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Tree species co‐occurrence patterns change across grains: insights from a subtropical forest
Author(s) -
BarMassada Avi,
Yang Qingsong,
Shen Guochun,
Wang Xihua
Publication year - 2018
Publication title -
ecosphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.255
H-Index - 57
ISSN - 2150-8925
DOI - 10.1002/ecs2.2213
Subject(s) - co occurrence , interspecific competition , spatial ecology , ecology , biology , sampling (signal processing) , filter (signal processing) , artificial intelligence , computer science , computer vision
Co‐occurrence is a basic measure of spatial relationships between species. This commonly used measure has many benefits and limitations, yet a basic property that can strongly affect it has been overlooked. Co‐occurrence analysis is based on discrete sampling in space, and therefore, its grain size may affect the results and their interpretation, because species interactions and their environmental responses are scale‐dependent. We utilized a large dataset on tree species from a full‐stem mapped forest plot in China as a template for testing the effects of grain on species co‐occurrence patterns. We quantified co‐occurrence patterns for large trees and saplings in nested sampling plots with increasing radii and analyzed the effect of plot size on co‐occurrence. Co‐occurrence patterns varied greatly across grains. More than half of the species in large trees we analyzed had significantly non‐random co‐occurrence patterns at some grain. In contrast, saplings exhibited much fewer non‐random co‐occurrences. The proportion of segregated species pairs of large trees had a unimodal relationship with grain, whereas the proportion of aggregated species was positively related to grain. These patterns disappeared in saplings, suggesting that spatial interactions among trees are more prominent among larger individuals. Therefore, co‐occurrence patterns are scale‐dependent, and this scale dependency reflects a mixture of ecological (interspecific interactions, environmental responses) and statistical (sampling effects) processes. Our results suggest that insights from single‐grained studies cannot be generalized.

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