Iwao’s patchiness regression through the origin: biological importance and efficiency of sampling applications

Autor: Michael J. Furlong, Kurt K. Benke, Edward K. Waters, Andrew J. Hamilton, James Grove
Rok vydání: 2013
Předmět:
Zdroj: Population Ecology.
ISSN: 1438-390X
1438-3896
DOI: 10.1007/s10144-013-0417-y
Popis: Iwao’s mean crowding-mean density relation can be treated both as a linear function describing the biological characteristics of a species at a population level, or a regression model fitted to empirical data (Iwao’s patchiness regression). In this latter form its parameters are commonly used to construct sampling plans for insect pests, which are characteristically patchily distributed or overdispersed. It is shown in this paper that modifying both the linear function and statistical model to force the intercept or lower functional limit through the origin results in more intuitive biological interpretation of parameters and better sampling economy. Firstly, forcing the function through the origin has the effect of ensuring that zero crowding occurs when zero individuals occupy a patch. Secondly, it ensures that negative values of the intercept, which do not yield an intuitive biological interpretation, will not arise. It is shown analytically that sequential sampling plans based on regression through the origin should be more efficient compared to plans based on conventional regression. For two overdispersed data sets, through-origin based plans collected a significantly lower sample size during validation than plans based on conventional regression, but the improvement in sampling efficiency was not large enough to be of practical benefit. No difference in sample size was observed when through-origin and conventional regression based plans were validated using underdispersed data. A field researcher wishing to adopt a through-origin form of Iwao’s regression for the biological reasons outlined above can therefore be confident that their sampling strategies will not be affected by doing so.
Databáze: OpenAIRE
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