Dataset Information

Estimation of functional diversity and species traits from ecological monitoring data.

ABSTRACT: The twin crises of climate change and biodiversity loss define a strong need for functional diversity monitoring. While the availability of high-quality ecological monitoring data is increasing, the quantification of functional diversity so far requires the identification of species traits, for which data are harder to obtain. However, the traits that are relevant for the ecological function of a species also shape its performance in the environment and hence, should be reflected indirectly in its spatiotemporal distribution. Thus, it may be possible to reconstruct these traits from a sufficiently extensive monitoring dataset. Here, we use diffusion maps, a deterministic and de facto parameter-free analysis method, to reconstruct a proxy representation of the species' traits directly from monitoring data and use it to estimate functional diversity. We demonstrate this approach with both simulated data and real-world phytoplankton monitoring data from the Baltic Sea. We anticipate that wider application of this approach to existing data could greatly advance the analysis of changes in functional biodiversity.

SUBMITTER: Ryabov A

PROVIDER: S-EPMC9618138 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Estimation of functional diversity and species traits from ecological monitoring data.

Ryabov Alexey A Blasius Bernd B Hillebrand Helmut H Olenina Irina I Gross Thilo T

Proceedings of the National Academy of Sciences of the United States of America 20221018 43

The twin crises of climate change and biodiversity loss define a strong need for functional diversity monitoring. While the availability of high-quality ecological monitoring data is increasing, the quantification of functional diversity so far requires the identification of species traits, for which data are harder to obtain. However, the traits that are relevant for the ecological function of a species also shape its performance in the environment and hence, should be reflected indirectly in i ...[more]

PMID: 36256813

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Project description:Predicting the evolutionary potential of natural tree populations requires the estimation of heritability and genetic correlations among traits on which selection acts, as differences in evolutionary success between species may rely on differences for these genetic parameters. In situ estimates are expected to be more accurate than measures done under controlled conditions which do not reflect the natural environmental variance. The aim of the current study was to estimate three genetic parameters (i.e. heritability, evolvability and genetic correlations) in a natural mixed oak stand composed of Quercus petraea and Quercus robur about 100 years old, for 58 traits of ecological and functional relevance (growth, reproduction, phenology, physiology, resilience, structure, morphology and defence). First we estimated genetic parameters directly in situ using realized genomic relatedness of adult trees and parentage relationships over two generations to estimate the traits additive variance. Secondly, we benefited from existing ex situ experiments (progeny tests and conservation collection) installed with the same populations, thus allowing comparisons of in situ heritability estimates with more traditional methods. Heritability and evolvability estimates obtained with different methods varied substantially and showed large confidence intervals, however we found that in situ were less precise than ex situ estimates, and assessments over two generations (with deeper relatedness) improved estimates of heritability while large sampling sizes are needed for accurate estimations. At the biological level, heritability values varied moderately across different ecological and functional categories of traits, and genetic correlations among traits were conserved over the two species. We identified limits for using realized genomic relatedness in natural stands to estimate the genetic variance, given the overall low variance of genetic relatedness and the rather low sampling sizes of currently used long term genetic plots in forestry. These limits can be overcome if larger sample sizes are considered, or if the approach is extended over the next generation.

Dataset Information

Estimation of functional diversity and species traits from ecological monitoring data.

Publications

Estimation of functional diversity and species traits from ecological monitoring data.

Similar Datasets

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