Functional trait metrics are sensitive to the completeness of the species' trait data?

By R. Pakeman. 2014

125 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6d1a35c522ae1324a5569a48ef3e7cc58dfb3b11

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Tim Newbold
2. Z. Botta‐Dukát
3. G. C. Costa
4. Melinda D. Smith
5. E. Arets
6. A. Knapp
7. Lourens Poorter
8. A. Etard

In Ecology, Evolution, Behavior and Systematics

1. Z. Botta‐Dukát
2. Lourens Poorter
3. A. Csecserits
4. T. Rédei
5. G. Ónodi
6. Miklós Kertész
7. B. Lhotsky
8. B. Kovács

In Nature and Landscape Conservation

1. Tim Newbold
2. M. Peña-Claros
3. E. Arets
4. Lourens Poorter
5. Z. Botta‐Dukát
6. Melinda D. Smith
7. S. Collins
8. T. Ocheltree

Citations in the corpus

• Shifts in plant functional composition following long‐term drought in grasslands (2019, background): “Estimates of functional diversity are highly sensitive to missing trait data (Pakeman, 2014).”
• Abiotic and biotic drivers of biomass change in a Neotropical forest (2017, background): “The community mean trait values weighted by species’ basal area can be accurately determined if it is based on the species that together compose at least 80% of the abundance (Pakeman & Quested 2007), but a higher coverage is needed to accurately determine trait diversity (Pakeman 2014).”
• How much of the world is woody? (2014, methodology): “This sampling bias also affects many commonly used methods in ecological and evolutionary research (e.g. Ackerly 2000; Nakagawa & Freckleton 2008; Pennell & Harmon 2013; Pakeman 2014) in addition to its well-understood effects on conventional statistics.”
• Changes in assembly rules along a stress gradient from open dry grasslands to wetlands (2016, background): “Journal of Ecology © 2015 British Ecological Society, Journal of Ecology, 104, 507–517 Quested 2007; Pakeman 2014) the 3% missing data does not cause considerable difference between the estimated and the actual community level trait indices.”
• Imputation of missing data in life‐history trait datasets: which approach performs the best? (2014, background): “When data are MAR, deleting missing values can lead to misleading results in comparative studies or biased estimates of evolutionary parameters (Hadfield 2008; Gonz alez-Su arez, Lucas & Revilla 2012; Pakeman 2014).”
• Global gaps in trait data for terrestrial vertebrates (2020, background): “Biases and gaps in trait data could also affect studies in closely related fields, such as functional ecology [for instance, past studies have shown that functional diversity indices are sensitive to missing data (Májeková et al., 2016; Pakeman, 2014)] or community assembly (Perronne et al., 2017).”
• Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides (2019, background): “Trait selection becomes especially significant when considering the sensitivity of different functional diversity measures to trait selection (Pakeman 2014).”