Evidence map›Paper›PMID 36032955›Full record

ArticlePeerJ2022

Estimating uncertainty in density surface models.

David L Miller, Elizabeth A Becker, Karin A Forney, Jason J Roberts, Ana Cañadas, Robert S Schick

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

David L MillerCentre for Research into Ecological & Environmental Modelling and School of Mathematics & Statistics, University of St Andrews, St Andrews, Fife, Scotland.
Elizabeth A BeckerOcean Associates, Inc. under contract to Marine Mammal and Turtle Division, Southwest Fisheries Science Center National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, United States of America.
Karin A ForneyMarine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Moss Landing, CA, United States of America.
Jason J RobertsMarine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America.
Ana CañadasMarine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America.
Robert S SchickMarine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales

Indexed as

EcosystemFin WhaleAnimalsCetaceaComputer SimulationUncertaintyDensity surface modelsDistance samplingEnvironmental uncertaintyModel uncertaintySpatial modellingSpecies distribution modellingUncertainty quantification

Identifiers

PMID36032955
PMCPMC9415456

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.