Conservation Genetics of the Saimaa Ringed Seal

dissertations

Mi a Va ltonen

Conservation genetics of seal ringed Saimaa of the genetics | Conservation Valtonen | No 159 | Mia the Saimaa ringed seal – insights into the history of a critically endangered population A critically endangered subspecies of the ringed seal has remained Mi a Va ltonen isolated in Lake Saimaa in Finland since the last glacial period, i.e., for nearly 10,000 years. The small Conservation genetics of population of ~300 seals is currently threatened by anthropogenic the Saimaa ringed seal factors, such as high by-catch mortality and climate change. This – insights into the history of a critically thesis examines changes in genetic endangered population diversity and population structure of the Saimaa ringed seal, and provides new information for conservation.

Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences No 159 isbn 978-952-61-1582-5 (printed) issnl 1798-5668 issn 1798-5668 isbn 978-952-61-1583-2 (pdf) issnl 1798-5668 issn 1798-5676

MIA VALTONEN

Conservation genetics of the Saimaa ringed seal

– insights into the history of a critically endangered population

Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences No 159

Academic Dissertation To be presented by permission of the Faculty of Science and Forestry for public examination in the Auditorium N100 in Natura Building at the University of Eastern Finland, Joensuu, on October, 31, 2014, at 12 noon

Department of Biology

Grano Joensuu, 2014 Editors: Prof. Pertti Pasanen, Prof. Pekka Kilpeläinen, Prof. Kai Peiponen, Prof. Matti Vornanen Cover photo: Mervi Kunnasranta Distribution: Eastern Finland University Library / Sales of publications [email protected] www.uef.fi/kirjasto

ISBN: 978‐952‐61‐1582‐5 (printed) ISSNL: 1798‐5668 ISSN: 1798‐5668

ISBN: 978‐952‐61‐1583‐2 (PDF) ISSNL: 1798‐5668 ISSN: 1798‐5676

Author’s address: University of Eastern Finland Department of Biology P.O. Box 111 80101 JOENSUU FINLAND email: [email protected]

Supervisors: Associate Professor Tommi Nyman, Ph.D. University of Eastern Finland Department of Biology P.O. Box 111 80101 JOENSUU FINLAND email: [email protected]

Senior Researcher Mervi Kunnasranta, Ph.D. University of Eastern Finland Department of Biology P.O. Box 111 80101 JOENSUU FINLAND email: [email protected]

Docent Minna Ruokonen, Ph.D. University of Oulu Department of Biology P.O. Box 3000 90014 OULU FINLAND

Docent Jukka Palo, Ph.D. University of Helsinki Hjelt Institute, Department of Forensic Medicine P.O. Box 40 00014 HELSINKI FINLAND email: [email protected]

Professor Jouni Aspi, Ph.D. University of Oulu Department of Biology P.O. Box 3000 90014 OULU FINLAND email: [email protected]

Reviewers: Professor David Coltman, Ph.D University of Alberta Department of Biological Sciences EDMONTON, ALBERTA T6G 2E9 CANADA email: [email protected]

Senior Researcher Liselotte Andersen, Ph.D University of Aarhus Department of Bioscience Grenåvej 14 8410 RØNDE DENMARK email: [email protected]

Opponent: Professor Craig Primmer, Ph.D University of Turku Department of Biology 20014 TURKU FINLAND email: [email protected]

ABSTRACT

Small and isolated populations lose genetic diversity, the raw material of evolution, more rapidly than do large populations, which may make them more vulnerable to demographic and environmental stochasticity. Fragmentation of an already small population may further increase its extinction risk by intensifying such effects in the even smaller subpopulations. The Saimaa ringed seal (Phoca hispida saimensis) represents an ideal study system for investigating the genetic and demographic effects of long isolation, small population size, and spatial subdivision. This critically endangered subspecies of c. 300 seals inhabits the highly fragmented Lake Saimaa in southeastern Finland. The population has remained completely isolated for c. 9,500 years and is currently threatened by anthropogenic factors, such as high by‐catch mortality and climate change. This thesis examines spatial and temporal variation in genetic diversity of the Saimaa ringed seal. For this, tissue samples collected from seal carcasses (N = 212) in 1980– 2008 and placentas (N = 66) collected from birth‐lair sites during 2000–2011 were examined for mtDNA and microsatellite variation. A new method of non‐invasive genetic sampling was developed, demonstrating the utility of placentas for reliable DNA genotyping. The diversity of the Saimaa population was contrasted with the levels found in populations sharing the same ancestry, Baltic (P. h. botnica; N = 21) and Ladoga (P. h. ladogensis; N = 16) ringed seals. The results show that genetic diversity of the Saimaa ringed seal is extremely low, with observed microsatellite heterozygosity for this subspecies (HE = 0.36) being the lowest recorded within the order Pinnipedia. Effective population sizes estimated for the total population and regional subpopulations were also very low (NE = 5–113), suggesting that the population is too small to maintain its current diversity in the long term. Although coalescent simulations indicated that most of the original diversity was lost during the long isolation, we observed a decrease in diversity also during the past decades, which suggests ongoing diversity loss in the population. Moreover, Bayesian clustering analyses revealed significant differentiation among the breeding areas. The fine‐scaled structuring of the Saimaa population is surprising, because in marine ringed seals only weak differentiation has been detected even among subpopulations located thousands of kilometres apart. In the Saimaa ringed seal, the population structure is most likely induced by the small subpopulation sizes and fragmented lacustrine habitat, but also by behavioural patterns of the seals. Overall gene flow within the lake is limited, as females are philopatric and, although males appear to be more prone to disperse, gene flow mediated by males is insufficient for counteracting the effects of genetic drift. The findings of the present study indicate that genetic diversity of the Saimaa ringed seal will inevitably continue to decrease unless its population size can be increased substantially. Additionally, the observed fine‐scaled structuring of the population raises concerns about the viability of subpopulations. Therefore, as rapid population growth is improbable in this slowly reproducing species, short‐term conservation efforts (e.g., translocations of adult seals) should focus on facilitating gene flow among breeding areas.

Universal Decimal Classification: 574.3, 575.113.2, 575.17, 575.22, 599.745.3

CAB Thesaurus: seals; Phoca hispida; effective population size; gene flow; genetic diversity; genetics; monitoring; placenta; mitochondrial DNA; microsatellites; heterozygosity; genotypes; population structure; spatial variation; temporal variation

Yleinen suomalainen asiasanasto: hylkeet; norppa; saimaannorppa; populaatiot; koko; rakenne; vaihtelu; genetiikka; geenit; geneettinen monimuotoisuus; genotyyppi; seuranta; näytteenotto; istukka; mitokondrio‐ DNA; mikrosatelliitit Preface

There are so many people that I wish to thank for their help, encouragement and support during this long journey. First of all, I wish to acknowledge my numerous supervisors located in different parts of the country, from whom each I learned so much. I want to thank Tommi Nyman for providing me the opportunity to study Saimaa ringed seal genetics and all his help during the preparation of this thesis. Your high standards taught me what it requires to make proper science. I am grateful to Mervi Kunnasranta, whose idea this PhD project originally was. You taught me how important it is to translate my results into common sense – what do they mean and what is their real‐ world relevance, if any. I also wish to express my gratitude to Jukka Palo, whose PhD work provided the basis for this study. Your enthusiasm for the subject has been extremely inspiring and your endless support invaluable along the sometimes not so smooth road. I also want to acknowledge Minna Ruokonen, who patiently guided my first steps into the field of population genetics. Sadly, she passed away two years ago. Jouni Aspi replaced Minna as my supervisor. I deeply appreciate that you shared your broad experience with me, your expert advice and never‐failing encouragement. I thank my co‐authors for their help with several issues: Hanna Buuri for her contribution to the placental lab work, Matti Heino for his huge effort in genotyping placentas and help in data analyses, and Tuomo Kokkonen, who tragically perished recently, for seamless co‐operation, especially in organising the field‐collection of placentas. I am grateful to Markku Viljanen for his help with the funding issues, Heikki Hyvärinen for his helpful comments, and Gernot Segelbacher for his major help and support during the final meters of the thesis. I sincerely thank David Coltman and Liselotte Andersen for their thorough reviews of this thesis. Further, I am grateful to Jukka Jernvall and Petri Auvinen for providing me the opportunity to continue and expand my research on the Saimaa ringed seal genetics. There are a number of sponsors which made this research possible: the Maj and Tor Nessling Foundation, Saimaa Ringed Seal Genome Project, Raija and Ossi Tuuliainen Foundation, Kuopio Naturalists’ Society, Nestori Foundation, Finnish Foundation for Nature Conservation, University of Eastern Finland, Finnish Graduate School in Environmental Science and Technology, Finnish Konkordia Fund and Oskar Öflunds Stiftelse are gratefully acknowledged for providing financial support for this study. Thanks are also due to the entire staff of the Department of Biology for providing excellent conditions for my research. Particularly, I wish to thank Riitta Pietarinen for helping with the laboratory analyses, Harri Kirjavainen for his help with the tissue bank, Eija Ristola and Marja Noponen for helping with many practical issues in the lab, and Matti Savinainen and Kirsti Kyyrönen for technical support. All people who contributed to field collection of placentas deserve big thanks, above all the divers Miina Auttila, Ismo and Paula Marttinen, Kari Ratilainen and Juha Taskinen. I also wish to acknowledge the personnel of Metsähallitus, especially Tero Sipilä, Jouni Koskela and Raisa Tiilikainen. Thanks are also due to Petri Timonen from the Finnish Game and Fisheries Research Institute for his help with the age determinations of seals and the Baltic ringed seal samples. I have had the privilege and pleasure to study in the “Söpöjen eläinten tutkimusryhmä” with many special people, who shared the ups and downs of this journey with me: Anni, Marja, Miina and Sanna (some people may find sawflies cute...) from the very beginning, and Sari, Riikka, Meeri and Anni joining in later – my warmest thanks for your help, support and friendship. I also wish to thank the whole Saimaa field crew for the memorable springs on the lake watching and catching seals. Those lake days offered a welcome and refreshing escape away from the computer and the seals themselves reminded me of why I started this thesis to begin with. My special thanks go to Juha for sharing many of those days with me and for teaching me the true nature of ‘hyle’. I thank all my fellow PhD students in the Biology department, both previous and current, for peer support – particularly Raisa and Ursula for refreshing company and lively discussions at lunch and coffee breaks, and Sari and Kaisa for offering a refuge at times when all went wrong. I also want to thank Jouni’s group in Oulu, for welcoming me, the eastern outlier, into your group. Many of my friends outside university have supported me all the way and lent an ear whenever I was in need of one and, equally importantly, provided me activities outside work and given me other things to think about. Especially Varpu, Suvi, Marjo, Ulla and Tiina, thank you for being there for me. I also wish to express my deepest gratitude to all of you who looked after my dog Mössi when I was travelling or needed a momentary relief at times when my everyday joy became a burden. Finally I want to thank my family for their faith in me although I often doubted myself. I am grateful to my mother for helping me with whatever and whenever I needed and for understanding when I was too stressed and needed some space. I thank my father and Armi for always encouraging me and helping with all kinds of practical issues when visiting Helsinki and travelling abroad. I also wish to thank my brother Juho for being my personal IT‐support person and for taking such good care of Mössi that he would forget my existence. My warmest thanks to my sister Niina and her husband Teijo who had their door open for me any time. Being surrounded by three lively children gave another perspective in life – a mistake in your data, the correction of which takes two days, does not seem such big an issue after all. Those three precious kids, Sara, Luka and Lumi, always remind me of the importance of being present in the present. LIST OF ABBREVIATIONS AND SYMBOLS a haplotypic richness A number of alleles AR allelic richness CR mitochondrial control region FIS inbreeding coefficient; departure from Hardy‐ Weinberg proportions within subpopulations FST index of population differentiation; proportion of genetic diversity due to differences among populations h haplotypic diversity hn number of haplotypes HE expected heterozygosity HO observed heterozygosity HV Haukivesi area IBD isolation by distance KV Kolovesi MHV Main Haukivesi area mtDNA mitochondrial DNA N sample size NC census population size NE effective population size NP number of polymorphic loci NS Northern Saimaa pn number of polymorphic sites PV Pihlajavesi area SD standard deviation SS Southern Saimaa uh number of unique haplotypes yob year of birth yob year of death ΦST index of population differentiation; proportion of genetic diversity (measured as the expected squared evolutionary distance between alleles) due to differences among populations π nucleotide diversity LIST OF ORIGINAL PUBLICATIONS

This thesis is based on data presented in the following articles, referred to by the Roman numerals I‐IV.

I Valtonen M, Palo J U, Ruokonen M, Kunnasranta M, Nyman T. Spatial and temporal variation in genetic diversity of an endangered freshwater seal. Conservation Genetics 13: 1231– 1245, 2012.

II Nyman T, Valtonen M, Aspi J, Ruokonen M, Kunnasranta M, Palo J U. Demographic histories and genetic diversities of Fennoscandian marine and landlocked ringed seal subspecies. Ecology and Evolution 4: 3420–3434, 2014.

III Valtonen M, Palo J U, Aspi J, Ruokonen M, Kunnasranta M, Nyman T. Causes and consequences of fine‐scale population structure in a critically endangered freshwater seal. BMC Ecology 14: 22, 2014.

IV Valtonen M, Heino M, Aspi J, Buuri H, Kokkonen T, Kunnasranta M, Palo J U, Nyman T. The utility of field‐ collected placentas for genetic monitoring of a critically endangered freshwater seal population. Submitted manuscript.

The publications are printed with kind permission of Springer and John Wiley and Sons. AUTHOR’S CONTRIBUTION

The present author contributed to the planning and to a minor part of the sample collection of all papers. She did the laboratory analyses for papers I–III, and was responsible for most data analyses and writing the original manuscripts for papers I, III and IV. She also participated in data analyses and writing of paper II. Contents

1 Introduction ...... 15 1.1 Genetic diversity in small populations ...... 16 1.2 Genetic monitoring of wildlife populations ...... 17 1.3 The study species ...... 19 1.3.1 The ringed seal as a species...... 19 1.3.2 History of the Saimaa ringed seal population ...... 20 1.3.3 Current status of the population ...... 20 1.4 Aims of the study ...... 23

2 Materials and methods ...... 25 2.1 Samples ...... 25 2.1.1 Sample division ...... 26 2.2 Molecular markers ...... 26 2.3 Genetic analyses ...... 27 2.3.1 Genetic diversity and inbreeding coefficient ...... 27 2.3.2 Present and historical effective population sizes ...... 28 2.3.3 Population differentiation and gene flow ...... 29 2.3.4 Identification of individuals ...... 30

3 Results and discussion ...... 33 3.1 Trajectory of genetic diversity in the Saimaa ringed seal in relation to the Baltic and Ladoga subspecies ...... 33 3.2 Ongoing diversity loss and extremely small effective population sizes in the Saimaa ringed seal ...... 36 3.3 Fine‐scale population structure and limited gene flow within Lake Saimaa ...... 38 3.4 Identification of individuals and the utility of placentas in genetic monitoring of the Saimaa population ...... 41

4 Conclusions and challenges for conservation ...... 45

5 References ...... 49

1 Introduction

“The world is changed. I feel it in the water. I feel it in the earth. I smell it in the air. Much that once was is lost; for none now live who remember it. [...] But there were some who resisted.” – Galadriel (The Lord of the Rings: The Fellowship of the Ring. 2001)

The importance of genetic diversity for the persistence of species and populations is nowadays commonly recognized (McNeely et al., 1990; Reed & Frankham, 2003; Frankham, 2005). Genetic diversity reflects the evolutionary potential of organisms, i.e., their capability to adapt to environmental changes. Small and endangered populations usually exhibit lower levels of genetic diversity than do closely related non‐endangered ones (Spielman et al., 2004) and, thus, are expected to have reduced adaptation capacity in a changing environment (Willi et al., 2006). Moreover, fitness of individuals is often reduced due to inbreeding (Reed & Frankham, 2003) and environmental stress (Willi et al., 2006), further elevating the extinction risk of small populations. The correlation between genetic diversity and viability of populations is, however, not always straightforward, particularly in stable and favourable environments, and there are examples of populations that thrive despite low diversity (Weber et al., 2000; Reed, 2010; Kekkonen et al., 2012). At present, many previously stable habitats are globally threatened by anthropogenic impacts, such as fragmentation, introduction of alien species, and climate change, that pose a challenge for many populations by altering the environmental conditions that they are adapted to. Unless a population is able to respond to environmental changes or to move to a more favourable habitat, its viability is severely compromised, which may lead to

extinction (Hoffmann & Sgrò, 2011). Therefore, knowledge on the levels of, and changes in, genetic diversity of small and isolated populations facing environmental changes is essential for efficient conservation management.

1.1 GENETIC DIVERSITY IN SMALL POPULATIONS

The current genetic diversity of any given population has been shaped by evolutionary forces during the history, and is also influenced by factors such as demographic history and reproductive biology of the species. The evolutionary forces influencing genetic diversity of a population include mutation, gene flow, selection, and genetic drift (e.g., Charlesworth, 2009). All genetic diversity is originally generated by mutations, but as only a minority of them are beneficial and the rate at which they occur is very low, lost adaptive diversity is regenerated extremely slowly. New alleles may also be brought into a population by immigrants arriving from other populations. Natural selection increases the frequency of alleles that are beneficial in prevailing conditions and reduces the frequency of those that are deleterious, while having no effect on neutral alleles and loci. Loss of genetic diversity is also caused by genetic drift, an inevitable, random process that causes allele frequencies to fluctuate from one generation to the next owing to sheer chance. In small populations, genetic diversity is lost through genetic drift more rapidly than is created by mutations, as the rate at which diversity is lost is inversely proportional to population size (Willi et al., 2006). At the same time, slightly negative mutations act as effectively neutral, and their fate is determined by genetic drift instead of natural selection, with the result that they may become fixed due to chance. Also inbreeding, i.e., mating between related individuals, which is unavoidable in small populations, reduces individual genetic diversity (heterozygosity), although does not directly influence the number of alleles. As homozygosity increases, deleterious

recessive alleles are exposed, resulting in fitness reduction of individuals. In consequence of both genetic drift and inbreeding, small populations often face an elevated risk of extinction due to reduced environmental adaptability (Frankham, 2005; Willi et al., 2006; Liao & Reed, 2009) and lowered fitness of individuals (Madsen et al., 1996; Reed & Frankham, 2003; Blomqvist et al., 2010; Mattila et al., 2012). Gene flow counteracts the effects of genetic drift and inbreeding by equalizing differences in allele frequencies among populations (Slatkin, 1985). If a small population is further divided into even smaller subunits, gene flow among subpopulations is essential for maintaining genetic diversity and alleviating the negative genetic consequences of small population size (Keller & Waller, 2002; Tallmon et al., 2004). Gene flow among and within populations may be impaired or even prevented by geographic and ecological barriers. This typically applies to island populations (Hoeck et al., 2010; Runemark et al., 2012), but also to species with specialised habitat requirements (Ferchaud et al., 2011; Gottelli et al., 2012) or limited dispersal capacity (Louy et al., 2007) living in fragmented landscapes. However, species‐specific behavioural patterns may also influence the level of gene flow, for example, due to sex‐dependent differences in dispersal. For example, in many mammals females are philopatric, while males are more prone to disperse (Greenwood, 1980; I, III).

1.2 GENETIC MONITORING OF WILDLIFE POPULATIONS

Introduction of genetic methods into population monitoring has considerably facilitated conservation and management of elusive species and small, endangered populations. Today, molecular methods are used for assessing the levels of genetic diversity and other genetic parameters of species and populations (e.g., Aspi et al., 2006; Schultz et al., 2009; Segelbacher et al., 2014). They also provide a means for examining many aspects of the species’ biology, such as

dispersal, mating patterns, reproductive success, and survival (Fedy et al., 2008; Brøseth et al., 2010; Ford et al., 2011), which are often difficult to study using traditional approaches, such as mark–recapture and telemetry methods. Estimating the level of genetic diversity is essential for management and conservation decisions. Assessing population structure and patterns of gene flow is also important, for example, when identifying management units (Palsbøll et al., 2007) and planning translocations among subpopulations (De Barba et al., 2010; Latch et al., 2011). In addition, identification of individuals from DNA samples can be used for estimating, for example, population census size (NC) and individual dispersal patterns and survival (Schwartz et al., 2007). Especially for species of conservation concern, effective population size (NE) is a much more important measure than is NC, as NE reflects the number of individuals contributing genes to the next generation. In most natural populations, NE is far lower than NC (Palstra & Ruzzante, 2008; Palstra & Fraser, 2012). Moreover, investigating the level of inbreeding, kinship, mating patterns, and individual reproductive success is often possible only by using genetic data. Genetic approaches can also be used for studying ecological and demographic changes in a population over time (Schwartz et al., 2007). This requires a time series of archived genetic data (tissue samples, extracted DNA, or records of genetic information from previous studies) with information on collection time and place of samples, but also multiple samples from each period (Jackson et al., 2011). Using such sample archives, it is possible to detect, for example, changes in genetic diversity of a population (Pichler & Baker, 2000, I, III), which may provide information on factors influencing the diversity and, hence, assist in designing appropriate management strategies. Technical advances have also enabled extraction of DNA from historical samples (e.g., hair, feather, skin, and bone) from hundreds to thousands of years old and, thus, direct assessments of historical levels of genetic diversity (e.g., Welch et al., 2012; Foote et al., 2012; Jansson et al., 2014; Segelbacher et al., 2014). However, past events can also be inferred from genetic

information obtained from current samples using coalescent approaches (Nordborg, 2010, I, II). Non‐invasive samples that can be collected without catching or even seeing the animal itself, such as hair, feather, shed skin, and faeces, provide a means for studying rare, elusive and endangered species without causing disturbance, danger, or stress to the animals (Swanson et al., 2006). At the same time, the use of such samples often enables obtaining large numbers of samples for monitoring purposes. Today, many terrestrial populations, including large carnivores (e.g., Brøseth et al., 2010; Kopatz et al., 2012; Davoli et al., 2013), are routinely monitored using non‐invasive genetic methods. Collection of non‐invasive samples in aquatic environments is often more challenging than in terrestrial habitats, but this approach is being increasingly utilised also in studies of marine mammals. For example, genetic information has been obtained from samples of shed skin in ringed seals (Martinez‐Bakker et al., 2013) and humpback whales (Baker et al., 2013), and from faeces in dolphins (Parsons et al., 2006) and marine otters (Valqui et al., 2010).

1.3 THE STUDY SPECIES

1.3.1 The ringed seal as a species The ringed seal (Phoca hispida) is a holarctically distributed species numbering a few million individuals in total, being at the same time the most northern and the most abundant of northern seals (Reeves, 1998). The species is one of the few pinnipeds capable of inhabiting fast ice areas during winter, as they can maintain breathing holes by their fore flipper claws. Not only can ringed seals survive in icy conditions, but ice and snow are indispensable for them as a breeding habitat. In comparison to other phocid seals, the ringed seal is genetically very diverse (Palo et al., 2001; Davis et al., 2008). Five different subspecies of ringed seal are recognised worldwide (Amano et al., 2002; but see Berta & Churchill, 2012), three of which are found in Fennoscandia: the Baltic (P. h. botnica), Ladoga (P. h.

ladogensis) and Saimaa (P. h. saimensis) ringed seals (Hyvärinen & Nieminen, 1990).

1.3.2 History of the Saimaa ringed seal population The current Fennoscandian ringed seal populations in Lake Saimaa, Lake Ladoga, and the Baltic Sea (Fig. 1A) descend from Arctic ringed seals (P. h. hispida) that colonized the Baltic basin from the Atlantic during the deglaciation, c. 10,000 years ago (Forstén & Alhonen, 1975; Ukkonen, 2002). Isostatic land‐uplift gave rise to numerous lakes, including lakes Saimaa and Ladoga, where parts of the Baltic population were trapped. The ringed seals in Lake Saimaa have lived in complete isolation for c. 9,500 years, during which they have evolved into a morphologically, ecologically, and genetically distinct subspecies (Hyvärinen & Nieminen, 1990; Kunnasranta, 2001; Palo, 2003; Palo et al., 2003; I, II). During its long isolation, the Saimaa ringed seal population has undergone substantial changes in size: it has been estimated that there were a few thousand seals in the lake before human impact (Hyvärinen et al., 1999), and still up to 1,000 seals at the turn of the 20th century (Kokko et al., 1999). During the last hundred years, the population experienced a human‐induced bottleneck: despite being placed under protection in 1955, the population continued to decrease mainly due to high by‐catch mortality and environmental pollutants (Hyvärinen et al., 1999) and reached its ultimate low of fewer than 150 individuals in the 1980s (Sipilä et al., 1990).

1.3.3 Current status of the population Since the end of the 20th century, the Saimaa ringed seal population has slowly increased, and it currently numbers slightly over 300 seals (Metsähallitus, 2014). However, the population is still very small and threatened by human activities (including by‐catch mortality and disturbance), and also by deterioration of breeding conditions associated with warming

Figure 1. The three water basins inhabited by ringed seals in Fennoscandia (A) and collection locations of Saimaa ringed seal specimens and the initial regional division of Lake Saimaa used in this study (B). Dot colours denote the type of the sample: red = carcass, blue = placenta.

winters. Hence, the subspecies is classified as critically endangered (Rassi et al., 2010; Kovacs et al., 2012). As is often the case in small and isolated populations, the level of genetic diversity of the Saimaa ringed seal is extremely low (Palo, 2003; Palo et al., 2003; Martinez‐Bakker et al., 2013; I– III), which may have an effect on the long‐term survival prospects of the subspecies. It has also been assumed that the population may be divided into several semi‐isolated subpopulations, since its habitat, Lake Saimaa, is naturally fragmented, with only narrow inlets connecting the main water basins (Fig. 1B). Additionally, behavioural studies have shown that although the seals are potentially very mobile, they exhibit a high degree of site fidelity, and no long‐distance migrations among different breeding areas have been observed (Kunnasranta, 2001; Koskela et al., 2002; Niemi et al., 2012; 2013a; 2013b). Division of this small population into even smaller units may hasten the loss of the remaining genetic diversity and, thus, make the Saimaa ringed seal even more vulnerable to environmental changes. However, in their study based on microsatellite variation of the Saimaa ringed seal, Palo et al., (2003) found no evidence of significant differentiation between the northern and southern parts of the lake, but this could be due to the limited numbers of markers and samples in the analysis. Therefore, more extensive surveys were needed for assessing the current levels of divergence among regional subpopulations, and also for evaluating the effect of the anthropogenic bottleneck on the genetic diversity of the population.

1.4 AIMS OF THE STUDY

The main aims of this work were to examine spatial and temporal changes in genetic diversity and population structure of the Saimaa ringed seal. This knowledge is essential in designing and allocating conservation measures for this critically endangered population. The specific objectives were to:

1. Study the genetic diversity of the Saimaa ringed seal in relation to larger populations of the same origin, i.e., the Baltic and Ladoga ringed seals (I, II) 2. Examine genetic structure and gene flow within the lake (I, III) 3. Investigate temporal changes in genetic diversity of the population (I–III) 4. Develop a method for genetic identification of Saimaa ringed seal individuals (IV) 5. Study the utility of non‐invasively collected placentas for genetic monitoring of the population (IV)

2 Materials and methods

A general outline of the materials and methods is presented here. Detailed descriptions of laboratory procedures and analytical methods are found in the original papers I–IV.

2.1 SAMPLES

The majority of the Saimaa ringed seal specimens used in this study were tissue samples that had been collected from carcasses found in different parts of Lake Saimaa during the years 1980–2008 (N = 212; I–VI). The samples had been deposited into a tissue bank maintained by the University of Eastern Finland and Natural Heritage Services of Metsähallitus, and stored at –20°C. Systematic searches for Saimaa ringed seal placentas were conducted in three consecutive springs during 2009–2011 (I, IV), as placentas can often be found from the vicinity of birth lairs situated along shorelines of islands and islets (Sipilä 2003) after the breeding season. A total of 59 placentas were found from 124 known birth lair sites, i.e., from nearly half of the inspected sites. Placentas collected during the years 2000–2007 were used as additional samples (N = 7; IV). Tissue samples from Baltic (N = 21, provided by the Finnish Game and Fisheries Research Institute) and Ladoga (N = 16, obtained from the tissue bank maintained by the University of Eastern Finland and Natural Heritage Services of Metsähallitus) ringed seals were used as reference, in order to compare the level of genetic diversity in the Saimaa population to those of the larger populations of the same origin (I, II).

2.1.1 Sample division The Saimaa ringed seal specimens were initially divided into four regional samples based on the topography of the lake (Northern Saimaa, Haukivesi area, Pihlajavesi area, Southern Saimaa), as well as into three temporal samples based on the collection decade of the seals (1980s, 1990s, 2000s; I, III). A decade is close to the estimated 11‐year generation time of ringed seals (Palo et al., 2003 after Smith, 1973) and, therefore, was considered appropriate for examining temporal changes in the genetic composition of the Saimaa population. In some analyses (I, III), the temporal division was based on the birth decade of seals, yielding five temporal samples (1963–1969, 1970s, 1980s, 1990s, 2000s; I, III).

2.2 MOLECULAR MARKERS

The molecular markers used in this study were mitochondrial DNA (mtDNA) sequences and nuclear microsatellites, both of which are considered neutral, i.e., they are typically not affected by selection. MtDNA is a haploid, circular molecule located in mitochondria, and many copies are found in each cell (Ballard & Whitlock, 2004). In most animals, mtDNA is maternally inherited, meaning that it is transmitted from mothers to their offspring and, thus, mtDNA can be used to study female lineages. The control region (CR) is a non‐coding region that is involved in regulation of mtDNA replication. Due to its high mutation rate, the CR usually shows a high level of polymorphism and, hence, multiple genetic lineages are often found both within and among populations (I). MtDNA is therefore widely used in phylogeographic and population‐ genetic studies. The effective population size of the haploid and uniparentally inherited mtDNA is only a quarter of that of diploid nuclear DNA (Ballard & Whitlock, 2004), which makes it particularly sensitive to demographic changes. Microsatellites are tandemly repeated DNA sequences that consist of 1–6 base pairs and are found at a high frequency

throughout nuclear genomes (Schlötterer, 2000). Polymorphism in microsatellites mainly results from variation in allelic length, which is due to differing numbers of repeats among alleles. Microsatellites have a high mutation rate as compared to base substitution rates in nuclear DNA. However, the flanking sequences surrounding microsatellite loci are often conserved, enabling the use of similar microsatellite‐amplifying primers across related species (II–IV). Microsatellites are biparentally inherited, i.e., each individual receives one allele from each parent, providing information on both maternal and paternal contributions to gene flow within and among populations. Because of their codominant inheritance and typically high polymorphism, microsatellites are frequently used as markers in population‐genetic studies as well as in identification of individuals and analyses of kinship (see, e.g., Chistiakov et al., 2006).

2.3 GENETIC ANALYSES

2.3.1 Genetic diversity and inbreeding coefficient Genetic diversity was estimated for the three Fennoscandian ringed seal subspecies and for regional and temporal samples of the Saimaa population. MtDNA diversity was estimated by numbers of different haplotypes (hn), unique haplotypes (uh), and polymorphic loci (pn), haplotypic richness (a), as well as haplotype (h) and nucleotide (π) diversities (I, III, IV). Haplotypic richness is the mean number of haplotypes per locus estimated using the rarefaction method (Kalinowski, 2004) taking the sample size into account. Haplotype diversity reflects numbers and frequencies of different haplotypes, and nucleotide diversity differences between haplotypes. Microsatellite diversity was estimated by numbers of polymorphic loci (NP) and alleles (NA), rarefied allelic richness (AR), and observed (HO) and expected (HE) heterozygosities (II– VI). Observed heterozygosity is the observed proportion of heterozygous individuals at a given locus, while expected

heterozygosity reflects the proportions estimated based on allele frequencies in the focal population. The level of inbreeding within each subspecies and Saimaa subsample was assessed by the inbreeding coefficient (FIS), i.e., the probability that both alleles at given locus of an individual are identical by descent (Wright, 1951).

