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Record W2908758646 · doi:10.1111/geb.12875

Large birds travel farther in homogeneous environments

2019· article· en· W2908758646 on OpenAlexafffund
Marlee A. Tucker, Όλγα Αλεξάνδρου, Richard O. Bierregaard, Keith L. Bildstein, Katrin Böhning‐Gaese, Chloe Bracis, John N. Brzorad, Evan R. Buechley, David Cabot, Justin M. Calabrese, Carlos Carrapato, André Chiaradia, L. Davenport, Sarah C. Davidson, Mark Desholm, Christopher R. DeSorbo, Robert Domenech, Peter Enggist, William F. Fagan, Nina Farwig, Wolfgang Fiedler, Christen H. Fleming, Alastair Franke, John M. Fryxell, Clara García‐Ripollés, David Grémillet, Larry Griffin, Roi Harel, Adam Kane, Roland Kays, Erik Kleyheeg, Anne E. Lacy, Scott LaPoint, Rubén Limiñana, Pascual López‐López, Alan D. Maccarone, Ugo Mellone, Elizabeth K. Mojica, Ran Nathan, Scott H. Newman, Michael Noonan, Steffen Oppel, Mark Prostor, Eileen C. Rees, Yan Ropert‐Coudert, Sascha Rösner, Nir Sapir, Dana G. Schabo, Matthias Schmidt, H. Schulz, Mitra Shariati, Adam Shreading, João Paulo Silva, Henrik Skov, Orr Spiegel, John Y. Takekawa, Claire S. Teitelbaum, Mariëlle L. van Toor, Vicente Uríos, Javier Vidal‐Mateo, Qiang Wang, Bryan D. Watts, Martin Wikelski, Kerri Wolter, Ramūnas Žydelis, Thomas Mueller

Bibliographic record

VenueGlobal Ecology and Biogeography · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsNunavut Arctic CollegeUniversity of GuelphUniversity of Alberta
FundersBC Cancer AgencyAustralian Research CouncilNederlandse Organisatie voor Wetenschappelijk OnderzoekAgence Nationale de la RechercheRobert Bosch StiftungIrish Research CouncilMAVA FoundationEnerginet.dkNational Science FoundationDepartment of Energy and Climate ChangeFundação para a Ciência e a TecnologiaU.S. Department of Energy
KeywordsGeographyEcologySpatial ecologyHomogeneousPopulationSpatial distributionPhysical geographyBiologyDemography

Abstract

fetched live from OpenAlex

Abstract Aim Animal movement is an important determinant of individual survival, population dynamics and ecosystem structure and function. Nonetheless, it is still unclear how local movements are related to resource availability and the spatial arrangement of resources. Using resident bird species and migratory bird species outside the migratory period, we examined how the distribution of resources affects the movement patterns of both large terrestrial birds (e.g., raptors, bustards and hornbills) and waterbirds (e.g., cranes, storks, ducks, geese and flamingos). Location Global. Time period 2003–2015. Major taxa studied Birds. Methods We compiled GPS tracking data for 386 individuals across 36 bird species. We calculated the straight‐line distance between GPS locations of each individual at the 1‐hr and 10‐day time‐scales. For each individual and time‐scale, we calculated the median and 0.95 quantile of displacement. We used linear mixed‐effects models to examine the effect of the spatial arrangement of resources, measured as enhanced vegetation index homogeneity, on avian movements, while accounting for mean resource availability, body mass, diet, flight type, migratory status and taxonomy and spatial autocorrelation. Results We found a significant effect of resource spatial arrangement at the 1‐hr and 10‐day time‐scales. On average, individual movements were seven times longer in environments with homogeneously distributed resources compared with areas of low resource homogeneity. Contrary to previous work, we found no significant effect of resource availability, diet, flight type, migratory status or body mass on the non‐migratory movements of birds. Main conclusions We suggest that longer movements in homogeneous environments might reflect the need for different habitat types associated with foraging and reproduction. This highlights the importance of landscape complementarity, where habitat patches within a landscape include a range of different, yet complementary resources. As habitat homogenization increases, it might force birds to travel increasingly longer distances to meet their diverse needs.

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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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.003
GPT teacher head0.197
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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

Citations64
Published2019
Admission routes2
Has abstractyes

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