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Record W1554169354

aMazing pattern: spatial self-organization in peatlands

2009· dissertation· en· W1554169354 on OpenAlexaboutno aff
Maarten B. Eppinga

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2009
Typedissertation
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPeatAbiotic componentEcosystemEcologyVegetation (pathology)EvapotranspirationEnvironmental scienceLandscape ecologySpatial ecologyBiotic componentPhysical geographyGeographyHabitatBiology
DOInot available

Abstract

fetched live from OpenAlex

Predicting how gradual changes in abiotic conditions affect ecosystem functioning is a key challenge in ecology and environmental science. For many ecosystems, the response to gradual changes may not be smooth, but rapid and almost irreversible shifts in ecosystem states may occur. Early warning signals for such catastrophic shifts are difficult to obtain. Recent research suggests that so-called self-organized patchiness (regular spatial vegetation patterning) can serve as an indicator for such sudden changes. Self-organized patchiness has been observed in a variety of ecosystems, including peatlands. Most research has focused on linear patterns along the contours of peatland slopes. More recently, aerial photographs from relatively flat ground in Siberia revealed peatlands with so-called maze-patterning, because this type of patchiness somewhat resembles the corridors of a maze. The striking self-organized patchiness has amazed many peatland scientists and has lead to considerable attention for peatland patterning in the literature. Until now, however, the driving mechanisms of peatland patchiness still remain elusive, despite more than a century of research on this phenomenon. This thesis investigates underlying mechanisms that explain self-organized patchiness in peatlands, and whether this patchiness could serve as an indicator for proximity to catastrophic shifts in peatland ecosystem states. A combination of theoretical and empirical approaches is used. We conclude that the potential importance of different driving mechanisms for peatland patterning depends on climatic conditions. Increased evapotranspiration in vegetation patches with high density may be particularly important in peatlands where most water leaves the system through evapotranspiration. Alternatively, in peatlands where water is lost via drainage or overland flow, a positive feedback between the thickness of the upper aerobic peat layer and the rate of peat formation the peat accumulation mechanism may be more important. Global climate models project for most peatland regions an increasing importance of evapotranspiration during the coming century, with the strongest increases being projected for Siberia and Canada. Based on the results in this thesis, we speculate that evapotranspiration may become the main driver of pattern formation in parts of these regions. We also conclude that a shift from an unpatterned state without hummocks and hollows toward a patterned state with hummocks and hollows already comprises a catastrophic shift in ecosystem functioning that is difficult to reverse. This means that a pattern cannot be used as an indicator of proximity to a catastrophic shift, but rather indicates that the shift has already happened. Moreover, the very slow development of peatlands calls for caution when applying equilibrium concepts, which are used in most mathematical models of pattern formation, to peatland dynamics. Investigation of the mechanisms that drive self-organized patchiness in ecosystems is a promising approach to increase our understanding of ecosystem functioning, and the response of ecosystems to changing abiotic conditions. This thesis exemplifies that studies on pattern formation need to include both theoretical and empirical approaches, because the driving mechanisms of self-organized patchiness may change with climatic regions and may therefore be site-specific.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.216
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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