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Record W7106013178 · doi:10.7939/83562

Broad and local-scale factors modifying ecosystem recovery after restoration

2025· dissertation· en· W7106013178 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemRestoration ecologyNovel ecosystemClimate changeAbiotic componentEcosystem servicesEcosystem managementPsychological resilienceEcosystem ecology

Abstract

fetched live from OpenAlex

The practice of restoration is one pillar of land stewardship that can reduce the impacts of ecosystem degradation. However, the complexity of biotic and abiotic interactions that underlie ecosystem responses to restoration renders it difficult to predict restoration outcomes and the extent of recovery, consequently affecting restoration success. At broad global scales, ecosystem responses to restoration may be influenced by climate, and at local scales, biotic interactions may play a stronger role. The overarching goal of my PhD research was to identify climatic and biotic factors that affect ecosystem responses to restoration at broad and local scales and to determine to what extent these factors can predict restoration outcomes. At the broad global scale, I investigated the role of a changing climate in ecosystem recovery across ecosystems and different disturbances. In a meta-analysis of 130 restoration studies across Asia, Europe, North America, and South America, I quantified the extent of recovery following restoration and tested whether the extent of deviation from historical climates and extreme climate events predicts ecosystem responses to contemporary restoration. I found that restoration is not achieving complete recovery, and that the effects of deviation from recent past climate on recovery after restoration were dependent on the ecosystem characteristic measured. Deviation and anomalies in mean annual temperatures from the recent past mostly decreased recovery following restoration. At the local-scale, I investigated the role of plant-soil interactions in facilitating dominance of non-native plants in the montane grasslands of Banff National Park, Canada, a critical habitat for wildlife. I first identified the plant, arbuscular mycorrhizal, and plant-pathogenic fungal communities in a field survey of reference ecosystems and partially restored ecosystems where non-native plants dominate. I found that the arbuscular mycorrhizal fungal community composition did not differ across ecosystems, suggesting establishment of native plants is not limited by this fungal group. However, distinct fungal pathogen communities coincided with the presence of non-native plant species. These pathogens, possibly conditioned by non-native plants, may impede the restoration of montane grasslands by affecting the establishment of native plant species. To test this hypothesis, I next performed a set of experiments using soil from the field as inoculum and growing plant species present in the montane grasslands to test whether different plant species condition distinct communities of soil pathogenic and arbuscular mycorrhizal fungi, and whether soil conditioned by different plant species has different effects on con- and heterospecific plant growth that may explain non-native plant dominance. I found plant species conditioned distinct communities of soil pathogenic fungi in some cases, but not arbuscular mycorrhizal fungi. Most plant species had less biomass in conspecific than in heterospecific soils (negative conspecific feedback), suggesting non-native plant dominance is not driven by increased positive feedbacks, and that soil pathogens affect the growth of native and non-native plants. In the field, non-native plant species may offset negative conspecific feedbacks by untested mechanisms explaining their dominance in the montane grasslands of Banff National Park. Overall, by addressing multiple scales and using a variety of methods, my research shows that climate and biotic factors can influence ecosystem responses to restoration and helps identify opportunities and limitations to the predictability of ecosystem responses to restoration at global and local scales.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.164
Teacher spread0.157 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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