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Record W3106435107 · doi:10.1002/ece3.6820

Climate change has different predicted effects on the range shifts of two hybridizing ambush bug (<i>Phymata</i>, Family <i>Reduviidae</i>, Order Hemiptera) species

2020· article· en· W3106435107 on OpenAlexafffund
Vicki Mengyuan Zhang, David Punzalan, Locke Rowe

Bibliographic record

VenueEcology and Evolution · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of VictoriaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaAmerican Museum of Natural History
KeywordsClimate changeHemipteraRange (aeronautics)EcologySpecies distributionBiologyRepresentative Concentration PathwaysHabitatClimate model

Abstract

fetched live from OpenAlex

Abstract Aim A universal attribute of species is that their distributions are limited by numerous factors that may be difficult to quantify. Furthermore, climate change‐induced range shifts have been reported in many taxa, and understanding the implications of these shifts remains a priority and a challenge. Here, we use Maxent to predict current suitable habitat and to project future distributions of two closely related, parapatrically distributed Phymata species in light of anthropogenic climate change. Location North America. Taxon Phymata americana Melin 1930 and Phymata pennsylvanica Handlirsch 1897, Family: Reduviidae, Order: Hemiptera. Methods We used the maximum entropy modeling software Maxent to identify environmental variables maintaining the distribution of two Phymata species, Phymata americana and Phymata pennsylvanica. Species occurrence data were collected from museum databases, and environmental data were collected from WorldClim. Once we gathered distribution maps for both species, we created binary suitability maps of current distributions. To predict future distributions in 2050 and 2070, the same environmental variables were used, this time under four different representative concentration pathways: RCP2.6, RCP4.5, RCP6.0, and RCP8.5; as well, binary suitability maps of future distributions were also created. To visualize potential future hybridization, the degree of overlap between the two Phymata species was calculated. Results The strongest predictor to P. americana ranges was the mean temperature of the warmest quarter, while precipitation of the driest month and mean temperature of the warmest quarter were strong predictors of P. pennsylvanica ranges. Future ranges for P. americana are predicted to increase northwestward at higher CO2 concentrations. Suitable ranges for P. pennsylvanica are predicted to decrease with slight fluctuations around range edges. There is an increase in overlapping ranges of the two species in all future predictions. Main conclusions These evidences for different environmental requirements for P. americana and P. pennsylvanica account for their distinct ranges. Because these species are ecologically similar and can hybridize, climate change has potentially important eco‐evolutionary ramifications. Overall, our results are consistent with effects of climate change that are highly variable across species, geographic regions, and over time.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0020.000

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 head0.033
GPT teacher head0.221
Teacher spread0.188 · 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 designSimulation or modeling
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

Citations9
Published2020
Admission routes2
Has abstractyes

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