2.3.2 Present and historical effective population sizes Effective population size (NE) is the size of an idealized Fisher– Wright population (i.e., a population with constant size, equal sex ratio, random mating, equal reproductive success of individuals, and non‐overlapping generations) that loses genetic diversity or becomes inbred at the same rate as the observed population (Waples, 2002). Current NE was estimated for the total Saimaa ringed seal population as well as for regional and temporal samples using two different approaches (III). The method based on linkage disequilibrium provides an NE estimate for a single population sample at a single point in time (Waples, 2006; Waples & Do, 2008), whereas the temporal method is based on the extent of changes in allele frequencies between samples taken at different time points (Jorde & Ryman, 2007). The trajectory of genetic diversity and past effective population sizes were estimated for the Saimaa, Baltic, and Ladoga subspecies using coalescent approaches (I, II). The coalescent framework was utilised to simulate the changes that have occurred in the genetic composition of each population after separation from the common ancestral population (Nordborg, 2010). As the separation time of the populations is known based on Fennoscandian geological history (Forstén & Alhonen, 1975; Ukkonen, 2002), past events could be inferred from the present‐day data. Firstly, the Bayesian serial coalescent model (Excoffier et al., 2000; Anderson et al., 2005) was used to infer mutation and population size parameters in the Saimaa ringed seal (I). Secondly, a Markov Chain Monte Carlo method under the isolation‐with‐migration model (Hey & Nielsen, 2007;

Hey, 2010) was used to estimate demographic parameters for the Saimaa and Baltic populations (I). Thirdly, an approximate Bayesian computation approach (Cornuet et al., 2008; 2010) was used to explore historical NEs and to assess the best‐fitting scenario for changes in NE through time for the Saimaa, Baltic, and Ladoga subspecies (II).

2.3.3 Population differentiation and gene flow Differences among mtDNA haplotypes of the three ringed seal subspecies were studied by constructing a haplotype network illustrating relationships and distances among haplotypes (I). Genetic differentiation among subspecies and Saimaa subsamples was estimated using F‐statistics (I–IV), describing the distribution of genetic diversity among different levels of the sampling hierarchy (individuals, subpopulations, and total population) (Wright, 1951; see also Excoffier et al., 1992). Differentiation based on both mtDNA and microsatellite variation for all sampling schemes was evaluated using FST, which measures differences in allelic frequencies among populations. For assessing mtDNA differentiation among subspecies, we also estimated ΦST, which takes differences between haplotypes into account. Genetic differences among seals originating from different populations (II) and Saimaa subpopulations (III) were examined using factorial correspondence analysis (FCA), which illustrates the distribution of genetic variation across individuals based on their microsatellite genotypes. Spatial structuring of the Saimaa ringed seal population was also investigated using Bayesian clustering analyses (Guillot et al., 2009; François & Durand, 2010; III). The analysis in general consists of two phases. First, the issue of model choice (i.e., how many subpopulations are most appropriate for interpreting the data) is considered without prior information of the number of locations at which the individuals were sampled, and into which location each individual belongs. Second, the individuals in the sample are assigned probabilistically to the selected number of

subpopulations on the basis of their multilocus genotypes. The basic hierarchical structure of the Saimaa population was inferred utilising only microsatellite genotypes of individuals without knowledge on sampling locations (Pritchard et al., 2000; Falush et al., 2003; Evanno et al., 2005), and finer‐scale structuring was examined by using an approach that incorporates information on the collection locations as well as the topography of the lake into the analysis (Chen et al., 2007; Durand et al., 2009). The presence of an isolation‐by‐distance (IBD) pattern was investigated for the Saimaa population. A finding of IBD indicates that dispersal of individuals is limited and, thus, individuals found close to each other tend to be more related to each other than those with greater geographic distances (Wright, 1943). Asymmetric migration rates among Saimaa regions were estimated using the Bayesian method of Wilson & Rannala (2003), which uses multilocus genotypes of individuals for inferring recent migration rates among subpopulations. As the data included few adults (< 14%), direct assessments of dispersal of different sexes could not be made. Therefore, the relative amounts of male‐ and female‐mediated gene flow were calculated indirectly based on FST values of maternally inherited mtDNA and biparentally inherited microsatellites (González‐ Suárez et al., 2009).

2.3.4 Identification of individuals A method for genetic identification of Saimaa ringed seal individuals was developed based on multiple microsatellite loci. The reliability of the method was evaluated by estimating the probability of identity (PI), i.e., the probability that two randomly chosen individuals have identical multilocus genotypes, as well as the corresponding value for siblings (PISIB; Taberlet & Luikart, 1999; Waits et al., 2001). Because the resolution of the method improves with increasing number of markers, but the probability of genotyping errors increases at the same time, the optimal number of loci was assessed by

computing expected and observed mismatch distributions for the marker system (Waits & Paetkau, 2005). In addition, we examined whether the marker system developed for individual identification was adequate also for inferring parentage and kinship using full‐pedigree likelihood methods utilising multilocus genotype data (Jones & Wang, 2010).

3 Results and discussion

3.1 TRAJECTORY OF GENETIC DIVERSITY IN THE SAIMAA RINGED SEAL IN RELATION TO THE BALTIC AND LADOGA SUBSPECIES

The three Fennoscandian ringed seal subspecies inhabiting the Baltic Sea and lakes Saimaa and Ladoga (Hyvärinen & Nieminen, 1990; Amano et al., 2002) descend from the same ancestral population that colonised the Baltic basin after the last glacial period (Forstén & Alhonen, 1975; Ukkonen, 2002), but they currently retain very different levels of genetic diversity (Table 1; I, II, see also Palo, 2003; Palo et al., 2003). The genetic diversity of the Baltic ringed seal is close that observed in Arctic ringed seals, possibly due to a large historical population size (I, II) and/or occasional incoming gene flow (Palo et al., 2001; Martinez‐Bakker et al., 2013). The populations of lakes Saimaa and Ladoga became isolated at roughly the same time (Donner, 1995; Saarnisto, 2011), but their genetic diversities differ considerably: the Saimaa ringed seal is genetically very uniform, whereas the Ladoga subspecies is nearly as diverse as the Baltic population (Table 1; I–III). This is most likely due to differences in their population sizes and habitats: the shallow and highly fragmented Lake Saimaa is currently inhabited by only some 300 seals (Metsähallitus, 2014), while Ladoga is deeper, more continuous, and four times larger, and maintains a population of a few thousand individuals (Sipilä et al., 1996; Trukhanova et al., 2013). Assuming that the diversity observed in the Baltic population today represents the original level in the lacustrine populations, the Saimaa ringed seal has lost 55% of its overall microsatellite heterozygosity, and 34% and 89% mtDNA haplotypic and nucleotide diversities, respectively (I, II). For the Ladoga subspecies, the diversity loss has been substantially milder, so that the corresponding

Table 1. Mitochondrial and microsatellite diversity in the three Fennoscandian ringed seal subspecies, and in the spatial and temporal subsamples of the Saimaa ringed seal population. mtDNA diversity microsatellite diversity

N hn (uh) a pn h ± SD π ± SD N NP NA ± SD AR HO ± SD HE ± SD FIS

Baltic Sea 19 16 (15) 13.77 46 0.98 ± 0.03 0.047 ± 0.038 21 17 9.00 ± 3.20 8.30 0.74 ± 0.20 0.80 ± 0.08 0.07**

Lake Ladoga 16 13 (12) 13.00 29 0.97 ± 0.04 0.015 ± 0.017 16 17 7.65 ± 2.76 7.65 0.69 ± 0.22 0.74 ± 0.16 0.06*

Lake Saimaa 215 8 (8) 4.04 10 0.65 ± 0.02 0.005 ± 0.005 172 17 3.47 ± 3.32 2.77 0.33 ± 0.21 0.36 ± 0.22 0.07***

NS 19 3 (0) 3.00 6 0.43 ± 0.12 0.004 ± 0.004 15 14 2.29 ± 1.16 2.27 0.34 ± 0.27 0.33 ± 0.24 -0.00

HV 116 4 (0) 3.06 7 0.60 ± 0.02 0.005 ± 0.005 99 15 3.24 ± 3.15 2.7 0.35 ± 0.23 0.38 ± 0.23 0.07***

KV 21 2 (0) 2.00 5 0.18 ± 0.10 0.001 ± 0.003 20 13 2.41 ± 1.66 2.32 0.37 ± 0.31 0.36 ± 0.28 -0.03

MHV 95 4 (0) 3.11 7 0.58 ± 0.03 0.005 ± 0.005 79 15 3.18 ± 2.92 2.63 0.34 ± 0.23 0.35 ± 0.24 0.02

PV 61 5 (1) 3.78 9 0.63 ± 0.03 0.006 ± 0.005 43 15 2.59 ± 1.42 2.26 0.31 ± 0.25 0.30 ± 0.23 -0.03

SS 19 5 (2) 5.00 8 0.78 ± 0.06 0.005 ± 0.004 15 14 2.47 ± 1.42 2.45 0.31 ± 0.24 0.30 ± 0.22 -0.05

yod 1980s 79 6 (0) 5.55 8 0.56 ± 0.05 0.005 ± 0.005 59 17 3.29 ± 2.62 3.21 0.35 ± 0.23 0.37 ± 0.23 0.05*

yod 1990s 54 7 (0) 7.00 9 0.61 ± 0.04 0.005 ± 0.005 48 17 3.29 ± 2.85 3.29 0.34 ± 0.22 0.37 ± 0.23 0.08*

yod 2000s 82 7 (1) 6.50 10 0.67 ± 0.04 0.005 ± 0.005 65 17 3.06 ± 2.38 2.96 0.32 ± 0.20 0.35 ± 0.22 0.09***

yob 1963-79 21 5 (0) 5.00 7 0.74 ± 0.06 0.005 ± 0.004 14 16 2.94 ± 1.75 2.94 0.38 ± 0.25 0.38 ± 0.23 -0.02

yob 1980s 67 5 (0) 3.31 8 0.49 ± 0.06 0.004 ± 0.005 54 17 3.06 ± 2.38 2.71 0.34 ± 0.23 0.37 ± 0.23 0.07**

yob 1990s 51 6 (0) 4.04 9 0.62 ± 0.04 0.005 ± 0.005 47 17 3.29 ± 2.85 2.69 0.33 ± 0.22 0.36 ± 0.23 0.07*

yob 2000s 71 7 (1) 5.07 10 0.69 ± 0.04 0.005 ± 0.005 54 17 3.06 ± 2.38 2.58 0.32 ± 0.19 0.35 ± 0.22 0.10** NS = Northern Saimaa, HV = Haukivesi area, KV = Kolovesi, MHV = Main Haukivesi area, PV = Pihlajavesi area, SS = Southern Saimaa, yod = year of death, yob = year of birht, N = nr of samples, hn = nr of haplotypes, uh = nr of unique haplotypes, a = haplotypic richness, pn = nr of polymorphic sites, h = haplotype

diversity, π = nucleotide diversity, NP = nr of polymorphic loci, NA = nr of alleles, AR = allelic richness, HO = observed heterozygosity, HE = expected hetero-

zygosity, FIS = fixation index, *P < 0.05, **P < 0.01, ***P < 0.001.

figures are 7%, 1%, and 68%, respectively. Consequently, the Saimaa population differs conspicuously from the other two populations (mtDNA: pairwise ΦST > 0.900, P < 0.001; microsatellites: FST > 0.340, P < 0.05), while differentiation between the Baltic and Ladoga subspecies is clearly weaker (mtDNA: pairwise ΦST = 0.157, P < 0.001; microsatellites: FST = 0.041, P < 0.05). Coalescent simulations were used to assess the historical population size of the Saimaa ringed seal, i.e., to find out how large the population must have been in the past to retain the level of genetic diversity observed today, and also to investigate the effects of a possible colonisation bottleneck (= founder effect) and the recent anthropogenic bottleneck (I, II). The analyses based on mtDNA and microsatellite data both suggested a historical population size of around ten thousand seals. This number clearly exceeds the estimated present‐day carrying capacity of Lake Saimaa (a few thousand seals; Hyvärinen et al., 1999). However, some 8,000 years ago the present‐day lake was part of an enormous lake complex (Saarnisto et al., 1999; Tikkanen, 2002; Oinonen et al., 2014) that could have supported even such a large population (Hyvärinen et al., 1999). Furthermore, the ABC analyses suggested that the colonisation bottleneck of the population during the formation of Lake Saimaa was severe and lasted for a long time, but indicated, as expected, that the recent, 20th‐century bottleneck has as of yet had a negligible effect on the genetic diversity of the Saimaa ringed seal (II). In contrast, both the colonisation and anthropogenic bottlenecks in the Ladoga population were suggested to have been less severe, with only a minor effect on the level of diversity. Hence, the Saimaa ringed seal has evidently lost most of its genetic diversity during its isolation of nearly 10,000 years (I, II, see also Palo, 2003; Palo et al., 2003). For the Ladoga population, which is separated from the Baltic Sea by a river only 70 km long, the possibility of occasional gene‐flow from the marine population cannot be ruled out (II).

3.2 ONGOING DIVERSITY LOSS AND EXTREMELY LOW EFFECTIVE POPULATION SIZES IN THE SAIMAA RINGED SEAL

Mammalian populations that have experienced demographic challenges exhibit > 20% lower levels of genetic diversity than do non‐endangered ones (Garner et al., 2005). Genetic variation in the Saimaa ringed seal (Table 1; I–III) was drastically lower than that of marine ringed seals (Palo et al., 2001, 2003; Palo, 2003; Davis et al., 2008; Martinez‐Bakker et al., 2013). Moreover, microsatellite diversity of this subspecies (Table 1; III) is among the lowest thus far reported for pinnipeds (Pastor et al., 2004; Schultz et al., 2009; Han et al., 2010; Sanvito et al., 2012). Also mtDNA diversity of the Saimaa ringed seal was notably low (Table 1; I). Nevertheless, as many as eight haplotypes were detected in the population. This is somewhat surprising for a population that has experienced a severe bottleneck of fewer than 150 individuals (Sipilä et al., 1990), as other mammalian populations that have undergone a comparable reduction in size retain only one or two mtDNA haplotypes (e.g., Pichler & Baker, 2000; Randi et al., 2000; Weber et al., 2000). With respect to other Fennoscandian ringed seal subspecies, the Saimaa haplotypes were all located in a single clade, with very small distances among them, which was reflected in the notably low nucleotide diversity (Table 1; I). As the geological history of Lake Saimaa and, thus, the origin of its ringed seal population are reasonably well established, the high number of haplotypes could only be explained by unusually high mutation rates and/or large historical population size, as indicated by coalescent simulations (I). When examining temporal changes in mtDNA haplotype frequencies, we detected high differentiation among the last three decades (overall and pairwise FST values ≥ 0.356, all P < 0.001; I). This result suggests that the population is currently so small that the effect of genetic drift is pronounced, causing haplotype frequencies to fluctuate even over such a short time span. Indeed, the total and regional effective population sizes

estimated for the Saimaa ringed seal based on microsatellites were extremely low (NE = 5 – 113; III). Comparable NEs have been estimated for other endangered and/or bottlenecked populations of large mammals (e.g., Aspi et al., 2006; Ortego et al., 2011; Casas‐Marce et al., 2013). Furthermore, a slight, but evident decrease was observed in both mtDNA variation and individual microsatellite

Figure 2. Cumulative mtDNA haplotype diversity (past‐to‐present) in the Saimaa population (A) and observed heterozygosity of Saimaa ringed seal individuals (B) in relation to year of birth. Error bars are ±1 SD. In (B), the number of seals in each 5‐ year category is given above the error bar.

heterozygosity based on the birth year of seals during the past few decades (Fig. 2AB; I, III), suggesting ongoing diversity erosion in the population. Hence, in addition to major diversity loss caused by the colonisation bottleneck and the long postglacial period of isolation (see above), the anthropogenic bottleneck in the 20th century together with subdivision of the population into small subpopulations (see below) seems to have had an effect on the genetic diversity of this landlocked subspecies.

3.3 FINE-SCALE POPULATION STRUCTURE AND LIMITED GENE FLOW WITHIN LAKE SAIMAA

Ringed seals are potentially highly mobile: Arctic ringed seals make seasonal migrations of hundreds to thousands of kilometres (Kelly et al., 2010; Harwood et al., 2012; Crawford et al., 2012; Martinez‐Bakker et al., 2013), and individual Baltic ringed seals have been observed travelling comparable distances (Oksanen S., unpublished). High mobility of the species is further supported by genetic studies, which have shown near‐ panmixia in marine ringed seals: only weak differentiation was detected between Arctic and Baltic populations located thousands of kilometres apart (Palo et al., 2001; Martinez‐Bakker et al., 2013). In contrast, Saimaa ringed seals are considered fairly sedentary (Kunnasranta, 2001; Koskela et al., 2002; Niemi et al., 2012; 2013b), although individual seals, especially juveniles, have been observed to regularly travel some tens of kilometres, a relatively long distance within the lake (Niemi et al., 2012; 2013a). However, no migrations between the main basins of the lake have been detected, which has raised concerns of isolation of breeding areas. Indeed, analyses based on both mitochondrial and nuclear markers indicated limited gene flow within the lake: differentiation among the four main basins (Fig. 3A) was statistically highly significant for both marker types (I, III). In contrast, a previous study based on eight microsatellite loci

suggested only weak differentiation between the northern and southern parts of the lake (Palo et al., 2003), which was evidently due to limited numbers of samples and loci in that study. Furthermore, a statistically significant isolation‐by‐distance pattern was detected for both markers (III). This result reflects

Figure 3. Collection sites of Saimaa ringed seal specimens used in mtDNA (A) and microsatellite (B) analyses. Initial (A) and updated (B) division of the lake. Different colours denote different mtDNA haplotypes (A) and different clusters indicated by a Bayesian genotype‐assignment analysis conducted using the program TESS (B).

closer relatedness of individual seals that had been found close to each other as compared to that of individuals with a greater distance from each other, further supporting the finding of restricted gene flow within the lake. Structuring of the Saimaa ringed seal population was also examined using only data on the microsatellite genotypes of individual seals, with no presumption of spatial division of the lake (III). Bayesian clustering analyses revealed surprisingly fine‐scaled structuring of the population (Fig. 3B). The analysis of the upper hierarchical population structure suggested two clusters within Lake Saimaa, one of which, quite surprisingly, almost exclusively included seals from the relatively small, labyrinthine Kolovesi basin, whereas the other represented the rest of the lake (III). A further analysis incorporating also the sampling‐site locations and topography of the lake (i.e., the actual dispersal routes of seals) as prior information indicated the presence of four clusters (Fig. 3B). The semi‐isolation of the Kolovesi basin was confirmed and, additionally, seals from Northern Saimaa formed one cluster, as was expected in the initial division of the lake (Fig. 3A). However, individuals from the Pihlajavesi area and Southern Saimaa were suggested to belong to a single cluster. The rest of the Haukivesi area (Kolovesi excluded) mainly formed one cluster, but also seemed to represent an admixture zone. Thus, we reassessed the initial spatial division of Lake Saimaa: we retained Northern Saimaa, the Pihlajavesi area, and Southern Saimaa in their original form based on significant FST values among them, but split the Haukivesi area into two subregions (Kolovesi and Main Haukivesi; Fig. 3B). Notably, the significant heterozygote deficit found in the total population (FIS = 0.075, P < 0.001) disappeared after division into the aforementioned five regions (Table 1; III). Migration rates estimated among these five regions based on microsatellite data were very low, except for the rate from the Pihlajavesi area to Southern Saimaa, which was 20.4% and the only rate significantly different from zero (III). However, the migration rate in the opposite direction was low and did not significantly depart from zero, suggesting that the Pihlajavesi

area, which is the most productive breeding area of the Saimaa ringed seal (Metsähallitus, 2014), serves as a source for Southern Saimaa. This is a particularly important finding, since identifying productive subpopulations, which may serve as sources, from less productive ones acting as sinks, is essential in designing and allocating conservation measures (Hansen, 2011). The regional differentiation revealed by maternally inherited mtDNA was very strong (overall and pairwise FST values ≥ 0.311, all P < 0.001; I, III), indicating that Saimaa ringed seal females have a tendency to stay in their natal region to reproduce (I). Female philopatry has not been reported in marine ringed seals, but it has been observed in related species, such as grey seals (Halichoerus grypus) (Allen et al., 1995; Pomeroy et al., 2000) and harbour seals (Phoca vitulina) (Stanley et al., 1996). In contrast, spatial differentiation in biparentally inherited microsatellites was moderate (overall five‐region FST = 0.107, pairwise FST values = 0.039 – 0.236, all P < 0.01; III). The difference in the level of differentiation in microsatellites and mtDNA could be due to differences in effective sizes of these markers. However, it more probably results from sex‐biased dispersal, as turned out to be the case also for the Saimaa ringed seal: gene flow mediated by males was estimated to be over sevenfold compared to that by females (III). The ratio is close to values reported, for example, for harbour seals (Herreman et al., 2009) and California sea lions (Zalophus californianus) (González‐ Suárez et al., 2009).

3.4 IDENTIFICATION OF INDIVIDUALS AND THE UTILITY OF PLACENTAS IN GENETIC MONITORING OF THE SAIMAA POPULATION

Monitoring Saimaa ringed seals is challenging, as they not only are very few in number, but also extremely elusive animals that spend about 80% of their time submerged (Hyvärinen et al., 1995; Niemi et al., 2013b). We developed a method for

identification of individuals from DNA samples for this genetically very uniform subspecies (IV). The marker system based on 17 microsatellite loci proved to be adequate for identifying individual Saimaa ringed seals: the probability that two randomly chosen individuals share identical genotypes at the studied loci was PI = 4.8 × 10–7, and the corresponding value for siblings was PISIB = 1.2 × 10–3. This means that, in the population of around 300 seals (Metsähallitus, 2014), the expected number of genotype matches is as low as 0.0001 for unrelated individuals and 0.36 for siblings. Although the corresponding values were slightly higher for the 11‐locus panel, it could be sufficient for spatially and/or temporally restricted surveys of the population. However, there is not enough power in the 17‐locus marker system for conducting kinship analyses, owing to the low genetic diversity of the subspecies (III, IV). Collecting non‐invasive samples of marine mammals is often challenging, although shed skin and faeces have been successfully utilised in genetic studies (Parsons et al., 2006; Valqui et al., 2010; Martinez‐Bakker et al., 2013; Baker et al., 2013). Our study using Saimaa ringed seal placentas in genetic analyses (IV) is the first to describe the use of placentas as non‐ invasive samples from a natural population. Postnatal consumption of the placenta (placentophagia), is routine behaviour among female mammals, but it does not occur within the order Pinnipedia (Kristal et al., 2012). Hence, we found a placenta at nearly half of the inspected birth‐lair sites, even though the collection was conducted two to three months after parturition. As the pinniped placenta is composed of tissues of both the female and its offspring in close union (Stewart & Stewart, 2009), we aimed at finding the optimal sampling spot for genotyping both the mother and the pup from the placentas. The pup’s genotype was found to be reliably obtainable from the umbilical cord (IV). Thus, identification of the pup from a placenta and later encounter of the adult individual would yield information on the dispersal of seals. Additionally, genetic

diversity and differentiation indices acquired using data on umbilical cord samples (i.e., samples of pups) were highly correspondent with those based on the reference datasets (I, III), indicating that placentas can be used for inferring standard population‐genetic parameters. However, we did not succeed in obtaining the mother’s genotype separately, which is most likely due to a high level of intermingling between the maternal and foetal tissues in the areas of contact. Nevertheless, fine‐tuning the sampling method together with the use of next‐generation sequencing technology may enable inferring also the mother’s genotype from the placenta in the near future.

4 Conclusions and challenges for conservation

This study, based on an extensive survey of neutral genetic variation of the Saimaa ringed seal, shows that the genetic diversity of this small and isolated population is extremely low and, worryingly, still declining (I, III). Microsatellite diversity in this subspecies (HE = 0.36) is the lowest thus far recorded within the order Pinnipedia (III). The present effective population size estimates (Ne = 5–113, III) were remarkably low, also indicating that the population is too small to maintain the current diversity in the long term. This was further seen in high temporal differentiation at mtDNA haplotype frequencies among the past few decades, which demonstrates strikingly the strong effect of genetic drift within such a short time span (I). Although most of the original diversity of the Saimaa ringed seal undoubtedly was lost during its long isolation of nearly 10,000 years (I, II, see also Palo et al., 2003), we observed evident decreases in both mtDNA variation and individual microsatellite heterozygosity even within the past few decades (Fig. 2AB, I, III). The ongoing loss of diversity suggested by our results is disconcerting given the critically endangered status of the population. Fragmentation of a population into even smaller units may further reduce its genetic diversity and increase the level of inbreeding (i.e., autozygosity) of the total population (Keyghobadi 2007). This seems to be the case for the Saimaa ringed seal, as we detected unexpectedly fine‐scaled differentiation among regional subpopulations (I, III). The structuring of the population is undoubtedly induced by the small population size and fragmented topography of the lake, but also by behavioural patterns of the seals. The present study demonstrated that Saimaa ringed seal females have a tendency to stay in their natal area for breeding (I), whereas males are

more prone to disperse. Thus, gene flow within the lake is predominantly male‐mediated, but nevertheless limited and insufficient to counteract the effect of genetic drift (III). Despite of the low genetic diversity of this small, landlocked population, no clear signs of inbreeding depression have been detected. However, environmental changes may drive populations to extinction, unless they are able to adapt to altered conditions (Hoffmann & Sgrò, 2011). In this respect, the low neutral genetic diversity of the Saimaa ringed seal is a cause of concern, as its adaptive variation may be similarly affected (cf. Bollmer et al., 2007; Babik et al., 2009; Smith et al., 2009), which could reduce its ability to respond to a warming climate and other environmental changes (Willi et al., 2006; O’Corry‐Crowe, 2008). Further, as small population size along with low diversity may weaken population‐level resilience to both demographic and environmental stochasticity (Lacy, 1997), the significant differentiation among breeding areas raises concerns about the viability of the even smaller subpopulations and, in particular, that of the Kolovesi basin (Fig. 1B). Genetic rescue, i.e., introduction of unrelated individuals into an inbred population, is often suggested and increasingly being implemented as a conservation measure for small and isolated populations (e.g., Hogg et al., 2006; Bouzat et al., 2009; Hedrick & Fredrickson, 2010). For safeguarding the Saimaa population, Saarnisto (2011) suggested translocations of Ladoga ringed seals to Lake Saimaa. However, as the Saimaa ringed seal is recognized as a morphologically and ecologically distinct subspecies (Hyvärinen & Nieminen, 1990; Kunnasranta, 2001), and differs also genetically from the other Fennoscandian subspecies (I, II), such actions would most likely be highly risky. Interbreeding between genetically diverged individuals may result in outbreeding depression (Tallmon et al., 2004), which could compromise the adaptation of the Saimaa subspecies to its unique lacustrine habitat. In addition, the immigrants might carry diseases towards which Saimaa ringed seals are not resistant. Therefore such extreme measures should only be taken as a last resort.

Hence, along with mitigating the most acute threats posed by by‐catch mortality and breeding‐habitat deterioration due to warming winters (Niemi 2013; Auttila et al., 2014), future conservation efforts should concentrate on preserving the current diversity of the Saimaa ringed seal and enhancing gene flow among subpopulations. The most efficient method for this would be increasing the overall population size, which would simultaneously restore both genetic diversity and gene flow. However, because rapid population growth is improbable in this slowly reproducing species, translocations of adult seals, especially females, should be considered as a short‐term conservation measure. For designing such actions, this study has helped in identifying management units within the population, i.e., distinguishing subpopulations that are productive and could serve as sources from ones that are semi‐ isolated and possibly not viable on their own in the long term. Furthermore, the non‐invasive genetic sampling method based on placentas developed here can provide an efficient means not only for monitoring the population in general, but also for evaluating the success of conservation efforts.

5 References

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dissertations

Mi a Va ltonen

RESEARCH ARTICLE

Spatial and temporal variation in genetic diversity of an endangered freshwater seal

Mia Valtonen • Jukka U. Palo • Minna Ruokonen • Mervi Kunnasranta • Tommi Nyman

Received: 29 June 2011 / Accepted: 15 May 2012 / Published online: 4 June 2012 Ó Springer Science+Business Media B.V. 2012

Abstract Molecular-genetic assessments of demographic gene pool of this critically endangered subspecies. The history, population structure, and loss of genetic variation mtDNA diversity of the Saimaa ringed seal is low, con- in small natural populations are often hampered by a lack sisting of only eight haplotypes. Nevertheless, coalescent of information on initial diversity, isolation time, and simulations show that the diversity is unexpectedly high migration rates. The landlocked population of currently when contrasted with the over 800 generations of isolation less than 300 ringed seals inhabiting Lake Saimaa in and the estimated historical carrying capacity of the lake. southeastern Finland offers a unique model for studying In the short term, marked temporal and spatial differenti- these questions in small populations. It has remained ation was observed among three decades and four regions completely isolated since the last ice age, information on of Lake Saimaa, suggesting extremely low effective sub- its initial genetic diversity can be inferred from the large population sizes and limited migration of females within marine source population in the Baltic Sea, and its ecology the lake. Our study strongly suggests that enhancing gene is well studied. We analyzed the mitochondrial DNA ﬂow and population growth is crucial for maintaining the diversity of 215 Saimaa ringed seals spanning three dec- remaining genetic diversity of the Saimaa ringed seal. ades in order to assess long- and short-term changes in the Keywords Genetic drift MtDNA Small population Spatio-temporal differentiation Coalescent simulation Electronic supplementary material The online version of this Saimaa ringed seal article (doi:10.1007/s10592-012-0367-5) contains supplementary material, which is available to authorized users.

