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Record W7135040165 · doi:10.5376/ijmec.2025.15.0025

Spatial Behavior and Population Ecology: The Role of Territoriality

2025· article· W7135040165 on OpenAlexvenueno aff
Xuming Lyu, Yeping Han

Bibliographic record

VenueInternational Journal of Molecular Ecology and Conservation · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsTerritorialityPopulationCommunityHabitatNiche constructionDisturbance (geology)NicheEcological successionDiversity (politics)

Abstract

fetched live from OpenAlex

This study introduces the definition and research history of territoriality, elaborates on the core position of territorial behavior in animal ecology and behavioral science, as well as its mechanism of action on population density regulation, reproductive success and individual fitness. Meanwhile, it was explored how territorial behavior affects niche differentiation, predator-prey relationships, and community stability and diversity in community structure. Through the analysis of typical cases of birds (such as songbirds), mammals (such as wolves and lions), fish and reptiles (such as cichlids and lizards), the different manifestations and ecological significance of territoriality are demonstrated. This study also discusses the methods of incorporating territoriality into population dynamic models, including the combination of spatial heterogeneity and individual-based models, as well as the significance of territoriality for long-term population succession prediction. From an application perspective, this paper clarifies the implications of territorial behavior research for habitat protection, species restoration and human disturbance management. Territoriality is not only an individual's behavioral strategy but also an important mechanism for regulating population structure and maintaining ecosystem stability. Integrating behavioral ecology with population dynamic models is conducive to enhancing ecological prediction capabilities and the scientific nature of conservation management.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.174
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.257
Teacher spread0.248 · 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.

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
Published2025
Admission routes1
Has abstractyes

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