Evidence map›Paper›PMID 34615895›Full record

ArticleScientific reports2021

An evaluation of multi-species empirical tree mortality algorithms for dynamic vegetation modelling.

Timothy Thrippleton, Lisa Hülsmann, Maxime Cailleret, Harald Bugmann

Abstract read
In one paragraph

Article in Scientific reports, 2021. 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

4 authors.

Timothy ThrippletonDepartment of Environmental Systems Science, Forest Ecology, Swiss Federal Institute of Technology (ETH Zurich), Universitätstrasse 16, 8092, Zürich, Switzerland. timothy.thrippleton@wsl.ch.
Lisa HülsmannTheoretical Ecology Lab, Faculty of Biology and Pre-Clinical Medicine, University of Regensburg, Universitätsstraße 31, 93053, Regensburg, Germany.
Maxime CailleretINRAE, Aix-Marseille University, UMR RECOVER, 3275 Route de Cézanne, CS 40061, Aix-en-Provence Cedex 5, France.
Harald BugmannDepartment of Environmental Systems Science, Forest Ecology, Swiss Federal Institute of Technology (ETH Zurich), Universitätstrasse 16, 8092, Zürich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tree mortality is key for projecting forest dynamics, but difficult to portray in dynamic vegetation models (DVMs). Empirical mortality algorithms (MAs) are often considered promising, but little is known about DVM robustness when employing MAs of various structures and origins for multiple species. We analysed empirical MAs for a suite of European tree species within a consistent DVM framework under present and future climates in two climatically different study areas in Switzerland and evaluated their performance using empirical data from old-growth forests across Europe. DVM projections under present climate showed substantial variations when using alternative empirical MAs for the same species. Under climate change, DVM projections showed partly contrasting mortality responses for the same species. These opposing patterns were associated with MA structures (i.e. explanatory variables) and occurred independent of species ecological characteristics. When comparing simulated forest structure with data from old-growth forests, we found frequent overestimations of basal area, which can lead to flawed projections of carbon sequestration and other ecosystem services. While using empirical MAs in DVMs may appear promising, our results emphasize the importance of selecting them cautiously. We therefore synthesize our insights into a guideline for the appropriate use of empirical MAs in DVM applications.

Indexed as

BiodiversityEcosystemForestsModels, TheoreticalTreesAlgorithmsClimateEurope

Identifiers

PMID34615895
PMCPMC8494886

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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.