The effects of seasonal and long-term climatic variability on Neotropical flowering phenology: An ecoinformatic analysis of aerial pollen data

Derek S. Haselhorst, David K. Tcheng, J. Enrique Moreno, Surangi W. Punyasena

Research output: Contribution to journalArticle

Abstract

The phenological behavior of tropical forests changes in response to seasonal, annual, and long-term variation in temperature, precipitation, and solar irradiance. However, detecting the respective influence of these variables is difficult due to the relatively small range of change that is observed in the tropics. Analysis is further constrained by the limited duration of many phenological datasets. To address these limitations, we developed a predictive ecoinformatic model using multivariate linear regression and slope correlation analysis that can uncover statistically significant biological responses within short, noisy ecological time series. Our approach correlates all possible combinations of climatic and taxonomic variables using a series of random determination trials on shuffled environmental data. Seasonal and annual fluctuations in temperature, precipitation, and sunlight were used to predict the reproductive response of each individual taxon. This predictive model was applied to two seasonally sampled aerial pollen records collected between 1996 and 2006 from two Panamanian forests, Barro Colorado Island and Parque Nacional San Lorenzo. Our results highlight the degree to which pollen output responds to fine-scale variability in climate. Our results lend support to the hypothesis that the pollen output of tropical species is diminished with prolonged periods of heavy rainfall and that pollen output is sensitive to small, seasonal increases in temperature. Our ecoinformatic approach can be expanded to other observational phenological datasets to better understand how communities will respond to climate change and our results demonstrate the ability of aerial pollen data to track long-term changes in flowering phenology.

Original languageEnglish (US)
Pages (from-to)54-63
Number of pages10
JournalEcological Informatics
Volume41
DOIs
StatePublished - Sep 2017

Keywords

  • Climate
  • Ecoinformatics
  • Ecology
  • Palynology
  • Phenology
  • Pollen

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Modeling and Simulation
  • Ecological Modeling
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

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  • Datasets

    Pollen and climate data from Haselhorst et al. (2017) Ecological Informatics: Table S2. Raw pollen counts and climatic data for each seasonal sampling period

    Haselhorst, D. S. (Creator), Tcheng, D. K. (Creator), Moreno, J. E. (Creator) & Punyasena, S. W. (Creator), University of Illinois at Urbana-Champaign, Jun 16 2017

    Dataset

    Model results from Haselhorst et al. (2017) Ecological Informatics: Table S3. Mean slope response for each predictive model used in the ecoinformatic analysis

    Haselhorst, D. S. (Creator), Tcheng, D. K. (Creator), Moreno, J. E. (Creator) & Punyasena, S. W. (Creator), University of Illinois at Urbana-Champaign, Jun 16 2017

    Dataset

    Pollen types from Haselhorst et al. (2017) Ecological Informatics: Table S1. Pollen types identified in the BCI and PNSL pollen rain data sets

    Haselhorst, D. S. (Creator), Tcheng, D. K. (Creator), Moreno, J. E. (Creator) & Punyasena, S. W. (Creator), University of Illinois at Urbana-Champaign, Jun 16 2017

    Dataset

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