Zero inflation in ecology

The April Eco-Stats Lab (Friday 24th, 2pm, Bioscience level 6) will be on zero inflated data in ecology. 

It's very common for ecological data to contain many zeros. To account for this we may need to:

1. Use zero inflated regression models

2. Do absolutely nothing (i.e. fit standard glm's)

In this lab we'll talk about why many zeros may occur in ecology, and the appropriate ways to account for them in your analysis.We will mostly use the pscl package in R.

Special Issue from Eco-Stats Symposium in MEE April 2015 issue

The Eco-Stats Symposium led to a series of papers which make up the April Special Issue of Methods in Ecology and Evolution.  Read the blog post on the topic for a little more info.  This includes contributions to species distribution modelling (point process models, measurement error, jointly estimating observer bias across multiple spp), multivariate analysis (a method for unconstrained ordination, trait analysis), diversity estimation (rarefaction for phylogenetic diversity, how to weight branches in functional diversity computation).

Planning is well underway for a follow-up Eco-Stats Conference in December 2015 at UNSW, featuring Otso Ovaskainen (Helsinki), Doug Wu (East Anglia), Jay ver Hoef (Alaska), Melodie McGeoch (Monash) and plenty more.  We'll send out a call for registrations and poster abstracts in the next month or so.

Species traits in multivariate analysis

The March Eco-Stats Lab (Friday 27th, 2pm, Bioscience level 6) will be on incorporating species traits into multivariate analysis (or equivalently, community level models), using some functions recently added to the mvabund package.

Often ecologists collect data (especially abundance or presence-absence data) simultaneously across many taxa, with the intention of studying what occurs where (and why). This tutorial focusses on the why - methods to help us move towards a functional explanation of community abundances. McGill et al (2006) and Shipley (2010) argue passionately for the need for this.

A common strategy in any field looking at "why" is to look for predictor variables that can explain the response. In the case of studying why some taxa are abundant at a site while others are not, the relevant predictors are species traits. These come in a matrix, different traits in different columns, different taxa in different rows.

We will explore methods for using such a matrix of traits in a multivariate analysis, using the mvabund package (version 3.10.1 or later).  Full details here:
             http://rpubs.com/dwarton/68823
We will also discuss the relationship of these methods with standard analysis of multivariate abundance data, SDMs, and Bill Shipley's CATS models.

*** Note you need mvabund 3.10.1 or later... ***

UPDATE (13/5/15): mvabund 3.10.4 is now available from CRAN.  It has a formula argument for more control of the traitglm model you fit, and composition and col.intercepts arguments to control whether or not you include row/column effects in the model (to focus on relative abundance), and a block resampling option on anova.traitglm (useful for example if you have repeated measures).

Congratulations Francis!

In the last few weeks Francis Hui has pulled off two big results:
- his PhD has been awarded, with his thesis going through without changes, but with plenty of complements.
- he was awarded the 2015 E&ERC prize for best student paper for the below article in the Journal of the American Statistical Association:
http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2014.951444

Well done Francis!  Super effort.  He is currently working on a post-doc at ANU with Alan Welsh and Samuel Mueller, developing new methodology for mixed models.

2014 Eco-Stats paper of the year

Time for our third annual Eco-Stats Paper of the Year awards.  Basically everyone in our group nominates the paper they were most impressed by this year, across the ecology and statistics literatures (although ideally somewhere between the two).  Then we have a vote for a winner.  A bit of a stats focus this year as it turns out, perhaps reflecting where our thinking has been recently.  Here is our shortlist:

Kitzes and Harte Beyond the species–area relationship: improving macroecological extinction estimates, Methods in Ecology and Evolution.  There is something of a disconnect between broad macroecological methods (like species-area relationships) and methods for modelling individual species (SDMs etc), and this paper tries to bridge that gap a little with some nice ideas.

