Evidence map›Paper›PMID 37532828›Full record

ArticleScientific reports2023

Interannual climate variability improves niche estimates for ectothermic but not endothermic species.

Dirk Nikolaus Karger, Bianca Saladin, Rafael O Wüest, Catherine H Graham, Damaris Zurell, Lidong Mo, Niklaus E Zimmermann

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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.

Dirk Nikolaus KargerSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland. dirk.karger@wsl.ch.
Bianca SaladinSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Rafael O WüestSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Catherine H GrahamSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Damaris ZurellSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Lidong MoSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Niklaus E ZimmermannSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate is an important limiting factor of species' niches and it is therefore regularly included in ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values, yet many species experience wide climatic variation over their lifespan and within their geographical range which is unlikely captured by long-term means. Further, depending on their physiology, distinct groups of species cope with climate variability differently. Ectothermic species, which are directly dependent on the thermal environment are expected to show a different response to temporal or spatial variability in temperature than endothermic groups that can decouple their internal temperature from that of their surroundings. Here, we explore the degree to which spatial variability and long-term temporal variability in temperature and precipitation change niche estimates for ectothermic (730 amphibian, 1276 reptile), and endothermic (1961 mammal) species globally. We use three different species distribution modelling (SDM) algorithms to quantify the effect of spatial and temporal climate variability, based on global range maps of all species and climate data from 1979 to 2013. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). The mean performance of SDMs using only climatic means as predictors was TSS = 0.71 and AUC = 0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS = 0.74, mean AUC = 0.92), as does the inclusion of temporal variability (mean TSS = 0.80, mean AUC = 0.94). Including both spatial and temporal variability in SDMs shows the highest scores in AUC and TSS. Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in ectotherm groups such as amphibians and reptiles, while for endothermic mammals no such improvement was observed. These results indicate that including long term climate interannual climate variability into niche estimations matters most for ectothermic species that cannot decouple their physiology from the surrounding environment as endothermic species can.

Indexed as

Climate ChangeEcosystemTemperature

Identifiers

PMID37532828
PMCPMC10397316

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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.