Glued Ecology Bob O'Hara University of Helsinki Finland

All models are wrong, but some are useful G.E.P. Box

Where Statistics Fits in Data Theory Scientific Knowledge

Where Statistics Fits In Data Theory Scientific Knowledge

Approaches to Statistical Inference

Graphics and Summary Stats

Traditional Statistical Models http://www.vias.org/science_cartoons/

Mechanistic Modelling http://www.vias.org/science_cartoons/

Which One To Use? http://www.vias.org/science_cartoons/

Whichever Works Best! http://www.vias.org/science_cartoons/

Graphics and Summary Stats Should be simple and easy to follow

Traditional Statistical Models Forces the analysis into (flexible) boxes Fitting and interpretation Well understood

Mechanistic Modelling Model behaviour can be difficult to understand Closer link to theoretical models

Community Dynamics Real work done by Crispin Mutshinda-Mwanza

All Trees Are Equal

... but should some be more equal than others?

Use Real Time Series Data

Discrete Time Neutral Theory n i,t – number of individuals of species i at time t N t – total community size at time t p i,t = n i,t / N t So,

In Other Words N t t p t N t t p t

Sampling Model True N Observed N q

Moth Data

Fit The Model...

Community Size Sampling Rate Immigration Rate

Impossible Results = possible range (0 to 1)

Directly fitting the model to data showed that it is wrong

What Next?

What Next? Add more mecahnisms!

The Environment

Competition

Other Interactions Between Individuals

A Gompertz Model log Abundances Growth rates Density Effects Environmental shocks

A

A Density Dependence

A Between-species Competition Density Dependence

e ~ N(0, V e )

Decompose the variation Interspecific variation: off-diagonal Intraspecific variation: leading diagonal Environmental variation

Data

Proportions of Environmental Variance Sampling variation also estimated

No Interspecific Interactions Largest Bayes Factor 1.25 <1: evidence against an effect 1 - 3: “Not worth more than a bare mention”

Environmental Correlations

Summary None Some Lots

What This Means

Models and data are brought closer together

How the data were collected is important

There are usually more things affecting the data than are in the model

The models that are fitted usually contain some traditional statistical components

Thoughts on Bayes

He's getting popular Data from Web of Science Number of papers with ”Bayes*”/Number of papers with ”Statisti*”

He's Flexible

But he can be difficult

What is more exciting is the models we can fit, not the methods we use to fit them