar
Figure 0

Figure 1. Photosymbiodemes of the lichen-forming fungus Sticta canariensis, showing dramatically different morphologies. A, chloromorph with small brown apothecia. B, cyanomorph with phyllidia (asexual propagules). Scales = 1 cm. In colour online.

Figure 1

Figure 2. Tree model for the lichen-forming fungus Sticta canariensis. Midpoint-rooted neighbour-joining population tree from PHYLIP based on chord distance between populations, calculated with 1000 bootstrap replicates. A = Azores, E = England, N = Norway, M = Madeira, C = Canary Islands. In colour online.

Figure 2

Table 1. Primers for 25 microsatellite markers developed for the lichen-forming fungus Sticta canariensis.

Figure 3

Table 2. Microsatellite diversity of the mycobiont Sticta canariensis, based on the analysis of 25 loci from 65 individuals.

Supplementary material: File

Goss and Werth supplementary material 1

Goss and Werth supplementary material
Download Goss and Werth supplementary material 1(File)
File 10.2 KB
Supplementary material: File

Goss and Werth supplementary material 2

Goss and Werth supplementary material
Download Goss and Werth supplementary material 2(File)
File 13.4 KB
Supplementary material: File

Goss and Werth supplementary material 3

Goss and Werth supplementary material
Download Goss and Werth supplementary material 3(File)
File 13.1 KB