M. Valtonen (&) M. Kunnasranta T. Nyman Introduction Department of Biology, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland e-mail: mia.valtonen@uef.fi Genetic variability, the raw material of evolution, is reduced more rapidly in small and isolated populations J. U. Palo than in large stocks due to intensified genetic drift, which Hjelt Institute, Department of Forensic Medicine, University eventually can mean increased vulnerability to environ- of Helsinki, P.O. Box 40, 00014 Helsinki, Finland mental stochasticity (Frankham 2005; Willi et al. 2006; M. Ruokonen Liao and Reed 2009). Unavoidable inbreeding in small Department of Biology, University of Oulu, populations may also have negative effects on individual- P.O. Box 3000, 90014 Oulu, Finland level survival and reproductive success (Madsen et al. M. Kunnasranta 1996; Zachos et al. 2007; Cristescu et al. 2009; Blomqvist Finnish Game and Fisheries Research Institute, Itaïnen Pitka¨katu et al. 2010). Population subdivision further reduces effec- 3A, 20520 Turku, Finland tive population sizes and intensifies these genetic adversities, increasing the risk of extinction due to ecological T. Nyman Institute of Systematic Botany, University of Zurich, or stochastic factors (Saccheri et al. 1998; Nieminen Zollikerstrasse 107, 8008 Zurich, Switzerland et al. 2001; O’Grady et al. 2006). Migration between 123 1232 Conserv Genet (2012) 13:1231–1245 subpopulations is therefore often crucial for maintaining genetic diversity (Keller and Waller 2002; Palstra and (a) Ruzzante 2008), but if the population as a whole is very small, even extensive gene flow cannot prevent the overall loss of variability (Ortego et al. 2011). In natural populations, assessments of demographic history and past changes in genetic diversity are often hampered by a lack of information on population origins, initial diversity, isolation time, and/or inter-population migration rates (cf. Hoelzel et al. 2002; Schultz et al. 2009; Hu¨rner et al. 2010; Smith et al. 2011). For studies on the genetic effects of small population size, the Saimaa ringed seal (Phoca hispida saimensis) population offers a unique model. The population has remained completely isolated for a known time, and its initial diversity can be approxi- mated from the large and thus relatively unchanged marine source population. Furthermore, the current population size and ecology of this subspecies is well known, and extensive sampling is enabled by systematic specimen collection (b) conducted since the 1970s. Yet, many aspects of the demographic history of the Saimaa ringed seal, such as past population sizes, remain unknown, but can be explored using genetic data. The Saimaa ringed seal is endemic to Lake Saimaa in southeastern Finland (Fig. 1a). It is a critically endangered landlocked subspecies of the holarctically distributed ringed seal (Phoca hispida). The Fennoscandian ringed seal populations in Lake Saimaa, Lake Ladoga (Phoca hispida ladogensis), and the Baltic Sea (Phoca hispida botnica) derive from Arctic ringed seals (Phoca hispida hispida), which colonized the Baltic basin from the Atlantic during the deglaciation of the Scandinavian ice sheet, c. 11,500 years ago (Forsteń and Alhonen 1975; Ukkonen 2002). Continuing land uplift gave rise to numerous lakes, including lakes Saimaa and Ladoga, where parts of the Baltic stock were trapped. The ringed seals in Lake Saimaa have thus lived in complete isolation for at least 9,500 years (c. 860 generations), evolving into a morphologically and ecologically distinct subspecies (Hyva¨rinen and Nieminen 1990; Kunnasranta 2001). Fig. 1 Map of the three water basins (the Baltic Sea, Lake Saimaa, and Lake Ladoga) inhabited by the focal ringed seal subspecies (a), In contrast to other ringed seal populations, the Saimaa and the regional division of Lake Saimaa (b). In b the first number in population has presumably never been large due to the parentheses refers to the current estimated size of the local limited dimensions of the lake: Hyva¨rinen et al. (1999) population, and the latter to the regional sample size estimated the carrying capacity of the lake to have been a few thousand seals before the human impact. Although population rebounded to 250–300 individuals. Neverthe- never particularly large, the population started to plummet less, the Saimaa ringed seal is still considered critically after the turn of the 19th century as a result of hunting, by- endangered (Rassi et al. 2010; Kovacs et al. 2012). catch mortality, disturbance, and pollution (Ranta et al. The extended period of isolation in combination with the 1996; Hyva¨rinen et al. 1998; Kokko et al. 1998). Despite a small population size has had a considerable impact on the ban on hunting introduced in 1955, the population decline genetic diversity of Saimaa seals: Previous studies based continued until a minimum of less than 150 individuals was on microsatellites (Palo et al. 2003) and mitochondrial reached in the 1980s (Sipila¨ et al. 1990). After this bot- control-region sequences (Palo 2003) have shown that most tleneck phase, conservation efforts were intensified and the of the initial variability has been lost. However, the main 123 Conserv Genet (2012) 13:1231–1245 1233 focus of both of these studies was on comparisons among left domain of the mtDNA control-region using primers ringed seal subspecies, while sampling within Lake Saimaa H16305 (50-AAGGAAGAGGTAACAACCC-30, Palo was limited. The microsatellite analysis revealed weak 2003) and L224 (50-GTGTACGTAACGTAACTATGT-30; differentiation between the northern and southern parts of own design). Primer numbers refer to nucleotide positions the lake, but the low sample sizes meant that temporal and in the complete mtDNA sequence of the harbor seal (Phoca finer-scale spatial variation could not be studied (Palo et al. vitulina) (GenBank accession no. X63726; A´ rnason and 2003). In addition, nuclear microsatellites do not neces- Johnsson 1992). PCR amplifications were performed in a sarily tell much about the movements of females if the total volume of 25 ll, each reaction containing 5–20 ng of sexes differ in their dispersal patterns, as is often the case genomic DNA, 0.4 lM of each primer, 0.75 U of GoTaq for mammals (Greenwood 1980). The aim of our present Flexi DNA polymerase (Promega Corp., Madison, Wis- study was to gain a better understanding of both long- and consin), 19 GoTaq Flexi reaction buffer (Promega), short-term changes in genetic diversity in this small natural 2.5 mM MgCl2, and 0.15 mM of each nucleotide (Finn- population with a relatively well-established history. To zymes Oy, Espoo, Finland). We used the PCR cycling this end, we conducted an extensive survey of mtDNA profile described in Palo (2003): 94 °C for 2 min, 6 cycles control-region variation in ringed seals collected from of 94 °C for 45 s, 56 °C for 20 s, 72 °C for 50 s, followed different parts of Lake Saimaa through three decades, and by 26 cycles of 94, 58, and 72 °C, 45 s each; the final used the larger populations of the Baltic Sea and Lake elongation step was extended to 5 min. PCR products were Ladoga as references. The results shed light on processes purified using the QIAquick PCR Purification Kit (Qiagen), that erode and create genetic diversity in small populations, and then sequenced with the PCR primers in both direc- and uncover pronounced temporal and spatial stochasticity tions with the BigDye Terminator v3.1 Cycle Sequencing in the small, isolated, and subdivided gene pool within Kit (Applied Biosystems, Foster City, California) and an Lake Saimaa. ABI 3730xl automated sequencer (Applied Biosystems). Sequences were read and edited using Sequencher version 4.9 (Gene Codes Corp., Ann Arbor, Michigan) and aligned Materials and methods using ClustalW version 2.0 (Larkin et al. 2007). The control-region segment of all Saimaa ringed seals was 704 bp Specimens long, while the corresponding fragment of the Baltic and Ladoga individuals varied between 704 and 706 bp, and A total of 215 ringed seals from different parts of Lake the length of the completed alignment was 707 bp. Saimaa were analyzed for mitochondrial DNA variation All sequences have been deposited in GenBank under (Table S1, Supplementary material). Most specimens accession numbers JX109584–JX109833. (N = 203) were obtained from the tissue bank maintained by the University of Eastern Finland and Natural Heritage MtDNA diversity and differentiation among ringed seal Services of Matsa¨hallitus, where specimens of Saimaa subspecies ringed seals found dead have been deposited since the 1970s. The individuals included in this study had been Mitochondrial DNA variability for each subspecies was found between the years 1980 and 2008. Additionally, estimated using standard indices of genetic variation: placentas (N = 12) were collected from the vicinity of Number of mtDNA haplotypes (hn), number of private birth lairs by scuba diving after the breeding season in haplotypes (hp), number of variable sites (vs), haplotype 2009. Placentas were collected during a single year only, in diversity (h), and nucleotide diversity (p). Diversity indices order to avoid sampling the same individuals twice. Baltic were computed with ARLEQUIN 3.11 (Excoffier et al. ringed seals (N = 19; provided by the Finnish Game and 2005), unless otherwise specified. ModelTest version 3.7 Fisheries Research Institute) and Ladoga ringed seals (Posada and Crandall 1998) was used in conjunction with (N = 16; obtained from the tissue bank of University of PAUP* version 4.0b10 (Swofford 2000) to find the optimal Eastern Finland) were used as reference specimens (Table substitution model for the whole dataset; both the Akaike S1, Supplementary material). information criterion and hierarchical likelihood ratio tests

indicated a TrN?I?C4 model as optimal. Genetic distances PCR and sequencing among haplotypes were calculated in PAUP* using the PaupUp graphical interface (Calendini and Martin 2005), Total genomic DNA was extracted from tissue specimens using the estimated proportion of invariant nucleotide sites stored at –20 °C using DNeasy Blood and Tissue Kits pinv = 0.8788 and gamma-shape parameter a = 1.408; the (Qiagen, Valencia, California), following the manufac- distances were then used for estimating the average pair- turer’s instructions. We PCR amplified a fragment of the wise distance among all haplotypes (i.e., nucleotide 123 1234 Conserv Genet (2012) 13:1231–1245 diversity p) and respective standard deviations for each et al. 2001), this roughly corresponds to the beginning of population. Haplotypic richness (a) was estimated using the 19th century, and is taken to represent the start of the the rarefaction method implemented in HP-Rare (Kali- negative human impact on population sizes. Henceforth, nowski 2005) separately for each subspecies using the the population size remains constant at the hypothetical smallest number of individuals in that group. carrying capacity until t = 860 when the Saimaa popula- A haplotype network was calculated in Network 4.5.1.6. tion is isolated from the Baltic Sea population. The Baltic (www.fluxus-engineering.com) using the Median-Joining population is modeled as hosting 2,000 females at t = 0, network algorithm (Bandelt et al. 1999), and the MP option increasing to 25,000 at t = 19, after which it remains was implemented to resolve the network (Polzin and constant in size. Daneshmand 2003). Networks were constructed weighting The initial population sizes of 50 and 2,000 effective transversions equal to, three, and ten times higher than females for Lake Saimaa and the Baltic Sea (present census transitions, and with parameter e values of 10, 20, and 29. size c. 10,000; Harkonen et al. 2008), respectively, were TCS version 1.21 (Clement et al. 2000) was also used to estimated based on the observation that c. 40 % of the reconstruct the haplotype network. The program uses sta- female seals reproduce during a given breeding season tistical parsimony (Templeton et al. 1992) to generate an (Smith 1973). For Saimaa, the population size at t B 19 unrooted cladogram based on a matrix of absolute differ- was picked from a uniform distribution between ences among haplotypes, and identifies the most probable Nef = 100–10,000. This historical Nef range is extremely ancestral haplotype according to coalescent theory (Cas- broad, as the natural population size of the ringed seals in telloe and Templeton 1994). The analysis was run with a Lake Saimaa has been estimated to be 2,000–4,000 in the 95 % parsimony connection limit, and gaps were treated as past (Hyva¨rinen et al. 1999). a fifth character state. For each run, 105 707-bp mtDNA datasets were simulated Analysis of molecular variance (AMOVA; Excoffier under the K2P mutation model with gamma-corrected rates et al. 1992) and pairwise comparisons were used to (8 rate classes, a = 0.01; estimated using ModelTest for the examine differentiation among the three subspecies. The K2P?C model) and with the proportion of transitions set to analyses were run in ARLEQUIN using both conventional 0.9 of all mutations. Target Saimaa diversity values (hn = 8, F-statistics (i.e., based on haplotype frequencies only) and h = 0.646, p = 0.012) for the K2P?C model were calcu- U-statistics estimated on the basis of the inter-haplotypic lated using ARLEQUIN 3.11. The mutation frequency was distance matrix calculated in PAUP* while assuming a picked from a uniform distribution between u = 0.0001–

TrN?I?C4 model of substitution. 0.01 substitutions/haplotype/generation. The mutation frequency estimated for the ringed seal control-region is l = Demographic history of the Saimaa ringed seal 3.9 9 10-9 substitutions/site/year, which for the current data corresponds to u = 0.0003 substitutions/haplotype/ The history of the Saimaa seal population was examined generation (Palo 2003). through two simulation approaches with differing logic and The combination of historical population size and background assumptions. Firstly, coalescent simulations in mutation rate providing diversity values similar to those Bayesian Serial SimCoal (BayeSSC; Excoffier et al. 2000; observed in Saimaa today were estimated simultaneously. Anderson et al. 2005) were performed to explore mutation Alternatively, u was fixed to certain values (u = 0.0001/ and population size parameters. Secondly, demographic 0.001/0.005/0.01), while Nef was allowed to vary across a parameters for the four Saimaa regions (Fig. 1b) and for the broad spectrum. From these simulated datasets, 1 % Baltic Sea population were estimated using a Markov Chain yielding diversity values closest to the observed ones were Monte Carlo method under the isolation with migration chosen to estimate the posterior probabilities for the sim- model implemented in the software IMa2 (Hey 2010). ulation variables (historical Nef and/or u). Each run was In the BayeSSC analyses, the mtDNA diversity indices repeated five times to ensure convergence. (hn, h, p) observed in the Saimaa population were con- Demographic split times (t), population sizes (h), and trasted with results from corresponding datasets simulated migration rates (m), all scaled by the per haplotype muta- under a number of demographical scenarios with varying tion rate u, were estimated using the IMa2 software. population sizes and mutation frequencies. The Saimaa and Importantly, the method implemented in IMa2 does not Baltic populations were both included in the historical assume mutation–migration–drift equilibrium. For the model, which was structured as follows: At t = 0 (present analyses involving five extant populations, the branching day) the Saimaa population hosts 50 (effective) females order must be defined. For this, we assumed a simple and reaches ‘‘historical population size’’ at t = 19. phylogenetic tree that first splits the Saimaa and Baltic Assuming an 11-year generation interval, estimated from subspecies, and then divides the main breeding areas within the age-specific fecundity scheme in Smith (1973; see Palo Lake Saimaa (see Figs. 1b, S1, Supplementary material). 123 Conserv Genet (2012) 13:1231–1245 1235

We also assumed no migration between the Baltic and Standard indices of genetic variation for each of the four Saimaa subspecies since the formation of Lake Saimaa. spatial and three temporal samples within Lake Saimaa The simulations were run under the HKY mutation model were calculated as described above. Diversity indices were (Hasegawa et al. 1985) and implementing uniform priors also calculated for temporal samples that were based on the with high upper bounds for the estimated parameters (see birth decade of seals: The 1960s (N = 7), the 1970s Fig. S1, Supplementary material, for prior limits); these (N = 14), the 1980s (N = 67), the 1990s (N = 51), and the prior values were chosen based on a large number of test 2000s (N = 71). The year of birth was calculated by sub- runs of varying upper bound settings and run lengths. tracting the age of an individual (determined by counts of Fourteen independent runs having a 500,000-step burn-in cementum layers of canine teeth; Stewart et al. 1996) from followed by 1.0–4.1 million steps with 100 simultaneous the collection year. The cut-off date for the age determi- chains and a geometric heating scheme (ha = 0.99, nation was set to the 1st of March, as pups are born hb = 0.75) were performed. Genealogies were sampled approximately at that time, and this was taken into con- every 100 steps. Convergence was evaluated based on the sideration when calculating the year of birth. consistency of results between the runs and on the auto- In order to ensure that our temporal analyses were not correlation of different estimates. The reported posterior compromised by the choice of discrete time intervals for probability estimates are based on 223,500 post-burnin classifying specimens, temporal variation in genetic diver- genealogies sampled from all 14 runs. sity was further studied by using bidirectional plots of The estimated scaled model parameters can be con- cumulative haplotype diversity (Pichler and Baker 2000). verted into demographic effective population sizes Curves based on the birth years of individuals were con-

(Ne = h/4u), divergence times (T = t 9 g/u), and migra- structed for the whole population sample (N = 210) as well tion rates per gene copy and generation (M = m 9 u) if the as for the two largest subpopulations (Haukivesi, N = 113; mutation rate (u) and generation interval (g) are known. and Pihlajavesi, N = 60). The cumulative plots were gen- Due to the use of an inheritance scalar of 0.25 in the runs, erated by dividing individuals into two temporal groups by population size estimates refer to Ne instead of Nef. each year within the study period, and then successively recording the haplotype diversity of both partitions (past-to- Population structure and gene flow within Lake Saimaa present and present-to-past) for each dividing year. The past- to-present curve of each dataset was started from the year In order to study how mtDNA diversity within the Saimaa when the sample size reached six individuals. ringed seal population varies in space and time, the sampled Analysis of molecular variance (AMOVA; Excoffier et al. individuals were divided into four spatial and three temporal 1992) and pairwise comparisons were used to examine dif- groups. The spatial division followed the morphology of the ferentiation among the spatial samples and the temporal lake: Northern Saimaa (N = 19), Haukivesi area (N = 116), samples based on collection decades. In addition, hierar- Pihlajavesi area (N = 61), and Southern Saimaa (N = 19) chical AMOVA analyses were run for spatial samples sub- (Fig. 1b). The aforementioned regions are connected only by divided into temporal subsamples and vice versa. Within- narrow straits and, additionally, the waterway connecting the Saimaa analyses were run using conventional F-statistics Haukivesi and Pihlajavesi areas runs through the town of only, since mutations are not relevant within the focal time Savonlinna. Currently, about 110 and 125 seals inhabit the scale. Pairwise comparisons and AMOVA analyses based on Haukivesi and Pihlajavesi areas, respectively, but Northern the same grouping schemes were also performed using a and Southern Saimaa harbor only c. 10 and 40 seals reduced dataset that included only Saimaa ringed seal pups (Metsa¨hallitus, Natural Heritage Services 2012). The small (\1 y). Statistical significance of the results was inferred sample sizes in Northern and Southern Saimaa thus reflect using 10,000 permutations. Sequential Bonferroni correc- the low number of seals living in those peripheral regions. tions (Rice 1989) were used to correct significance levels The temporal division in our main analyses followed the whenever multiple comparisons were made. collection (= death) decade of each specimen: The 1980s (N = 79), the 1990s (N = 54), and the 2000s (N = 82). A decade roughly corresponds to the aforementioned ringed Results seal generation time of 11 years (Palo et al. 2001). We note that the interpretation of some of the AMOVA results below MtDNA diversity and genetic differentiation could be affected if individuals had been collected unequally among the ringed seal subspecies from different parts of the lake at different time periods; however, no evidence for a spatial bias in sampling across For all data, the length of the control-region alignment was decades was observed (Chi-square test for homogeneity, 707 bp and included 51 variable sites. The majority of the v2 = 8.98, d.f. = 6, P = 0.175). nucleotide variation was found within the first 467 base 123 1236 Conserv Genet (2012) 13:1231–1245

Table 1 MtDNA sequence variability in the control-region for the Baltic, Ladoga and Saimaa ringed seals, as well as for spatial and temporal samples of the Saimaa population Nhn(hp) avsh± SD p ± SD

Baltic Sea 19 16 (15) 13.77 46 0.977 ± 0.027 0.047 ± 0.038 Lake Ladoga 16 13 (12) 13.00 29 0.967 ± 0.036 0.015 ± 0.017 Lake Saimaa 215 8 (8) 4.04 10 0.646 ± 0.020 0.005 ± 0.005 Northern Saimaa 19 3 (0) 3.00 6 0.433 ± 0.117 0.004 ± 0.004 Haukivesi area 116 4 (0) 3.06 7 0.600 ± 0.021 0.005 ± 0.005 Pihlajavesi area 61 5 (1) 3.78 9 0.626 ± 0.035 0.006 ± 0.005 Southern Saimaa 19 5 (2) 5.00 8 0.784 ± 0.056 0.005 ± 0.004 Saimaa yod 1980s 79 6 (0) 5.55 8 0.563 ± 0.049 0.005 ± 0.005 Saimaa yod 1990s 54 7 (0) 7.00 9 0.614 ± 0.045 0.005 ± 0.005 Saimaa yod 2000s 82 7 (1) 6.50 10 0.670 ± 0.040 0.005 ± 0.005 Saimaa yob 1960s 7 5 (0) 5.00 7 0.905 ± 0.103 0.006 ± 0.004 Saimaa yob 1970s 14 4 (0) 3.23 7 0.692 ± 0.094 0.005 ± 0.004 Saimaa yob 1980s 67 5 (0) 2.38 8 0.489 ± 0.056 0.004 ± 0.005 Saimaa yob 1990s 51 6 (0) 2.75 9 0.618 ± 0.039 0.005 ± 0.005 Saimaa yob 2000s 71 7 (1) 3.33 10 0.689 ± 0.038 0.005 ± 0.005 yod year of death, yob year of birth Numbers of specimens (N), haplotypes (hn), private haplotypes (hp) and variable sites (vs) as well as haplotypic richness (a), haplotype diversity (h) and nucleotide diversity (p) are given pairs, while only two variable sites were observed in the Saimaa haplotypes. Calculations using lower values of e remaining 30 end of the segment. All diversity indices (10 and 20) yielded simpler, but essentially identical net- showed that mtDNA variability is substantially lower in works (results not shown). The statistical parsimony Lake Saimaa compared with the Baltic and Ladoga sub- method recovered the same network structure for the species (Table 1). Ten variable sites were observed in ‘Saimaa clade’ (Fig. 2b), but determined a different Saimaa (all transitions), defining eight haplotypes. In the ancestral haplotype, since TCS considers the most common reference populations, 46 variable sites were found among haplotype in the population to be the ancestral one. The the Baltic (44 transitions, one transversion, and two indels) remaining Baltic and Ladoga haplotypes were separated and 29 among Ladoga (26 transitions and three indels) from the ‘Saimaa clade’ by at least 11 mutational steps, and samples, resulting in 16 and 13 distinct haplotypes, therefore could not be connected into the same network respectively. The haplotypic richness of Saimaa seals was with 95 % confidence (results not shown). only about one-third of that observed in the two other The differences between the Baltic, Ladoga, and Saimaa subspecies (a = 4.04 vs. a C 13.00, respectively). Con- ringed seal populations are also evident as measured with trasted with the nucleotide diversity of the Baltic ringed F-statistics, both at the haplotype and nucleotide levels seals (p = 0.047 ± 0.038), diversity was reduced not only (FST = 0.219, P \ 0.001; UST = 0.900, P \ 0.001). Pair- in Saimaa (p = 0.005 ± 0.005), but also in the Ladoga wise differentiation between the Saimaa and the two other population (p = 0.015 ± 0.017). populations (FST [ 0.227, P \ 0.001; UST [ 0.901, In constructing the median-joining haplotype network, P \ 0.001) was substantially greater than that between the weighting transversions equal to, or three, or ten times Baltic and Ladoga subspecies (FST = 0.028, P = 0.007; higher than transitions had no influence on the results, UST = 0.157, P = 0.013). mainly due to the presence of a single transversion in the data matrix. The MJ network (ti/tv-ratio = 1, e = 29; Demographic history of the Saimaa ringed seal Fig. 2a) clearly illustrates the difference in mtDNA population diversity between the Saimaa and the reference populations: Baltic and Ladoga haplotypes form a complex net- In order to find the approximate bounds for the historical work with long inter-haplotypic distances, whereas all population size in the Saimaa population, we explored Saimaa haplotypes are placed in a single tight clade. No different female population sizes and mutation frequencies haplotypes are shared between Saimaa and Baltic or that would yield mtDNA diversity values similar to those between Saimaa and Ladoga samples but, interestingly, observed in Saimaa ringed seals today using the Bayesian one Baltic haplotype is placed in the same clade as the Serial SimCoal approach. Simultaneous estimation of the

123 Conserv Genet (2012) 13:1231–1245 1237

Saimaa H8 (a) Saimaa H8 (b) (N = 3) (N = 3) Saimaa H7 Saimaa H7 (N = 5) Saimaa H1 (N = 5) Saimaa H1 (N = 98) (N = 98)

Saimaa H3 Saimaa H3 (N = 81) Saimaa H5 (N = 81) Saimaa H5 (N = 3) (N = 3) Saimaa H2 (N = 16) Saimaa H2 (N = 16)

Saimaa H4 (N = 6)

Saimaa H6 Saimaa H4 (N = 3) (N = 6)

Saimaa H6 (N = 3)

100 Saimaa 1980s Hypotethical haplotype Saimaa 1990s Most probable 50 Saimaa 2000s ancestral haplotype 10 1 Baltic Sea 1 mutational step

Northern Saimaa Baltic Sea 100 Haukivesi area Lake Ladoga 50 10 Pihlajavesi area Median vector 1 Southern Saimaa 1 mutational step

Fig. 2 Inferred genealogical network of ringed seal mtDNA haplo- corresponding haplotype, and branch lengths are proportional to the types based on a median-joining algorithm to illustrate phylogenetic number of inferred mutations. For the Lake Saimaa population, each relationships among ringed seal haplotypes found in Lake Saimaa, the haplotype is identiﬁed (H1–H8) and the numbers of individuals found Baltic Sea, and Lake Ladoga, and b statistical parsimony for the Lake with the given haplotype are shown. The pie charts show the Saimaa population alone (including one Baltic Sea haplotype). The frequencies of each Lake Saimaa haplotype a in different regions and size of each circle is proportional to the frequency of the b within three decades

mutation frequency and the historical population size ﬁxed mutation rates ranging from 0.0001 to 0.01 (Fig. 3). resulted in bimodal posterior distributions (results not The simulations indicated that, in order to retain the levels shown), apparently due to parallel effects of population of diversity present in the population today, the mutation size and mutation frequency on the maintenance of frequency should have been u C 0.005 or the effective sequence variability. The bimodal peaks for the historical female population size Nef [ 2,000. population size lie around 2,000 and 7,000 females, and for For a more ﬁne-grained analysis of demographic the mutation frequency per gene and per generation at parameters, altogether 223,500 genealogies from 14 inde- around 0.001 and 0.008. The latter estimates correspond to pendent IMa2 runs were combined. For population sizes 1.3 9 10-7 and 1.0 9 10-6 substitutions/nucleotide/year, (h) and split times (t), all runs gave rather stable posterior respectively. estimates that were well below the prior upper bounds (Fig. Due to the uninformative bimodality observed in the S1, Supplementary material). However, no reliable esti- posterior distributions above, the probabilities of different mates of migration rates (m) among subpopulations were historical population sizes were explored using several obtainable, despite an extensive number of trial runs

123 1238 Conserv Genet (2012) 13:1231–1245

Population structure and gene ﬂow within Lake Saimaa

0.00040 Out of the eight haplotypes identiﬁed within the Saimaa population, two (H1 and H3) were common (45.6 and 37.7 %, respectively) and were shared among all four u = 0.01 regions of the lake (Fig. 2a and Fig. S3a, b, Supplementary 0.00030 u = 0.0001 material). The remaining six haplotypes comprised only 16.7 % of haplotypes altogether (H2 7.4 %, and H4–H8 \ 3 % each). Three haplotypes were found only in a single u = 0.005 region (H5 and H6 in Southern Saimaa and H8 in the Pi- hlajavesi area) and another three (H2, H4, and H7) were u = 0.001

Posterior probability shared between two geographically adjacent regions. Within-region haplotypic richness and haplotype diversity were highest in Southern Saimaa (Table 1), despite the population being currently rather small in that area. In all temporal samples, H1 and H3 were the predominant haplotypes, but their relative abundances changed during the

0.00000 0.00010 0.00020 study period: H1 was the most common haplotype in the 0 2000 4000 6000 8000 10000 1980s and 1990s (60.8 and 51.9 %, respectively), whereas Effective population size (females) H3 became the most common haplotype in the 2000s Fig. 3 Posterior probabilities of historical effective female popula- (50.0 %) (Fig. 2b and Fig. S3c, d, Supplementary material). tion sizes of Saimaa ringed seals obtained with observed mtDNA No clear change in the overall level of diversity was observed diversity values and simulated with four different mutation across the three decades when individuals were classified frequencies according to their year of death (Table 1). However, the alternative analysis in which decadal groups were instead performed to find suitable run parameters (Fig. S2, Sup- based on birth years points towards higher diversity in the plementary material). The posterior estimates for m were 1960s (Table 1) and, likewise, the bidirectional cumulative always heavily dependent on (uniform or exponential) plots of haplotype diversity suggest that diversity decreased prior values, in many cases concentrating near the upper through the 1960s and 1970s and then remained roughly bound. However, during the trial runs, some of which were stable for the following three decades (Fig. 4a). This pattern extensive, we found no correlation between migration and found in the whole data is, however, less clear in the separate other model parameters. Therefore, we subsequently focus analyses of the main breeding areas, as haplotype diversity in only on the estimates obtained for h and t. Furthermore, the Haukivesi area (Fig. 4b) shows no large changes while, due to the shapes of the posterior distributions, most esti- in the Pihlajavesi area, yearly present-to-past cumulative mates had very large 95 % confidence intervals (Fig. S1, diversities always exceed past-to-present values, signifying Supplementary material), and they should therefore be a rise in diversity since the early 1980s (Fig. 4c). interpreted with caution. Significant differentiation at haplotype frequencies was Very different split times were obtained for the two found among both spatial (regions) and temporal (collec- northern Saimaa subpopulations (t = 0.006) and the two tion decades) samples: In both cases, nearly 40 % southern ones (t = 0.118). For the separation of these two (P \ 0.001) of the variation could be attributed to variation groups the analysis suggests a much older time at among samples (Table 2a, b). However, hierarchical AM- t = 1.007. Finally, a split time of t = 1.732 was obtained OVA analyses revealed that when spatial samples were for the Saimaa and Baltic subspecies. The population size further subdivided into temporal subsamples, virtually all parameter h estimates for the four Saimaa regions varied of the variation was due to variance among decades within between hNS = 0.49 and hSS = 3.07, increasing from north regions, and among individuals within decades (c. 45 and to south. For the Baltic population, the estimated popula- 55 %, respectively, P \ 0.001; Table 2c). Subdividing tion size was two orders of magnitude higher at temporal samples into spatial subsamples yielded compa- hB = 125.80. Complying with the current understanding of rable results: None of the variation could be attributed to the history of the Saimaa population, significantly higher variation among groups, i.e., decades (Table 2d). The estimates were obtained for historical population sizes, analyses using a reduced dataset (Northern and Southern varying between h = 2.3 and h = 9.5 depending on the Saimaa excluded due to small sample sizes) produced region and time period (see Fig. S1, Supplementary results (Table S2, Supplementary material) similar to those material). of the whole dataset. 123 Conserv Genet (2012) 13:1231–1245 1239

(a) Lake Saimaa differentiation and geographical distance among the 1.0 Cumulative diversity regions could be detected. Past-to-present Approximately similar levels of differentiation were 0.9 Present-to-past found in pairwise comparisons of temporal samples (col- 0.8 lection decades) of the Saimaa population, with

0.7 FST = 0.414 between the 1980s and 1990s, FST = 0.356 between the 1990s and 2000s, and F = 0.384 between 0.6 ST the 1980s and 2000s (all P \ 0.001 after sequential Bon- 0.5 ferroni corrections). For pairwise comparisons of subsam-

Haplotype diversity Haplotype 0.4 ples (each region further divided into three temporal samples), Northern and Southern Saimaa were excluded, 0.3 since they had small temporal subsample sizes. All subs- 0.2 amples of the Haukivesi and Pihlajavesi areas differed 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 signiﬁcantly from each other, as did the temporal samples within each region (Table 3). In addition, a slight, albeit (b) 1.0 Haukivesi area statistically non-signiﬁcant, temporal decrease in pairwise 0.9 FST estimates between the Saimaa seals’ main breeding 0.8 areas Haukivesi and Pihlajavesi was detected during the study period (Table 3). 0.7 AMOVA analyses and pairwise comparisons performed 0.6 using a dataset including only Saimaa ringed seal pups

0.5 (\1 y) yielded results (Tables S3–S6, Supplementary material) comparable to the ones based on the whole

Haplotype diversity Haplotype 0.4 dataset. 0.3

0.2 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Discussion

0.9 Of the three studied Fennoscandian ringed seal popula- 0.8 tions, mtDNA control-region diversity was clearly lowest 0.7 in Lake Saimaa and, as expected, highest in the Baltic Sea; previous studies (Palo et al. 2001, Palo 2003) have shown 0.6 that haplotypic diversity in the Baltic subspecies is nearly 0.5 as high as within the Arctic ringed seal (comprising

