Dataset Information

Not that kind of tree: Assessing the potential for decision tree-based plant identification using trait databases.

ABSTRACT: Premise:Advancements in machine learning and the rise of accessible "big data" provide an important opportunity to improve trait-based plant identification. Here, we applied decision-tree induction to a subset of data from the TRY plant trait database to (1) assess the potential of decision trees for plant identification and (2) determine informative traits for distinguishing taxa. Methods:Decision trees were induced using 16 vegetative and floral traits (689 species, 20 genera). We assessed how well the algorithm classified species from test data and pinpointed those traits that were important for identification across diverse taxa. Results:The unpruned tree correctly placed 98% of the species in our data set into genera, indicating its promise for distinguishing among the species used to construct them. Furthermore, in the pruned tree, an average of 89% of the species from the test data sets were properly classified into their genera, demonstrating the flexibility of decision trees to also classify new species into genera within the tree. Closer inspection revealed that seven of the 16 traits were sufficient for the classification, and these traits yielded approximately two times more initial information gain than those not included. Discussion:Our findings demonstrate the potential for tree-based machine learning and big data in distinguishing among taxa and determining which traits are important for plant identification.

SUBMITTER: Almeida BK

PROVIDER: S-EPMC7394705 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Not that kind of tree: Assessing the potential for decision tree-based plant identification using trait databases.

Almeida Brianna K BK Garg Manish M Kubat Miroslav M Afkhami Michelle E ME

Applications in plant sciences 20200731 7

<h4>Premise</h4>Advancements in machine learning and the rise of accessible "big data" provide an important opportunity to improve trait-based plant identification. Here, we applied decision-tree induction to a subset of data from the TRY plant trait database to (1) assess the potential of decision trees for plant identification and (2) determine informative traits for distinguishing taxa.<h4>Methods</h4>Decision trees were induced using 16 vegetative and floral traits (689 species, 20 genera). ...[more]

PMID: 32765978

Similar Datasets

Project description:Background and objectiveProfiles of people with chronic obstructive pulmonary disease (COPD) often do not describe treatable traits, lack validation and/or their stability over time is unknown. We aimed to identify COPD profiles and their treatable traits based on simple and meaningful measures; to develop and validate a decision tree and to explore profile stability over time.MethodsAn observational, prospective study was conducted. Clinical characteristics, lung function, symptoms, impact of the disease (COPD Assessment Test-CAT), health-related quality of life, physical activity, lower-limb muscle strength and functional status were collected cross-sectionally and a subsample was followed-up monthly over six months. A principal component analysis and a clustering procedure with k-medoids were applied to identify profiles. A decision tree was developed and validated cross-sectionally. Stability was explored over time with the ratio between the number of timepoints that a participant was classified in the same profile and the total number of timepoints (i.e., 6).Results352 people with COPD (67.4 ± 9.9 years; 78.1% male; FEV1 = 56.2 ± 20.6% predicted) participated and 90 (67.6 ± 8.9 years; 85.6% male; FEV1 = 52.1 ± 19.9% predicted) were followed-up. Four profiles were identified with distinct treatable traits. The decision tree included CAT (< 18 or ≥ 18 points); age (< 65 or ≥ 65 years) and FEV1 (< 48 or ≥ 48% predicted) and had an agreement of 71.7% (Cohen's Kappa = 0.62, p < 0.001) with the actual profiles. 48.9% of participants remained in the same profile whilst 51.1% moved between two (47.8%) or three (3.3%) profiles over time. Overall stability was 86.8 ± 15%.ConclusionFour profiles and treatable traits were identified with simple and meaningful measures possibly available in low-resource settings. A decision tree with three commonly used variables in the routine assessment of people with COPD is now available for quick allocation to the identified profiles in clinical practice. Profiles and treatable traits may change over time in people with COPD hence, regular assessments to deliver goal-targeted personalised treatments are needed.

Project description:The worldwide plant economic spectrum hypothesis predicts that leaf, stem, and root traits are correlated across vascular plant species because carbon gain depends on leaves being adequately supplied with water and nutrients, and because construction of each organ involves a trade-off between performance and persistence. Despite its logical and intuitive appeal, this hypothesis has received mixed empirical support. If traits within species diverge in their responses to an environmental gradient, then interspecific trait correlations could be weakened when measured in natural ecosystems. To test this prediction, we measured relative growth rates (RGR) and seven functional traits that have been shown to be related to fluxes of water, nutrients, and carbon across 56 functionally diverse tree species on (1) juveniles in a controlled environment, (2) juveniles in forest understories, and (3) mature trees in forests. Leaf, stem, and fine root traits of juveniles grown in a controlled environment were closely correlated with each other, and with RGR. Remarkably, the seven leaf, stem, and fine root tissue traits spanned a single dimension of variation when measured in the controlled environment. Forest-grown juveniles expressed lower leaf mass per area, but higher wood and fine root tissue density, than greenhouse-grown juveniles. Traits and growth rates were decoupled in forest-grown juveniles and mature trees. Our results indicate that constraints exist on the covariation, not just the variation, among vegetative plant organs; however, divergent responses of traits within species to environmental gradients can mask interspecific trait correlations in natural environments. Correlations among organs and relationships between traits and RGR were strong when plants were compared in a standardized environment. Our results may reconcile the discrepancies seen among studies, by showing that if traits and growth rates of species are compared across varied environments, then the interorgan trait correlations observed in controlled conditions can weaken or disappear.

Dataset Information

Not that kind of tree: Assessing the potential for decision tree-based plant identification using trait databases.

Publications

Not that kind of tree: Assessing the potential for decision tree-based plant identification using trait databases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets