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Record W7106503055 · doi:10.25932/publishup-69057

Past and future dynamics of boreal forests in Siberia and North America derived by population genetics and individual-based modelling

2025· article· en· W7106503055 on OpenAlexaboutno aff

Bibliographic record

Venuepublish.UP (University of Potsdam) · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsTaigaGlacial periodBorealPopulationRange (aeronautics)Last Glacial MaximumLarchClimate change

Abstract

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Understanding the historical and ecological drivers of boreal forest dynamics is essential for predicting species-specific responses to ongoing climate change. This thesis integrates landscape genetics, palaeobotanical evidence, and individual-based modelling to examine how glacial legacies, refugial histories, and ecological constraints have shaped the postglacial recolonisation and future migration potential of dominant boreal tree species – Larix spp. in Siberia and Picea spp. in North America. First, we investigated the demographic impacts of Quaternary glacial cycles (Hypothesis 1). Genome-wide single nucleotide polymorphisms (SNPs), derived by genotyping-by-sequencing (GBS), combined with Bayesian demographic inference, revealed contrasting trajectories between Larix and Picea. In Larix, genetic diversity was shaped by geographic isolation, topographic barriers, and repeated Pleistocene range contractions. Effective population size closely tracked glacial–interglacial temperature oscillations, highlighting strong climate-driven demographic sensitivity. Divergence between Larix cajanderi and L. gmelinii began during the Last Glacial Maximum (LGM, ~ 20 ka BP), likely due to range contraction and reduced gene flow across the north–south-oriented Verkhoyansk Mountains, followed by secondary contact and interbreeding in the mid-Holocene (~ 5 ka BP) as conditions improved. In North America – where, unlike Siberia, much of the boreal zone was glaciated – ice sheets strongly influenced Picea population structure. In Picea mariana, genetic patterns indicate divergence predating the LGM, with long-term isolation maintaining separation between eastern and western lineages, divided by the Laurentide and Cordilleran Ice Sheets. In contrast, P. glauca exhibits high genetic connectivity, consistent with a more recent and widespread postglacial recolonisation, limited mainly by the Alaskan Coastal Range. Second, we assessed the role of glacial refugia in shaping genetic diversity and recolonisation dynamics (Hypothesis 2). In Larix, Bayesian clustering, Approximate Bayesian Computation (ABC) modelling, and palaeobotanical data support long-term persistence in cryptic northern refugia, such as the Verkhoyansk Mountains and Tschuch’ye Lake region. Rather than a complete northward recolonisation from southern populations, Holocene expansion seems to have benefitted from these refugial populations ahead of the treeline. In contrast to early-Holocene dynamics, however, current migration is likely to be slower, due to the absence of extant refugia in the far north. The two Picea species show divergent refugial histories. In P. mariana, genetic data suggest survival in multiple refugia east and west of the major ice sheets, with limited postglacial gene flow. Recolonisation of Alaska–Yukon likely occurred via eastern ice-free corridors across the Rockies and Mackenzie Mountains. Picea glauca, by contrast, underwent rapid expansion from Beringian and/or southeastern Alaskan refugia, facilitated by high dispersal capacity andecological generalism. These divergent histories have left lasting imprints on genetic structure, connectivity, and present-day distributions. Third, we investigated how ecological constraints – particularly snow dynamics – modulate mountain treeline migration under climate change (Hypothesis 3). Using the individual-based, spatially explicit forest model LAVESI, originally developed for Larix and here adapted for Picea, we conducted multi-site sensitivity analyses in Alaska, Canada, and Russia. Migration responses were highly site-specific, shaped by local climate, wind, slope, and species traits. Counterintuitive patterns – such as accelerated migration with delayed maturation or higher seedling mortality – highlight the complexity of demographic–environmental interactions. Incorporating a snow dynamics module further revealed that snow cover duration and depth are critical constraints on seedling establishment, growth, and dispersal. Snow acted variably as a barrier or facilitator, depending on site conditions and its interaction with topography and wind, producing often non-linear treeline responses. These results underscore the importance of including fine-scale snow processes in models of northern forest dynamics to improve climate change projections. Finally, our genetic findings suggest species-specific differences in demographic resilience. Picea mariana, with its fragmented genetic structure and ecological specialisation, may be more vulnerable to climate change. In contrast, the high genetic connectivity and ecological breadth of P. glauca imply a potentially greater adaptive potential under future conditions. Collectively, this thesis demonstrates that boreal forest dynamics are shaped by the interplay of demographic history, ecological traits, landscape configuration, and climatic constraints. By integrating landscape genetics, palaeobotanical data, and process-based modelling, it provides robust support for three linked hypotheses: (1) that Pleistocene climate and geographical factors shaped genetic structure and forest migration; (2) that refugia influence genetic diversity and postglacial recolonisation; and (3) that treeline migration is modulated by ecological constraints, particularly snow conditions. These findings highlight the need to account for both historical legacies and present-day ecological limits when predicting forest migration and resilience in high-latitude and alpine environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.186
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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