ORCID Profile
0000-0002-8003-7844
Current Organisation
Universidad de Jaén
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Publisher: Wiley
Date: 03-01-2022
DOI: 10.1002/ECY.3923
Abstract: Plant recruitment interactions (i.e., who recruits under whom) shape the composition, ersity and structure of plant communities. Despite the huge body of knowledge on the mechanisms underlying recruitment interactions between species, we still know little about the structure of the recruitment networks emerging in ecological communities. Modeling and analyzing the community-level structure of plant recruitment interactions as a complex network can provide relevant information on ecological and evolutionary processes acting both at the species and ecosystem levels. We report a data set containing 143 plant recruitment networks in 23 countries across five continents, including temperate and tropical ecosystems. Each network identifies the species under which another species recruits. All networks report the number of recruits (i.e., in iduals) per species. The data set includes >850,000 recruiting in iduals involved in 118,411 paired interactions between 3,318 vascular plant species across the globe. The cover of canopy species and open ground is also provided. Three s ling protocols were used: 1) The Recruitment Network (RN) protocol (106 networks) focuses on interactions between established plants ("canopy species") and plants in their early stages of recruitment ("recruit species"). A series of plots are delimited within a locality and all the in iduals recruiting and their canopy species are identified 2) The paired Canopy-Open (pCO) protocol (26 networks) consists in locating a potential canopy plant and identifying recruiting in iduals under the canopy and in a nearby open space of the same area 3) The Georeferenced plot (GP) protocol (11 networks) consists in using information from georeferenced in idual plants in large plots to infer canopy-recruit interactions. Some networks incorporate data for both herbs and woody species, while others focus exclusively on woody species. The location of each study site, geographical coordinates, country, locality, responsible author, s ling dates, s ling method and life habit of both canopy and recruit species are provided. This database will allow researchers to test ecological, biogeographical and evolutionary hypotheses related to plant recruitment interactions. There are no copyright restrictions on the data set please cite this data paper when using these data in publications.
Publisher: Wiley
Date: 31-12-2019
DOI: 10.1111/GCB.14904
Abstract: Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to bio ersity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on in idual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
No related grants have been discovered for Julio Manuel Alcántara Gámez.