Haplotype diversity Haplotype 0.4 millions of individuals; Reeves 1998). Assuming that the mtDNA control-region diversity of 0.3 the Lake Saimaa ringed seal was initially equal to that 0.2 observed in the Baltic today, the Saimaa subspecies has 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 lost 33.9 and 89.4 % of its haplotype and nucleotide Year diversity, respectively. As a consequence, the Saimaa Fig. 4 Bidirectional cumulative haplotype diversity curves in the subspecies differs conspicuously from the two reference ringed seal populations of a Lake Saimaa, b Haukivesi area, and populations (pairwise UST [ 0.900). The ringed seal pop- c Pihlajavesi area. For each consecutive dividing year, haplotype ulation inhabiting Lake Ladoga, the largest lake in Europe, diversities of the past-to-present partition are shown by closed circles, while open circles show values of the present-to-past partition (see has not suffered as pronounced a reduction in mtDNA legend). Error bars are ±1SD diversity: When compared to the Baltic population, loss of variation is detected only in nucleotide diversity and in the Population pairwise comparisons conﬁrmed signiﬁcant number of variable sites (Table 1). The difference in differentiation among all four Lake Saimaa regions, with genetic variation between the two landlocked subspecies, FST values ranging from 0.311 to 0.454 (all P \ 0.001 after which were isolated at roughly the same time (Donner a sequential Bonferroni correction; Table S3, Supplemen- 1995), is most likely explained by the disparate carrying tary material). No obvious relationship between genetic capacities and morphologies of their respective habitats. 123 1240 Conserv Genet (2012) 13:1231–1245

Table 2 AMOVA results for Grouping Variance spatial and temporal samples Component Variance F-statistics P value (based on collection years) of the Saimaa ringed seal (a) Regions population Northern Saimaa (NS) AP 0.195 FST = 0.390 \0.001 Haukivesi area (HV) WP 0.304 Pihlajavesi area (PV) Southern Saimaa (SS) (b) Decades

Saimaa 1980s AP 0.192 FST = 0.384 \0.001 Saimaa 1990s WP 0.308 Saimaa 2000s (c) Three decades within four regions

NS 1980s, 1990s, 2000s AG 0.004 FCT = 0.007 0.097

HV 1980s, 1990s, 2000s AP/WG 0.223 FSC = 0.449 \0.001

PV 1980s, 1990s, 2000s WP 0.275 FST = 0.453 \0.001 SS 1980s, 1990s, 2000s (d) Four regions within three decades

Variance component: AG 1980s NS, HV, PV, SS AG -0.003 FCT =-0.006 0.663 among groups, AP/WG among 1990s NS, HV, PV, SS AP/WG 0.228 FSC = 0.454 \0.001 populations/within groups, WP 2000s NS, HV, PV, SS WP 0.275 F = 0.451 \0.001 within populations ST

Table 3 Genetic differentiation (pairwise FST) between Saimaa ringed seals from Haukivesi (HV) and Pihlajavesi (PV) areas from three decades N HV 1980s HV 1990s HV 2000s PV 1980s PV 1990s

HV 1980s 43 HV 1990s 32 0.452 HV 2000s 41 0.472 0.426 PV 1980s 19 0.487 0.433 0.459 PV 1990s 16 0.452 0.394 0.423 0.431 PV 2000s 26 0.419 0.364 0.392 0.393 0.350

Differentiation in all comparisons was statistically highly signiﬁcant at P \ 0.001 after a sequential Bonferroni correction. Pairwise FST values between the regions from each decade are in bold

Hence, the present number of Ladoga ringed seals is over example, in Hector’s dolphin (Cephalorhynchus hectori)in tenfold compared to the Saimaa subspecies, and the Ladoga New Zealand (Pichler and Baker 2000), but the loss of population has undoubtedly been substantially larger in the mtDNA variation in the Saimaa ringed seal does not appear past as well (Sipila¨ et al. 1996; Hyva¨rinen et al. 1999; to result from this recent phenomenon: Although decadal Kokko et al. 1999; Metsa¨hallitus, Natural Heritage Ser- comparisons based on individual birth years (Table 1)as vices 2012). The absence of shared haplotypes between the well as the cumulative plots of haplotype diversity two landlocked populations speaks directly against the (Fig. 4a) suggest that genetic diversity diminished from the recent suggestion by Saarnisto (2011), that there has been 1960s to the 1970s, no further decreases seem to have occasional migration of seals between Ladoga and Saimaa followed. In addition, diversity actually seems to have risen via the River Vuoksi. Although the river has connected the in the Pihlajavesi area (Fig. 4c), apparently due to an two lakes since its formation c. 5,700 years ago, its many increased number of sampled rare haplotypes and more rapids evidently prevent seal migration. even haplotype frequencies during the latter half of the During the 20th century, the Saimaa ringed seal popu- study period. This could result from a post-1980s popula- lation entered into a decline that was not reversed before an tion recovery in this breeding area but, considering the extreme low of less than 150 individuals had been reached overall pattern, it may also simply reﬂect genetic drift in a in the 1980s (Sipila¨ et al. 1990). Even less severe bottle- still small subpopulation (see the following). However, it necks have induced rapid losses of genetic diversity, for should be noted that the conﬁdence intervals of most

123 Conserv Genet (2012) 13:1231–1245 1241 temporal diversity estimates are wide and partly overlap- In the case of the IMa2 results, the aforementioned ping, and the results should therefore be interpreted with uncertainty concerning mutation rates affects the conver- caution. Analyses of museum samples dating from the sion of the obtained model parameters to ‘real’ demo- early 20th century could provide a means for elucidating graphic units. To overcome this, we used the Baltic– the level of genetic diversity in the population immediately Saimaa separation, firmly believed to have taken place c. prior to the human-induced bottleneck. 9,500 years ago, as a reference point. In order to comply Despite the low overall diversity of the Saimaa ringed with the estimated t = 1.732 for this split, a mutation rate seal, it is noteworthy that as many as eight haplotypes are of l = 0.000183 substitutions/haplotype/year or, assuming found within the lake. As a reference, an isolated wolf an 11-year generation interval, u = 0.002 substitutions/ (Canis lupus) population in Italy and the northern elephant haplotype/generation, is required. seal (Mirounga angustirostris) have both experienced a Using this mutational value, split time parameters yield severe bottleneck of less than 100 individuals, and retain estimates of merely 33 years for the NS/HV split and only one and two mtDNA haplotypes, respectively (Randi 642 years for the PV/SS split. However, the separation of et al. 2000; Weber et al. 2000). Intriguingly, all Saimaa the northern Saimaa subpopulations (NS ? HV) from the haplotypes belong to a single tight clade (Fig. 2). This ones inhabiting the southern half of the lake (PV ? SS) is would be an unlikely result of random lineage sorting from substantially older, and would be placed at c. 5,500 years an mtDNA pool similar to that observed in the Baltic ago. Interestingly, this estimate roughly corresponds with population today, especially when noting that haplotypes the formation of the River Vuoksi, which lowered the water belonging to the ‘Saimaa clade’ are rare in Baltic ringed level in Lake Saimaa by five meters, and thereby led to a seals: In addition to the single marine individual with a significant decrease in the size of the lake, as well as to ‘Saimaa clade’ haplotype in our sample, Palo (2003) found increased fragmentation of the lake area (Saarnisto et al. three other ‘Saimaa clade’ sequences in the Baltic Sea. 1999). However, the large confidence interval of the split However, none of them was common, so the combined age prevents firm conclusions. frequency of ‘Saimaa clade’ haplotypes in the current Converted estimates for the effective population sizes marine population is likely to be well below 10 %. for the Saimaa subpopulations range from Ne = 61 (NS) to This enigma of low but at the same time surprisingly high Ne = 381 (SS), while a notably higher Ne = 15,615 was diversity in the Saimaa population was further investigated obtained for the Baltic Sea seals. Note that due to differ- by Bayesian Serial SimCoal coalescent simulations. ences in split time estimates, these values represent long- Although this approach contains a number of potential error term averages over different time periods. Relatively high sources and cannot give exact answers, the simulations too historical population sizes were recorded for different parts suggest that Saimaa ringed seals hold unexpectedly high of Saimaa in the past: An effective population size of levels of mtDNA diversity. If the control-region mutation Ne = 286 was estimated for the northern parts of the lake, rates have been similar to those found in other large mam- and Ne = 658 for the southern parts. Finally, the analysis -8 mals [c. 2 9 10 substitutions/site/year (Pesole et al. gave an Ne = 1,182 for the ancestral population inhabiting 1999), which corresponds to u & 0.0002 substitutions/ Lake Saimaa after the initial formation of the lake but haplotype/generation for the present data], preservation of before the separation of its northern and southern parts. the observed diversity through the c. 860 generations of Despite differences in methodology, assumptions, and isolation would require a female effective population size run parameters, the two simulation approaches produced that is at least twice the estimated natural census size roughly comparable results. In particular, both BayeSSC (Hyva¨rinen et al. 1999; Kokko et al. 1999). Even when and IMa2 results strongly suggest that mutation rates have considering that c. 6,000 years ago, before formation of the been very high. For instance, assuming a ‘normal’ mutation Vuoksi outlet, Lake Saimaa was 36 % larger than today rate of u = 0.0002 would in the case of IMa2 estimates (Saarnisto et al. 1999; Tikkanen 2002), and that the number indicate a Pleistocene separation c. 95,000 years ago for of seals could have reached 10,000 during that period (Hy- the Baltic and Saimaa ringed seals. The high mutation rates va¨rinen et al. 1999), the population size suggested by the required for even marginally realistic demographic esti- simulations is unrealistic. This estimate is also in stark mates could perhaps be explained by mutational hotspots. contrast with the previously suggested long-term Ne & 370 This is suggested also by the network topology (Fig. 2), in inferred from microsatellite markers (Palo et al. 2003). which four haplotypes are separated from the two most

Alternatively, when assuming a marginally realistic Nef common ones (H1, H3) by single transitions. These two of about 1,000, obtaining the current diversity would common haplotypes could represent the ancestral diversity necessitate mutation frequencies (u C 0.005) that are sev- retained in the lake. In fact, when performing Serial Sim- eral orders of magnitude higher than the aforementioned Coal coalescent simulations based on diversity values estimates for the mammalian control-region. derived from a dataset in which the aforementioned rare 123 1242 Conserv Genet (2012) 13:1231–1245 haplotypes are binned with the common ones (i.e., H5 and regions, which suggests that Saimaa ringed seal males are H8 with H1, and H2 and H7 with H3), high posterior more mobile than females. Female philopatry is common probabilities for marginally realistic ancestral effective in mammals (Greenwood 1980), and population subdivi- populations sizes are observed with substantially lower sion in maternally inherited markers due to sex-specific (and more realistic) mutation rates (u = 0.0003; data not dispersal has been observed in many pinniped species (e.g., shown). Slade et al. 1998; Campbell et al. 2008, Lowther et al. On the other hand, one must ask whether the current 2012). understanding of the population history is correct. The Interestingly, significant differentiation in mtDNA hap- combination of near-monophyly and relatively high lotype frequencies was detected between the three decades. diversity in Saimaa ringed seals could also have resulted While many studies have demonstrated variation between from longer isolation. As the Saimaa region was covered temporal samples (e.g., Martıńez-Cruz et al. 2007; Riccioni by a continental ice sheet until c. 11 kya, this would et al. 2010; Ruokonen et al. 2010; Ortego et al. 2011), only necessitate a separate, hitherto unknown, glacial refugium. few (e.g., Heath et al. 2002; Hoeck et al. 2010) have

Late-glacial colonization from other regions, e.g., from the reported FST values as high as those detected here. The fact White Sea, has been proposed for several fish species that all pairs of samples (in both datasets), irrespective of inhabiting the same area (Koskinen et al. 2000; Kontula the basis of division (spatial or temporal), significantly and Vaïno¨la¨ 2001; Nilsson et al. 2001). However, the differed from each other (FST [ 0.300), firmly demon- current understanding of the Fennoscandian postglacial strates the strong effect of genetic drift and, thus, the cur- geology does not support this possibility (Saarnisto et al. rent small size of the population. 1995). Conservation implications Spatial and temporal differentiation within Lake Saimaa Our results show that the genetic diversity of the Saimaa ringed seal is strikingly low as compared to other ringed Our analyses revealed significant population structure in seal subspecies, and that the population is subdivided into mitochondrial control-region variation among Lake Saimaa small semi-isolated units experiencing high genetic drift. regions (Fig. 1b), suggesting that female gene flow is These factors may pose a threat to the long-term survival of insufficient to counteract genetic drift in the ringed seal the Saimaa subspecies, as a high level of inbreeding may subpopulations of different water basins. Equally high reduce fitness (Madsen et al. 1996; Acevedo-Whitehouse levels of genetic differentiation have been found mostly in et al. 2003; Blomqvist et al. 2010). Yet, the correlation cases in which subpopulations are separated from each between individual fitness and neutral genetic variation is other due to natural physical barriers (e.g., Przewalski’s seldom straightforward (Szulkin et al. 2010), and examples gazelles, Procapra przewalskii; Lei et al. 2003), human- of seemingly viable populations with negligible genetic induced habitat fragmentation (e.g., golden brown mouse diversity exist (Visscher et al. 2001; Habel et al. 2009; lemurs, Microcebus ravelobensis; Guschanski et al. 2007), Reed 2010). or long geographical distances (e.g., harbor seals; Andersen As of yet, no obvious signs of inbreeding depression in et al. 2011). In Lake Saimaa, no impenetrable physical the Saimaa ringed seal population have been reported, barriers or distances long enough to prevent migration although it must be emphasized that this aspect of the exist. However, the lake is extremely labyrinthine, with biology of the subspecies is woefully understudied. The only narrow straits connecting the main water basins apparent lack of inbreeding depression may be explained (Fig. 1b), which may restrict migration between different by the history of the population: Our mtDNA results are in breeding areas. Radio telemetry studies have likewise line with the microsatellite-based suggestion by Palo et al. shown that adult Saimaa seals exhibit a high degree of site (2003), that the Saimaa population remained relatively fidelity (Kunnasranta 2001; Koskela et al. 2002), but also large and that its neutral genetic diversity therefore have suggested that young individuals are more prone to diminished slowly during the c. 860 generations of post- long-distance movements (Kunnasranta 2001; Niemi et al. glacial isolation. Such gradual loss of variation is consid- 2011). However, spatial differentiation in mtDNA was ered optimal for the purging of deleterious alleles (Keller equally high regardless of whether using a pre-dispersal and Waller 2002). Nevertheless, the population’s impov- dataset (pups \ 1 y), or data subsequent to gene flow erished neutral diversity is a cause of concern, because the (adults included) (Table S3, Supplementary material). same may apply to the still unstudied levels of adaptive Contrary to our results, a previous study based on genetic variation (cf. Bollmer et al. 2007; Babik et al. 2009; biparentally inherited microsatellite markers (Palo et al. Smith et al. 2010). In particular, reduced adaptive genetic 2003) found only weak differentiation among Lake Saimaa diversity could affect the population’s ability to respond to 123 Conserv Genet (2012) 13:1231–1245 1243 impending global warming (cf. Willi et al. 2006; Ehlers Anderson CNK, Ramakrishnan U, Chan YL, Hadly EA (2005) Serial et al. 2008; O’Corry-Crowe 2008), which will lead to SimCoal: a population genetic model for data from multiple populations and points in time. 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123 Demographic histories and genetic diversities of Fennoscandian marine and landlocked ringed seal subspecies Tommi Nyman1,2, Mia Valtonen1, Jouni Aspi3, Minna Ruokonen3, Mervi Kunnasranta1 & Jukka U. Palo4 1Department of Biology, University of Eastern Finland, PO Box 111, Joensuu FI-80101, Finland 2Institute for Systematic Botany, University of Zurich, Zollikerstrasse 107, Zurich CH-8008, Switzerland 3Department of Biology, University of Oulu, PO Box 3000, Oulu FI-90014, Finland 4Laboratory of Forensic Biology, Hjelt Institute, University of Helsinki, PO Box 40, Helsinki FI-00014, Finland

Keywords Abstract Approximate Bayesian computing, bottleneck, demographic history, founder Island populations are on average smaller, genetically less diverse, and at a event, genetic diversity, island populations. higher risk to go extinct than mainland populations. Low genetic diversity may elevate extinction probability, but the genetic component of the risk can be Correspondence affected by the mode of diversity loss, which, in turn, is connected to the Tommi Nyman, Department of Biology, demographic history of the population. Here, we examined the history of University of Eastern Finland, PO Box 111, genetic erosion in three Fennoscandian ringed seal subspecies, of which one Joensuu FI-80101, Finland. inhabits the Baltic Sea ‘mainland’ and two the ‘aquatic islands’ composed of Tel: +358 2944 53081; Fax: +358 13 318 039; Lake Saimaa in Finland and Lake Ladoga in Russia. Both lakes were colonized E-mail: Tommi.Nyman@uef.ﬁ by marine seals after their formation c. 9500 years ago, but Lake Ladoga is larger and more contiguous than Lake Saimaa. All three populations suffered dra- Funding Information matic declines during the 20th century, but the bottleneck was particularly Financial support for this project was severe in Lake Saimaa. Data from 17 microsatellite loci and mitochondrial con- provided mainly by the Maj and Tor Nessling trol-region sequences show that Saimaa ringed seals have lost most of the Foundation, with additional funding provided by the Raija and Ossi Tuuliainen Foundation genetic diversity present in their Baltic ancestors, while the Ladoga population and the Academy of Finland. has experienced only minor reductions. Using Approximate Bayesian computing analyses, we show that the genetic uniformity of the Saimaa subspecies Received: 8 July 2014; Revised: 21 July 2014; derives from an extended founder event and subsequent slow erosion, rather Accepted: 22 July 2014 than from the recent bottleneck. This suggests that the population has persisted for nearly 10,000 years despite having low genetic variation. The relatively high – Ecology and Evolution 2014; 4(17): 3420 diversity of the Ladoga population appears to result from a high number of ini- 3434 tial colonizers and a high post-colonization population size, but possibly also doi: 10.1002/ece3.1193 by a shorter isolation period and/or occasional gene ﬂow from the Baltic Sea.

Introduction it has been hypothesized that gradual erosion of variation allows purging of detrimental alleles, which would lessen Island populations have higher rates of extinction than inbreeding depression (Crnokrak and Barrett 2002; mainland ones, mainly because they tend to be small and, Bouzat 2010). However, experimental evidence and hence, more vulnerable to environmental and demo- investigations of captive populations have cast doubt on graphic stochasticity (Ricklefs 2010; Wootton and Pﬁster the existence or effectiveness of purging (Boakes et al. 2013). Small population size also has inexorable genetic 2007; Leberg and Firmin 2008). consequences – loss of neutral and adaptive variation and Assessing standing genetic diversity is routine, but increase of inbreeding – that can hasten extinction by determining the rate of diversity loss is far more challeng- reducing individual ﬁtness (Frankham 1998; O’Grady ing, as this requires knowledge on the initial levels of var- et al. 2006). However, the adversity of genetic effects variation as well as on isolation times (Wayne et al. 1991; ies from species to species (Jamieson 2007), and may also Wang et al. 2014). Perhaps surprisingly, a promising depend on the rate at which diversity is lost; in particular, model system for studying insular genetic erosion is

3420 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 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. T. Nyman et al. Genetic Erosion in Fennoscandian Ringed Seals provided by ringed seals (Phoca hispida) that inhabit the motion c. 10,000 years ago when the last glacial period large Fennoscandian lakes Saimaa and Ladoga (Figs. 1, 2). was coming to an end. As the Scandinavian ice shield From the perspective of seals, these lakes represent retreated northwest, isostatic land uplift in the pre-Baltic ‘islands’ separated from the marine ‘mainland’ and, Yoldia Sea area led to the formation of numerous lakes in importantly, the initial diversity of the landlocked popula- which ringed seals could be trapped (Eronen et al. 2001). tions can be estimated on the basis of extant marine seals. Of these – possibly numerous – lacustrine populations, In addition, the ages of the lakes are well known, as they only two survive to this day: the Saimaa ringed seal owe their origin to geological events that were set in (P. h. saimensis) in southeastern Finland, and the Ladoga ringed seal (P. h. ladogensis) in northwestern Russia (Ukkonen 2002; Saarnisto 2011). During their extended isolation, these populations have evolved into distinct subspecies that are morphologically, behaviorally, and genetically differentiated from each other, as well as from the direct descendants of their Yoldian ancestors in the current Baltic Sea (P. h. botnica) (Sipil€a et al. 1996; Hyv€arinen et al. 1999; Amano et al. 2002; Berta and Churchill 2012; Valtonen et al. 2012). Previous assessments based on nuclear and mitochondrial markers have shown that the Saimaa subspecies is genetically depauperate compared to its Fennoscandian sister populations (Palo et al. 2003; Valtonen et al. 2012). However, the trajectory of the loss remains uncertain, partly because all Fennoscandian seal populations underwent dramatic human-caused declines during the 20th century (Kokko et al. 1999). The recent bottleneck was particularly severe in Lake Saimaa, in which the number of seals fell below 200 in the mid-1980s (Hyv€arinen et al. 1999; Sipil€a 2003). The population has since slowly recovered to slightly over 300 individuals (Mets€ahallitus Natu- ral Heritage Services 2013), but it remains critically endangered. Reductions were equally dramatic in the Bal-

Figure 1. Adult Saimaa ringed seal (Phoca hispida saimensis) hauling tic and Ladoga populations, but their head counts out on lakeside rocks. Photograph by Mervi Kunnasranta. remained in the thousands (currently c. 15,000 and >5000, respectively; Sipil€a et al. 1996; Sundqvist et al. 2012; Trukhanova et al. 2013), which could maintain genetic variation at or near pre-bottleneck levels. In this study, we aimed at estimating genetic diversity in, and differentiation among, Fennoscandian ringed seal (Arctic) populations, with a specific focus on the demographic histories of the two landlocked subspecies. In particular, we wanted to find out whether the genetic uniformity of Lake the Saimaa subspecies is a legacy of gradual diversity ero- Saimaa sion during its long isolation, or a result of more abrupt losses during the colonization of the lake (i.e., a founder effect) and/or the recent bottleneck. On this account, we Baltic combined new data from 17 microsatellite loci (Valtonen Sea Lake et al. 2014) with previously published mitochondrial con- Ladoga trol-region sequence data (Valtonen et al. 2012) to estimate the divergence times and past effective population 0 300 km sizes of the three focal subspecies. Specifically, we took advantage of the ability of recently developed approxi- Figure 2. Map showing the distributions of the marine and mate Bayesian computation methods to evaluate the fit of landlocked Fennoscandian ringed seal subspecies. alternative, increasingly complex hypotheses, which

ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3421 Genetic Erosion in Fennoscandian Ringed Seals T. Nyman et al. assume either constant postglacial sizes in all populations, and because the estimated null-allele frequencies in these or invoke an assumption of bottlenecks at colonization loci were relatively high (r ≥ 0.18) in the Baltic and and/or during the last decades. Ladoga samples (see Results), global and pairwise FST values were also calculated while correcting for the Methods presence of null alleles in these loci in FREENA. Genetic differences among individuals originating from the three populations were visualized with factorial corre- Data acquisition spondence analysis (FCA) implemented in the program For Saimaa and Ladoga seals, we used samples from a tis- GENETIX v. 4.05.2 (Belkhir et al. 2004). Distinctness of sue bank maintained by the University of Eastern Finland the subspecies was further studied using Bayesian geno- and the Natural Heritage Services of Mets€ahallitus, while type assignment analyses in STRUCTURE v. 2.3.4 (Prit- Baltic individuals were provided by the Finnish Game chard et al. 2000; Hubisz et al. 2009), while excluding and Fisheries Research Institute. Microsatellite variation prior information on the sampling population. We used was inferred at 17 variable loci; detailed methods and data the admixture model with correlated allele frequencies, characteristics are described in Valtonen et al. (2014). and tested the number of clusters (K) from 1 to 10 using The possibility of genotyping errors was investigated by a burn-in period of 100,000 followed by 500,000 MCMC analyzing the data in MICRO-CHECKER v. 2.2.3 (van iterations, with 20 replicate analyses per each value of K Oosterhout et al. 2004) using Bonferroni-adjusted 95% to ensure convergence. The analysis was repeated using confidence intervals, and null allele frequencies at each the no-admixture model with otherwise similar settings. locus for each population were estimated using Chapuis In both cases, we used the ad hoc approach of Evanno and Estoup’s (2007) FREENA software. The microsatellite et al. (2005), implemented in STRUCTURE HARVESTER data were supplemented with 707 bp of mitochondrial v. 0.6.93 (Earl and vonHoldt 2011), to determine the control-region sequences from the same individuals number of clusters that best fits the data. collected by Valtonen et al. (2012). For the Saimaa subspecies, microsatellite genotypes were obtained for 172 ABC analyses individuals (=Nms), and mtDNA sequences for 166 of these (=Nmt). For the Ladoga subspecies Nms = Nmt = 16, Before approximate Bayesian computing (ABC) analyses and for the Baltic population Nms = 21 and Nmt = 19. in DIYABC v. 1.0.4.46 (Cornuet et al. 2008, 2010), we defined four incrementally complex hypotheses on the colonization and population histories of lakes Saimaa and Estimation of genetic diversity and Ladoga (Fig. 3). All scenarios assumed that the two lakes differentiation were colonized independently from a Baltic source popu- We used ARLEQUIN v. 3.5.1.2 (Excoffier and Lischer lation. Scenario ‘No Colonization Bottleneck – No Recent 2010) to estimate basic genetic diversity measures [num- Bottleneck’ (NCB–NRB) assumed constant population ber of alleles (A), and observed (HO) and expected (HE) sizes through time in all populations, Scenario CB–NRB heterozygosities] as well as Wright’s in breeding coeffi- included independent initial colonization bottlenecks in cient (FIS) for each subspecies. HP-RARE v. 1.1 (Kalinow- the landlocked populations, Scenario NCB–RB assumed ski 2004) was used to estimate allelic richness (AR)by only a recent bottleneck starting in all populations during rarefaction to the smallest sample size (N = 16). Devia- the 20th century, and Scenario CB–RB involved both col- tions from Hardy–Weinberg equilibrium (HWE) in indi- onization bottlenecks and a marked recent reduction in vidual loci, as well as linkage disequilibrium across locus population size in all subspecies. pairs, were evaluated in ARLEQUIN. The overall loss of Broad uniform priors for current effective population genetic diversity over time in the landlocked populations sizes (Fig. 3) were set based on published estimates (Sipil€a € € was estimated as Fe = 1 – (Hlake/HBaltic), assuming that et al. 1996; Sipila 2003; Sundqvist et al. 2012; Metsahall- the (observed) heterozygosity in the Baltic population itus Natural Heritage Services 2013; Trukhanova et al. represents the level at the start of the lacustrine isolation 2013) and preliminary test runs. The independent U[200– (cf. Palo et al. 2003). 1100] generations priors for the timing of both coloniza- To investigate the amount of genetic differentiation tion events correspond to 2200–12,100 years assuming an among the subspecies, we performed AMOVA (Excoffier 11-year generation time for ringed seals, estimated from et al. 1992) and estimated pairwise FST values in ARLE- the age-specific fecundity scheme in Smith (1973; see Palo QUIN. Significance levels were determined by 10,000 per- et al. 2001). This range should encompass the origination mutations. Because significant deviations from HWE were times of both lakes (Eronen et al. 2001; Saarnisto 2011), observed in the loci Hg1.4 and Hg3.6 in all populations, and the lower limit is close enough to the Recent to take

3422 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. T. Nyman et al. Genetic Erosion in Fennoscandian Ringed Seals

Colonization bottlenecks No colonization bottlenecks in Saimaa and Ladoga

Scenario NCB–NRB Scenario CB–NRB

Lake Baltic Lake Lake Baltic Lake Saimaa Sea Ladoga Saimaa Sea Ladoga

NS NB NL NS NB NL [10– [10–100,000] [10–100,000] [10– [10–100,000] [10–100,000] 10,000] 10,000]

dScb NScb NLcb dLcb [1–50] {[2–100] [2–100] } [1–50] tBS tBL tBS tBL [200–1,100] [200–1,100] [200–1,100] [200–1,100] No recent bottlenecks

Scenario NCB–RB Scenario CB–RB

Lake Baltic Lake Lake Baltic Lake Saimaa Sea Ladoga Saimaa Sea Ladoga

NSrb NBrb NLrb NSrb NBrb NLrb [50–200] [500–5,000] [500–2,000] [50–200] [500–5,000] [500–2,000] } trb } trb [3–10] [3–10]

NS NB NL NS NB NL [10– [10–100,000] [10–100,000] [10– [10–100,000] [10–100,000] 10,000] 10,000]

dScb { NScb NLcb } dLcb [1–50] [2–100] [2–100] [1–50] tBS tBL tBS tBL [200–1,100] [200–1,100] [200–1,100] [200–1,100] in all populations Recent bottlenecks

Abbreviations of demographic and event-time parameters

NS Long-term effective population size in Lake Saimaa NB Long-term effective population size in Baltic Sea NL Long-term effective population size in Lake Ladoga tBS Time of colonization of Lake Saimaa from Baltic Sea (generations) tBL Time of colonization of Lake Ladoga from Baltic Sea (generations) NScb Effective population size in Lake Saimaa during colonization bottleneck Figure 3. Schematic illustration of the four NLcb Effective population size in Lake Ladoga during colonization bottleneck hypothetical scenarios evaluated using dScb Duration of colonization bottleneck in Lake Saimaa (generations) dLcb Duration of colonization bottleneck in Lake Ladoga (generations) approximate Bayesian computing (ABC), with NSrb Effective population size in Lake Saimaa during recent bottleneck the uniform prior ranges used for demographic NBrb Effective population size in Baltic Sea during recent bottleneck and event-time parameters given in square NLrb Effective population size in Lake Ladoga during recent bottleneck brackets. Note that relative times and trb Start time of recent bottleneck (generations) population sizes are not to scale. into account the possibility of a wide connection between For the microsatellite loci, we employed a generalized Lake Ladoga and the Baltic Sea until c. 4000 years ago stepwise mutation model (Estoup et al. 2002) with a max- (see below). For the hypothesized colonization bottle- imum of 40 contiguous dinucleotide allelic states allowed necks, we set the effective population size prior as U[2– for each locus. Default prior ranges were used for all 100] and the duration prior to U[1–50] generations in parameters, with the exception of the lower bound of the both lakes, reﬂecting the common assumption that colo- mean mutation rate, which was lowered to 10À5, and the nization bottlenecks are relatively brief (e.g., Keller et al. corresponding value of the individual locus mutation rate, 2012). The temporal prior of the recent bottleneck was which was set to 10À6 (Table S1 in Appendix S1). These set to U[3–10] generations, which, given the aforemen- changes were made on the basis of exploratory runs tioned generation time, corresponds to a sharp popula- showing that posterior distributions of mutation-rate tion decline commencing between 1903 and 1980 in all parameters tended to ﬁt poorly within the default prior subspecies (cf. Sipil€a et al. 1996; Kokko et al. 1999; Sipil€a bounds (see also Fontaine et al. 2012). For mitochondrial 2003; Sundqvist et al. 2012). control-region sequences, we used a HKY+I+G model of

ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3423 Genetic Erosion in Fennoscandian Ringed Seals T. Nyman et al. substitution, with the proportion of invariant sites set and then estimating the relative posterior probability of to 88% and the gamma shape parameter to 1.40 (see each competing scenario with respect to each POD, using Valtonen et al. 2012). For the transition/transversion rate the same methods and settings as for the real data. Type I ratio (kappa), we used default prior limits, but the upper error is defined as the probability of the best-fitting sce- bound of the prior on the mutation rate (per site and nario not having the highest posterior probability when it generation) was raised to 10À5 (Table S1 in Appendix S1). was in fact used for POD simulation. Conversely, Type II As above, this change was based on preliminary test runs, error is the proportion of cases in which the best-fitting but an elevated upper limit also conforms with control- scenario was selected although it was not the one used for region mutation rates estimated by Valtonen et al. (2012). POD simulation. For measuring the fit of simulated datasets to the observed data, we selected three single-sample summary Results statistics (mean number of alleles, expected heterozygosity, and mean Garza–Williamson’s M) and three two-sample Genetic diversity within and among statistics (mean number of alleles, F , and classification ST subspecies index) for microsatellites. For mitochondrial DNA sequences, we used four single-sample targets (numbers of Consistent genotyping results across all 17 microsatellite haplotypes and segregating sites, and mean and variance loci were obtained for 168 Saimaa, 15 Baltic, and 16 La- of pairwise differences) and three two-sample statistics doga ringed seals, while four Saimaa and six Baltic individ-

(numbers of haplotypes and segregating sites, and FST). uals lacked data for a single locus each. No consistent Following the recommendation of Cornuet et al. (2010), linkage disequilibria across pairs of loci were found in all subsequent model checking was based on a separate set of populations (at P < 0.05), indicating absence of structural summary statistics (Table S2 in Appendix S1). linkage of markers. Tests for presence of null alleles were A total of four million simulated datasets were gener- significant for six loci in Lake Saimaa (Hg1.4, Hg3.6, ated using the Vuori cluster of the Center for Scientific Hg6.1, Hg8.9, Hgdii, and Pvc78), but the low estimated fre- Computing in Espoo, Finland. This corresponds to quencies (all r < 0.1) suggest that this result simply reflects roughly one million simulations per scenario, given that the presence of population structure within the lake (see the prior probability of scenarios was set to equal. Poster- Valtonen et al. 2014). Null alleles were also indicated for ior support for each scenario was assessed based on (1) a two loci in the Baltic Sea (Hg1.4 and Hg3.6), and three in direct estimate, in which 500 datasets with summary sta- Lake Ladoga (Hg1.4, Hg2.3, and Hg3.6); for Hg1.4 and tistics closest to the target values were extracted from the Hg3.6, frequency estimates of these alleles were high at simulated pool, and (2) a polychotomous logistic regres- r = 0.181–0.286 (Table S3 in Appendix S1).These loci also sion approach (Cornuet et al. 2008) based on the best-fit- deviated significantly from HWE due to heterozygote defi- ting 1% (40,000) of simulated datasets. Posterior ciency in all three populations (Table S4 in Appendix S1). distributions of parameters according to individual sce- The average number of alleles per locus in the Saimaa narios were estimated by local linear regression on logit- population was 3.5, ranging from 2 to 4 in 16 loci, the sole transformed parameter values on the basis of 0.1% (1000) exception being locus Hl15, at which 16 alleles were found. of best-fitting simulated datasets. In the Baltic and Ladoga populations, the average numbers Because ABC inferences theoretically can be influenced of alleles per locus were 9.0 and 7.6, respectively, ranging by the presence of null alleles, the analyses described above from 4 to 15, even though their sample sizes were an order were repeated with a dataset from which we excluded the of magnitude smaller than in Saimaa. Consequently, allelic two microsatellite loci (Hg1.4 and Hg3.6) with relatively richness in Saimaa was only about one-third, and expected high estimated null-allele frequencies in the Baltic and heterozygosity less than half, of the values observed in the Ladoga samples (see Results). The overall results (not Baltic and Ladoga samples (Table 1). Effective inbreeding shown) and inferences resulting from the analysis of this coefficients, summing the drift within the lacustrine popu-

15-locus + mtDNA dataset did not differ from the results lations in relation to the Baltic, were Fe = 0.550 for of the complete 17-locus + mtDNA dataset, so only the Saimaa, and Fe = 0.069 for Ladoga. outputs of the main analyses are presented below. As expected (see Chapuis and Estoup 2007), global and

We evaluated the level of conﬁdence in our choice of a pairwise FST estimates based on the original microsatellite best scenario by using simulated pseudo-observed datasets dataset were slightly higher than values calculated while (PODs) to infer the discriminatory power of the analyses correcting for the presence of null alleles. The estimates (see Cornuet et al. 2010). This was done by simulating were nevertheless very similar and, hence, only the values 500 PODs for each of the four scenarios by drawing taking the null alleles into account are reported here. parameter values from their respective prior distributions, Overall differentiation among the subspecies was high

3424 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. T. Nyman et al. Genetic Erosion in Fennoscandian Ringed Seals

Table 1. Indices of genetic diversity in Baltic, Ladoga, and Saimaa ringed seals based on analyses of 17 microsatellite loci. Numbers of samples

(N), mean number of alleles (A), allelic richness (AR), observed heterozygosity (HO), expected heterozygosity (HE), and inbreeding coefﬁcient (FIS) are given

Subspecies NAÆ SD AR HO Æ SD HE Æ SD FIS

Baltic Sea 21 9.00 Æ 3.20 8.30 0.74 Æ 0.20 0.80 Æ 0.08 0.07** Lake Ladoga 16 7.65 Æ 2.76 7.65 0.69 Æ 0.22 0.74 Æ 0.16 0.06* Lake Saimaa 172 3.47 Æ 3.32 2.77 0.33 Æ 0.21 0.36 Æ 0.22 0.07***

*P < 0.05; **P < 0.01; ***P < 0.001.

6 the latter result by performing an additional admixture- Baltic Sea (N = 21) model run using only Baltic and Ladoga seals. This Lake Ladoga (N = 16) resulted in highest probability of the data at K = 1, maxi- 4 Lake Saimaa (N = 172) mum DK at K = 4, and ambiguous assignments of all individuals if a four-cluster solution was enforced (results 2 not shown).

0 Effective population sizes and divergence Axis 2 (4.86 %) times –2 The direct-estimate comparison of posterior probabilities favored Scenario CB–RB over the simpler alternatives –4 – –0.5 0.0 0.5 1.0 1.5 2.0 2.5 (Fig. 5). The superiority of Scenario CB RB is clearer in Axis 1 (10.95 %) the logistic approach, in which the posterior probability of this model at 40,000 closest simulations is 0.66 [95% Figure 4. Factorial correspondence analysis (FCA) plot of ringed seal CI 0.62–0.69]. However, the posterior probability of Sce- individuals from the Baltic Sea and lakes Saimaa and Ladoga based on nario CB–NRB is also non-negligible at 0.27 [0.24 –0.30]. their genotypes at 17 microsatellite loci. As described above, these two scenarios share the assumption of an initial colonization bottleneck in the lacustrine

(FST = 0.331; 95% CI = 0.247–0.422). Differentiation was populations, but Scenario CB–RB also invokes a recent statistically significant (P < 0.05) across all pairwise bottleneck commencing during the 20th century. By con- comparisons, but markedly higher between Saimaa trast, the posterior probabilities of Scenario NCB–NRB and the two other populations (FST(Saimaa–Baltic) = 0.367; (0.04[0.03–0.06]) and Scenario NCB–RB (0.03[0.02– FST(Saimaa–Ladoga) = 0.343) than between Ladoga and the 0.03]), neither of which involves a founder event, are so Baltic Sea (FST(Ladoga–Baltic) = 0.041). For comparison, we low that they can safely be rejected as an explanation for note that corresponding indices estimated by Valtonen the observed data. et al. (2012) based on mtDNA control-region sequences Estimations of confidence in scenario choice revealed a were: overall FST = 0.219; FST(Saimaa–Baltic) = 0.227; fairly high capability of the applied ABC method to dis- FST(Saimaa–Ladoga) = 0.233; and FST(Ladoga–Baltic) = 0.028. criminate among the scenarios, both when measured as In the FCA plot, Saimaa individuals were tightly Type I (direct estimate = 0.14; logistic approach = 0.18) grouped and clearly separated from the two other subspe- and Type II (direct estimate mean = 0.12; logistic cies, while Ladoga and Baltic seals formed loose, partially approach mean = 0.06) error rates (Table 2). However, overlapping clusters (Fig. 4). Regardless of the model the mean values of Type II error hide large variation used, Bayesian assignment analyses favored a two-cluster among the three nonoptimal scenarios, because the rates solution for the data (Fig. S1a in Appendix S1; results of were notably higher than the means when using PODs the no-admixture runs not shown). The corresponding simulated under Scenario CB–NRB (direct esti- STRUCTURE assignment plot shows that while Saimaa mate = 0.30; logistic approach = 0.13). By contrast, the seals are readily identifiable, individuals from the Baltic probability of making a Type II error was <5% for PODs and Ladoga cannot be distinguished from each other (Fig. derived from Scenarios NCB–NRB and NCB–RB S1b in Appendix S1). Because Kalinowski (2011) has (Table 2). recently shown that the outcome of STRUCTURE analy- Posterior distributions of parameter estimates from the ses can be influenced by unequal sample sizes, we checked best-fitting Scenario CB–RB reveal a rather complex

ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3425 Genetic Erosion in Fennoscandian Ringed Seals T. Nyman et al.

(A)

(B)

Figure 5. Posterior probabilities of the four alternative hypothetical scenarios based on (a) the direct-estimate approach and (b) the logistic approach.

appears to have been colonized by a larger number of Table 2. Ability of the applied approximate Bayesian computing seals that quickly grew in numbers. In the Baltic popula- (ABC) methods to discriminate among the four hypothetical scenarios, measured as rates of Type I and Type II errors. The latter are shown tion, the long-term median effective size is 14,200 with a – for all alternative scenarios combined (mean), as well as separately for 95% CI of [5690 39,100], while corresponding values in each suboptimal scenario Lake Saimaa are 1270 [220–8850] and in Lake Ladoga 70,800 [15,200–98,800]. For Lake Ladoga, however, the Method posterior distribution is broad. The timing of the recent Error Direct estimate Logistic approach bottleneck seems uncertain, but the posterior distribution of effective population size tends toward the lower end of Type I 0.144 0.182 Type II (mean) 0.116 0.055 the prior range in Saimaa and Ladoga (yet with much Type II (Scenario NCB–NRB) 0.010 0.006 higher numbers in the latter), but toward the higher end Type II (Scenario CB–NRB) 0.296 0.134 in the Baltic population (Fig. 6). Type II (Scenario NCB–RB) 0.042 0.026 The second-best hypothesis, Scenario CB–NRB, likewise provides little information on lake colonization times, but NCB–NRB, No Colonization Bottleneck – No Recent Bottleneck. also suggests that Saimaa was colonized earlier than Ladoga (Fig. S2 in Appendix S1).The scenario also agrees pattern (Fig. 6). Estimates for colonization times are with Scenario CB–RB with respect to the relative duration equivocal, but the posterior distribution is shifted toward and severity of the colonization bottlenecks in the two the lower end of the prior range in Lake Ladoga. The lakes. The median for effective population size subsequent results further suggest that post-colonization effective to the colonization phase in the Baltic Sea is 16,900 [6500 population size within Lake Saimaa remained within a –45,500], in Lake Saimaa 373 [109–2080], and in Lake few dozen for tens of generations, while Lake Ladoga Ladoga 52,200 [6990–97,600].

3426 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. T. Nyman et al. Genetic Erosion in Fennoscandian Ringed Seals

Figure 6. Prior (red lines) and posterior (green lines) distributions of event-time and demographic parameters in three Fennoscandian ringed seal subspecies according to the best-ﬁtting scenario (CB–RB), and estimates of mutation-model parameters for microsatellites and mtDNA sequences (inset).

ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3427 Genetic Erosion in Fennoscandian Ringed Seals T. Nyman et al.

For comparison, we note that the simple but disfavored presumedly more panmictic Ladoga population (cf. Amos Scenario NCB–NRB, which assumes constant sizes in all and Harwood 1998; Caballero et al. 2009). Especially in populations, gives the following median estimates of the case of the Saimaa subspecies, a long-standing issue long-term effective population sizes: 12,800 [4410–38,500] has been as to whether the majority of the diversity loss for the Baltic Sea, 414 [191–736] for Saimaa, and 7810 can be attributed to a founder effect, long-term erosion, [1620–70,900] for Ladoga. The likewise disfavored or to the recent bottleneck (Palo et al. 2003). Scenario NCB–RB, which includes only the known recent These kinds of questions were practically intractable bottleneck, gives the following pre-bottleneck Ne esti- until very recently, but the development of coalescent mates: Baltic Sea 10,800 [4000–31,600]; Lake Saimaa 567 simulation methods, in association with increased com- [253–1040]; Lake Ladoga 10,500 [1850–73,400]. During puting power, is starting to provide the tools necessary the recent bottleneck phase, corresponding values are for answering them (Hoban et al. 2012). One of the 3040 [712–4900] for the Baltic Sea, 164 [65–199] for Lake approaches, approximate Bayesian computing, uses simu- Saimaa, and 1380 [584–1960] for Lake Ladoga, but the lated datasets for evaluating how well alternative evolu- posterior distributions are weakly defined especially for tionary scenarios incorporating differential assumptions the Baltic and Ladoga populations. concerning (among others) demographic histories explain Model-checking analyses, in which 200 datasets per pre-selected population-genetic ‘target’ parameters (Corn- favored scenario (CB–RB and CB–NRB) were simulated uet et al. 2008; Csillery et al. 2010). ABC has proven to with parameter values drawn from their respective poster- be a versatile tool that can be used for tackling complex ior distributions, revealed that both ‘winning’ scenarios research questions, such as colonization routes of invasive could produce the observed data with high probability; a organisms (Boissin et al. 2012; Keller et al. 2012) and single outlying summary statistic (at P < 0.05) was found hindcasting sizes of endangered (Fontaine et al.2012) or for both scenarios when using alternative targets (results even extinct (De Bruyn et al. 2014) populations. In an not shown). For Scenario CB–RB, this was seen in that ABC framework, a clear benefit of the focal Fennoscan- the clouds formed by simulated datasets were centered on dian seal populations is that the relative simplicity of the the observed data in principal components (PCA) space, study system makes it possible to formulate distinct alter- both when using the original summary statistics (results native demographic hypotheses that can then be tested not shown) and when using an alternative set of target against each other. values (Fig. S3 in Appendix S1). Genetic diversity within and differentiation Discussion among subspecies Molecular-genetic surveys of island populations have pro- As would be expected for a highly mobile circumpolar vided important insights into how small population size, species with a census size in the millions (Reeves 1998), limited gene flow, inbreeding, stochasticity, and isolation the ringed seal – as a species – is genetically extremely time influence the loss of genetic variability in nature variable (Palo et al. 2001; O’Corry-Crowe 2008; Olsen (Frankham 1998; Cardoso et al. 2009; Jensen et al. 2013; et al. 2011; Martinez-Bakker et al. 2013). This species- Wang et al. 2014). The three Fennoscandian ringed seal level diversity does not apply to the three Fennoscandian subspecies constitute an excellent model system for study- populations, which have all been more or less isolated ing such diversity-eroding processes, because the geologi- from the main Arctic stock since the end of the Pleisto- cal history of the Baltic Sea region is well known, and the cene. The reduction in diversity is relatively mild in the deglaciation of the area c. 10,000 years ago sets a hard Baltic subspecies, for which previous genetic and ecologi- upper limit for all divergence times (Ukkonen 2002). cal modeling analyses suggest connectivity with the Arctic Lakes Saimaa and Ladoga were formed in close succession population and a historical census size exceeding 100,000 c. 9500 years ago (Saarnisto 2011), but their physical fea- individuals (Harding and H€arkonen 1999; Kokko et al. tures immediately suggest differences in the genetic trajec- 1999; Palo et al. 2001). In the scenarios favored by our tories of their endemic seal populations: Ladoga is deeper ABC analyses, the long-term effective size of the Baltic and over four times larger than Saimaa, which is directly population is estimated to be between 14,000 and 17,000, reflected in the estimated natural carrying capacities of which is comparable to estimates obtained by Valtonen these lakes (Sipil€a et al. 1996; Hyv€arinen et al. 1999; et al. (2012) in simulations based on mitochondrial con- Kokko et al. 1999). In addition, the labyrinthine shape of trol-region sequences (when assuming high mutation Lake Saimaa has been shown to impose population frag- rates). In combination, these results therefore indicate mentation on the seals (Valtonen et al. 2012, 2014), that large historical population size – instead of frequent which could speed up genetic erosion in relation to the incoming gene flow from the Arctic, as suggested by the

3428 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. T. Nyman et al. Genetic Erosion in Fennoscandian Ringed Seals microsatellite-based analyses of Martinez-Bakker et al. of many isolated populations (Swatdipong et al. 2010; Ul- (2013) – underlies the relatively high genetic diversity in ler and Leimu 2011; Kolbe et al. 2012). the Baltic Sea. Nevertheless, both our results and the weak In the case of the focal Fennoscandian seals, our ABC

Arctic–Baltic differentiation (FST = 0.011–0.028) found in results decisively favor the two models assuming founder the geographically more extensive survey of Martinez- events in both landlocked populations (Fig. 5). However, Bakker et al. (2013) point to the conclusion that postglacial while posterior distributions suggest an extended initial changes in the Baltic population have been relatively period of small population size in Lake Saimaa, the colo- minor, meaning that its extant diversity can be assumed nization bottleneck in Lake Ladoga appears to have been to represent reasonably well the initial diversity of the both shorter and less severe (Fig. 6 and Fig. S2 in Appen- founders of the Saimaa and Ladoga populations. dix S1), which could partly explain the preservation of When it comes to microsatellite intrapopulation diver- variability in the Ladoga subspecies (cf. Clegg et al. 2002; sity and interpopulation differentiation, our results are Kekkonen et al. 2012; Keller et al. 2012; see also Valtonen conveniently summarized in the FCA plot: 172 Saimaa et al. 2012). Posterior distributions of effective population seals form a tight cluster well separated from the others, sizes in the lakes during the known recent bottlenecks while, despite the smaller numbers of individuals from the remain somewhat uncertain, but are shifted toward the Baltic Sea and Lake Ladoga, high genotypic variability in lower end of the prior range in both Saimaa and Ladoga these populations leads to two wide, partly overlapping (yet with far larger numbers in the latter). The median

‘clouds’ (Fig. 4). Our estimate of a 55% overall loss of het- ABC estimate for Lake Saimaa Ne during the recent bot- erozygosity in the Saimaa population is slightly lower than tleneck (Ne = 114) is comparable to values estimated by the 69% estimate of Palo et al. (2003), but the Saimaa Valtonen et al. (2014) on the basis of linkage disequilib- ringed seal still ranks among the genetically most uniform rium (Ne = 12.3–32.7) and temporal changes in allele fre- seal populations known (Hoelzel 1999; Gelatt et al. 2010; quencies during the last three decades (Ne = 69–113).This Han et al. 2010; Schultz et al. 2010; Valtonen et al. 2014; preferred, two-bottleneck interpretation of the history of see also O’Corry-Crowe 2008), especially remembering the Saimaa population exhibits some striking similarities that the reduced nuclear variation is paralleled by losses in with long-term demographic trends in Black Sea harbor mitochondrial haplotype (33.9%) and nucleotide (89.4%) porpoises (Phocoena phocoena relicta) estimated recently diversity (Valtonen et al. 2012). Prior assessments of by Fontaine et al. (2012). nuclear variation are not available for the Ladoga subspe- However, it is noteworthy that incorporating the well- cies, but the observed relatively minor reduction (6.9%) in known 20th-century bottlenecks into the scenario assum- comparison to the Baltic population is in line with the ing only lacustrine founder events improves model fit, mtDNA results of Valtonen et al. (2012), which showed but not statistically significantly so (Fig. 5). Power analy- near-equal haplotype diversities in these large populations ses based on simulated, pseudo-observed datasets also cast (1.0% loss in Ladoga), but also a clear (68.1%) reduction doubt on the ability of the applied ABC methods to dis- in nucleotide diversity within Lake Ladoga. tinguish between the two best scenarios (Table 2). This may indicate that the recent population crashes have not yet had a marked effect, or that they are at least of minor Demographic history of the marine and importance compared to the diversity losses incurred by landlocked subspecies the preceding long isolation of the lacustrine subspecies. All Fennoscandian ringed seal populations underwent Diversity erosion during even severe bottlenecks may be dramatic, human-caused crashes during the 20th century, slow (Nei et al. 1975), especially in long-lived species with but while the Saimaa subspecies only narrowly escaped overlapping generations (Hoffman et al. 2011; Kekkonen extinction in the 1980s, the populations of the Baltic Sea et al. 2012). The worst phase of the current bottleneck and Lake Ladoga remained at (presumably) safer levels has lasted as little as five generations, which evidently is (Sipil€a et al. 1996; Hyv€arinen et al. 1999; Sundqvist et al. not enough to have left a clear signal in the population- 2012; Trukhanova et al. 2013). These events may have genetic parameters of the studied subspecies (see also contributed substantially to the observed differences in Schultz et al. 2010; Peery et al. 2012). As a matter of fact, genetic diversity among the populations. However, a via- Lander et al. (2011) recently demonstrated that ABC ble alternative hypothesis is that variation in the Saimaa approaches may have a low power to discriminate among population diminished gradually during its long isolation scenarios on average, and a low ability to accurately esti- (Palo et al. 2003), or that the population has always been mate effective population sizes and divergence dates when genetically depauperate, which could be the case if it was the temporal scale used is short. established by a small number of colonists. Such founder Interestingly, incorporating the recent bottleneck into events have been found to underlie the genetic uniformity the founder-event scenario allows a relatively large

ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3429 Genetic Erosion in Fennoscandian Ringed Seals T. Nyman et al. between-bottleneck population within Lake Saimaa: 2012), but we also propose that this could be caused by a assuming an Ne/Nc ratio between 0.1 (Frankham 1995) violation of one of the central assumptions in simulations and 0.16 (Palstra and Ruzzante 2008), the median effective in DIYABC, namely, the absence of gene flow after popu- size of 1270 would correspond to survey size of about lation separation (Cornuet et al. 2010; Lander et al. 2011). 8000–13,000 seals. Like the mtDNA-based long-term esti- After its initial formation, Lake Ladoga remained only a mates of Valtonen et al. (2012), these combined-data few meters above sea level and broadly connected with the numbers clearly exceed the estimated carrying capacity of Baltic basin during at least some parts of the Litorina Sea the present-day Lake Saimaa (Hyv€arinen et al. 1999), but stage, c. 7500–4000 BP (Ukkonen 2002; Saarnisto 2011). the estimates can be considered plausible if the wider geo- Even today, the elevation difference between Lake Ladoga logical history of the ‘lake district’ of southern Finland is and the Baltic Sea is only five meters, meaning that marine taken into consideration. Due to faster postglacial bedrock seals could conceivably reach the lake via its 70 km long rebound along the northern Baltic Sea coastline, the origi- current outlet, river Neva (Saarnisto 2011). Ladoga seals nal, northwest-flowing outlets of southern Finnish lakes are morphologically differentiated from the Baltic subspe- dried up around 8000 BP (Saarnisto et al. 1999). This cies (Sipil€a et al. 1996), and the general genetic composi- damming led to the formation of the Great Lake of Central tions of the populations clearly differ (Fig. 4), but it is Finland, an enormous lake complex (Saarnisto et al. 1999; noteworthy that Bayesian assignment analyses based on Tikkanen 2002) that could have provided enough space presumably neutral microsatellites have difficulties with and resources to support up to 10,000 seals (Hyv€arinen identifying the source population of Baltic or Ladogan et al. 1999). When new, southbound rivers finally burst individuals (Fig. S1 in Appendix S1). open c. 6000 BP onwards, the lake complex slowly became divided into multiple fragments, including Lake Saimaa Conclusions (Tikkanen 2002; Oinonen et al. 2014). This hypothesis of a wider lacustrine distribution in the past naturally requires Our ABC simulations shed light on the origin of the that ringed seal populations in the (smaller) lakes adjacent widely differing levels of nuclear and mitochondrial diver- to Saimaa have gone extinct. In addition to the Saimaa sity in the three Fennoscandian ringed seal subspecies. area, subfossil seal bones have indeed been recovered from The results strongly suggest that Lake Saimaa was colo- archaeological sites located in the vicinity of the adjacent nized by a small number of seals, and that a significant Lake P€aij€anne (Ukkonen 2002), but their identification portion of the genetic variation present in the marine col- and dating remains disputed (Saarnisto 2011). onizers was lost already during this prolonged founder Although our ABC analyses provide novel insights into event, with slow further erosion taking place throughout the demographic histories of Fennoscandian ringed seals, the post-colonization period. The ensuing low variability they unfortunately leave a few questions open. First, evidently has not prevented the Saimaa population from despite the well-established geological chronology of the persisting for nearly ten thousand years, a possibility that lakes, the genetic data gave relatively poor estimates for is supported by numerous examples of long-term survival their colonization times within the specified prior range. of genetically homogeneous species (Schultz et al. 2010; However, in both favored scenarios, the posterior distribu- Rodrıguez et al. 2011) and even rapid population growth tions suggest that Lake Ladoga was colonized later than under suitable conditions (Hoelzel 1999; Taylor et al. Saimaa (Fig. 6 and Fig. S2 in Appendix S1). Second, pos- 2005; Bouzat 2010; Rollins et al. 2013). This indicates that terior distributions of long-term effective population sizes lack of heritable variation per se would not prevent recov- in Lake Ladoga are more or less uninformative in the two ery of the Saimaa population, if current anthropogenic models that include founder events (Fig. 6 and Fig. S2 in threats could be controlled effectively. The best-fitting Appendix S1). This could indicate that enforcing a scenario also includes the known 20th-century population hypothesis of a small colonizing population in the case of crash in all subspecies, but the effect is weak, indicating Ladoga necessitates overcompensation in the simulated that the bottleneck is too recent to manifest clearly in the long-term effective population sizes. While the disfavored genetic composition of the populations. However, we scenarios NCB–NRB and NCB–RB produced more note that Valtonen et al. (2014) found evidence for a slow focused posteriors within Ladoga (Fig. S4 in Appendix (yet statistically significant) decline in individual heterozy-

S1), median Ne estimates in these are still only slightly gosity within the Saimaa population during the last ﬁve lower than the Baltic numbers and, hence, when converted decades; preventing further diversity losses in this criti- to ‘real’ seals, are too high even for such a large lake. Espe- cally endangered subspecies therefore most likely requires cially in the case of the more complex scenarios, these increasing its head count. ambiguities are almost certainly partly brought about by Ladoga ringed seals present a more complicated case, the relatively small Ladogan sample size (cf. Hale et al. because the population is genetically nearly as diverse as

3430 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. T. Nyman et al. Genetic Erosion in Fennoscandian Ringed Seals the Baltic subspecies in both nuclear microsatellites (this Amos, W., and J. Harwood. 1998. Factors affecting levels of study) and mitochondrial control-region sequences genetic diversity in natural populations. Philos. Trans. R. (Valtonen et al. 2012). Our simulations indicate that the Soc. Lond. B Biol. Sci. 353:177–186. lake was colonized by a large number of seals, and that Belkhir, K., P. Borsa, L. Chikhi, N. Raufaste, and F. the initial bottleneck – if it can be called such – was brief. Bonhomme. 2004. GENETIX 4.0.5.2., logiciel sous Although lake colonization times cannot be inferred with WindowsTM pour la genetique des populations. confidence based on the present data, all evaluated Berta, A., and M. Churchill. 2012. Pinniped taxonomy: scenarios indicate that that the final separation of the review of currently recognized species and subspecies, Ladoga population occurred later than in Lake Saimaa, and evidence used for their description. Mammal Rev. which is in line with the prevailing view of the geological 42:207–234. history of these large lake basins (Ukkonen 2002; Saarnis- Boakes, E. H., J. Wang, and W. Amos. 2007. An investigation to 2011). Estimating the size of the Ladoga population of inbreeding depression and purging in captive pedigreed – likewise proved to be problematic: posterior distributions populations. Heredity 98:172 182. in the favored scenarios are quite uninformative, while Boissin, E., B. Hurley, M. J. Wingfield, R. Vasaitis, J. Stenlid, the disfavored scenarios produce numbers close to the C. Davis, de Groot P., R. Ahumada, A. Carnegie, and A. Baltic estimates. Such large populations cannot be supported Goldarazena. 2012. Retracing the routes of introduction of by the lake, but the results could reflect intermittent invasive species: the case of the Sirex noctilio woodwasp. – incoming gene flow from the Baltic Sea. Future studies Mol. Ecol. 21:5728 5744. Bouzat, J. L. 2010. Conservation genetics of population of Ladoga ringed seals, employing larger numbers of bottlenecks: the role of chance, selection, and history. markers and individuals, are clearly warranted. Conserv. Genet. 11:463–478. Caballero, A., S. T. Rodrıguez-Ramilo, V. Avila, and J. Acknowledgments Fernandez. 2009. Management of genetic diversity of subdivided populations in conservation programmes. We thank the Natural Heritage Services of Mets€ahallitus Conserv. Genet. 11:409–419. (especially Tero Sipil€a and Tuomo Kokkonen) and the Cardoso, M. J., M. D. B. Eldridge, M. Oakwood, B. Rankmore, Finnish Game and Fisheries Research Institute for assis- W. B. Sherwin, and K. B. Firestone. 2009. Effects of founder tance in sample collection, and the Center for Scientific events on the genetic variation of translocated island Computing for providing access to their clusters. populations: implications for conservation management of Ari-Matti Siren kindly gave advice on running the CSC the northern quoll. Conserv. Genet. 10:1719–1733. computers, Matti Heino assisted in microsatellite geno- Chapuis, M.-P., and A. Estoup. 2007. Microsatellite null alleles typing, and three anonymous referees provided construc- and estimation of population differentiation. Mol. Biol. tive criticism that helped in improving the manuscript. Evol. 24:621–631. Financial support for this project was provided by the Clegg, S. M., S. M. Degnan, J. Kikkawa, C. Moritz, A. Estoup, Maj and Tor Nessling Foundation, the Raija and Ossi and I. P. Owens. 2002. Genetic consequences of sequential Tuuliainen Foundation, and the Academy of Finland founder events by an island-colonizing bird. Proc. Natl. (project 14868 to TN). Acad. Sci. USA 99:8127–8132. Cornuet, J.-M., F. Santos, M. A. Beaumont, C. P. Robert, Data Accessibility J.-M. Marin, D. J. Balding, T. Guillemaud, and A. Estoup. 2008. Inferring population history with DIY ABC: a Microsatellite genotypes and mtDNA sequences have been user-friendly approach to approximate Bayesian deposited as a DIYABC-formatted text file in the DRYAD computation. Bioinformatics 24:2713–2719. database, along with the reftableHeaders file containing Cornuet, J.-M., V. Ravigne, and A. Estoup. 2010. Inference on the scenarios, priors, and settings used in the main analy- population history and model checking using DNA ses (doi:10.5061/dryad.r25g1). sequence and microsatellite data with the software DIYABC (v1.0). BMC Bioinformatics 11:401. Conflict of Interest Crnokrak, P., and S. C. H. Barrett. 2002. Purging the genetic load: a review of the experimental evidence. Evolution None declared. 56:2347–2358. Csillery, K., M. G. B. Blum, O. E. 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Trukhanova, I. S., E. Gurarie, and R. A. Sagitov. 2013. Appendix S1. Supplementary tables and figures. Distribution of hauled-out Ladoga ringed seals (Pusa hispida Table S1. Mutation-model priors used for microsatellite ladogensis) in spring 2012. Arctic 66:417–428. loci and mitochondrial control-region sequences in the Ukkonen, P. 2002. The early history of seals in the northern ABC analyses. Baltic. Ann. Zool. Fenn. 39:187–207. Table S2. Target summary statistics used during ABC Uller, T., and R. Leimu. 2011. Founder events predict changes simulations and model-checking analyses. in genetic diversity during human-mediated range Table S3. Observed and estimated microsatellite allele fre- expansions. Glob. Change Biol. 17:3478–3485. quencies in the three ringed seal subspecies. Valtonen, M., J. Palo, M. Ruokonen, M. Kunnasranta, and Table S4. Sample sizes, diversity indices, estimated null- T. Nyman. 2012. Spatial and temporal variation in genetic allele frequencies, and deviations from Hardy–Weinberg diversity of an endangered freshwater seal. Conserv. Genet. equilibrium at 17 microsatellite loci in the focal ringed – 13:1231 1245. seal subspecies. Valtonen, M., J. U. Palo, J. Aspi, M. Ruokonen, M. Figure S1. (a) DK values for each number of K from Kunnasranta, and T. Nyman. 2014. Causes and STRUCTURE runs, and (b) assignment of Saimaa, Ladog- consequences of fine-scale population structure in a a, and Baltic ringed seal individuals into two population critically endangered freshwater seal. BMC Ecol. 14:22. clusters in STRUCTURE. Wang, S., W. Zhu, X. Gao, X. Li, S. Yan, X. Liu, J. Yang, Figure S2. Prior (red lines) and posterior (green lines) Z. Gao, and Y. Li. 2014. Population size and time since distributions of event-time and demographic parameters island isolation determine genetic diversity loss in insular in three Fennoscandian ringed seal subspecies according frog populations. Mol. Ecol. 23:637–648. to the second-best scenario (CB–NRB), and estimates of Wayne, R. K., N. Lehman, D. Girman, P. J. P. Gogan, D. A. mutation-model parameters for microsatellites and Gilbert, K. Hansen, R. O. Peterson, U. S. Seal, A. Eisenhawer, mtDNA sequences (inset). and L. D. Mech. 1991. Conservation genetics of the Figure S3. PCA plots from a model-checking analysis endangered Isle Royale gray wolf. Conserv. Biol. 5:41–51. based on alternative target statistics and datasets simu- Wootton, T. J., and C. A. Pfister. 2013. Experimental lated using parameter values drawn from the posterior separation of genetic and demographic factors on extinction distributions from Scenario CB–RB. risk in wild populations. Ecology 94:2117–2123. Figure S4. Prior (red lines) and posterior (green lines) distributions for effective population size in Ladoga Supporting Information ringed seals in each of the four scenarios evaluated in the ABC analyses. Additional Supporting Information may be found in the online version of this article:

3434 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Valtonen et al. BMC Ecology 2014, 14:22 http://www.biomedcentral.com/1472-6785/14/22

RESEARCH ARTICLE Open Access Causes and consequences of fine-scale population structure in a critically endangered freshwater seal Mia Valtonen1*, Jukka U Palo2, Jouni Aspi3, Minna Ruokonen3ˆ, Mervi Kunnasranta1 and Tommi Nyman1,4

Abstract Background: Small, genetically uniform populations may face an elevated risk of extinction due to reduced environmental adaptability and individual fitness. Fragmentation can intensify these genetic adversities and, therefore, dispersal and gene flow among subpopulations within an isolated population is often essential for maintaining its viability. Using microsatellite and mtDNA data, we examined genetic diversity, spatial differentiation, interregional gene flow, and effective population sizes in the critically endangered Saimaa ringed seal (Phoca hispida saimensis), which is endemic to the large but highly fragmented Lake Saimaa in southeastern Finland.

