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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».