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Record W7018847519

The effect of landscape structure on insect herbivory and biodiversity: Implications for forest ecosystem services in the Monteregie, Québec

2015· other· en· W7018847519 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemHerbivoreExclosureEcosystem servicesEcosystem healthContext (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

Anthropogenic disturbances are fragmenting forested areas around the world, fundamentally changing the structure of landscapes in a manner that has poorly understood consequences for biodiversity, ecological processes, and ecosystem services. This lack of understanding makes it difficult for land managers to make optimal decisions that balance environmental benefits and costs of land use change. In this thesis, I address this gap in our understanding by using a combination of theoretical and empirical approaches to quantify how and when fragmentation affects ecosystem processes that have distinct impacts on forest ecosystem services. Specifically, I test the role that insect herbivory plays in mediating the effects of landscape structure on ecosystem services in forest ecosystems in the Montégerie in southwest Quebec. Insect herbivory is a good model system to test these effects because it is sensitive to landscape structure, can have strong effects on ecosystem services, and can be either beneficial or harmful depending on the herbivore species and the ecosystem context. In this thesis, I quantify the effects of landscape structure on insect herbivory and biodiversity, and assesses the implications of these effects for forest ecosystem services in four steps: 1) In the second chapter examine the known relationships between landscape structure, herbivory and ecosystem services through a semi-quantitative literature review and show that herbivory plays an important role mediating effects of connectivity on ecosystem services. The results of this review show assessing the mechanisms that regulate the response of insect herbivores to landscape structure would allow for better predictions regarding this process. 2) In the third chapter, I quantify the relationship between landscape structure and herbivory, and test potential mechanisms (i.e. predation pressure) driving the response, using a manipulative field experiment in the Montérégie. I find that landscape structure alone does not explain levels of herbivory, even though there was strong vertebrate predation pressure on herbivores that varied with landscape structure. Because these results show structure at the landscape scale does not explain patterns of herbivory, my next step was to determine whether insect herbivory is spatially heterogeneous at a finer scale. 3) In the fourth chapter, I measure the spatial heterogeneity of insect herbivory in remnant forest patches at both the landscape and patch level, quantifying herbivore damage on sugar maple trees at the edge, interior, and canopy of remnant forest patches that differ in size and connectivity. I find that patterns of herbivory are affected by the interaction of landscape and patch level sources of spatial heterogeneity. 4) Finally, to better understand the mechanism by which these finer scale patterns occur, I measure the effects of landscape structure and location within a forest patch (i.e., edge, interior, canopy) on arthropod functional and taxonomic biodiversity. I find that canopies of fragmented forest patches are important reservoirs of arthropod biodiversity, even in fragmented forest ecosystems. Human activities, such as fragmentation or restoration, continue to affect the structure of forest ecosystems, with unknown consequences for the ecosystem services provided by forests. My thesis advances our understanding of the effects of landscape structure on ecosystem services by using herbivory as a model system to provide a conceptual framework with which to balance the social and environmental costs and benefits associated with altering landscape structure.

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 score0.022
Threshold uncertainty score0.160

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 studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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
Published2015
Admission routes1
Has abstractyes

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