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Assessing plant‐nutrient relationships in highly invaded Californian grasslands using non‐normal probability distributions
Author(s) -
GeaIzquierdo Guillermo,
Gennet Sasha,
Bartolome James W.
Publication year - 2007
Publication title -
applied vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.096
H-Index - 64
eISSN - 1654-109X
pISSN - 1402-2001
DOI - 10.1111/j.1654-109x.2007.tb00433.x
Subject(s) - species richness , abundance (ecology) , ecology , introduced species , native plant , grassland , nutrient , soil water , grazing , poisson distribution , agronomy , biology , environmental science , mathematics , statistics
Question: Is native species occurrence related to soil nutrients in highly invaded Californian annual grasslands? What is the best method to analyze this relationship, given that native species occur in very low numbers and are absent from many locations? Location: California, USA. Methods: We investigated the effects of soil characteristics and livestock grazing on native plant occurrence at 40 plots from six sites during the period 2003–2005. Low absolute cover (< 5.8%) of native species resulted in strongly skewed, zero‐inflated data sets. To overcome problems in the analysis created by non‐normality and correlations within plots, we used GLMs and GLMMs, either with a Poisson or a negative binomial distribution, to analyse native species richness and Nassella pulchra cover. Results:N. pulchra cover was strongly associated with low phosphorus in sandy soils, whereas native species richness was highest in soils with low available nitrogen (high C:N). Conclusion: Under current conditions, phosphorus seems to be a critical factor influencing abundance of N. pulchra . Low fertility soils may provide refugia for native species in highly invaded California grasslands as they may be below a threshold required for non‐native annuals to completely dominate. By using non‐normal distributions in linear models with random components, we report well fitted models with more accurately tested significant covariates.