Literature DB >> 34755895

Macrophenology: insights into the broad-scale patterns, drivers, and consequences of phenology.

Amanda S Gallinat1, Elizabeth R Ellwood2,3, J Mason Heberling4, Abraham J Miller-Rushing5, William D Pearse6, Richard B Primack7.   

Abstract

Plant phenology research has surged in recent decades, in part due to interest in phenological sensitivity to climate change and the vital role phenology plays in ecology. Many local-scale studies have generated important findings regarding the physiology, responses, and risks associated with shifts in plant phenology. By comparison, our understanding of regional- and global-scale phenology has been largely limited to remote sensing of green-up without the ability to differentiate among plant species. However, a new generation of analytical tools and data sources-including enhanced remote sensing products, digitized herbarium specimen data, and public participation in science-now permits investigating patterns and drivers of phenology across extensive taxonomic, temporal, and spatial scales, in an emerging field that we call macrophenology. Recent studies have highlighted how phenology affects dynamics at broad scales, including species interactions and ranges, carbon fluxes, and climate. At the cusp of this developing field of study, we review the theoretical and practical advances in four primary areas of plant macrophenology: (1) global patterns and shifts in plant phenology, (2) within-species changes in phenology as they mediate species' range limits and invasions at the regional scale, (3) broad-scale variation in phenology among species leading to ecological mismatches, and (4) interactions between phenology and global ecosystem processes. To stimulate future research, we describe opportunities for macrophenology to address grand challenges in each of these research areas, as well as recently available data sources that enhance and enable macrophenology research.
© 2021 Botanical Society of America.

Entities:  

Keywords:  biogeography; ecological mismatch; ecosystem processes; herbarium specimens; macroecology; plant phenology; range limits; remote sensing

Mesh:

Year:  2021        PMID: 34755895     DOI: 10.1002/ajb2.1793

Source DB:  PubMed          Journal:  Am J Bot        ISSN: 0002-9122            Impact factor:   3.844


  1 in total

1.  Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.

Authors:  Rachel A Reeb; Naeem Aziz; Samuel M Lapp; Justin Kitzes; J Mason Heberling; Sara E Kuebbing
Journal:  Front Plant Sci       Date:  2022-01-17       Impact factor: 5.753

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.