Evidence map›Paper›PMID 38779431›Full record

ArticleConservation physiology2024

Physiologically informed organismal climatologies reveal unexpected spatiotemporal trends in temperature.

Aubrey Foulk, Tarik Gouhier, Francis Choi, Jessica L Torossian, Allison Matzelle, David Sittenfeld, Brian Helmuth

Abstract read
In one paragraph

Article in Conservation physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

7 authors.

Aubrey FoulkDepartment of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA.ORCID https://orcid.org/0009-0005-6369-5465
Tarik GouhierDepartment of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA.ORCID https://orcid.org/0000-0002-9537-7358
Francis ChoiDepartment of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA.ORCID https://orcid.org/0000-0003-4389-8087
Jessica L TorossianDepartment of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA.
Allison MatzelleDepartment of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA.
David SittenfeldCenter for the Environment, Museum of Science, Boston, MA 02114, USA.
Brian HelmuthDepartment of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA.ORCID https://orcid.org/0000-0003-0180-3414

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature-collected via remote sensing or weather stations-are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature ('organismal climatologies') during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or 'corrected' environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered.

Indexed as

Body temperatureforecastingheterogeneityintertidalquantile regressionthermal stressAbbreviations: RBM, robomussel; WS, weather station; CFSR, Climate Forecast System Reanalysis; OLS, ordinary least squares

Identifiers

PMID38779431
PMCPMC11109819

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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.