I don’t know why, but it took me a little while to properly make sense of these diagnostics, so I wanted to develop a very simple illustration of the logic behind these concepts. ROC stands for Receiver Operating Characteristics, while AUC is the area under this curve, which is used as a metric for model performance in a classification problem. Perfomance is measured as the ability to maximise true positives, while minimising false positives.
Soil structure and health is critical to water availability, nutrient cycling, and plant productivity. By extension, soils will have strong associations with invertebrate community assemblage, diversity, and abundance and is thus a invertebrate science.
Modern spatial datasets on soil are available to help us intergrate variation in soil characteristics in the prediction of invertebrate processes. In Austalia, one of the newer soil data sets is Soil and Landscape grid of Australia.
Proportions are a funny thing in statistics. Some people just seem to love percentages. But there is a dark side to modelling a response variable as a percentage.
For example, I might be tempted to fit a linear model to mortality data on some insects exposed to heat stress for some time. To prove the point I will simulate some data.
library(tidyverse) time = rep(0:9, 10) n = 100 a = -1 b = 0.
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.