I have known for a while that functional traits are at the core of ecology. Unfortunately, for most of my experiments I focus solely on biomass and abundance. However, at ESA this year I noticed many researchers who measured community abundance and used a secondary database such as TRY for extracting functional traits per plots. This is a great re-purposing of already collected data to be reused to answer different questions. I am definitely going to consider analyzing data sets I currently have the species composition for to answer questions about functional diversity rather than typical species richness.
There was a considerable amount of NMDS usage this year, furthering that ecological analyses are becoming more complicated and requiring ordinations. Although ecologists may love NMDS, there is a preference in the statistical world to avoid it. I won’t get into the pros and cons here, but I believe ordinations such as PCoA, CA and RDA can accomplish the same as NMDS with less limitations. I will need to explore this further but I found this document a good starting point. Permuted ANOVAs were also another particularly popular statistical test and one I need to find a good R package for.
Another great talk from the conference discussed the advantages of the Negative Binomial distributions particularly for species abundance distributions. The talk discussed the tendency to fit discrete count data into log-normal distributions or Poisson. Often these distributions do not fit the data, while negative binomial does. The author A. Rominger promised me that he would be publishing an article detailing this commonality later in the year. I will hold him to that! In the mean time, here is an R package that he developed for species abundance distributions and the negative binomial distribution called pika.
Species distribution modeling
I had the opportunity to sit in on a talk by C. Merow that discussed used “expert maps” to further refined predictions of species distributions. It was a great talk and introduced a new concept to refine species predictions. Using the Map of Life (MOL), ecologists create species boundary maps that are would go into an SDM. The maps are compatible with MaxEnt and were shown to be effective at better predicting species occurrence. While a much better technique, I think the major limitation is that specific species will not have these maps delineated yet. However, until MOL catches up, big picture questions can be addressed with a greater degree of accuracy. The talk also reminded me I need to learn more Point Process Poisson modeling
The other instance SDMs that I thought was extremely informative was by Leung discussing co-occurrence models for invasion. This models have strong similarities with our work except instead of testing how an invasive species co-occurs with native species, we are trying to determine how a facilitating benefactor species interacts with neighbouring species. The way this is done is either using a proxy measurement or by using multi-species models. The example Leung used for a proxy measurement was boat movement in Ontario as a function of invasibility for a particular non-native species. I though this was a great idea and something I will consider for my next SDM experiment. The multi-species models is something that was a bit more complicated and that I will need to explore further.
Overall ESA went well! The attendance appeared a bit lower than last year, but I felt I was still super busy. It was a great experience and I learned a lot more that the highlights I am listing here. We also received some great feedback on micronet on how to improve it and I go some ideas to develop an R package that hopefully satisfies everyone’s micro-environmental challenges. A full list of my participation at ESA below: