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Record W4313462446 · doi:10.1101/2022.12.31.521874

The search behavior of terrestrial mammals

2023· preprint· en· W4313462446 on OpenAlexafffund
Michael Noonan, Ricardo Martínez‐García, Christen H. Fleming, Benjamin Garcia de Figueiredo, Abdullahi H. Ali, Nina Attias, Jerrold L. Belant, Dean E. Beyer, Dominique Berteaux, Laura R. Bidner, Randall B. Boone, Stan Boutin, Jorge Brito, Michael B. Brown, Andrew Carter, Armando Castellanos, Francisco X. Castellanos, Colter Chitwood, Siobhan Darlington, J. Antonio de la Torre, Jasja Dekker, Christopher S. DePerno, Amanda Droghini, Mohammad S. Farhadinia, Julian Fennessy, Claudia Fichtel, Adam T. Ford, Ryan Gill, Jacob R. Goheen, Luiz Gustavo Rodrigues Oliveira‐Santos, Mark Hebblewhite, Karen E. Hodges, Lynne A. Isbell, René Janssen, Peter M. Kappeler, Roland Kays, Petra Kaczensky, Matthew J. Kauffman, Scott LaPoint, Marcus A. Lashley, Peter Leimgruber, Andrew R. Little, David W. Macdonald, Symon Masiaine, Roy McBride, Emília Patrícia Medici, Katherine Mertes, Chris Moorman, Ronaldo Gonçalves Morato, Guilherme Mourão, Thomas Mueller, Eric W. Neilson, Jennifer Pastorini, Bruce D. Patterson, Javier A. Pereira, Tyler R. Petroelje, Katie Piecora, R. John Power, Janet L. Rachlow, Dustin H. Ranglack, David Roshier, Kirk Safford, Dawn M. Scott, Robert Serrouya, Melissa Songer, Nucharin Songsasen, Jared A. Stabach, Jenna Stacy‐Dawes, Morgan Swingen, Jeffrey J. Thompson, Marlee A. Tucker, Marianella Velilla, Richard W. Yarnell, Julie K. Young, William F. Fagan, Justin M. Calabrese

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsParks CanadaCanadian Forest ServiceNatural Resources CanadaUniversity of AlbertaUniversité du Québec à RimouskiOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaKfW EntwicklungsbankThreatened Species Recovery HubNational Commission for Science, Technology and InnovationConsejo Nacional de Ciencia y TecnologíaUniversity of California, DavisFundação de Amparo à Pesquisa do Estado de São PauloNorth Carolina State UniversityUniversity of OxfordInstituto SerrapilheiraAustralian Wildlife ConservancyICTP South American Institute for Fundamental ResearchSimons FoundationNuclear Safety and Security CommissionArcticNetNational Aeronautics and Space AdministrationLeakey FoundationAustralian GovernmentAbdus Salam International Centre for Theoretical PhysicsU.S. Department of DefenseGiraffe Conservation FoundationNational Science Foundation
KeywordsPredationResource (disambiguation)PredatorAbundance (ecology)EcologyBiologyMovement (music)Apex predatorGeographyBalance (ability)Computer scienceNeuroscience

Abstract

fetched live from OpenAlex

Summary Animals moving through landscapes need to strike a balance between finding sufficient resources to grow and reproduce while minimizing encounters with predators 1,2 . Because encounter rates are determined by the average distance over which directed motion persists 1,3–5 , this trade-off should be apparent in individuals’ movement. Using GPS data from 1,396 individuals across 62 species of terrestrial mammals, we show how predators maintained directed motion ~7 times longer than for similarly-sized prey, revealing how prey species must trade off search efficiency against predator encounter rates. Individual search strategies were also modulated by resource abundance, with prey species forced to risk higher predator encounter rates when resources were scarce. These findings highlight the interplay between encounter rates and resource availability in shaping broad patterns mammalian movement strategies.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.028
GPT teacher head0.242
Teacher spread0.214 · 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 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

Citations7
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicWildlife Ecology and Conservation→French-language works237,207→