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Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web

Despite intensive research, predicting pairwise species associations in pollination networks remains a challenge. The morphological fit between flowers and pollinators acts as a filter that allows only some species within the network to interact. Previous studies emphasized the depth of floral tubes...

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Autores principales: Ornai, Alon, Keasar, Tamar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694153/
https://www.ncbi.nlm.nih.gov/pubmed/33114435
http://dx.doi.org/10.3390/plants9111432
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author Ornai, Alon
Keasar, Tamar
author_facet Ornai, Alon
Keasar, Tamar
author_sort Ornai, Alon
collection PubMed
description Despite intensive research, predicting pairwise species associations in pollination networks remains a challenge. The morphological fit between flowers and pollinators acts as a filter that allows only some species within the network to interact. Previous studies emphasized the depth of floral tubes as a key shape trait that explains the composition of their animal visitors. Yet, additional shape-related parameters, related to the handling difficulty of flowers, may be important as well. We analyzed a dataset of 2288 visits by six bee genera to 53 flowering species in a Mediterranean plant community. We characterized the plant species by five discrete shape parameters, which potentially affect their accessibility to insects: floral shape class, tube depth, symmetry, corolla segmentation and type of reproductive unit. We then trained a random forest machine-learning model to predict visitor identities, based on the shape traits. The model’s predictor variables also included the Julian date on which each bee visit was observed and the year of observation, as proxies for within- and between-season variation in flower and bee abundance. The model attained a classification accuracy of 0.86 (AUC = 0.96). Using only shape parameters as predictors reduced its classification accuracy to 0.76 (AUC = 0.86), while using only the date and year variables resulted in a prediction accuracy of 0.69 (AUC = 0.80). Among the shape-related variables considered, flower shape class was the most important predictor of visitor identity in a logistic regression model. Our study demonstrates the power of machine-learning algorithms for understanding pollination interactions in a species-rich plant community, based on multiple features of flower morphology.
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spelling pubmed-76941532020-11-28 Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web Ornai, Alon Keasar, Tamar Plants (Basel) Article Despite intensive research, predicting pairwise species associations in pollination networks remains a challenge. The morphological fit between flowers and pollinators acts as a filter that allows only some species within the network to interact. Previous studies emphasized the depth of floral tubes as a key shape trait that explains the composition of their animal visitors. Yet, additional shape-related parameters, related to the handling difficulty of flowers, may be important as well. We analyzed a dataset of 2288 visits by six bee genera to 53 flowering species in a Mediterranean plant community. We characterized the plant species by five discrete shape parameters, which potentially affect their accessibility to insects: floral shape class, tube depth, symmetry, corolla segmentation and type of reproductive unit. We then trained a random forest machine-learning model to predict visitor identities, based on the shape traits. The model’s predictor variables also included the Julian date on which each bee visit was observed and the year of observation, as proxies for within- and between-season variation in flower and bee abundance. The model attained a classification accuracy of 0.86 (AUC = 0.96). Using only shape parameters as predictors reduced its classification accuracy to 0.76 (AUC = 0.86), while using only the date and year variables resulted in a prediction accuracy of 0.69 (AUC = 0.80). Among the shape-related variables considered, flower shape class was the most important predictor of visitor identity in a logistic regression model. Our study demonstrates the power of machine-learning algorithms for understanding pollination interactions in a species-rich plant community, based on multiple features of flower morphology. MDPI 2020-10-24 /pmc/articles/PMC7694153/ /pubmed/33114435 http://dx.doi.org/10.3390/plants9111432 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ornai, Alon
Keasar, Tamar
Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web
title Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web
title_full Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web
title_fullStr Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web
title_full_unstemmed Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web
title_short Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web
title_sort floral complexity traits as predictors of plant-bee interactions in a mediterranean pollination web
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694153/
https://www.ncbi.nlm.nih.gov/pubmed/33114435
http://dx.doi.org/10.3390/plants9111432
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