Results: Microsatellite diversity within the subspecies (HE = 0.36) ranks among the lowest thus far recorded within the order Pinnipedia, with signs of ongoing loss of individual heterozygosity, reflecting very low effective subpopulation sizes. Bayesian assignment analyses of the microsatellite data revealed clear genetic differentiation among the main breeding areas, but interregional structuring was substantially weaker in biparentally inherited microsatellites (FST = 0.107) than in maternally inherited mtDNA (FST = 0.444), indicating a sevenfold difference in the gene flow mediated by males versus females. Conclusions: Genetic structuring in the population appears to arise from the joint effects of multiple factors, including small effective subpopulation sizes, a fragmented lacustrine habitat, and behavioural dispersal limitation. The fine-scale differentiation found in the landlocked Saimaa ringed seal is especially surprising when contrasted with marine ringed seals, which often exhibit near-panmixia among subpopulations separated by hundreds or even thousands of kilometres. Our results demonstrate that population structures of endangered animals cannot be predicted based on data on even closely related species or subspecies. Keywords: Cryptic population structure, Effective population size, Gene flow, Genetic erosion, Landscape genetics, Small population

Background from purely behavioural patterns, e.g., site fidelity [4,5], Efficient management of endangered animal populations social systems [6,7], or individual-level foraging special- requires information on the species’ biology and behav- isation [8]. iour, but also on census size, effective size, population Studying spatial connectivity and levels of gene flow in structure, and migration rates. Especially in the absence rare, elusive, and endangered mammals is difficult using of incoming migration and gene flow, fragmentation of traditional approaches like mark–recapture or telemetry an already small population may increase its extinction methods [9]. To substitute and complement traditional risk by exacerbating demographic and genetic stochasti- methods, a number of genetic tools have been devel- city in the even smaller subpopulations [1]. Gene flow oped. While keeping in mind that genetic methods may be prevented by physical barriers [2] or by a lack of typically provide little information on the demographic suitable corridors connecting habitat patches [3], but consequences of interpopulation migration [10], they are sometimes fine-scaled spatial substructuring may result nevertheless a powerful and often the only tool for assessing connectivity within and among populations [11]. * Correspondence: [email protected] The landlocked Saimaa ringed seal (Phoca hispida ˆ Deceased saimensis) provides an excellent model system for studying 1Department of Biology, University of Eastern Finland, Joensuu, Finland Full list of author information is available at the end of the article how small population size and spatial subdivision influence

© 2014 Valtonen et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Valtonen et al. BMC Ecology 2014, 14:22 Page 2 of 14 http://www.biomedcentral.com/1472-6785/14/22

key genetic and demographic parameters and processes. by the University of Eastern Finland and Natural Heri- This endemic ringed seal subspecies derives from marine tage Services of Metsähallitus [22]. The majority of the seals that became trapped in Lake Saimaa in southern sampled individuals were pups (<1 yrs; 76%), and the Finland after the last glacial period, i.e., nearly 10,000 years main cause of death of both adults and pups was ago [12]. The Saimaa ringed seal population experienced a entanglement in fishing gear (66%). All specimens had serious anthropogenic bottleneck during the 20th century been stored at −20°C. [13,14] and, despite a post-1980s recovery, the population Total genomic DNA was extracted using DNeasy Blood still numbers only c. 300 seals [15]. Previous studies [16,17] and Tissue Kits (Qiagen) following the manufacturer’s have shown that the Saimaa ringed seal is genetically very instructions. Microsatellite loci were amplified using primers depauperate in terms of both nuclear and mitochondrial originally designed for grey seal (Halichoerus grypus) [23-26], variability. As the population has remained completely iso- leopard seal (Hydrurga leptonyx) [27], and harbour seal lated for hundreds of generations, the trajectory of its gene (Phoca vitulina) [28,29] (Additional file 1: Table S1). PCR poolisdeterminedlargelybypopulationsizeandinternal reactions were set up in 10 μl volumes, each containing 25 – population structure. 50 ng genomic DNA, 1 μM each primer, 0.5 U AmpliTaq Lake Saimaa extends nearly 200 km in the north– Gold DNA polymerase (Applied Biosystems), 1X PCR buffer, 2 south direction and covers over 4,400 km [18], but the 1.25 – 1.5 mM MgCl2, and 0.1 mM of each dNTP lake is very fragmented, with narrow straits separating (Finnzymes). The thermal cycler profile consisted of initial its main basins (Figure 1). Telemetry studies have shown denaturation at 94°C for 3 min, followed by 30 – 35 cycles that adult Saimaa ringed seals are relatively sedentary of denaturation at 94°C for 30 s, annealing at 50 – 58°C for [19,20], but have also indicated that population connect- 30s, and extension at 72°C for 30s, with a final extension step ivity could be maintained by dispersal of immature at 72°C for 5 min (Additional file 1: Table S1). PCR products individuals [21]. A scenario of substantial gene flow was were run on an ABI 3730 DNA Analyzer (Perkin Elmer Ap- supported by Palo et al. [16], showing weak differenti- plied Biosystems), and allele peaks scored using GeneMap- ation at microsatellite loci between the northern and per v. 4.0 (Applied Biosystems). Micro-Checker v. 2.2.3 [30] southern parts of the lake. However, Valtonen et al. [17] was used to test for the presence of null alleles, allelic recently reported strong mtDNA differentiation among dropout, and scoring errors due to stuttering, with the four main basins of Lake Saimaa. These contradict- Bonferroni-adjusted 95% confidence intervals. The fre- ory results could be explained by the low amount of data quency of null alleles at each locus was estimated using in the earlier microsatellite assessment, but also by the software FreeNA [31]. sex-biased gene flow. The microsatellite dataset (Additional file 2: Table S2) Here we have examined the genetic diversity, popula- was supplemented by, and contrasted with, mtDNA tion structure, and dispersal patterns in the Saimaa sequence variation in 215 individuals from the same ringed seal based on data from 172 individuals geno- population, obtained by Valtonen et al. [17]. The 704-bp typed at 17 microsatellite loci. We first tested the hy- mtDNA fragment spans the 5′ domain of the mitochon- pothesis that subpopulations in the four main breeding drial control region. areas within the lake are differentiated. As the initial analysis revealed autosomal differentiation between the Spatial and temporal division of specimens main regions, we proceeded to investigate the spatial The genotyped individuals originate from different parts distribution of genetic variation in more detail, taking of Lake Saimaa and represent a time span of three advantage of the specific collection locations of the geno- decades (1980 – 2008). In order to analyse the spatial typed individuals. Using also previously published mtDNA genetic structure of the population, the specimens were data [17], we then explored isolation-by-distance patterns, initially divided into four regional samples following the migration rates among subpopulations, presence of sex- topography of the lake (Figure 1B): Northern Saimaa biased gene flow, local effective population sizes, and (microsatellites: Nms = 15, and mtDNA: Nmt = 19), temporal trends in diversity. Especially when contrasted Haukivesi area (Nms = 99 and Nmt = 116), Pihlajavesi area with recent analogous studies on marine ringed seals, our (Nms = 43 and Nmt = 61), and Southern Saimaa (Nms =15 results provide insights into factors that induce population and Nmt = 19). subdivision, and into the effects of spatial structuring on In addition, the individuals were divided into three tem- the genetic composition of small, isolated populations. poral samples based on their collection decade: the 1980s (Nms =59and Nmt = 79), 1990s (Nms =48and Nmt =54), Methods and 2000s (Nms =65andNmt = 82). A decade was consid- Laboratory analyses ered an appropriate time span to detect temporal changes, We used tissue specimens from Saimaa ringed seals since the estimated generation time of the ringed seal is c. found dead and deposited in a tissue bank maintained 11 years (estimated by Palo et al. [16] after Smith [32]). Valtonen et al. BMC Ecology 2014, 14:22 Page 3 of 14 http://www.biomedcentral.com/1472-6785/14/22

A N 20º B N

Haukivesi area Northern Saimaa

Lake Saimaa Pihlajavesi area

60º mtDNA H1 H2 Baltic H3 Sea H4 H5 H6 Southern H7 Saimaa H8

C NND

Kolovesi area Kolovesi area Northern Northern Saimaa Saimaa

Main Haukivesi area Main Haukivesi area

Pihlajavesi area Pihlajavesi area

TESS Cluster 1 Structure Cluster 2 Cluster 1 Cluster 3 Cluster 2 Southern Southern Cluster 4 Admixed Saimaa Saimaa Admixed

01020 01020 km km Permit MML/VIR/TIPA/5012/13 © National Land Survey of Finland Permit MML/VIR/TIPA/5012/13 © National Land Survey of Finland Figure 1 Location of Lake Saimaa in Finland (A) and collection sites of the ringed seal specimens (B – D). Initial (B) and updated (C, D) regional division used in this study. Different colours imply different mtDNA haplotypes (B), and different clusters identified by Structure (C) and TESS (D) (see Figure 5A,C). Valtonen et al. BMC Ecology 2014, 14:22 Page 4 of 14 http://www.biomedcentral.com/1472-6785/14/22

Genetic diversity indices were also calculated across an [42] and G”ST [44], which correct for genetic diversity as alternative temporal division scheme, in which individ- well as the number of subpopulations in the analysis; de- uals were grouped based on their year of birth: 1965– partures from zero were inferred based on 999 permuted 1979 (Nms = 14), the 1980s (Nms = 54), the 1990s (Nms = 47), datasets. To facilitate comparisons between the micro- and the 2000s (Nms = 54) (see [17] for details on age satellite and mtDNA datasets (see [47]), we estimated determination). global Dest also for the mtDNA sequence data using a script [48] employing the seqinr [49] and ape [50] pack- Estimation of genetic diversity and effective population ages in R 3.0.1 [51]; statistical significance was assessed size based on 10,000 permutations of the data. The distribu- We used Arlequin v. 3.5.1.2 [33] to estimate the number tion of microsatellite variation across individual seals of alleles (A), observed (HO) and expected (HE) heterozy- from the main regions of the lake was visualized with gosities, and Wright’s inbreeding coefficients (FIS) for factorial correspondence analysis (FCA) implemented in each spatial and temporal sample. Departures from the program GENETIX v. 4.05.2 [52]. The statistical Hardy–Weinberg equilibrium and the presence of link- significance of differences in FCA axis 1 and 2 scores age disequilibrium between pairs of loci were tested with among regional groups were tested using a multi- Arlequin and GENEPOP v. 4.1.3 [34]. Estimates of allelic response permutation procedure (MRPP) test in PC- richness (AR) were obtained using the rarefaction method ORD v. 5.33 [53]. implemented in HP-Rare [35]. AR was estimated separ- Genetic structuring of the population was further ately for each spatial and temporal sample, based on the studied using Bayesian genotype-assignment approaches. smallest sample size within each division scheme. Individ- Analyses in Structure v. 2.3.4 [54,55] were run without ual observed heterozygosities (HO) were calculated using prior information on the sampling locality. We employed IR macroN4 ([36]; www.zoo.cam.ac.uk/directory/william- the admixture model with correlated allele frequencies, amos), and the relationship between heterozygosity and and implemented a burn-in period of 100,000 followed by year of birth was tested by performing a linear regression 500,000 MCMC iterations. We set the number of clusters analysis in SPSS Statistics v. 19 (IBM). (K) from 1 to 8, and ran 20 replicate analyses per each K Effective sizes for the total population, as well as for to ensure convergence. Subsequently, we determined the different regional and temporal subsamples within the most likely number of clusters by estimating the logarithm lake, were estimated using two different methods. First, of the data probability given each number of K (logP(X|K); we used the single-sample method based on linkage [54]), and on the basis of ΔK values estimated using the disequilibrium implemented in the software LDNe [37], ad hoc approach of Evanno et al. [56] implemented in while assuming a lowest allele frequency of 4% in order Structure Harvester v. 0.6.93 [57]. to prune singletons also from the smallest regional sam- Because collection locations were known for all but ples (= Northern and Southern Saimaa). Second, utilis- one of the sampled 172 seals, we used TESS v. 2.3.1 ing the sampling from different decades, we estimated [58,59] for a more detailed evaluation of spatial differen- Ne with the temporal method in TempoFS [38], assum- tiation within the lake. In these analyses, individual ing Waples sampling scheme 1 (see [39] and references sampling-site coordinates were included as prior infor- therein). For both methods, 95% confidence intervals mation. Before the analyses, we removed some links were obtained through jackknifing. The sample size from from the default neighbourhood system created by the the Haukivesi area allowed also estimating effective program, in order to improve its match to the geography population sizes separately for different decades. of Lake Saimaa (Additional file 3: Figure S1). Thereafter, we ran TESS using a no-admixture model with 200,000 Assessments of population structure iterations, of which the first 100,000 were excluded as a Genetic differentiation among the spatial and temporal burn-in, to test the number of clusters (K) from 2 to 8, samples was investigated using analysis of molecular with ten replicates for each K. Plots of the deviance in- variance (AMOVA) [40,41] and by estimating pairwise formation criterion (DIC) against K were used to identify FST values between samples using Arlequin. For the two the most likely number of clusters, which was then used regions with the largest sample sizes (Haukivesi and Pih- in 100 replicate runs with the admixture model, using lajavesi), we also ran a hierarchical AMOVA with spatial the same number of iterations as above. Finally, we averaged samples subdivided into temporal subsamples, and vice results from the ten runs having the highest likelihoods versa. Significance levels were estimated on the basis of using CLUMPP v. 1.1.2 [60], and visualised the results using 10,000 permutations. Because FST is influenced by the Distruct v. 1.1 [61]. The initial hypothesis concerning the level of heterozygosity (HS) in the studied subpopula- regional division of the Saimaa ringed seal population, which tions [42-44], we used GenAlEx v. 6.501 [45,46] to esti- was based on the topography of the lake, was then reas- mate overall spatial differentiation also based on Dest sessed according to the Structure and TESS results. Valtonen et al. BMC Ecology 2014, 14:22 Page 5 of 14 http://www.biomedcentral.com/1472-6785/14/22

The presence of an isolation-by-distance (IBD) pattern one locus each (Additional file 2: Table S2) [22]. Signifi- among seals within the lake was tested by using SPA- cant heterozygote deficit was observed at eight loci, but GeDi v. 1.3 [62] to contrast the degree of relatedness the finding was not consistent through regional or tem- among individuals with their geographic distances on a poral samples. Over all samples, Micro-Checker analyses logarithmic scale [63]. Using the aforementioned loca- suggested presence of null alleles at six loci (Hg1.4, tion information, we estimated IBD for the microsatellite Hg3.6, Hg6.1, Hg8.9, Hgdii, and Pvc78) with low frequen- (N = 171) and mtDNA (N = 214) datasets using the cies (r < 0.1) in all cases, most likely due to population kinship coefficient of Loiselle et al. [64], which is consid- substructure (see below). Scoring errors suggested for ered suitable for datasets containing rare alleles [65]. For the loci Hg3.6 and Hg8.9 were ruled out by independent mtDNA, we also used Nij, a kinship analogue based on gen- manual rechecking of the data by two researchers. In etic distances among haplotypes [66]. Between-haplotype Arlequin, consistent and significant linkage disequilib- distances were estimated based on a TrN + I + Γ substitution rium in all temporal samples after a sequential Bonfer- model using parameter values from Valtonen et al.[17].In roni correction was found between loci Hg4.2 and all three analyses, ten spatial distance classes were created Pvc78, whereas Bonferroni-corrected GENEPOP results using the equal-frequency method, which generates uneven indicated no disequilibrium. We consider the latter result distance intervals with roughly equal numbers of pairwise more reliable, because the permutational likelihood-ratio comparisons. Significance levels for the mean kinship coeffi- test implemented by Arlequin assumes that all loci are in cient (departure from zero) for each distance class, as well Hardy–Weinberg equilibrium [70], which is not the case as for the regression slope (b), were obtained by 10,000 here (Additional file 2: Table S2). Therefore, the main ana- permutations, and standard errors were estimated by lyses were performed using the full 17-locus dataset. jackknifing over loci. The average number of alleles per locus was 3.5, but the range was broad, so that 16 loci had 2 to 4 alleles, while Estimation of migration rates and sex-biased patterns of 16 were found in locus Hl15 (Table 1 and Additional gene flow file 2: Table S2). The low allelic diversity was reflected Migration rates among Lake Saimaa regions were esti- in the very low overall level of expected heterozygosity mated using the Bayesian multilocus genotyping method in the population (HE =0.36±0.22). implemented in BayesAss v. 1.3 [67]. We performed 1 × No obvious differences in genetic diversity were found 106 burn-in iterations followed by 9 × 106 iterations with a among regions (Table 1). The data also suggested diver- sampling frequency of 2,000, with Δ parameters of allele sity decrease along the decades, but there is not enough frequency, migration rate, and inbreeding coefficient set to statistical power for testing the pattern. However, there 0.15, 0.17, and 0.17, respectively, to achieve the recom- was a weak but statistically significant negative relation- mended acceptance rates of between 40% and 60%. The ship between individual observed heterozygosity and 2 analysis was repeated three times with different initial seed year of birth (HO = 3.762–0.002*birth year; r =0.030, numbers to ensure convergence. P = 0.025; Figure 2). Temporal FIS estimates increased In order to infer whether dispersal within the lake is over the study period from effectively zero for seals sex-biased, we estimated the relative amount of male born before 1980 to a significantly positive FIS = 0.100 and female gene flow following the approach of González- (P = 0.001) in the 2000s (Table 1). Suárez et al. [68], which takes into account the different Estimates of effective population sizes, based on both mode of inheritance of mitochondrial and nuclear genes, linkage disequilibrium and temporal changes of allele as well as their differing numbers of gene copies of within frequencies, were extremely low (Table 2). For the total a population (see also [69]). This indirect method was population, the LD-based estimate was Ne = 14.7 (95% chosen because the small number of subadults and adults CI 9.9 – 20.7), and similarly low numbers were obtained in the data [22] prevented direct assessments of sex- for different decades, ranging from Ne = 10.7 (6.1 – 17.4) specific differentiation. We first estimated the amount in the 1990s to Ne = 32.7 (18.6 – 66.1) in the 2000s. Esti- of male differentiation using equation 1b in González- mates based on the temporal approach were somewhat Suárez et al. [68], and then assessed the ratio of male higher, but with broad confidence intervals: 1980s – and female gene flow rates based on equation 2c in the 1990s Ne = 69 (33 – inf.); 1990s – 2000s Ne = 53 (31 – same paper. 171); and Ne = 113 (51 – inf.) over two generations (1980s – 2000s). Effective population sizes within the Results four main regions ranged from 4.7 to 45.9 (Table 2). The Data characterization, genetic diversity, and effective highest estimate (yet with a rather broad confidence population size interval) was found in the Pihlajavesi area and the lowest Genotypes for all 17 microsatellite loci were obtained for in Northern Saimaa. As for the whole population, Nes 168 individuals, while four individuals lacked data for within Haukivesi were highest during the 2000s. Valtonen et al. BMC Ecology 2014, 14:22 Page 6 of 14 http://www.biomedcentral.com/1472-6785/14/22

Table 1 Measures of genetic diversity in spatial and temporal samples of the Saimaa ringed seal population based upon analyses of 17 microsatellite loci

Sample N NP NA ±SD AR HO ±SD HE ±SD FIS Lake Saimaa 172 17 3.47 ± 3.32 – 0.33 ± 0.21 0.36 ± 0.22 0.075*** Northern Saimaa 15 14 2.29 ± 1.16 2.27 0.34 ± 0.27 0.33 ± 0.24 −0.004 Haukivesi area 99 15 3.24 ± 3.15 2.70 0.35 ± 0.23 0.38 ± 0.23 0.074*** Kolovesi 20 13 2.41 ± 1.66 2.32 0.37 ± 0.31 0.36 ± 0.28 −0.031 Main Haukivesi area 79 15 3.18 ± 2.92 2.63 0.34 ± 0.23 0.35 ± 0.24 0.024 Pihlajavesi area 43 15 2.59 ± 1.42 2.26 0.31 ± 0.25 0.30 ± 0.23 −0.034 Southern Saimaa 15 14 2.47 ± 1.42 2.45 0.31 ± 0.24 0.30 ± 0.22 −0.045 yod 1980s 59 17 3.29 ± 2.62 3.21 0.35 ± 0.23 0.37 ± 0.23 0.054* yod 1990s 48 17 3.29 ± 2.85 3.29 0.34 ± 0.22 0.37 ± 0.23 0.077* yod 2000s 65 17 3.06 ± 2.38 2.96 0.32 ± 0.20 0.35 ± 0.22 0.092*** yob 1965-1979 14 16 2.94 ± 1.75 2.94 0.38 ± 0.25 0.38 ± 0.23 −0.019 yob 1980s 54 17 3.06 ± 2.38 2.71 0.34 ± 0.23 0.37 ± 0.23 0.075** yob 1990s 47 17 3.29 ± 2.85 2.69 0.33 ± 0.22 0.36 ± 0.23 0.073* yob 2000s 54 17 3.06 ± 2.38 2.58 0.32 ± 0.19 0.35 ± 0.22 0.100**

NP, number of polymorphic loci; NA, average number of alleles per locus; AR, allelic richness; HO, observed heterozygosity; HE, expected heterozygosity; FIS, inbreeding coefficient; yod, year of death; yob, year of birth. Statistically significant FIS values are marked with asterisks: ***P < 0.001; **P < 0.01; *P < 0.05. Regional estimates are shown both for the initial four-region division, and for the updated five-region scheme in which Haukivesi is split into the Kolovesi and Main Haukivesi areas (in italics).

Population structure in space and time to estimates based on mitochondrial control-region se- In microsatellite analyses, significant overall differenti- quences, which were consistently higher for overall differ- ation was detected among the four main regions of Lake entiation (FST =0.390, P < 0.001 [17]; Dest =0.256, P = Saimaa (FST = 0.065, Dest = 0.043, G”ST = 0.121; all P ≤ 0.047) as well as for pairwise FST values (Table 3). A highly 0.001), and the result was confirmed by pairwise FST significant negative relationship between relatedness estimates among regions (Table 3). These patterns can and spatial distance between pairs of individuals was be seen in the FCA plot, in which seals from the same found using both microsatellites (b = −0.039, P < 0.001; region tended to cluster together, even though there was Figure 4A) and mtDNA (bLoiselle = −0.055; bNij = −0.067; wide overlap among groups (Figure 3A). FCA axis loads both P <0.001;Figure4B). of the regional groups differed significantly from each Overall microsatellite differentiation among temporal other (MRPP test, P < 0.001), and all pairwise compari- samples based on decades (1980s, 1990s and 2000s) was sons were statistically significant (P < 0.01 in each case). very weak (FST = 0.002, P < 0.001), and this was true also Microsatellite differentiation was moderate in comparison for all pairwise comparisons (all FST ≤ 0.002, P ≥ 0.293). Hierarchical AMOVA of the two regions with the largest

0.7 sample sizes (= Haukivesi and Pihlajavesi) likewise Ho = 3.762 – 0.002 * birth year showed that, regardless of the order of the hierarchy, P 0.6 = 0.025 most of the differentiation could be attributed to vari- 0.5 ation among regions and individuals, while the effect of decades was weak and statistically non-significant 0.4 (results not shown). In contrast, mtDNA-based ana- 0.3 lyses showed highly significant differentiation among

Heterozygosity decades (FST = 0.384, P < 0.001; see discussion in [17]). 0.2 In Bayesian assignment analyses in Structure, mean 0.1 log-likelihood was maximized at K = 4 (Additional file 4: Figure S2A), but the Evanno et al. [56] approach 0.0 favoured a two-cluster model (Additional file 4: Figure 1960 1970 1980 1990 2000 2010 2020 S2B). In the two-cluster results, 22 individuals were Birth year assigned to cluster 1 and 146 to cluster 2 (red and light Figure 2 Observed heterozygosity of Saimaa ringed seal blue in Figure 5A, respectively), while the assignments individuals in relation to their year of birth. of four individuals remained ambiguous (membership Valtonen et al. BMC Ecology 2014, 14:22 Page 7 of 14 http://www.biomedcentral.com/1472-6785/14/22

Table 2 Effective population sizes for the total Saimaa ringed seal population as well as for regional and temporal samples based on linkage disequilibrium and temporal changes in allele frequencies Linkage disequilibrium Temporal method All 1980s 1990s 2000s 1980s – 1990s 1990s – 2000s 1980s – 2000s Lake 14.7 12.3 10.7 32.7 69 53 113 Saimaa (9.9 – 20.7) (8.1 – 18.1) (6.1 – 17.4) (18.6 – 66.1) (33 – inf.) (31 – 171) (51 – inf.) Northern 4.7 –– – Saimaa (2.1 – 13.6) Haukivesi 8.8 3.3 4.5 30.7 61 24 92 area (5.6 – 12.3) (2.5 – 5.3) (2.7 – 7.6) (15.7 – 86.1) (17 – inf.) (12 – inf.) (47 – 1873) Kolovesi 10.7 –– – (4.6 – 27.0) Main 21.8 10.8 7.5 53.6 46 15 59 Haukivesi area (12.1 – 40.3) (6.2 – 19.5) (3.0 – 15.4) (22.5 – 2366.1) (15 – inf.) (8 – 135) (23 – inf.) Pihlajavesi 45.9 –– – area (20.6 – 251.3) Southern 15.3 –– – Saimaa (6.0 – 100.1) inf. = infinity. Estimates are shown both for the initial four-region division, and for the updated five-region scheme in which Haukivesi is split into the Kolovesi and Main Haukivesi areas (in italics). 95% confidence intervals are given in parentheses. coefficient Q < 0.7, following, e.g., Kopatz et al. [71]). corresponds to Pihlajavesi and Southern Saimaa. Cluster Notably, all individuals assigned to cluster 1 originated 1 (red) individuals were largely restricted to the afore- from the Haukivesi area (Figure 5A) and, upon closer mentioned Kolovesi basin (Figure 1D), whereas the rest examination, 86% of them were found to be from the of the Haukivesi area seemed to represent an admixture Kolovesi basin in northeastern Haukivesi (Figure 1C). zone: although 35% of individuals belonged to Cluster 2 The Kolovesi individuals were similarly identifiable in (green), 44% remained weakly assigned. the four-cluster plot, but the geographic pattern of the Based on these results, we reassessed our initial, other three clusters was less apparent (Figure 5B). topography-based division of Lake Saimaa by splitting In the TESS assignment analyses, DIC reached a plat- the Haukivesi area (Figure 1B) into two subregions, eau at K = 4 (Additional file 4: Figure S2C). However, the Kolovesi and Main Haukivesi (Figure 1C,D). For the rest spatial distribution of individuals in these clusters did of the lake, we retained the original division scheme, on not entirely follow our predefined structure (Figures 1D grounds of the significantly non-zero pairwise FST values and 5C). Instead, Northern Saimaa was the only prede- (Table 3). As expected, using the five-region division ele- termined region that was more or less identifiable in its vated global estimates of differentiation, based on both original form in the TESS chart (Cluster 3, blue), while microsatellites (FST = 0.107 vs. 0.065; Dest = 0.074 vs. the distribution of Cluster 4 (yellow) individuals roughly 0.043; G”ST = 0.196 vs. 0.121) and mtDNA (FST = 0.444

Table 3 Genetic differentiation (pairwise FST) between regional samples of the Saimaa ringed seal population based on microsatellite and mtDNA variation Northern Haukivesi Main Haukivesi Pihlajavesi Southern

Nms Nmt Saimaa area Kolovesi area area Saimaa Northern Saimaa 15 19 – 0.454 0.698 0.467 0.446 0.392 Haukivesi area 99 116 0.039** – ––0.389 0.333 Kolovesi 20 21 0.161 –– 0.544 0.534 0.527 Main Haukivesi area 79 95 0.047 – 0.170 – 0.398 0.344 Pihlajavesi area 43 61 0.096 0.063 0.236 0.057 – 0.311 Southern Saimaa 15 19 0.153 0.071 0.209 0.075 0.054 –

Nms = sample size in microsatellite analyses; Nmt = sample size in mtDNA analyses. Estimates based on microsatellites are given below diagonal and mtDNA variation above diagonal. Results are shown for the initial four-region division, and for the updated five-region scheme in which Haukivesi is split into the Kolovesi and Main Haukivesi areas (in italics). Differentiation in all comparisons was statistically significant at P < 0.001 after a sequential Bonferroni correction, except for **P < 0.01. Valtonen et al. BMC Ecology 2014, 14:22 Page 8 of 14 http://www.biomedcentral.com/1472-6785/14/22

vs. 0.390; Dest = 0.298, P < 0.001, vs. 0.256). Pairwise FST 1 A estimates showed that Kolovesi is highly differentiated from the rest of the lake (Table 3), which can also be seen in the five-region FCA plot (Figure 3B; MRPP test, 0 overall P <0.001;pairwisecontrasts,allP < 0.001). Furthermore, the significantly positive FIS =0.074, P = 0.0002 of the undivided Haukivesi area disappeared when Main Haukivesi and Kolovesi were analyzed sep-

Axis 2 (5.59%) -1 − Northern Saimaa arately (FIS = 0.024, P =0.183; and FIS = 0.031, P = Haukivesi area 0.741, respectively) (Table 1). Separating the Kolovesi Pihlajavesi area and Haukivesi individuals elevated LD-based Ne esti- Southern Saimaa mates in the new Main Haukivesi area, while estimates -2 -1.2-0.8 -0.40.0 0.4 based on the temporal method decreased (Table 2). Axis 1 (10.50%) Migration rates and sex-biased patterns of gene flow 1 B BayesAss runs implementing the initial four-region scheme did not converge, so only results based on the five-region division are reported here. Migration rates among different 0 parts of Lake Saimaa were generally low (Table 4): point estimates of self-recruitment exceeded 87% in four regions, and Southern Saimaa was the only region estimated to receive immigrants at a considerable rate (27.8%). These Northern Saimaa

Axis 2 (5.59%) -1 Kolovesi immigrants seem to originate from the adjacent Pihlajavesi Main Haukivesi area area, since the 20.4% rate of migration from Pihlajavesi to Pihlajavesi area Southern Saimaa was the only interregional estimate that Southern Saimaa was significantly higher than zero. -2 -1.2-0.8 -0.40.0 0.4 The expected level of differentiation for paternally Axis 1 (10.50%) inherited genes (FST(males) = 0.099) among the five Lake Figure 3 Factorial correspondence analysis (FCA) plot of ringed Saimaa regions was only slightly lower than overall seals from different regions of Lake Saimaa. Individuals are microsatellite differentiation (FST = 0.107), and the same marked with different symbols based on (A) the initial division to was true for nearly all regional pairs (Tables 3 and 5). four main regions and (B) on the updated division to five regions The overall ratio of male to female gene flow was 7.26, (see legends). and ratios estimated for separate regional pairs ranged from 3.87 to 18.55 (Table 5).

