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Record W2089275709 · doi:10.1080/17513758.2015.1027309

Allee effects and population spread in patchy landscapes

2015· article· en· W2089275709 on OpenAlexaff
Gabriel Andreguetto Maciel, Frithjof Lutscher

Bibliographic record

VenueJournal of Biological Dynamics · 2015
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsUniversity of Ottawa
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsAllee effectHomogenization (climate)HomogeneousPopulationStatistical physicsBiological dispersalSpatial heterogeneityEcologyMathematicsEconometricsBiologyPhysicsBiodiversityDemography

Abstract

fetched live from OpenAlex

Invasion of alien species is one of the major threats for natural community structures, potentially leading to high economic and environmental costs. In this work, we study through a reaction-diffusion model the dynamics of an invasion in a heterogeneous environment and in the presence of a strong Allee effect. We model space as an infinite landscape consisting of periodically alternating favourable and unfavourable patches. In addition, we consider that at the interface between patch types individuals may show preference for more favourable regions. Using homogenization techniques and a classical result for spread with Allee effect in homogeneous landscapes, we derive approximate expressions for the spread speed. When compared with numerical simulations, these expressions prove to be very accurate even beyond the expected small-scale heterogeneity limit of homogenization. We demonstrate how rates of spatial spread depend on demographic and movement parameters as well as on the landscape properties.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.298
Teacher spread0.266 · 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 designSimulation or modeling
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

Citations56
Published2015
Admission routes1
Has abstractyes

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