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Record W7024878062

Toward a theory of abundance at large spatial scales

2011· dissertation· en· W7024878062 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2011
Typedissertation
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersMcGill University
KeywordsAbundance (ecology)Interspecific competitionCompetition (biology)Variation (astronomy)Relative species abundanceGeneralitySpatial ecologySpatial variabilityForaging
DOInot available

Abstract

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Fundamentally, ecology is the study of the diversity, distribution, and abundance of organisms.Recent advances in technology coupled with expanding research goals have lead to studies of how the first two of these properties vary over large spatial scales.There has been relatively few cases documenting large scale spatial variation in abundance and very little theoretical development explaining such variation.Yet a general pattern exists: a species is abundant in very few places and rare in most places in its range.Current theory suggests that such a pattern of abundance reflects underlying spatial variation in the environment.In this thesis, I used observational, experimental, theoretical, and statistical approaches to test the type of environmental variation and how such environmental variation combines with interspecific competition to generate spatial variation in abundance.For two species of hummingbirds, I found that different environmental factors related to abundance than to occupancy.Interspecific competition altered spatial variation in abundance in different ways depending on the niche differences among competing species.Interspecific competition also mediated the effect of the environment on abundance by influencing the relative costs and benefits of different hummingbird foraging strategies.I also found that abundance data can be used to predict species' response to climate change because statistical models minimize the noise inherent in abundance datasets.Despite my findings, a theory of abundance is still in its infancy.It is not known whether there is generality in the number and identity of large scale environmental gradients that affect abundance.Similarly, more work needs to be done connecting the small scale interplay between environment, species traits, behaviour, and competition to a broader geographic ii context.There are also dispersal and non-niche based approaches to spatial variation in abundance that need to be reconciled with current theory.In this way, a more general theory relating macroevolutionary dynamics to macroecological patterns can be developed.Résumé L'écologie est l'étude de la diversité, des distributions et des abondances des organismes vivants.Les avancées technologiques récentes couplées à une expansion des objets de recherche ont permis à une étude approfondie de la variation de ces deux premières propriétés sur de très grandes échelles spatiales.Les variations en abondance sont, quant à elles, peu documentées aux grandes échelles spatiales et les développements théoriques correspondant restent limités.Il existe pourtant un pattern prévalent : une espèce donnée est généralement abondante dans une partie extrêmement réduite de sa zone géographique et rare partout ailleurs.Cette observation est aujourd'hui communément expliquée par une variation environnementale sous-jacente.Cette thèse s'appuie sur des approches à la fois empiriques et expérimentales, statistiques et théoriques pour tester le type de variation environnementale ainsi que les interactions entre environnement et compétition interspécifique pouvant générer les variations spatiales en abondances observées.Il est montré que présence-absence et abondance sont affectées par des facteurs environnementaux distincts.Il apparaît en outre que l'effet de la compétition interspécifique dépend des différences de niches entre espèces et module l'impact de l'environnement sur l'abondance en modifiant des coûts et bénéfices relatifs des différentes stratégies d'acquisition iii des ressources.Finalement, la possibilité de prédire les réponses aux changements climatiques grâce aux données d'abondance et à des modèles statistiques minimisant le bruit inhérent à ce type de données est démontrée.Pour autant, une véritable théorie des distributions d'abondance reste à développer.Le nombre, et a fortiori l'identité, des gradients environnements affectant les abondances à grande échelle spatiale sont encore mal connus.Un effort de recherche considérable est ainsi nécessaire pour améliorer la compréhension du lien entre phénomènes locaux, dont l'interaction entre environnement, traits, comportement et compétition, et patterns à grandes échelles.Par ailleurs, l'unification entre approches basées sur la dispersion, négligeant les différences de niches, avec la théorie actuelle doit encore être accomplie pour qu'une véritable théorie générale des dynamiques macro-évolutive et patterns macro-écologiques puisse voir le jour.Traduit par Jurgis Sapijanskas Acknowledgements Brian McGill, my supervisor, was a constant source of inspiration throughout my PhD.One of his words could capture the essence of my thinking.One of his ideas could send me off to explore a subject in new and deeper ways.I greatly respect his work and approach to science and I have grown as an ecologist because of him.He has spawned in me an enthusiasm for the discipline due to his clear sense of the field.My committee of Andy Gonzalez, Luc-Alain Giraldeau, and Murray Humphries kept me on track and they were always available for a quick chat about my work.Marty Lechowicz was exceptionally generous in making me feel iv welcome in his lab and with his lab group.One of the highlights of my PhD career was TAing ENVR 301 and for this I owe gratitude to Colin Chapman.The McGill lab group was as good of a group of peers and friends a student could ever ask for.At times, we spanned three universities or four countries but still managed to trade jokes, advice, and critical analysis.John Donoghue always provided constructive feedback.Julie Messier has been, at times, a housemate, a travel buddy, and, always, a good friend.Peter White, you were alongside me in the whole process and I congratulate you on

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.010
Scholarly communication0.0040.016
Open science0.0030.004
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.238
Teacher spread0.209 · 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 designTheoretical or conceptual
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

Citations1
Published2011
Admission routes1
Has abstractyes

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