A B

0.2 0.2 Nij *** Loiselle *** 0.1 *** 0.1

*** * 0.0 0.0 * *** *** *** * *** *** * * Kinship coefficient Kinship coefficient -0.1 -0.1 *** ***

-0.2 -0.2 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 Mean ln(distance) Mean ln(distance) Figure 4 Average kinship coefficient plotted against logarithmic distance between Saimaa ringed seal individual pairs. The plots are

based on (A) 17 microsatellite loci, and (B) mtDNA haplotypes (Loiselle) and genetic distance between haplotypes (Nij). Distance classes differing significantly from the mean kinship of the population are marked with asterisks: ***P < 0.001; *P < 0.05. Valtonen et al. BMC Ecology 2014, 14:22 Page 9 of 14 http://www.biomedcentral.com/1472-6785/14/22

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Northern Haukivesi area Pihlajavesi area Southern Saimaa Saimaa

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Northern Haukivesi area Pihlajavesi area Southern Saimaa Saimaa

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Northern Haukivesi area Pihlajavesi area Southern Saimaa Saimaa Figure 5 Assignment of individual Saimaa ringed seals into population clusters based on microsatellite data. Results are shown as indicated by Structure (A) for K = 2 and (B) K = 4, and (C) by TESS for K = 4. Each bar represents a single individual, and the height of each bar represents the relative probability of it belonging to a given cluster. Individuals are grouped by the four main sampling areas, inverted triangles above the plots denote individuals that originate from the Kolovesi part of the Haukivesi area. Cluster colours in (A) and (C) correspond to the colours used in Figure 1C,D. The arrow below the plot in (C) indicates an individual that was excluded from the analysis in TESS due to lacking detailed location information.

Discussion [78], see also [79]). The reduced diversity of the Saimaa Genetic diversity and effective population size population is not plausibly explained by ascertainment bias The Saimaa ringed seal has very low genetic diversity [78,80], which could stem from the use of loci developed (HE = 0.36) in comparison to marine ringed seal popula- for other seal species, since it should similarly affect the tions, in which microsatellite-based estimates of ex- levels of diversity in marine ringed seals. On the contrary, pected heterozygosities range from HE = 0.80 to HE = heterozygosity estimates in marine ringed seals [72-74] are 0.89 [72-74]. Indeed, microsatellite variation within the in most cases higher than in the species for which the loci subspecies is, to our knowledge, the lowest that has thus were originally designed [25,27,29]. Coalescent simulations far been found within the order Pinnipedia in studies in indicate that the main loss of genetic diversity in the which monomorphic loci have been excluded (cf. Medi- Saimaa ringed seal predates the 20th-century anthropogenic terranean monk seal (Monachus monachus), HE = 0.40 bottleneck [16,17], but the question remains as to [24]; Hawaiian monk seal (Monachus schauinslandi), whether the current population – given its size and HE = 0.49 [75]; spotted seal (Phoca largha), HE =0.51 spatial structure – will be able to maintain the remaining [76]; northern elephant seal (Mirounga angustirostris), variability. HE = 0.40 [77]), and heterozygosity is low even when con- Unfortunately, it seems that the answer to this ques- trasted with the mean for placental mammals that have tion is “No”: Estimates of total and regional effective experienced a recent demographic threat (HE = 0.50 ± 0.03; population sizes were very low, ranging roughly between Valtonen et al. BMC Ecology 2014, 14:22 Page 10 of 14 http://www.biomedcentral.com/1472-6785/14/22

Table 4 Migration rates of Saimaa ringed seals among five regions of the lake based on 17-locus microsatellite genotypes To From N Northern Saimaa Kolovesi Main Haukivesi area Pihlajavesi area Southern Saimaa Northern Saimaa 15 0.874 ± 0.114 0.011 ± 0.013 0.016 ± 0.019 0.004 ± 0.006 0.013 ± 0.016 Kolovesi 20 0.010 ± 0.015 0.969 ± 0.021 0.025 ± 0.011 0.002 ± 0.004 0.012 ± 0.015 Main Haukivesi area 79 0.022 ± 0.033 0.006 ± 0.008 0.888 ± 0.068 0.003 ± 0.006 0.050 ± 0.052 Pihlajavesi area 43 0.084 ± 0.102 0.009 ± 0.012 0.068 ± 0.067 0.987 ± 0.012 0.204 ± 0.061 Southern Saimaa 15 0.009 ± 0.014 0.006 ± 0.009 0.003 ± 0.004 0.003 ± 0.005 0.722 ± 0.033 The estimates (m ± SD) are averaged over three BayesAss runs. Rates are estimated in both directions, from source regions given in the first column (“From”)to destination regions in the following columns (“To”). Self-recruitment within each region is shown along the diagonal (in italics), and interregional rates in which 95% confidence intervals do not include zero are in bold.

5 and 113 (Table 2). Comparable values have been sampled individuals detected slowly declining individual obtained for other isolated and/or bottlenecked popula- heterozygosity in microsatellite loci during the past few tions of large mammals, e.g., Finnish wolves (Canis decades (Figure 2), which is in line with the post-1960s lupus), Ne =37.8 – 43.0 [81], Western Canadian moun- decrease in mtDNA variation observed by Valtonen tain goats (Oreamnos americanus), Ne = 23.2 – 65.9 [82], et al. [17]. The fact that the overall inbreeding coeffi- and Iberian lynxes (Lynx pardinus), Ne = 8.5 – 23.1 [79]. cient has risen at the same time (Table 1) suggests that It should be noted that historical, long-term Nesofsev- the increasing autozygosity is due to a progressive eral hundred, estimated by Palo et al. [16] and Valtonen increase in differentiation among and/or within regions et al. [17], are far higher than our contemporary rather than to loss of alleles. Examples of similar trends estimates, suggesting that the population has been are accumulating from other species that have under- substantially larger in the past. Notably, estimates based gone recent anthropogenic collapses [85] and range on linkage disequilibrium were clearly lower than those fragmentations [79,86]. based on temporal changes in allele frequencies (Table 2). This is probably due to population structure lowering Population structure and gene flow LD-based estimates (see [83]), as is indicated by the rise Gene flow among populations is often restricted or even in Main Haukivesi Ne after exclusion of Kolovesi individ- prevented by geographic barriers, but assessing what uals (Table 2). We therefore consider the higher esti- constitutes an obstacle for a given species is not always mates based on the temporal method to be more straightforward [11,87,88]. In analyses of spatial genetic applicable for the Saimaa ringed seal. When reflected to variation, a clear benefit of the Saimaa ringed seal popu- the current census size (310 in the year 2012 [15]), the lation is that the topography of the lake unambiguously temporal Ne estimates yield a ratio of Ne/Nc = 0.17 – determines possible routes of dispersal among subpopu- 0.36. The lower end of this range is consistent with the lations, and the level of differentiation then depends on average ratio observed in wild populations (0.16), and the migration rates through these routes. Telemetry the higher end would comply with the higher-than- studies have shown that adult home ranges may span average Ne/Nc ratios observed in small threatened popu- sites nearly 40 km apart [19] and that even first-year lations [84]. pups can travel up to 15 km per day after being weaned Nevertheless, even the higher temporal Nes apparently [21]. However, it has hitherto remained unclear whether are not enough for safeguarding the existing diversity: such occasional long-distance movements lead to gene our detailed analysis based on the birth years of the flow among the main breeding areas.

Table 5 Estimates of sex-specific differentiation and gene flow in the Saimaa ringed seal population Northern Kolovesi Main Haukivesi Pihlajavesi Southern Saimaa area area Saimaa Northern Saimaa – 15.67 18.55 8.18 3.87 Kolovesi 0.129 – 7.22 5.01 5.44 Main Haukivesi area 0.045 0.142 – 11.31 6.50 Pihlajavesi area 0.090 0.186 0.055 – 7.79 Southern Saimaa 0.143 0.170 0.074 0.055 –

Estimates show the expected level of differentiation for paternally inherited genes (FST(males); below diagonal) and the ratio of male- and female-mediated gene flow (m(males)/m(females); above diagonal) among ringed seals from five regions of Lake Saimaa. Valtonen et al. BMC Ecology 2014, 14:22 Page 11 of 14 http://www.biomedcentral.com/1472-6785/14/22

Our results demonstrate that ringed seal subpopulations demographic independence of subpopulations, which inhabiting the main regions of Lake Saimaa are differenti- could significantly increase the risk of stochastic extinc- ated with respect to both autosomal and mitochondrial tion by lowering the demographic effective population variation (see also [17]). However, Bayesian assignment size (Nd) well below the census size [93]. analyses showed that genetic structuring within the lake is Microsatellites exhibited lower levels of interregional more subtle than we originally envisioned, by uncovering differentiation (measured as FST and Dest)andasmoother semi-isolation of the Kolovesi basin, which essentially con- isolation-by-distance pattern than did mtDNA (Figure 4A, stitutes a labyrinthine cul-de-sac separated from the main B), suggesting that gene flow is mainly mediated by males, parts of Haukivesi by a narrow strait (Figure 1C,D). All in as is often the case in pinnipeds [5,94,95] and mammals all, the level of genetic differentiation among different in general [96]. The roughly sevenfold male-to-female parts of Lake Saimaa (FST =0.065 – 0.107) is remarkable gene flow ratio is comparable to values reported for, e.g., considering the short distances among the main basins harbour seals [97] and California sea lions (Zalophus (Figure 1). As a comparison, Palo et al. [72] showed that californianus)[68]. ringed seals of the Baltic Sea are essentially panmictic (FST = 0.000), and geographic differentiation is weak also Conclusions in grey seals, in which Graves et al. [89] found Saimaa-like The critically endangered ringed seal subspecies endemic FST values only between Baltic and North Sea breeding to Lake Saimaa is genetically impoverished and fragmen- colonies (FST =0.068– 0.097), while differentiation within ted into several partially isolated subpopulations. Micro- the Baltic was clearly lower (FST ≤ 0.023). In general, inter- satellite diversity within the subspecies is the lowest population genetic differentiation in marine seals is often recorded so far within the order Pinnipedia and, worry- negligible – or at least lower than that observed within ingly, our analyses reveal an ongoing downward trajec- Lake Saimaa – even across thousands of kilometres tory in both microsatellite (this study) and mtDNA [72-74,90-92]. A potential caveat here is that the depend- variation [17]. Although further studies are needed for ence of FST on overall heterozygosity [42,44] means that investigating whether the low diversity in these presum- direct comparisons are complicated by the very disparate ably neutral markers correctly reflects the level of variation levels of genetic diversity in the Saimaa ringed seal and its in adaptively important loci, and although genetically based marine counterparts. We therefore used the approach of adversities have thus far not been demonstrated in the Heller & Siegismund [43] and Meirmans & Hedrick [44] population, maintaining the existing variability is recom- to convert overall differentiation indices among marine mendable, especially considering the need of the subspe- ringed seal populations estimated by Davis et al.[73] cies to adapt to a substantially warmer climate in the (FST = 0.005) and Martinez-Bakker et al.[74](FST = coming decades (cf. [98,99]). The observed diversity de- 0.0086) into G”ST values, based on the reported FST s, clines are fortunately relatively slow, as is expected for mean values of HS, and numbers of sampled populations. a long-lived species with overlapping generations (cf. The converted estimates (G”ST =0.047,and G”ST =0.056, [100-102]). Hence, both negative trends most likely respectively) resulting from these studies, both of which can be reversed if the still-fragile recovery of the popu- include populations on different continents, are still below lation can be sustained and strengthened by conserva- the overall differentiation among regions within Lake tion actions. Managed translocations of individual seals Saimaa (G”ST =0.121– 0.196). could restore demographic connectivity (cf. [91,103,104]), As can be expected based on the high spatial differen- and would simultaneously aid in protecting the remaining tiation, interregional migration estimates (Table 4) were genetic variation; according to our results, such efforts very low in comparison to values estimated for other should be concentrated on females, which otherwise seem large mammals inhabiting fragmented landscapes (e.g., to be particularly reluctant dispersers. [2,11]). Across comparable geographic distances, equally The level of genetic differentiation among animal pop- low rates have been detected only for the endangered ulations is a function of multiple factors, including time, Ethiopian wolf (Canis simensis), in which remaining effective population sizes, degree of geographic separ- populations are restricted to mountaintops separated by ation, and species-specific dispersal ability and propen- unsuitable lowlands [3]. Although the migration esti- sity [105,106]. Hence, large and mobile mammals tend mates were generally near zero, it appears that Pihlaja- to exhibit coarse-grained or widely clinal genetic struc- vesi, the most important breeding area producing some tures, except when differentiation follows from behav- 40 – 50% of the pups born annually [15], serves as a iourally mediated dispersal limitation caused by, for source for the small Southern Saimaa subpopulation. example, individual-level habitat or resource specialization Assessing whether the estimated migration rates are [87,107] or spatial clustering of related individuals [4,81]. high enough to maintain demographic connectivity is Our analyses uncovered cryptic and remarkably fine- difficult (cf. [10]), but our results suggest at least partial scaled genetic structure within the Saimaa ringed seal Valtonen et al. BMC Ecology 2014, 14:22 Page 12 of 14 http://www.biomedcentral.com/1472-6785/14/22

population, despite potentially high individual dispersal Nestori Foundation, the Finnish Foundation for Nature Conservation, and the ability [19,21]. Small effective subpopulation sizes and the Academy of Finland (project 14868 to TN). subdivided geography of the lake are undoubtedly central Author details factors underlying this spatial structuring, but purely 1Department of Biology, University of Eastern Finland, Joensuu, Finland. 2 behavioural traits must also play a role, considering the Laboratory of Forensic Biology, Hjelt Institute, University of Helsinki, Helsinki, Finland. 3Department of Biology, University of Oulu, Oulu, Finland. 4Institute differing levels of male- and female-mediated gene flow for Systematic Botany, University of Zurich, Zurich, Switzerland. and the short distances that are involved. The fine-scaled differentiation within Lake Saimaa contrasts markedly Received: 28 January 2014 Accepted: 3 July 2014 Published: 9 July 2014 with the population structures of marine Baltic and Arctic ringed seals, in which breeding colonies located hundreds or even thousands of kilometres apart often constitute References essentially panmictic units [72,74]. Therefore, our results 1. Reed DH: Extinction risk in fragmented habitats. Animal Conserv 2004, 7:181–191. provide a striking demonstration that population struc- 2. Zhu L, Zhang S, Gu X, Wei F: Significant genetic boundaries and spatial tures of endangered animals cannot be predicted based on dynamics of giant pandas occupying fragmented habitat across data from even closely related species or subspecies. southwest China. Mol Ecol 2011, 20:1122–1132. 3. 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Kokko H, Lindström J, Ranta E, Sipilä T, Koskela J: Estimating the • Immediate publication on acceptance demographic effective population size of the Saimaa ringed seal (Phoca hispida saimensis Nordq.). Animal Conserv 1998, 1:47–54. • Inclusion in PubMed, CAS, Scopus and Google Scholar 94. Hoelzel AR, Campagna C, Arnbom T: Genetic and morphometric • Research which is freely available for redistribution differentiation between island and mainland southern elephant seal populations. Proc Biol Sci 2001, 268:325–332. Submit your manuscript at www.biomedcentral.com/submit Ann. Zool. Fennici 52: 51–65 ISSN 0003-455X (print), ISSN 1797-2450 (online) Helsinki 17 April 2015 © Finnish Zoological and Botanical Publishing Board 2015

Genetic monitoring of a critically-endangered seal population based on field-collected placentas

Mia Valtonen1,2,*, Matti Heino3, Jouni Aspi3, Hanna Buuri3, Tuomo Kokkonen4,†, Mervi Kunnasranta1, Jukka U. Palo5 & Tommi Nyman1

1) Department of Biology, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland (*corresponding author’s email: [email protected]) 2) Institute of Biotechnology, P.O. Box 56, FI-00014 University of Helsinki, Finland 3) Department of Biology, P.O. Box 3000, FI-90014 University of Oulu, Finland 4) Parks & Wildlife Finland, Akselinkatu 8, FI-57130 Savonlinna, Finland 5) Laboratory of Forensic Biology, Hjelt Institute, P.O. Box 40, FI-00014 University of Helsinki, Finland

Received 11 July 2014, final version received 13 Nov. 2014, accepted 13 Nov. 2013

Valtonen, M., Heino, M., Aspi, J., Buuri, H., Kokkonen, T., Kunnasranta, M., Palo, J. U. & Nyman, T. 2015: Genetic monitoring of a critically-endangered seal population based on field-collected placentas. — Ann. Zool. Fennici 52: 51–65.

Genetic analyses of non-invasively collected samples are increasingly being used in the monitoring of wildlife populations and individuals. This study is the first describing the use of placentas as non-invasive genetic samples from a natural population. We collected 66 placentas from birth-lair sites of Saimaa ringed seals (Phoca hispida saimensis) after the breeding seasons, with the aim of obtaining DNA from both the pup and the mother. Umbilical cord samples proved to yield the pup genotypes, but mothers could not be genotyped with confidence. Comparisons with existing mtDNA and microsatellite reference data sets showed that placentas can be used for inferring population-level genetic parameters. Our microsatellite panel provided sufficient resolution for genetic identification of individuals but, due to the extremely low variability of the population, parentage and sibship could not be inferred reliably. Field-collected placentas could provide means for genetic monitoring of many other seal species as well.

Introduction kau 2005, Schwartz et al. 2007). Non-invasive approaches have become more or less estab- Genetic analyses of non-invasively collected lished practice in studies on large terrestrial samples, such as hair and faeces, are being mammals, which are hard to find and capture increasingly utilized in wildlife research, man- (Arandjelovic et al. 2011, Kopatz et al. 2012, agement, and conservation. Applications of these Davoli et al. 2013). In marine mammals, the methods include, for example, identification of collection of non-invasive samples is often more species or individuals, and estimation of home challenging, but shed skin (Swanson et al. 2006, range size, gene flow, local population size, and Baker et al. 2013, Martinez-Bakker et al. 2013), individual reproductive success (Waits & Paet- faeces (Parsons et al. 2006, Valqui et al. 2010), 52 Valtonen et al. • Ann. ZOOL. Fennici Vol. 52

20° ABN

Lake Saimaa

60° Baltic Sea Kolovesi

0 300 km Northern Saimaa ▼

▼▼ Main Haukivesi area

Pihlajavesi area + + ▼ + ▼ ▼ ▼ ▼ ▼▼++ ▼▼▼ ▼ Haplotype: ▼ ▼ + ▼▼ ▼ H1 H2 H3 ¤ H4 + H7 ¤ H8 Year: ▼ ▼ 2000 Southern ▼▼ 2001 Saimaa 2002 Fig. 1. (A) Location of 2004 2009 Lake Saimaa in Finland, 2010 and (B) collection sites of 2011 Saimaa ringed seal placentas. Different symbols denote different mtDNA 015 0203040 haplotypes and colours Permit MML/VIR/TIPA/5012/14 © National Land Survey of Finland km the year of collection. and even environmental DNA from the water could be substantially enhanced by remotely col- column (Foote et al. 2012) have been success- lected genetic samples. It has been estimated that fully used as a source of genetic information. around 70%–80% of the adult females give birth Here, we describe how field-collected pla- annually (Sipilä 2003), and that some 50–60 centas can be used for non-invasive genetic pups are born each spring. Placentas are rela- monitoring. The target population was the land- tively easy to collect post-partum: females give locked Saimaa ringed seal (Phoca hispida sai- birth to a single pup in subnivean lairs dug mensis). This endemic subspecies inhabits Lake into snowdrifts formed along shores of islands Saimaa in southeastern Finland (Fig. 1A). The and islets (Sipilä 2003) and, after the breeding population of currently circa 300 seals is threat- season, the placenta can often be found from ened mainly by high mortality of juveniles due the bottom of the lake within a few meters from to entanglement in fishing gear and by climate the lair. Preservation of the tissue, and DNA, is change and, therefore, is classified as critically extended by the low temperature of the water endangered (Rassi et al. 2010, Kovacs et al. during and right after the ice-covered season. 2012). Given the still-precarious situation of Ideally, placentas could provide a unique the subspecies, there is a clear need for con- opportunity for obtaining the DNA and geno- tinuous monitoring of the population, which types of two different individuals from a single Ann. Zool. Fennici Vol. 52 • Genetic monitoring of seals based on placentas 53 sample, because mammalian placentas are com- Amniotic cavity posed of both foetal and maternal tissue. Like other carnivorans, pinnipeds have an endothelio- FS MS chorial placenta (Stewart & Stewart 2009) with extensive intermingling of uterine and chorionic tissues, which causes a part of the uterine com- PLACENTA ponent to be torn away along with the placenta at birth. In the case of the Saimaa ringed seal, identification of females based on shed -placen Uterine wall tas could potentially be used to infer individual-level breeding-site fidelity and reproductive Foetus OP success, and could also lead to more accurate UC population-size estimates. Similarly, genotyping Fig. 2. Structure of the placenta, and different sampling pups using placentas could yield information on spots used in this study: MS = maternal side, FS = long-term dispersal patterns and survival prob- foetal side, UC = umbilical cord, OP = orange particles. abilities, if the individuals are recaptured and genotyped later. The main aims of this study were to (1) 2010 (28 and 46 sites, respectively), and from investigate whether it is possible to identify all known sites in 2011 (50 sites). A total of 59 seal mothers and/or offspring based on genetic placentas were recovered (n2009 = 13, n2010 = 21, profiling of field-collected placentas, and (2) n2011 = 25), meaning that a placenta was found find out the optimal spot for extracting DNA from 48% of the inspected lair sites. An unusual of each of these individuals from the placentas. visual observation of nursed seal twins in 2009 We also wanted to (3) evaluate whether pla- was confirmed by our finding of two placentas centas could be used as a source of information only one meter apart on the lake bottom below in genotype-based mark–recapture and kinship a nearby birth-lair site (see below). In addition, studies, and (4) examine the utility of placentas seven placentas that had been collected in 2000– in inferring population-genetic parameters of the 2006 and deposited in a tissue bank maintained Saimaa ringed seal using both nuclear (micro- by the University of Eastern Finland and Parks satellites) and mitochondrial (mtDNA control- & Wildlife Finland were genotyped. All placen- region sequences) markers. tas were stored at –20 °C. The state of decomposition of each placenta was assessed visually using the following three- Material and methods stage ordinal scale: (1) fresh, (2) partly decomposed and (3) decomposed. Of the 66 placentas Sample collection and handling sampled (Appendix), nearly half were classified as partly decomposed (48%), while 23% were Lake Saimaa is a large (ca. 4400 km2) and shal- categorized as fresh, and 29% as decomposed. low lake (mean depth 12 m, max. 85 m) with In order to identify the placental sampling over 13 000 islands (Kuusisto 1999). Birth lairs spots from which the mother’s and pup’s DNA are identified during annual lair censuses con- could be extracted separately, samples were ducted throughout the lake during each April taken from four different parts of each intact pla- (Metsähallitus 2014). A few placentas used in centa (Fig. 2): maternal (i.e., uterine) side (MS); this study were found from collapsed lairs during foetal (i.e., membrane) side (FS); umbilical cord, the censuses, but most placentas were collected or in absence of it, a vein (UC); and orange by scuba diving from birth-lair sites after ice particles (OP), which are bilirubine-containing break-up in May, some 2–3 months after the particles found on the maternal side of the pla- birth of pups (see Auttila et al. 2014). centa (Van den Broeck 1904). We were able to Placentas were searched for at birth-lair sites take all four subsamples from 58 intact placentas in the main breeding areas (Fig. 1B) in 2009 and (Appendix). For eight placentas with only shreds 54 Valtonen et al. • Ann. ZOOL. Fennici Vol. 52 of the membrane left, only an FS sample was were run on an ABI 3730 DNA Analyzer (Perkin taken. An additional blood sample (BS) was col- Elmer Applied Biosystems), and genotypes were lected from four very fresh placentas that had inferred using GENEMAPPER ver. 4.0 (Applied been collected frozen on ice in April in 2000, Biosystems). 2001 and 2009. Altogether 244 tissue samples MICRO-CHECKER ver. 2.2.3 (Van Ooster- from 66 placentas were analysed, and for the hout et al. 2004) was used for identifying pos- laboratory analysis each sample was given a sible genotyping errors (i.e., stuttering, allelic random number in order to prevent subjective dropout, and null alleles) for UC samples, which interpretation of genotypes (i.e., samples were were found to yield the pups’ genotypes (see genotyped blind). Results), with Bonferroni-adjusted 95% con- Samples from five pups (three stillborn and fidence intervals. FreeNA (Chapuis & Estoup two by-caught pups with known natal sites) for 2007) was used to estimate the frequency of which the corresponding placenta was available null alleles at each locus. In order to determine were used as reference samples. These samples whether the data set was affected by allelic drop- were used to determine which of the sampling out due to poor sample quality, we used MICRO- spots was optimal for obtaining the pup’s geno- DROP ver. 1.01 (Wang et al. 2012) to test for a type, and which for obtaining the mother’s geno- correlation between the amount of missing data type. and homozygosity across individuals and loci. The mean error rate per locus and observed error rate per multilocus genotype were calculated by Laboratory analyses replicated genotyping of a subset of the samples: PCR was repeated three times for each locus for Total genomic DNA was extracted using the the UC sampling spots of the placentas of the DNeasy Blood and Tissue Kits (Qiagen) accord- twin individuals, and for the four UC samples for ing to the manufacturer’s protocol. Each sample which a corresponding reference pup was avail- was genotyped at eleven microsatellite loci orig- able. In addition, a 704 bp-fragment from the 5´ inally developed for other pinnipeds (annealing domain of the mitochondrial control region was temperature (°C) and number of PCR cycles in sequenced for all placentas as described by Val- parentheses): Hg3.6 (58, 40), Hg4.2 (61, 40), tonen et al. (2012). Hg6.1 (58, 40), Hg8.9 (58, 45), Hg8.10 (53, 40), SGPv9 (60, 40) (Allen et al. 1995), Hgdii (58, 40) (Allen et al. 1995, Twiss et al. 2006), Hl15 Data analysis (53, 43) (Davis et al. 2002), SGPv10 (55, 40), SGPv11 (55, 40) and SGPv16 (51, 43) (Good- A χ2-test for homogeneity was used to test man 1997). These loci have previously been whether the four main placental sampling spots used in genetic analyses of the Saimaa ringed (MS, FS, UC, OP) differed with respect to over- seal (see Valtonen et al. 2014), but the exist- all genotyping success: a full 11-locus genotype ing laboratory protocols were modified in order vs. more than two alleles at any of the loci (sug- to optimise amplification success for placental gesting mixture of pup’s and mother’s DNA), or DNA: PCR reactions contained 1 µl template an otherwise unclear genotype at any locus (e.g., DNA, 0.4 µM each primer, 0.6 U AmpliTaq locus did not amplify at all, or the signal of the Gold DNA polymerase (Applied Biosystems), shorter allele was weaker than that of the longer

1X PCR buffer, 1.75 mM MgCl2, 0.2 mM of allele, which could indicate genotype mixture). each dNTP (Finnzymes), and 1 mg ml–1 BSA We compared the genotypes obtained from (Thermo Scientific) in a total reaction volume the different sampling spots (MS, FS, UC, OP, of 10 µl. Reactions were performed under the BS) within each placenta to investigate whether following conditions: 95 °C for 10 min followed the mother’s and/or pup’s genotype could be by 40–45 cycles of 95 °C for 30 s, 51–61 °C for inferred. Data from the five reference pups were 30 s, and 72 °C for 1 min, followed by a final used to determine the placental sampling spots extension at 72 °C for 10 min. PCR products that produced the best match with the corre- Ann. Zool. Fennici Vol. 52 • Genetic monitoring of seals based on placentas 55 sponding pup’s genotype. PCR was repeated equal-frequency method, which produces dis- three times for all these placental samples, in tance intervals with uneven distances, but with order to acquire reliable genotyping results for roughly equal numbers of pairwise comparisons. comparison with the “correct” pup’s genotype. The mean kinship coefficient of each distance As no reference samples of mothers were avail- class, as well as the overall regression slope, able, the optimal sampling spot for obtaining were tested for a significant departure from zero the mother’s DNA was assessed indirectly, i.e., by 10 000 permutations, and standard errors by comparing the multilocus genotypes of the were estimated by jackknifing over loci. reference pups with different placental sampling The utility of placentas for estimating pop- spots, in order to determine which ones produced ulation-level genetic parameters was evaluated clearly incompatible results. by contrasting the aforementioned diversity and The effect of the quality of the placenta (state differentiation estimates with reference values of decomposition) on amplification success, obtained in previous genetic analyses of Saimaa measured as the number of loci successfully ringed seals: Valtonen et al. (2012) sequenced the amplified from UC samples, which were found mtDNA control-region from 203 dead individu- to yield the pups’ genotypes (see Results), was als (stillborns, weaned pups, and adults) and 12 tested using one-way ANOVA in SPSS Statistics placentas, and Valtonen et al. (2014) analyzed ver. 19 (IBM). variation at 17 microsatellite loci in 172 seals. The placental samples used by Valtonen et al. (2012) were excluded from the mtDNA refer- Genetic diversity and isolation-by- ence data (but included in our present data set), distance and the microsatellite data set of Valtonen et al. (2014) was in our main comparisons reduced to Population-level genetic parameters were esti- include the same 11 loci as our placental data. To mated based on the UC samples, because these correct for unequal sample sizes when comparing were found to yield the pups’ genotypes (see microsatellite allelic richness (AR) and mtDNA Results). We estimated the number of alleles haplotype richness (a), the reference microsatel-

