Pruunkaru (Ursus arctos) populatsiooni struktuur, demograafilised protsessid ja toitumisvariatsioonid Põhja-Euraasias

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Populatsioonigeneetika ja fülogeograafia on olulised aspektid liikide ja populatsioonide kaitsks ja ohjamiseks, kuna sisaldavad informatsiooni populatsiooni ajaloo, sidususe ja elujõulisuse kohta. Pruunkarude populatsioonigeneetika ja fülogeograafia on pälvinud palju tähelepanu kogu maailmas tulenevalt liigi majandamis- ja kaitsevajadustest ning ka pruunkaru 'mudelliigi' staatuse tõttu fülogeograafias. Käesoleva väitekirja eesmärgiks on uurida Põhja-Euraasia karupopulatsiooni populatsioonigeneetikat, toitumist ja fülogeograafiat, et leida, milline on sealse populatsiooni geneetiline struktuur ja toitumise eripärad ning üritada nende kujunemisele selgitust saada ajaloolistest ja klimaatilis-ökoloogilistest tingimustest. Populatsioonigeneetiliste ja fülogeograafilise uurimuste vahelised sarnasused võimaldavad järeldada, et tänapäevase Põhja-Euraasia mandriosa pruunkarupopulatsiooni geneetiline struktuur ei ole kujunenud ainult tänapäevaste tegurite mõjul, vaid ka kaugemas minevikus toimunu tagajärjel. Kuigi õnnestus tuvastada laiuskraadiline gradient karu toitumises, siis geneetiline struktuur paistab olema pigem pikkuskraadiline. Mitmete geneetilist struktuuri kujundavate tegurite ilmnemine ja nende mõju ulatus pruunkarupopulatsiooni geneetilise struktuuri kujunemisele sellel ulatuslikul alal vajab edasist uurimist.Knowledge of population genetics and phylogeography of living organisms are important for conservation and management of species, since they provide information about species viability and integrity. There have been numerous genetic studies of brown bears in parts of the species’ range to inform local management and conservation approaches, and due to the status of the brown bear as a 'model species' in phylogeographic studies. This thesis provide new information about the population genetics, diet and phylogeography of brown bears in northern continental Eurasia, to describe population genetic structure and to assess the importance of historical and ecological conditions in generating and maintaining structure. Similarities between the results of the population genetic and phylogeographic studies allow us to conclude that population structure in modern northern continental Eurasian brown bear population is not only a result of present conditions, but also contains a historical signature. Although we found a latitudinal gradient in brown bear diet, genetic structure appears to be rather longitudinal. Understanding the precise occurrence of factors which have played a role in the formation of genetic structure and the amount of their impact on brown bear genetic structure in this vast area will require further research

Data from: Spatial genetic analyses reveal cryptic population structure and migration patterns in a continuously harvested grey wolf (Canis lupus) population in north-eastern Europe

Spatial genetics is a relatively new field in wildlife and conservation biology that is becoming an essential tool for unravelling the complexities of animal population processes, and for designing effective strategies for conservation and management. Conceptual and methodological developments in this field are therefore critical. Here we present two novel methodological approaches that further the analytical possibilities of STRUCTURE and DResD. Using these approaches we analyse structure and migrations in a grey wolf (Canis lupus) population in north-eastern Europe. We genotyped 16 microsatellite loci in 166 individuals sampled from the wolf population in Estonia and Latvia that has been under strong and continuous hunting pressure for decades. Our analysis demonstrated that this relatively small wolf population is represented by four genetic groups. We also used a novel methodological approach that uses linear interpolation to statistically test the spatial separation of genetic groups. The new method, which is capable of using program STRUCTURE output, can be applied widely in population genetics to reveal both core areas and areas of low significance for genetic groups. We also used a recently developed spatially explicit individual-based method DResD, and applied it for the first time to microsatellite data, revealing a migration corridor and barriers, and several contact zones

Spatial distribution of genetic differentiation between individuals in the Estonian-Latvian wolf population (n = 166) based on results of the spatially explicit DResD procedure at three spatial scales: (a-c) - the average D_LR-index (based on 16 microsatellite loci) between sample pairs, corrected for isolation by distance and interpolated across the study area using inverse distance weighting.

<p>The full coloured areas represent the 5 km grid points where the tested value deviates significantly from the null-model (IBD alone – a value of 0; p ≤0.05 according to 1000 iterations). The dots represent sample locations, and dashes denote locations and directions of sample pair midpoints lying at a particular distance range; the black section of the scale-bar in the top-left corner of each image represents the distance range of sample pairs included in the respective calculation.</p

Schematic representation of DResD results (shown in Figure 5).

<p>Migration directions and strength were determined with BayesAss (see Information S2), with dark green arrows indicating stronger and light green arrow weaker dispersal strength. Red prohibiting signs designate migration barriers (note that the barrier in the form of the Gulf of Riga was clearly identified by the analysis, whereas the city of Riga and its surrounding infrastructure are proposed to explain the evidence for a barrier in that approximate location). CoZ: contact zones for genetically distant individuals.</p

Spatial Genetic Analyses Reveal Cryptic Population Structure and Migration Patterns in a Continuously Harvested Grey Wolf (<i>Canis lupus</i>) Population in North-Eastern Europe

<div><p>Spatial genetics is a relatively new field in wildlife and conservation biology that is becoming an essential tool for unravelling the complexities of animal population processes, and for designing effective strategies for conservation and management. Conceptual and methodological developments in this field are therefore critical. Here we present two novel methodological approaches that further the analytical possibilities of STRUCTURE and DResD. Using these approaches we analyse structure and migrations in a grey wolf (<i>Canis</i><i>lupus</i>) population in north-eastern Europe. We genotyped 16 microsatellite loci in 166 individuals sampled from the wolf population in Estonia and Latvia that has been under strong and continuous hunting pressure for decades. Our analysis demonstrated that this relatively small wolf population is represented by four genetic groups. We also used a novel methodological approach that uses linear interpolation to statistically test the spatial separation of genetic groups. The new method, which is capable of using program STRUCTURE output, can be applied widely in population genetics to reveal both core areas and areas of low significance for genetic groups. We also used a recently developed spatially explicit individual-based method DResD, and applied it for the first time to microsatellite data, revealing a migration corridor and barriers, and several contact zones.</p> </div

Geographical ranges of four genetic groups (A–D) in the Estonian-Latvian wolf population based on distance weighted interpolation of Structure membership coefficients.

<p>As determined by 1000 bootstrap permutations the dark coloured grid points (5×5 km) denote group core areas. Individuals are represented by multi-coloured pies which reflect the membership coefficient for each cluster (zoom to see the details).</p

Sampling locations of wolves in Estonia and Latvia.

<p>Background colours show MODIS land cover categories: green – forests, yellow – agricultural open habitats, red – settlement, blue – waterbodies.</p

Autosomal microsatellite data for Estonian-Latvian wolf population

The data file includes 16 microsatellite locus data for 166 wolves from Estonia and Latvia. Tissue samples were collected across the species range in Estonia and Latvia between 2004-2009

Geographical ranges of four genetic groups (A–D) presented separately.

<p>The dark coloured grid points (5×5 km) denote the core area of the group (as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075765#pone-0075765-g003" target="_blank">Figure 3</a>), whereas the light coloured areas represent near random group probability, and white areas are significantly outside of the range of the group.</p