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Record W7030340877

Monarch Butterfly (Danaus plexippus) Roost Site-Selection and Viability East of the Appalachian Mountains

2020· article· en· W7030340877 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalCommons (California Polytechnic State University) · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsMonarch butterflyHabitatFlywayContext (archaeology)PopulationHabitat destructionButterflyClimate changeEnvironmental niche modelling
DOInot available

Abstract

fetched live from OpenAlex

The Monarch butterfly is a flagship species and pollinator whose populations have declined by approximately 85% in the last two decades. Their largest population overwinters in Mexico, then disperses across the eastern United States and Canada during April to August. Between September-December, the butterflies return south using two migratory flyways, one spanning the central United States and another following the Atlantic coast. They fly during the day and at night roost in large groups. Roosting habitat is essential to the continuation of the Monarchs’ migration, however, threats such as anthropogenic habitat disturbance and climate change potentially endanger sustainability of these habitats. The criteria that Monarchs use to select specific roost sites, and the landscape context where those sites are found, have received little study. I developed ecological niche models for the Atlantic Flyway roost sites using modeling algorithms, citizen scientist observations, and environmental variables that are known to affect Monarchs in the adult stage prior to migration. MaxEnt variable jackknifing identified proximity to surface water (Euclidean Distance to Coast, Lakes, and Rivers), elevation (Above Mean Sea Level), and vegetative cover (Land and Crop Cover Type) as the most important criteria. My model predicts 2.6 million ha of suitable roosting habitat in the Atlantic flyway, with greatest availability along the Atlantic coastal plain and Appalachian Mountain ridges. These models can be used to help prioritize survey and conservation efforts for Monarchs in areas most suitable for their roosting. I developed two novel methods for validating the models: a smartphone application to engage citizen scientists, and peer-informed comparisons with Google Earth imagery. I conducted a vulnerability assessment of predicted suitable roost habitat, assessed the connectivity of the habitat with Morphological Spatial Pattern Analysis, used Zonation software to create a relative value ranking of the Atlantic flyway region for Monarch roost site conservation, and mapped areas of high conservation value in the flyway in regards to their current predicted vulnerability. Predicted suitable roost habitats occurring in coastal areas (1 million ha) were more vulnerable than those further inland (1.6 million ha), where they parallel the Appalachian Mountains chain. The majority (73%) of roosting habitat occurs within non-fragmented core patches, and many of these patches are within the average daily flight distance (45 km) of migrating Monarchs. Although the flyway contains 18.5 million hectares of lands in conservation management, there was little overlap between the areas of high conservation value for migrating Monarch butterflies and current conservation lands, with only 7% of predicted suitable roost habitat currently in conservation holdings. These findings suggest that conservation of the Monarch migratory phenomenon may benefit most from land management action outside of current conservation lands to promote roost habitat.

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 score0.123
Threshold uncertainty score0.245

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.201
Teacher spread0.182 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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