As pest scientists, it is important to understand how crop seasonality overlaps with pest seasonality.
But vegetable and fruit seasonality in Australia is important for a few other reasons. In season produce is cheaper, fresher, with a lower carbon footprint, compared with imported produce, due to increased local availability. In addition, different areas in Australia have different fruit production outputs e.g. high melon production in New South Wales but not in Victoria, so it is important to now what is grown near you.
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.
A practical question in species surveillance is “How much search effort is required for detection?”. This can be quantified under controlled conditions where the number and location of target species are known and participants are recruited to see how success rate varies.
Let’s use an example of an easter egg hunt where the adult (the researcher) wants to quantify how much effort it takes a child (the participant) to find an easter egg.
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.
How many probability distributions can we generate by imagining simple natural processes? In this post I use a simple binomial random number generator to produce different random variables with a variety of distributions. Using built in probability densities functions in R, I show how the simulated data (plot bars) approach the exact probability density (plot lines) and provide an intuitive interpretation of model parameters of commonly encountered distributions.
A biological example “Nothing in Biology Makes Sense Except in the Light of Evolution” - Theodosius Dobzhansky, 1973