Insights on plant interaction between dominating species from patterns of plant association: expected covariance of pin-point cover measurements of two species

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Christian Damgaard, Bodil K. Ehlers, Johannes C.G. Ransijn, Inger Kappel Schmidt, Jens-Christian Svenning

Abstract It has been suggested that in order to infer ecological processes from
observed patterns of species abundance we need to investigate the covariance in species abundance. Consequently, an expression for the expected covariance of pin-point cover measurements of two speciesisdeveloped.By comparing the observed covariance with the expected covariance we get a new type of information on the spatial arrangement of two species. Here the discrepancy between the observed and expected covariance may be thought of as a measure of the spatial configuration of the two species that may indicate underling ecological processes. The method is applied in a case study of Calluna vulgaris and Deschampsia flexuosa on dry heathland sites. The observed covariance of Calluna and Deschampsia at the level of the sites was positively and significantly correlated with the expected covariance. Negative covariance was observed
on sites where both Calluna and Deschampsia had a high cover, which is in gree-
ment with the notion that both species form distinct patches. Oppositely, at sites where both species have a low cover, we found that both the expected and observed covari-ance were positive. The proposed measure for the expected covariance of two species does capture information on the combined spatial configuration of the two species if the species are common. We show how this may be relevant for understanding the underlying ecological processes leading to the observed covariance.
Original languageEnglish
JournalEnvironmental and Ecological Statistics
Volume25
Issue number2
Pages (from-to)221-235
Number of pages15
ISSN1352-8505
DOIs
Publication statusPublished - 2018

    Research areas

  • The Faculty of Science - Covariance · Distribution of plant abundance · Ecological processes ·

ID: 189257503