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Record W3174052223 · doi:10.82308/41715

Long-term recovery of ecosystem services following forest harvest in coastal temperate rainforests of Vancouver Island, British Columbia, Canada

2016· article· en· W3174052223 on OpenAlexaboutno aff
Ira J. Sutherland

Bibliographic record

VenueeScholarship@McGill (McGill) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsRainforestTemperate rainforestGeographyTerm (time)Temperate climateEcosystemTemperate forestForest ecologyForestryEnvironmental scienceAgroforestryEcology

Abstract

fetched live from OpenAlex

All ecosystems, and the ecosystem services (ES) they provide, are susceptible to potentially lasting impacts of resource extraction. For example, in forests, timber harvesting provides near-term services (such as wood products), but can be responsible for declines in other services such as carbon storage or wild edible foods, which may take decades or even centuries to recover. Some ES may recover quickly with forest regrowth, while others recover either slowly or not at all. However, the long-term recovery of multiple forest ES has rarely been quantified. My fundamental goal in this thesis is to build an improved understanding of how multiple ES recover following forest harvest, using the heavily harvested coastal temperate forests of western Vancouver Island, British Columbia, Canada, as my study system. First, I used a forest chronosequence to estimate the recovery trajectories for eight ES over a 212-year period. I used changes in key forest structures to estimate the provision of the following services: wood volume, carbon storage, potential nesting platforms used by an emblematic old-growth associate bird species the marbled murrelet (Brachyramphus marmoratus), habitat services provided by coarse woody debris, habitat services provided by dead trees, large heritage trees, wild edible berries, and large redcedar (Thuja plicata) used in traditional First Nations carving. ES recovered along varying non-linear trajectories and within markedly different timeframes. Wood volume stocks, dead tree biomass, and carbon storage recovered the fastest, reaching their maximum rates of recovery at around 65 years. In contrast, recovery of wild edible berries, heritage large trees, and habitat for marbled murrelet did not even commence until 70-100 years. Large heritage trees and large redcedar did not recover to old-growth baseline (forests >250 years old) during the 212-year period of my chronosequence. Second, I examined how ES recovery differed in two forest types: riparian and upland forests. With field assistance from a local First Nations crew, I estimated ten ES in old-growth (late seral stands >250 years in age) and second-growth stands (~35 years age) within each forest type. In addition to those services sampled in Chapter 1, I also estimated cedar bark for use in traditional First Nations weaving, salal (Gualtheria shallon) merchantable greenery, and fish habitat provided by instream woody debris. The abundance of ES differed significantly by forest type and forest age. For example, large cedar and potential nesting platforms for marbled murrelets were absent in second-growth stands, and significantly higher in riparian sites relative to their presence in upland forests. Old-growth riparian forests were hotspots of many ES, providing the highest levels of all services except salal merchantable greenery. The long timeframes and varying trajectories of recovery highlight the need to avoid ES declines proactively, for example by preserving sites with high levels of ES or working with First Nations to identify key areas with high levels of desired ES. Forest age and forest type have significant and major effects on multiple ES, and are thus two key variables for managing multiple ES in forested landscapes. Overall, this thesis provides insights into the effects of forest harvesting on multiple ES of ecological, cultural and economic importance. By applying forest ecological understanding to track changes in a bundle of ES, I identify influences of site conditions, long timeframes of successional recovery, and impacts from management to gain a broader understanding of the factors shaping forest ES. By building an improved empirical and conceptual understanding of multiple ES and their change through time, I have provided novel insights as well as practical solutions towards the challenge of long-term forest planning to sustain multiple ES.

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 score0.014
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.004
GPT teacher head0.173
Teacher spread0.169 · 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
Published2016
Admission routes1
Has abstractyes

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