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Record W6996667138

Spatial and dietary niche variation associated with diverse resource availability, competitive environment, and landscape heterogeneity; ecology and conservation of bear-salmon systems in coastal British Columbia

2019· dissertation· en· W6996667138 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2019
Typedissertation
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsNicheContext (archaeology)Niche segregationIntraspecific competitionEcological nicheRange (aeronautics)Environmental niche modellingSpatial ecologyNiche differentiation
DOInot available

Abstract

fetched live from OpenAlex

The niche concept provides a tractable measure of the ecological roles and requirements of organisms, which can inform our understanding of the patterns of biodiversity, and subsequently, direct conservation policy. Although niche is most commonly considered for species, variation nested within lower hierarchies of biological organization (i.e., phenotypes, genotypes) also contributes to maintaining biodiversity. Herein I examine spatial and dietary niche variation among and within species in a consumer–resource system where resources and competition are structured by a heterogeneous environment. An underlying theme of this dissertation was developing research through a community-engaged approach that not only contributed to conceptual advancements in niche theory but also to applied conservation priorities. The marine archipelago of the central coast of British Columbia, Canada structures salmon (Oncorhynchus spp.) resources and the inter- and intraspecific competitive context for coastal grizzly (Ursus arctos) and black bears (Ursus americanus). This assembly of ursids represents nested levels of biological organization (i.e., species, phenotypes (white vs black-coated morphs of black bears) and genotypes (dominant homozygote black-coated black bears, heterozygote black-coated black bears, recessive homozygote white-coated ‘Spirit’ black bears)), which allows for investigation into niche variation across and within species. I investigated niche variation using a suite of non-invasive methods. Local and Traditional Ecological Knowledge (LEK/TEK) provided complementary information to genetic and stable isotope (13C and 15N) data from hair samples. First, I investigated changes in the spatial niche of coastal grizzly bears. By combining western scientific approaches with TEK/LEK interviews I found the range of coastal grizzly bears has expanded westward onto several coastal islands. The economic, cultural, and ecological impacts of this shift in spatial niche are not yet understood. Second, I tested hypotheses related to variation in the foraging niche of black bears in response to the competitive environment and salmon resource availability. I found that grizzly bear presence reduced the proportion of salmon in black bear diets by ~40%. I also found that salmon species diversity, and not biomass abundance, was positively correlated to salmon in black bear diets. This highlights the importance of resource diversity to consumers beyond the consideration of abundance. Third, I explored spatial niche patterns of Spirt bears in relation to protected areas. I found that landscape-level Spirit bear allele frequency and population estimates were lower than previously reported. Approximately ~50% of Spirit bear allele hotspots corresponded to protected areas. This finding suggests Spirit bears are rarer and less protected than previously assumed. Finally, I tested hypotheses related to niche variation between phenotype and genotypes of Spirit bear populations. I found both phenotypes and genotypes diverged in foraging niche, with Spirit bears and black-coated heterozygotes having elevated stable isotope signatures compared to black-coated homozygotes. This result supports the role of ‘multi-niche’ mechanisms in maintaining this rare polymorphism. Results from all chapters contributed to land- and marine-use stewardship efforts of collaborating First Nations. Collectively, this dissertation offers novel contributions towards understanding how niche variation at multiple levels of biological organization can contribute to conservation planning.

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 score0.059
Threshold uncertainty score0.119

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 integrity0.0000.000
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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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
Published2019
Admission routes1
Has abstractyes

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