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Record W3179495001 · doi:10.1080/11956860.2021.1934299

Impact of urbanization and landscape changes on the vegetation of coastal dunes along the Gulf of Mexico

2021· article· en· W3179495001 on OpenAlexvenueno aff
Karla Salgado, M. Luisa Martínez, Lucero Álvarez-Molina, Patrick A. Hesp, Miguel Equihua, Ismael Mariño‐Tapia

Bibliographic record

VenueEcoscience · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
Fundersnot available
KeywordsUrbanizationSpecies richnessGeographyUrban sprawlEcologyBiodiversityVegetation (pathology)Plant communitySand dune stabilizationUrban planningBiology

Abstract

fetched live from OpenAlex

In Mexico, as in other countries, coastal urbanization and landscape changes are occurring at an unprecedented scale and rate, with potential negative impacts on local biodiversity. Nevertheless, studies of the impacts that such changes have on coastal dune vegetation are relatively scarce. In this study, we examined (a) the trends of urban sprawl and landscape changes along the coast of Veracruz for more than 20 years; (b) the impact of urban sprawl on plant species richness and plant functional groups; c) how landscape changes have affected plant diversity and community structure. Our results show an increasing urbanization along the coasts of the Gulf of Mexico, occurring at different rates, and being higher in locations closest to tourist areas. Plant species richness decreased with urban expansion while the proportion of plant functional types was altered. Inland species not tolerant to the beach-dune environment became more abundant in the most urbanized locations while the abundance of psammophytes decreased. Community structure (the dominant species) was modified with landscape changes. Our results are useful for an adaptive management strategy and will help develop sustainable beach management plans that should include the conservation of native and highly specialized species, such as psammophytes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.364

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 studies0.0000.000
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 head0.010
GPT teacher head0.220
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 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

Citations24
Published2021
Admission routes1
Has abstractyes

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