Godoy, Kraft and Levine Phylogenetic relatedness and the determinants of competitive outcomes, Ecology Letters.  An interesting mix of empirical work and theory, meticulously collecting data on vital rates in a competition experiment to parameterise a fancy Chesson mathematical model for competition and look at what this implied concerning the pairwise competitive interactions between a set of 18 Californian grassland species.  Results question the widely held expectation that more distantly related species can more readily coexist.

Delaigle Nonparametric Kernel Methods with Errors-in-Variables: Constructing Estimators, Computing them, and Avoiding Common Mistakes, Australian and New Zealand Journal of Statistics. An insightful review of ways to handle measurement error in kernel estimation, with a section on common mistakes seen in the literature.  Aurore Delaigle (Melbourne) is one to watch on the Australian Statistics scene, a Peter Hall protege and winner of the 2012 Moran Medal.

Viladomat, .., Hastie Assessing the significance of global and local correlations under spatial autocorrelation: A nonparametric approach, Biometrics.  Not the first Stanford Stats entry, and not the last either!  A permutation test for association between two spatial variables, which works by smoothing and scaling values in the permuted variable in such a way that it preserves autocorrelation.  An original idea working away at the problematic area of design-based inference for spatial data.

And the two joint winners this year:
Kleiner et al A scalable bootstrap for massive data, JRSSB.  Bootstrapping target quantities in large datasets by breaking the data into groups and boostrapping the summary statistics for each group.  This idea makes bootstrapping doable for large datasets, if you have the right sort of statistic.

Lockhart, .., Tibshirani A significance test for the lasso, Annals of Statistics.  The LASSO is a big deal these days but a sticking point has always been inference about coefficients.  This paper proposes an amazingly simple significance test for coefficients entering the model.  This is destined to become a citation classic.

Feel free to share your own opinions on the highlights of 2014!

Congratulations, farewell and good luck to Francis Hui!

Congratulations to Francis - on submitting his PhD thesis earlier this month.  It is a really nice thesis with papers already accepted in Ecology, Methods in Ecology and Evolution, and the Journal of the American Statistical Association.  And a few more cracking papers in review.
Farewell - because he is off to a post-doc position at ANU in January, working on mixed models with Alan Welsh and Samuel Mueller.  Francis will be missed.  He is a friendly, thoughtful, energetic person, who has also been a big contributor in our group and beyond.  While keeping his thesis kicking along some other highlights include managing a number of productive collaborations with ecologists on the side, developing and presenting a few EcoStats labs (you can see these in his prev 2014 posts in this blog), and volunteering to teach a full day of introductory statistics material in the now-annual BEES postgrad workshop on intermediate statistics.  And co-ordinating Eco-Stats meetings.
 Good luck - well everyone could use a little luck every now and then.  But the systematic component of the model looks good... Francis has shown himself to be a pretty talented researcher and communicator, so we reckon he is well-placed for an exciting career in statistics methodology and its applications, especially (we hope!) in ecology.  Wishing you all the best, Francis!

(Generalised) linear (mixed) models with R2jags: an extremely brief primer...

The purpose of this lab (Friday 28th 2pm) is to provide a very basic primer on running basic lm, glm and glmms in a Bayesian framework using the R2jags package (and of course JAGS). As such, the goal is not to debate the relative merits of Bayesian vs frequentist approaches, but hopefully to demystify the fitting of Bayesian models, and more specifically demonstrate that in a wide variety of (more basic) use cases the parameter estimates obtained from the two approaches are typically very similar.

We will be attempting to reproduce a small element of the analysis from a recently published article in Journal of Ecology (for which all the data is available at datadryad.org).

Kessler, M., Salazar, L., Homeier, J., Kluge, J. (2014), Species richness-productivity relationships of tropical terrestrial ferns at regional and local scales. Journal of Ecology, 102: 1623-1633.

Code (jagstut.Rmd) and data (KessDiv.csv) available from dropbox or github. View the compiled html version here.

See you Friday,
Andrew