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Unifying facilitation and recruitment networks
Author(s) -
Alcántara Julio M.,
Garrido José L.,
MontesinosNavarro Alicia,
Rey Pedro J.,
ValienteBanuet Alfonso,
Verdú Miguel
Publication year - 2019
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.1111/jvs.12795
Subject(s) - facilitation , ecological network , ecology , ecological systems theory , community structure , stability (learning theory) , computer science , conceptual framework , network structure , plant community , environmental resource management , biology , sociology , neuroscience , distributed computing , ecological succession , environmental science , ecosystem , machine learning , social science
Ecological network studies are providing important advances about the organization, stability and dynamics of ecological systems. However, the ecological networks approach is being integrated very slowly in plant community ecology, even though the first studies on plant facilitation networks (FNs) were published more than a decade ago. The study of interaction networks between established plants and plants recruiting beneath them, which we call Recruitment Networks (RNs), can provide new insights on mechanisms driving plant community structure and dynamics. RN s basically describe which plants recruit under which others, so they can be seen as a generalisation of the classic FNs since they do not imply any particular effect (positive, negative or neutral) of the established plants on recruiting ones. RN s summarise information on the structure of sapling banks. More importantly, the information included in RN s can be incorporated into models of replacement dynamics to evaluate how different aspects of network structure, or different mechanisms of network assembly, may affect plant community stability and species coexistence. To allow an efficient development of the study of FN s and RN s, here we unify concepts, synthesise current knowledge, clarify some conceptual issues, and propose basic methodological guidelines to standardise sampling methods that could make future studies of these networks directly comparable.

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