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Record W2139958458 · doi:10.1111/jbi.12111

The combined effects of land‐use legacies and novel fire regimes on bird distributions in the Mediterranean

2013· article· en· W2139958458 on OpenAlexaff
Miquel De Cáceres, Núria Aquilué, Marie‐Josée Fortin

Bibliographic record

VenueJournal of Biogeography · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsShrublandBiological dispersalHabitatEcological successionEcologyMediterranean climateClimate changeFire regimeEnvironmental scienceVegetation (pathology)GeographyOccupancyBiodiversityDistribution (mathematics)Species distributionLand coverFire ecologyLand use, land-use change and forestryLand useEcosystemBiology

Abstract

fetched live from OpenAlex

Abstract Aim We investigate first whether fire regimes resulting from the combination of climate change and fire‐fighting policy may affect species distributions in Mediterranean landscapes, and second to what extent distributional dynamics may be constrained by the spatial legacy of historical land use. Location Catalonia (north‐eastern Spain). Methods We modelled the distributional responses of 64 forest and open‐habitat bird species to nine fire‐regime scenarios, defined by combining different levels of climate change and fire suppression efficiency. A fire‐succession model was used to stochastically simulate land‐cover changes between 2000 and 2050 under these scenarios. We used species distribution models to predict habitat suitability and occupancy dynamics under either no dispersal or full dispersal assumptions. Results Under many simulated scenarios, the succession from shrubland to forest dominated over the creation of new low‐vegetation areas derived from wildfires. Consequently, open‐habitat specialists were the group most affected by losses of suitable habitat. Fire regimes obtained under scenarios including high fire suppression efficiency resulted in a larger number of bird species experiencing reductions in their distribution area. Main conclusions Anthropogenic factors, such as historical land‐use change and fire suppression, can drive regional distribution dynamics in directions opposite to those expected from climatic trends. This raises the question of what drivers and interactions should be given priority in the prediction of biodiversity responses to global change at the regional scale.

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 score0.027
Threshold uncertainty score0.201

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 head0.006
GPT teacher head0.197
Teacher spread0.190 · 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

Citations35
Published2013
Admission routes1
Has abstractyes

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