## Better use of transfer functions?

#### 16 December 2015 /posted in: Science

Transfer functions have had a bit of a hard time of late following Steve Juggins (2013) convincing demonstration that 1) secondary gradients can influence your model, and 2) that variation down-core in a secondary variable can induce a signal in the thing being reconstructed. This was followed up by further comment on diatom-TP reconstructions (Juggins et al. 2013), and not to be left out, chironomid transfer functions have come in from some heat, if the last (that I went to) IPS meeting was any indication. In a session at the 2015 Fall Meeting of the AGU, my interest was piqued by Yarrow Axford’s talk using chironomid temperature reconstructions, but not for the reasons you might be thinking.

Juggins, Steve. 2013. “Quantitative Reconstructions in Palaeolimnology: New Paradigm or Sick Science?” Quaternary Science Reviews 64 (0): 20–32. doi:http://doi.org/10.1016/j.quascirev.2012.12.014.

Juggins, Steve, N John Anderson, Joy M Ramstack Hobbs, and Adam J Heathcote. 2013. “Reconstructing Epilimnetic Total Phosphorus Using Diatoms: Statistical and Ecological Constraints.” Journal of Paleolimnology 49 (3). Springer Netherlands: 373–90. doi:http://doi.org/10.1007/s10933-013-9678-x.

## AGU Fall Meeting 2015

#### 14 December 2015 /posted in: Science

My poster, Rapid ecological change in lake ecosystems (GC13G-1236) in the Sedimentary records of threshold change (GC13G Moscone South Poster Hall 1340–1800, monday 14th December) describes some of my recent research into methods to analyse palaeoenvironmental time series from sediment cores. Using data from a varved lake, Baldeggersee, Switzerland, I use location scale generalised additive models to simultaneously model the mean (trend) and the variance of a time series of diatom counts. Wavelets were used to investigate further variation in species dynamics during the well-documented history of eutrophication at the lake.

## Are some seasons warming more than others?

#### 23 November 2015 /posted in: R

I ended the last post with some pretty plots of air temperature change within and between years in the Central England Temperature series. The elephant in the room1 at the end of that post was is the change in the within year (seasonal) effect over time statistically significant? This is the question I’ll try to answer, or at least show how to answer, now.

1. well, one of the elephants; I also wasn’t happy with the AR(7) for the residuals

## Climate change and spline interactions

#### 21 November 2015 /posted in: R

In a series of irregular posts1 I’ve looked at how additive models can be used to fit non-linear models to time series. Up to now I’ve looked at models that included a single non-linear trend, as well as a model that included a within-year (or seasonal) part and a trend part. In this trend plus season model it is important to note that the two terms are purely additive; no matter which January you are predicting for in a long timeseries, the seasonal effect for that month will always be the same. The trend part might shift this seasonal contribution up or down a bit, but all January’s are the same. In this post I want to introduce a different type of spline interaction model that will allow us to relax this additivity assumption and fit a model that allows the seasonal part of the model to change in time along with the trend.

1. here, here, and here

## User-friendly scaling

#### 08 October 2015 /posted in: R

Back in the mists of time, whilst programming early versions of Canoco, Cajo ter Braak decided to allow users to specify how species and site ordination scores were scaled relative to one another via a simple numeric coding system. This was fine for the DOS-based software that Canoco was at the time; you entered 2 when prompted and you got species scaling, -1 got you site or sample scaling and Hill’s scaling or correlation-based scores depending on whether your ordination was a linear or unimodal method. This system persisted; even in the Windows era of Canoco these numeric codes can be found lurking in the .con files that describe the analysis performed. This use of numeric codes for scaling types was so pervasive that it was logical for Jari Oksanen to include the same system when the first cca() and rda() functions were written and in doing so Jari perpetuated one of the most frustrating things I’ve ever had to deal with as a user and teacher of ordination methods. But, as of last week, my frustration is no more…

## ESA's publishing deal with Wiley Notes from ESA Council

#### 11 August 2015 /posted in: Science

One of the big announcements about the society made by ESA in the run up to the annual meeting in Baltimore this week was the news that ESA has chosen to partner with John Wiley & Sons as publisher of the society journals. At the time of the announcement few details about the deal or the process by which this decision was made were available. I was attending the ESA Council as the incoming Chair of the Paleoecology Section where some further details were provided and members of Council were able to ask questions about the deal. These are my notes from that meeting.

## My aversion to pipes

#### 03 June 2015 /posted in: R

At the risk of coming across as even more of a curmudgeonly old fart than people already think I am, I really do dislike the current vogue in R that is the pipe family of binary operators; e.g. %>%. Introduced by Hadley Wickham and popularised and advanced via the magrittr package by Stefan Milton Bache, the basic idea brings the forward pipe of the F# language to R. At first, I was intrigued by the prospect and initial examples suggested this might be something I would find useful. But as time has progressed and I’ve seen the use of these pipes spread, I’ve grown to dislike the idea altogether. here I outline why.

## Something is rotten in the state of Denmark

#### 02 June 2015 /posted in: R

On Twitter and elsewhere there has been much wailing and gnashing of teeth for some time over one particular aspect of the R ecosphere: CRAN. I’m not here to argue that everything is peachy — far from it in fact — but I am going to argue that the problems we face do not begin and end with CRAN or one or more of it’s maintainers.

## Drawing rarefaction curves with custom colours

#### 16 April 2015 /posted in: R

I was sent an email this week by a vegan user who wanted to draw rarefaction curves using rarecurve() but with different colours for each curve. The solution to this one is quite easy as rarecurve() has argument col so the user could supply the appropriate vector of colours to use when plotting. However, they wanted to distinguish all 26 of their samples, which is certainly stretching the limits of perception if we only used colour. Instead we can vary other parameters of the plotted curves to help with identifying individual samples.