(NA), observed (HO) and expected (HE) heterozy- lite and mtDNA data sets were rarefied in HP- gosities, and Wright’s inbreeding coefficients RARE (Kalinowski 2005) based on the numbers

(FIS) using ARLEQUIN ver. 3.5.1.2 (Excoffier of placentas. Differences in microsatellite allele & Lischer 2010). GENEPOP ver. 4.1.3 (Rous- frequencies between placentas and the 11-locus set 2008) was used to test for departures from reference data set were tested in GENEPOP using Hardy-Weinberg equilibrium and for the pres- an exact G-test. Additionally, differences in allele ence of linkage disequilibrium between pairs of frequencies were assessed between placentas and microsatellite loci. Haplotype (h) and nucleotide a subset of the reference data (n = 65) spanning (π) diversities for the mtDNA control-region the same time period as the placental samples sequence data set were estimated using ARLE- (the 2000s). The correlation between mtDNA QUIN. haplotype frequencies in the placental and refer- The presence of an isolation-by-distance pat- ence data sets was tested using Spearman’s rank- tern among placentas was tested by contrast- order correlation in SPSS. ing pairwise microsatellite- and mtDNA-based genetic relatedness with geographical distance on a logarithmic scale in SPAGEDI ver. 1.3 Identification of individuals and kinship (Hardy & Vekemans 2009). We chose the kin- analyses ship coefficient of Loiselle et al. (1995) as a pairwise estimator of genetic relatedness, as it To evaluate whether our panel of 11 microsatel- is considered suitable for data sets containing lite loci allows reliable identification of Saimaa rare alleles, and does not assume Hardy-Wein- ringed seal individuals, we estimated the prob- berg equilibrium (Vekemans & Hardy 2004). Ten ability of identity (PI, i.e., the probability that spatial distance classes were created using the two randomly chosen individuals have identical 56 Valtonen et al. • Ann. ZOOL. Fennici Vol. 52 multilocus genotypes) as well as the correspond- reference data sets, the two lowest and two high- ing value for siblings (PISIB) using GENALEX est categories. ver. 6.41 (Peakall & Smouse 2006, 2012). For The adequacy of the 11- and 17-locus marker comparison, we calculated these probabilities systems for inferring sibship and parentage was also for the reference data set of Valtonen et al. further studied using COLONY ver. 2.0.4.1 (2014) using both 11 and 17 loci. For this and (Jones & Wang 2010). First, we inferred full- further analyses, missing data were not allowed and half-sibs from the data on placentas alone; at any loci; after exclusion of individuals with in this analysis, the aforementioned placentas incomplete genotypes, the 11- and 17-locus ref- of the confirmed twins were used as an addi- erence data sets comprised 171 and 168 indi- tional reference to determine whether they could viduals, respectively. be recognised as siblings. Second, we intro- GENALEX was also used to calculate the duced potential adult fathers (n = 6) and mothers probability of exclusion (PE) in the placental (n = 4) from the reference data set into the analy- and reference data sets, in order to estimate the sis of placentas, in order to assign parentage and power and utility of the 11- and 17-locus micro- sibship simultaneously. Finally, we analysed the satellite panels in parentage analysis. PE was full microsatellite reference data to infer sibship estimated for three alternative scenarios: PE1 = and parentage based on 17 loci. Three independ- probability for excluding a putative parent when ent replicate runs were carried out with each data the other parent is known; PE2 = probability of set to ensure convergence. Allele frequencies exclusion of a putative parent when the other were estimated for each data set by COLONY, parent is unknown; PE3 = probability of exclud- polygamy was assumed for both sexes, and no ing two putative parents (Jamieson & Taylor sibship prior was used. For placentas alone, we 1997). used the full-likelihood method with high preci- In order to determine the optimal number sion and ‘long’ run length. Otherwise similar of loci for identification of Saimaa ringed seal settings were used for the analysis involving individuals (see Waits & Paetkau 2005), we used placentas and potential parents, but run length MM-DIST (Kalinowski et al. 2006) to compute was set to ‘medium’. For the large reference expected and observed mismatch distributions data with 17 loci, the pairwise-likelihood score for placentas as well as for the 11- and 17-locus method and ‘very long’ run length were chosen. reference data sets. MM-DIST calculates probability distributions for genotypic differences, i.e., the numbers of loci that differ among indi- Results viduals in a population. The program creates the observed distribution for the studied data set, Genotyping as well as expected distributions for individuals with different degrees of relatedness: unrelated Genotyping success varied among different pla- individuals, fullsibs, and parent–offspring pairs. cental sampling spots (Table 1). Full, unambigu- The congruence between observed and expected ous 11-locus genotypes were obtained for 51% MM-distributions, as well as between observed of UC (umbilical cord/vein) samples, but for distributions estimated for the placental and only 0%–12% of the other sampling spots. More 11-locus reference data sets, were tested using than two alleles at one locus, indicating a mixture a χ2-test for goodness of fit. In order to meet the of the mother’s and pup’s DNA, were detected test’s assumption of all expected frequencies most often in MS (maternal side) samples and exceeding 1, the following mismatch categories least frequently in UC samples (34.5% and 3.6%, were combined: for placentas, the two lowest respectively). A χ2-test of homogeneity showed a and three highest categories; for the 11-locus highly significant difference in genotyping suc- reference data, the two lowest categories; for the cess among MS, FS, UC, and OP sampling spots 17-locus data, the three lowest and three highest (χ2 = 56.45, df = 6, p < 0.001). categories; and for the comparison of observed When comparing the genotypes of the five MM-distributions in the placental and 11-locus reference pups with those of their corresponding Ann. Zool. Fennici Vol. 52 • Genetic monitoring of seals based on placentas 57

Table 1. Genotyping success at 11 microsatellite loci for each placental sampling spot. MS = maternal side, FS = foetal side, UC = umbilical cord/vein, OP = orange particles, BS = blood sample.

MS FS UC OP BS ntotal

n % n % n % n % n %

Unambiguous genotype 5 8.6 8 12.1 28 50.9 7 12.1 0 0 48 More than 2 alleles 20 34.5 9 13.6 2 3.6 8 13.8 0 0 39 Unclear genotype 33 56.9 49 74.2 25 45.5 43 74.1 4 100 154 ntotal 58 66 55 58 4 placentas, UC samples were the only ones that were conducted with the 47 placentas that had produced completely matching multilocus con- full UC genotypes at 11 microsatellite loci. sensus genotypes (Table 2). None of the remain- Population-level microsatellite diversity esti- ing main sampling spots (MS, FS, OP) yielded a mated on the basis of placentas was very low, but genotype that was both unambiguous and clearly observed (HO) and expected (HE) heterozygosities different from the pup’s genotype, indicating corresponded closely with estimates obtained for that the mothers’ genotypes could not be reliably the reference data sets of 11 and 17 loci (Table determined. 3). Allelic richness (AR) was slightly higher in the The MICRO-CHECKER analysis suggested 11-locus reference data set than in the placental the presence of null alleles for three loci (Hg8.9, samples even after correcting for sample-size SGPv11 and SGPv16), but estimated null-allele differences by rarefaction (Table 3), which was, frequencies were low (r ≤ 0.08) at each locus, however, expected given that the individuals in and the result is likely caused by the presence the reference data set represent a longer period of population structure (Valtonen et al. 2014). and a wider geographic area, and that some pla- Scoring errors suggested for Hg8.9 were ruled centas may represent siblings due to the fact that out by rechecking the data independently by they had been collected from the same birth-lair two researchers. Further, no significant correla- sites during consecutive springs (see below). tion between the amount of missing data and FIS values estimated for the pooled placental homozygosity was found either across individu- sample as well as for the two regional sub- als (r = 0.163, p = 0.123) or loci (r = –0.247, samples did not depart from zero at p ≤ 0.05 p = 0.738). The estimated mean error rate per (Table 3), but the pooled data set was not in locus was 0.036, and the observed error rate per Hardy-Weinberg equilibrium (Fisher’s exact test multilocus genotype 0.313. Degree of decompo- over all loci, p = 0.006). However, when the sition had no effect on amplification success in Main Haukivesi (n = 17) and Pihlajavesi areas

UC samples (one-way ANOVA: F2,52 = 0.609, p = 0.548). Table 2. Congruence (i.e., percentage of full matches at 11 loci) between consensus genotypes of different placental sampling spots and those of corresponding Comparison of population-genetic reference pups (note that only an FS sample could be parameters estimated from the placental obtained from the placenta of Pup 3). MS = maternal and reference data sets side, FS = foetal side, UC = umbilical cord/vein, OP = orange particles. Based on the above, UC samples were taken to MS FS UC OP represent the genotypes of the pups. Unclear UC genotypes (i.e., longer allele producing a Pup 1 64 55 100 45 taller peak than the shorter allele, see above) Pup 2 73 91 100 45 were accepted as a true genotype, but samples Pup 3 – 45 – – with missing data or more than two alleles at any Pup 4 64 45 100 55 Pup 5 82 73 100 27 locus were discarded; hence, further analyses 58 Valtonen et al. • Ann. ZOOL. Fennici Vol. 52

(n = 24; see Fig. 1B) were analysed separately, (π ± SD) diversities as well as haplotypic rich- deviations from Hardy-Weinberg equilibrium ness (a) were very low (0.625 ± 0.037, 0.005 were found neither over all loci nor at any indi- ± 0.005, and 6.00, respectively), but similar to vidual locus after a sequential Bonferroni cor- those observed for the reference data set (0.649 rection. ± 0.021, 0.005 ± 0.005 and 6.69, respectively; Significant linkage disequilibrium remained the estimate of a in the reference data set was between loci Hg8.9 and Hl15 even after a obtained by rarefaction to n = 63). sequential Bonferroni correction; this most A weak but statistically significant negative likely reflects differentiation between the Pihla- relationship between relatedness and spatial dis- javesi and Main Haukivesi areas. Allele frequen- tance between pairs of placentas was detected cies in placentas and the 11-locus reference data using both microsatellites (b = –0.025, p = 0.009; set differed significantly (χ2 = 41.03, df = 22, p Fig. 3A) and mtDNA control-region sequences = 0.008), but the difference disappeared after (b = –0.128, p < 0.001: Fig. 3B). These results reducing the reference data to represent the same corresponded well with the previous study of time span as the placentas (χ2 = 23.17, df = 22, p Valtonen et al. (2014). = 0.392). Six mtDNA haplotypes were detected in the 63 placentas successfully analysed for mtDNA Identification of individuals, and kinship variation (Fig. 1B; see Valtonen et al. 2012 for analyses details of haplotypes). Haplotypes H1 and H3 dominated the sample with relative frequen- The probabilities of identity (i.e., probability that cies of 36.5% and 49.2%, respectively, while two individuals share the same multilocus geno- H7 comprised 9.5% of the haplotypes, and H2, type; PI for randomly chosen individuals and

H4, and H8 were found in a single placenta PISIB for siblings), were very low, but slightly each. When considering all haplotypes (H1– higher in placentas than in the 11-locus refer- H8) found by Valtonen et al. (2012), placental ence data set (Table 4). With six additional loci haplotype frequencies correlated strongly with (17 loci in total), PISIB values were an order and estimates derived from the mtDNA reference PI values two orders of magnitude lower. The data set (Spearman’s ρ = 0.805, p = 0.016). estimated probabilities of exclusion were suf- Estimated haplotype (h ± SD) and nucleotide ficiently high (p ≥ 0.99) for excluding two puta-

Table 3. Estimates of genetic diversity in the Saimaa ringed seal population based upon analyses of 11 microsatellite loci in UC samples of placentas, and 11 and 17 loci in the reference data set. Average total number of alleles

(NA), allelic richness (AR), observed heterozygosity (HO), expected heterozygosity (HE), and inbreeding coefficient

(FIS) are given. Allelic richness (AR) estimates in the reference data sets were obtained by rarefaction to the sample size (47, 17, or 24) in the corresponding sample in the placental data set. P(HWE) values indicating significant deviations from Hardy-Weinberg equilibrium are set in boldface.

Sample n NA ± SD AR HO ± SD HE ± SD FIS P(HWE)

Placentas Total sample 47 2.91 ± 2.12 2.91 0.33 ± 0.24 0.35 ± 0.23 0.057ns 0.006 Main Haukivesi area 17 2.64 ± 1.57 2.64 0.35 ± 0.27 0.39 ± 0.27 0.100ns 0.396 Pihlajavesi area 24 2.60 ± 1.35 2.45 0.35 ± 0.23 0.34 ± 0.22 –0.020ns 0.987 11-locus reference data Total sample 172 3.73 ± 4.10 3.38 0.35 ± 0.22 0.38 ± 0.24 0.073*** < 0.001 Main Haukivesi area 79 3.45 ± 3.56 2.90 0.38 ± 0.24 0.40 ± 0.25 0.038ns < 0.001 Pihlajavesi area 43 2.55 ± 1.63 2.35 0.32 ± 0.25 0.30 ± 0.23 –0.065ns 0.280 17-locus reference data Total sample 172 3.47 ± 3.32 3.14 0.33 ± 0.21 0.36 ± 0.22 0.075*** < 0.001 Main Haukivesi area 79 3.18 ± 2.92 2.70 0.34 ± 0.23 0.35 ± 0.24 0.024ns < 0.001 Pihlajavesi area 43 2.59 ± 1.42 2.43 0.31 ± 0.25 0.30 ± 0.23 –0.034ns 0.144 ns = not significant, *** p < 0.001. Ann. Zool. Fennici Vol. 52 • Genetic monitoring of seals based on placentas 59

0.08 A 0.30 A Placentas 11 loci *** *** 0.25 0.04 y 0.20

0 0.15 Frequenc 0.10

Kinship coefficient –0.04 ** ** 0.05

0 –0.08 8910 11 12 02468 10 12 14 16 18

0.3 B *** 0.30 B Reference data 11 loci 0.2 ** 0.25 t

0.1 * y 0.20

0 0.15 Frequenc 0.10 –0.1 * Kinship coefficien 0.05 *** –0.2 *** 0 –0.3 8910 11 12 02468 10 12 14 16 18 Mean ln(distance) 0.30 Fig. 3. Average Loiselle’s kinship coefficient plotted C Reference data 17 loci against logarithmic distance between pairs of Saimaa 0.25 ringed seal placentas. The plots are based on (A) 11 microsatellite loci, and (B) mtDNA haplotypes. Aster- y 0.20 isks denote distance classes that differ significantly 0.15 from mean kinship: ***p < 0.001; **p < 0.01; *p < 0.05. Frequenc 0.10 tive parents (PE3) for both reference data sets, 0.05 but not for placentas. Moreover, none of the data 0 sets provided enough resolution for excluding one parent (PE and PE ; Table 4). 02468 10 12 14 16 18 1 2 Mismatches Expected distribution for unrelated individuals Expected distribution for fullsibs Table 4. Probabilities of identity and exclusion esti- Expected distribution for parents-offspring mated for Saimaa ringed seal placentas genotyped at Observed distribution 11 loci, as well as for the 11- and 17-locus reference data sets. PI = probability of identity for unrelated indi- Fig. 4. Mismatch distributions among genotypes of Saimaa ringed seal placentas based on (A) 11 micros- viduals, PISIB = probability of identity for siblings, PE1 = probability for excluding a putative parent when the atellite loci, and within the reference data set based on (B) 11 loci and (C) 17 loci. other parent is known, PE2 = probability for excluding a putative parent when the other parent is unknown, PE3 = probability of excluding two putative parents.

Placentas Reference data set Mismatch distributions estimated for placentas and the 11- and 17-locus reference data sets 11 loci 17 loci were fairly consistent with the expected distribu- PI 2.098 ¥ 10–4 4.581 ¥ 10–5 4.817 ¥ 10–7 tions of unrelated individuals (Fig. 4). Observed ¥ –2 ¥ –2 ¥ –3 PISIB 1.695 10 1.010 10 1.193 10 and expected distributions did not differ in the 2 PE1 0.878 0.930 0.978 case of placentas (χ = 9.85, df = 8, p = 0.276), PE 0.638 0.734 0.839 2 but a highly significant deviation was found in PE 0.972 0.990 0.999 3 both reference data sets (χ2 = 30.39, df = 10, 60 Valtonen et al. • Ann. ZOOL. Fennici Vol. 52 p < 0.001 for the 11-locus reference data set, and population-genetic analyses have demonstrated χ2 = 111.43, df = 13, p < 0.001 for the 17-locus very low genetic variability in the population data set). Deviations from expectation are, how- (Palo et al. 2003, Valtonen et al. 2012, Martinez- ever, not pronounced (Fig. 4B and C), and are Bakker et al. 2013, Valtonen et al. 2014), and probably explained by spatial genetic structure have revealed clear spatial structuring among the in the population (see above) and the relatively main breeding areas, indicating limited move- large sample sizes in the reference data sets. ment of Saimaa ringed seals and especially The observed mismatch distributions of placen- females (Valtonen et al. 2012, 2014). However, tas and the 11-locus reference data set differed those analyses were mainly based on stillborn, significantly (χ2 = 206.26, df = 9, p < 0.001), by-caught, and stranded seals, of which only because the distribution based on placental geno- about 25 are recovered each year (Metsähallitus types is slightly shifted towards smaller numbers 2014). In addition, the samples were “retrospec- (Fig. 4A and B). tive” in the sense that the individuals were dead No pairs of individuals with fully matching and, hence, removed from the population. multilocus genotypes were found within any Under these circumstances, placentas col- of the data sets. There were four pairs with a lected from breeding sites could provide a highly single mismatch (1MM-pairs) in the placental useful source for non-invasive genetic sampling and 30 in the 11-locus reference data, whereas of live individuals: because of the thorough none were observed in the 17-locus data set. springtime breeding-site inspections conducted The number of 2MM-pairs was 33 for placentas, by Parks & Wildlife Finland (the authority while 206 and 8 pairs were present in the 11- and responsible for monitoring and conservation of 17-locus reference data sets, respectively. How- the Saimaa ringed seal), placentas of nearly half ever, when the placental and 11-locus reference of the pups born each year can be recovered with data sets were combined, we found five UC sam- a reasonable effort. Compared with other non- ples having full match with a genotype present invasive samples such as hair and faeces, placen- in the reference data set; however, the matching tas provide a large amount of DNA, as well as reference individuals had died 3–30 years before the theoretical prospect of genotyping both the collection of the placentas. offspring and its mother from a single sample In COLONY analyses, replicate runs of each (cf. Stewart & Stewart 2009). As our analyses data set produced different results, and the analy- show, the pup’s multilocus genotype can be ses generally did not seem to have enough power reliably inferred from umbilical cord samples. to resolve relationships. The results suggested Unfortunately, we did not succeed in genotyp- parentage of individuals that had died before the ing the mothers, most likely due to mixture of birth of suggested offspring, and also produced maternal and offspring tissues and, therefore, unrealistically numerous full- and half-sibling genotypes, on the maternal side of the placenta. pairs. Moreover, the aforementioned placentas of Despite the limitation concerning the identi- seal twins were not recognised as siblings. fication of breeding females, our results demonstrate that genetic tags obtained from umbilical cords could be used for studies on postnatal Discussion movements and long-term survival of Saimaa ringed seal individuals. Such studies could be The ecology of the critically endangered Saimaa conducted by comparing placental multilocus ringed seal is well known as a result of three genotypes to tags that are later obtained from, for decades of intensive field research and telemetry example, hair samples collected from haul-out studies (e.g., Hyvärinen et al. 1995, Rautio et sites, from live seals captured during telemetry al. 2009, Niemi et al. 2012). Nevertheless, as studies, or from by-caught or stranded carcasses. the population density is low, and the animal The extremely low microsatellite diversity itself elusive and difficult to capture, all aspects of the Saimaa ringed seal (HE = 0.36; Val- of its ecology and behaviour cannot be studied tonen et al. 2014) poses a clear challenge for using traditional monitoring approaches. Recent genetically-based tagging (cf. Waits & Paetkau Ann. Zool. Fennici Vol. 52 • Genetic monitoring of seals based on placentas 61

2005). However, sufficient resolution for indi- level of mtDNA variation and the presence of vidual identification is provided by our panel of an isolation-by-distance pattern within the lake 17 microsatellite loci: the estimated probability (Fig. 3, and Valtonen et al. 2014). Importantly, of identity for unrelated individuals (PI = 4.8 ¥ however, the analytical impediments that follow –7 10 ) is lower, and that for siblings (PISIB = 1.2 from lack of marker resolution can be overcome ¥ 10–3) very close, to the conservative threshold by applying new, genome-scan based methods values (1 ¥ 10–6 and 1 ¥ 10–3, respectively) rec- (e.g., Tokarska et al. 2009), so field-collected ommended by McKelvey and Schwartz (2004). placentas undoubtedly can also be applied for In the current population of approximately 300 studying breeding-site fidelity of females in the seals (Metsähallitus 2014), our figures translate near future. to 0.0001 expected full genotype matches for Interestingly, our results demonstrate that unrelated individuals and 0.36 matches for sib- key population-level diversity and differentiation lings, although the true numbers may be slightly indices can be estimated based on placental sam- higher due to the pronounced spatial genetic dif- ples. Both microsatellite and mtDNA diversities ferentiation in the population found by Valtonen of placentas corresponded closely with previous et al. (2012, 2014), i.e., because seals within results derived from large reference data sets each region tend to be more closely related comprising individuals found dead during a time and, thus, have more similar genotypes than span of 30 years (Valtonen et al. 2012, 2014), individuals on average in the population. PI and especially after applying rarefaction to correct

PISIB values are higher for the 11-locus placental for differences in sample size. No differences in and reference data sets, and numbers of 1MM- microsatellite allele frequencies were detected and 2MM-pairs in their mismatch distributions between placentas and a subset of the reference (Fig. 4A and B) exceed the recommendations data representing the same time span, the 2000s, of Waits and Paetkau (2005) (< 1–2 1MM- and and mtDNA haplotype frequencies in placentas < 10 2MM-pairs in the data). Nevertheless, as and the reference data set were likewise highly pointed out by Waits and Paetkau (2005), the correlated. Also the isolation-by-distance pat- number of loci required also depends on the terns of placentas were very similar to the find- number of individuals to be compared, so even ings of Valtonen et al. (2014). the 11-locus panel could be sufficient for spatially and/or temporally restricted surveys of the focal population. Conclusions and further prospects Although our marker system allows genetic identification of individuals, the low population- This study is the first describing the utility of level variability means that the limits of even the placentas in non-invasive genetic monitoring of full 17-locus panel are reached when attempting a natural population. Saimaa ringed seal pla- to infer parentage or sibship among the sampled centas are relatively easily collected from birth- individuals and/or placentas. This is especially lair sites, and comparisons with existing refer- seen in the kinship analyses in COLONY, in ence data sets demonstrate that placentas can be which the output of all runs suggested implau- used for estimating standard population-genetic sible or impossible parentage and sibship. The parameters in separate breeding areas or within unfortunate consequence of this is that one of our the whole lake. Genotyping pre-dispersal juve- main goals could not be reached, because pups niles from umbilical cord samples provides a (placentas) collected from the same site during unique opportunity for tracking individuals from different springs could not be confidently deter- their natal site to later recapture(s) or death, mined to be siblings. At least some sibling pairs yielding information on their dispersal patterns are likely to be present in our data set, because and long-term survival prospects. In addition, placentas having identical mtDNA haplotypes determining the pups’ gender from umbilical were collected from the same or closely located cord samples using genetic markers (Curtis et al. birth-lair sites in different years (Fig. 1B), but 2007) would yield information on the sex ratio this could also be explained by the generally low within the population and in different breeding 62 Valtonen et al. • Ann. ZOOL. Fennici Vol. 52 areas. Unfortunately, even our 17-locus micro- rus grypus) shows evidence of genetic differentiation between two British breeding colonies. — Molecular satellite panel does not provide enough dis- Ecology 4: 653–662. criminatory power for pedigree construction and Arandjelovic, M., Head, J., Rabanal, L. I., Schubert, G., kinship analyses, due to the fact that this small Mettke, E., Boesch, C., Robbins, M. M. & Vigilant, L. population with extremely low genetic diversity 2011: Non-invasive genetic monitoring of wild central and significant structuring (Valtonen et al. 2012, chimpanzees. — PLoS One 6: e14761, doi:10.1371/ 2014) is inevitably inbred, but such analyses journal.pone.0014761. 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Appendix. Collection information on the Saimaa ringed seal placentas included in this study. Collection date and location, samples taken (MS = maternal side; FS = foetal side; UC = umbilical cord/ vein; OP = orange particles; BS = blood) and quality (1 = fresh; 2 = partly decomposed; 3 = decomposed) of each placenta are given.

ID Code collection date Location Samples Quality

I-18 i-18_P_y2000 14 Apr. 2000 Pihlajavesi area MS, FS, UC, OP, BS 1 I-19 i-19_P_y2000 17 Apr. 2000 Pihlajavesi area MS, FS, UC, OP 2 I-20 i-20_P_y2001 12 Apr. 2001 Pihlajavesi area MS, FS, UC, OP, BS 1 I-21 i-21_P_y2002 22 Apr. 2002 Pihlajavesi area MS, FS, UC, OP 2 I-22 i-22_S_y2004 14 Apr. 2004 Southern Saimaa FS 2 I-23 i-23_P_y2004 6 Apr. 2004 Pihlajavesi area MS, FS, UC, OP 2 I-24 i-24_S_y2006 2006 Southern Saimaa FS 2 I-26 i-26_S_y2009 18 Apr. 2009 Southern Saimaa MS, FS, UC, OP, BS 1 I-27 i-27_H_y2009 19 Apr. 2009 Main Haukivesi area MS, FS, UC, OP, BS 1 I-28 i-28_P_y2009 25 Apr. 2009 Pihlajavesi area MS, FS, UC, OP 2 I-29 i-29_H_y2009 19 May 2009 Main Haukivesi area MS, FS, UC, OP 3 I-30 i-30_H_y2009 19 May 2009 Main Haukivesi area MS, FS, UC, OP 3 I-31 i-31_H_y2009 19 May 2009 Main Haukivesi area MS, FS, UC, OP 3 I-32 i-32_P_y2009 28 May 2009 Pihlajavesi area MS, FS, UC, OP 2 I-33A i-33A_P_y2009 28 May 2009 Pihlajavesi area MS, FS, UC, OP 3 I-33B i-33B_P_y2009 28 May 2009 Pihlajavesi area MS, FS, UC, OP 2 I-34 i-34_P_y2009 28 May 2009 Pihlajavesi area MS, FS, UC, OP 2 I-35 i-35_P_y2009 28 May 2009 Pihlajavesi area FS 3 I-36 i-36_P_y2009 29 May 2009 Pihlajavesi area MS, FS, UC, OP 3 I-37 i-37_P_y2009 29 May 2009 Pihlajavesi area MS, FS, UC, OP 3 I-38 i-38_P_y2010 14 May 2010 Pihlajavesi area MS, FS, OP 2 I-39 i-39_P_y2010 13 May 2010 Pihlajavesi area MS, FS, UC, OP 1 I-40 i-40_P_y2010 13 May 2010 Pihlajavesi area MS, FS, UC, OP 3 I-41 i-41_P_y2010 13 May 2010 Pihlajavesi area MS, FS, UC, OP 2 I-42 i-42_P_y2010 11 May 2010 Pihlajavesi area MS, FS, UC, OP 1 I-43 i-43_P_y2010 12 May 2010 Pihlajavesi area MS, FS, UC, OP 1 I-44 i-44_P_y2010 13 May 2010 Pihlajavesi area MS, FS, UC, OP 2 I-45 i-45_P_y2010 13 May 2010 Pihlajavesi area MS, FS, OP 3 I-46 i-46_P_y2010 13 May 2010 Pihlajavesi area MS, FS, UC, OP 2 I-47 i-47_P_y2010 12 May 2010 Pihlajavesi area MS, FS, UC, OP 3 I-48 i-48_P_y2010 13 May 2010 Pihlajavesi area MS, FS, UC, OP 3 I-49 i-49_S_y2010 27 May 2010 Southern Saimaa MS, FS, UC, OP 3 I-50 i-50_H_y2010 10 May 2010 Main Haukivesi area MS, FS, UC, OP 3 I-51 i-51_H_y2010 3 May 2010 Main Haukivesi area MS, FS, UC, OP 3 I-52 i-52_H_y2010 3 May 2010 Main Haukivesi area MS, FS, UC, OP 2 I-53 i-53_H_y2010 12 May 2010 Main Haukivesi area MS, FS, UC, OP 2 I-54 i-54_H_y2010 6 May 2010 Main Haukivesi area MS, FS, UC, OP 1 I-55 i-55_H_y2010 12 May 2010 Main Haukivesi area MS, FS, UC, OP 1 I-56 i-56_H_y2010 15 May 2010 Main Haukivesi area MS, FS, UC, OP 2 I-57 i-57_H_y2010 12 May 2010 Main Haukivesi area MS, FS, UC, OP 1 I-58 i-58_H_y2010 12 May 2010 Main Haukivesi area MS, FS, UC, OP 2 continued Ann. Zool. Fennici Vol. 52 • Genetic monitoring of seals based on placentas 65

Appendix. Continued.

ID Code collection date Location Samples Quality

I-59 i-59_N_y2011 19 May 2011 Northern Saimaa MS, FS, UC, OP 2 I-60 i-60_K_y2011 23 May 2011 Kolovesi MS, FS, UC, OP 2 I-61 i-61_H_y2011 12 May 2011 Main Haukivesi area FS 3 I-62 i-62_H_y2011 12 May 2011 Main Haukivesi area MS, FS, UC, OP 3 I-63 i-63_H_y2011 16 May 2011 Main Haukivesi area MS, FS, UC, OP 2 I-64 i-64_H_y2011 12 May 2011 Main Haukivesi area MS, FS, UC, OP 2 I-65 i-65_H_y2011 13 May 2011 Main Haukivesi area MS, FS, UC, OP 1 I-66 i-66_P_y2011 9 May 2011 Pihlajavesi area MS, FS, UC, OP 1 I-67 i-67_P_y2011 9 May 2011 Pihlajavesi area FS 2 I-68 i-68_P_y2011 10 May 2011 Pihlajavesi area MS, FS, UC, OP 2 I-69 i-69_P_y2011 10 May 2011 Pihlajavesi area MS, FS, UC, OP 2 I-70 i-70_P_y2011 10 May 2011 Pihlajavesi area FS 2 I-71 i-71_P_y2011 11 May 2011 Pihlajavesi area MS, FS, UC, OP 1 I-73 i-73_P_y2011 11 May 2011 Pihlajavesi area MS, FS, UC, OP 1 I-74 i-74_P_y2011 11 May 2011 Pihlajavesi area MS, FS, UC, OP 2 I-75 i-75_P_y2011 12 May 2011 Pihlajavesi area MS, FS, UC, OP 2 I-76 i-76_P_y2011 12 May 2011 Pihlajavesi area MS, FS, UC, OP 2 I-77 i-77_P_y2011 12 May 2011 Pihlajavesi area MS, FS, OP 2 I-78 i-78_P_y2011 12 May 2011 Pihlajavesi area MS, FS, UC, OP 3 I-79 i-79_S_y2011 7 May 2011 Southern Saimaa MS, FS, UC, OP 3 I-82 i-82_S_y2011 8 May 2011 Southern Saimaa FS 2 I-84 i-84_S_y2011 8 May 2011 Southern Saimaa MS, FS, UC, OP 3 I-85 i-85_H_y2011 23 Apr. 2011 Main Haukivesi area MS, FS, UC, OP 1 I-86 i-86_H_y2011 20 Apr. 2011 Main Haukivesi area FS 2 I-87 i-87_H_y2011 2011 Main Haukivesi area MS, FS, UC, OP 2

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