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Record W7019335494

A Framework to Model Current and Future Nonbreeding Distributions for Four Declining Nearctic-Neotropical Migratory Forest Birds to Inform Conservation Planning

2024· article· en· W7019335494 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalCommons (California Polytechnic State University) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatLand coverClimate changePopulationDistribution (mathematics)Habitat conservationBird conservationOccupancy
DOInot available

Abstract

fetched live from OpenAlex

The full-annual cycle of a Nearctic-Neotropical migratory bird consists of a summer breeding period in North America, southward migration in autumn, a winter stationary nonbreeding period in the Neotropics, and northward migration in spring back to breeding areas. In nonbreeding periods, conservation strategists lack understanding of environmental factors affecting species distributions, stationary nonbreeding distributions, and migratory airspace and stopover habitats. Successful full-annual cycle conservation planning requires knowledge of these critical components to reverse the decline of imperiled species. We focused our efforts on 4 Nearctic-Neotropical migratory forest birds experiencing population declines: Cardellina canadensis (Canada Warbler, CAWA), Setophaga cerulea (Cerulean Warbler, CERW), Vermivora chrysoptera (Golden-winged Warbler, GWWA), and Hylocichla mustelina (Wood Thrush, WOTH). In chapter 1, we quantified current (2012 to 2021) and projected future (2050) suitable climatic and land use/land cover conditions as components of stationary nonbreeding distributions. Multi-source occurrence data and covariates from 3 global coupled climate models (CMCC-ESM2, FIO-ESM-2-0, MIROC-ES2L) and 2 shared socioeconomic pathways (SSP2-RCP4.5, SSP5-RCP8.5) were used in an ensemble modeling approach to predict distribution responses to climate and land use/land cover change. Our findings suggest that 3 of 4 focal species will experience distribution contraction, upslope elevational shifts in suitable conditions, and limited latitudinal and longitudinal shifts. We proposed two conservation strategies for directing limited resources to important stationary nonbreeding areas involving currently suitable landscapes and landscapes likely to be suitable in the future. Chapter 2 utilized a three-scale approach to delineate migratory airspaces, identify high-use stopover landscapes within migratory airspaces, and assess habitat and protected area characteristics within selected stopover landscapes. At the continental scale, we quantified autumn and spring migratory airspace in a modified three-stage framework by estimating migratory connectivity, developing randomized least-cost paths, and incorporating telemetry movement data to produce generalized additive mixed model prediction surfaces. At the regional scale, we predicted autumn and spring stopover landscapes for each focal species in an ensemble modeling framework with 2014 to 2023 eBird occurrence records and global covariates. The stopover landscapes were used in a summation exercise to identify two high-use stopover hotspots for case studies where we characterized habitat and protected area status at the local scale. Our findings at the continental scale suggest 3 of 4 focal species cross the Gulf of Mexico during both migratory seasons. At the regional scale, stopover landscapes in the eastern U.S. revealed an urban effect with other high-use stopover areas in riparian habitats and the Appalachian Mountains. Neotropical stopover landscapes were mostly associated with the highlands of Central America. At the local scale for the two case studies, Neotropical migrants used riparian forests at lower elevations in the Upper Mississippi River watershed, and evergreen broadleaf forests and croplands at lower elevations in coastal Honduras. However, our findings that many areas within stopover hotspots are unprotected draw attention to the need for coordinated conservation action in places that often overlap with multiple political jurisdictions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.048
GPT teacher head0.279
Teacher spread0.231 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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