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Record W2909051107

Diversity and Diversification Across the Global Radiation of Extant Bats

2018· article· en· W2909051107 on OpenAlexfundno aff
Jeff J. Shi

Bibliographic record

VenueDeep Blue (University of Michigan) · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
FundersHorace H. Rackham School of Graduate Studies, University of MichiganMcGill UniversityNational Science Foundation
KeywordsDiversification (marketing strategy)Extant taxonDiversity (politics)GeographyEconomic geographyEvolutionary biologyEcologyBiologyBusinessSociologyAnthropology
DOInot available

Abstract

fetched live from OpenAlex

Diversity is not distributed equally across the tree of life. This fundamental observation is central to ecology and evolutionary biology, and spans both spatial and temporal scales. Species richness, for example, is unevenly distributed both within and across clades. Biodiversity is often spatially concentrated in the tropics, with lower richness in temperate biomes. Some clades are characterized by extremely high ecological and morphological diversity, while others remain static across geologic timescales. This dissertation highlights these patterns of diversity across extant bats, the Order Chiroptera, and seeks to understand the evolutionary processes of diversification that govern them. Chapter 1 serves as both an introduction to the major questions of the dissertation and an overview of extant bat diversity, and how it varies spatially, phylogenetically, and ecologically across the globe. In this chapter, I primarily focus on spatial variation in regional richness patterns, and on the major differences between temperate and tropical bat diversity. In Chapter 2, I assemble a species-level molecular phylogeny of the order that is time-calibrated with fossil data. Using this phylogeny, I infer speciation dynamics across the order, and find that despite the imbalances in species richness, speciation rates are relatively homogeneous. I only infer strong evidence for more rapid rates within the subfamily Stenodermatinae, a clade of Neotropical phyllostomid bats. In Chapter 3, I develop models to test whether bat species co-occurrence is constrained by relatedness or ecomorphological similarity. Contrary to theoretical predictions and results from other major clades, I find that neither of these metrics of divergence controls co-occurrence in sympatry across most bats and realms. The only exception is the Neotropical realm, where bat species are most likely to co-occur when they are the most ecomorphologically similar to one another. This potentially indicates that Neotropical bat communities and species pools, at broad regional scales, are sorted by filtering processes that cluster bats with similar ecologies together in space. For Chapters 4 and 5, I assess how ecology and morphology are linked in New World bats. Chapter 4 describes an open-access, X-ray computed microtomography database of bat skulls, and how this resource can be used by the broader scientific and educational community. Chapter 5 combines crania from that database with diet data across New World bats, and tests whether ecological and morphological evolution are correlated in this group. Surprisingly, I find that patterns of ecological, trophic evolution are largely decoupled from morphological evolution. There is considerable heterogeneity in how readily different clades transition among trophic guilds, yet cranial shape evolution is surprisingly homogeneous. This decoupled pattern is potentially driven by underestimated trophic plasticity and omnivory among noctilionoid bats, as well as high lability among bat crania. Finally, in Chapter 6, I conclude with a summary of our major findings, and some thoughts on ecological and macroevolutionary inference both within bats and across the tree of life.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.195
Teacher spread0.179 · 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 teacher head, not a consensus.

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
Published2018
Admission routes1
Has abstractyes

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