Aggregation measures for pests - Taylor's power law and Iwao's patchiness

Aggregation measures for pest abundance have been widely used as summary statistics for aggregation levels as well as in designing surveillance protocols. Taylor’s power law and Iwao’s patchiness are two methods that are used most commonly. To be frank, I find the measures a little strange, particularly when they appear in papers as “cookbook statistics” (sometimes incorrectly presented) with little reference to any underpinning theory. But I managed to find some useful sources which helped to clarrify things for me.

Accessing SILO climatic data for Australia in R

This short post will describe how to access SILO climatic data for Australia. The data is available as both csv and json, but here we work with the two-dimensional csv format to make use of R’s powerful data table functionality. At the time of writing there were 18937 stations. We can access the metadata on each station (including location and years of available data), which will later become useful for selecting an appropriate weather station.

Prey population growth contrained by predators

What if a growing population gets eaten by another population? In a previous post I showed why we might expect a population to grow exponentially when not resource limited. We then extended this to the case where a population reaches some carrying capacity (using a simple and non-mechanistic logistic function). But population growth can also be curtailed through interactions with another species population, such as a predator. In my area of study, we deal with a lot of herbivorous pests of agricultural systems.

Modelling density dependent population growth (logistic growth)

Let’s derive some more population growth functions! The logistic population growth function In a previous post we derived a function for population growth based on the vital rates of reproduction and mortality. We assumed that the growth rate was constant with respect to the number of individuals in the population or (\frac{dt}{dN} = rN). This led to the unrealistic prediction that populations will grow indefinitely. Of course, populations will eventually run into resource problems (e.

Unconstrained population growth

Let’s derive some population growth functions! How to grow Populations grow. They grow positively, if rates of reproduction > mortality, or negatively, if reproduction < mortality. For an unconstrained population of size (N) this diffence in per capita reproduction and mortality is referred to as the intrinsic growth rate, (r) and has the units individuals per individual per time (N.N^{-1}.t^{-1}) . The value of (r) is a constant if the age-distribution is constant (e.