The influence of environment on foliose lichen growth and its ecological significance

This chapter considers various aspects of the influence of the environment on the growth of foliose lichens and its significance in determining the ecology of individual species. Radial growth (RaG) and growth in mass of foliose lichens is influenced by climate and microclimate and also by substratum factors such as rock and bark texture, substrate chemistry, and nutrient enrichment. Seasonal fluctuations in growth, as measured by radial growth rate (RaGR) per month, often correlate best with average or total rainfall, the number of rain days, or rainfall in a specific season. Temperature has also been identified to be an important climatic factor influencing growth in some studies. Interactions between microclimatic factors and especially light intensity, temperature, and moisture status are important in determining differences in growth in relation to aspect and slope of the substratum. The physical and chemical nature of the substratum has a profound influence on the growth of foliose lichens. Hence, the effects of texture, porosity, rate of drying, and the physical changes of the substratum on growth are likely to influence lichen distributions. Bird droppings may influence growth and survival by smothering the thalli, altering the pH, or adding inhibitory and stimulatory compounds. Nitrogen and phosphate availability may also influence growth. Chemical factors also have an important influence on lichens of maritime rocks, the effect of salinity and calcium ions being of particular importance. Effects of environmental factors on growth influence the competitive ability of a lichen and ultimately its ecology and distribution.

Statnote 36: Do the data fit the Poisson distribution

In previous Statnotes, many of the statistical tests described rely on the assumption that the data are a random sample from a normal or Gaussian distribution. These include most of the tests in common usage such as the ‘t’ test ), the various types of analysis of variance (ANOVA), and Pearson’s correlation coefficient (‘r’) . In microbiology research, however, not all variables can be assumed to follow a normal distribution. Yeast populations, for example, are a notable feature of freshwater habitats, representatives of over 100 genera having been recorded . Most common are the ‘red yeasts’ such as Rhodotorula, Rhodosporidium, and Sporobolomyces and ‘black yeasts’ such as Aurobasidium pelculans, together with species of Candida. Despite the abundance of genera and species, the overall density of an individual species in freshwater is likely to be low and hence, samples taken from such a population will contain very low numbers of cells. A rare organism living in an aquatic environment may be distributed more or less at random in a volume of water and therefore, samples taken from such an environment may result in counts which are more likely to be distributed according to the Poisson than the normal distribution. The Poisson distribution was named after the French mathematician Siméon Poisson (1781-1840) and has many applications in biology, especially in describing rare or randomly distributed events, e.g., the number of mutations in a given sequence of DNA after exposure to a fixed amount of radiation or the number of cells infected by a virus given a fixed level of exposure. This Statnote describes how to fit the Poisson distribution to counts of yeast cells in samples taken from a freshwater lake.