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"Boot Camp" in Wildlife Statistics

 

WCS Malaysia program has regularly run statistics workshops for their own staff, and the "Boot Camp" aims to make this training available to people in other organisations. We have run 24 sessions to date, in Kuala Lumpur, Kuching, Kota Kinabalu, Vientiane (Lao PDR), Astana (Kazakhstan), Bangkok (Thailand), Phnom Penh (Cambodia) and Khulna (Bangladesh).

The content has evolved since the first Boot Camps in 2007, and is still evolving: starting in July 2015, we introduced Bayesian methods on the second day. However, the Boot Camp approach to learning stats is the same.


What's different?

Why hold a special workshop for wildlife study design and data analysis? What's different from normal biostatistics?

  • Our work is relevant to wildlife management, and we should produce results which are useful for decision-making.
     
  • We deal directly with complex ecological systems with many interacting factors; we can't do experiments.
     
  • We collect binomial data (present/absent, dead/alive,...) and count data, and have small samples. Normality assumptions rarely apply.
     
  • Our results are affected by the data collection process: we rarely detect all the animals or species present and must incorporate detection probability in the analysis.

What does it cover?

We will begin with the basics:

  • What is statistics all about? - the nature of statistical inference.
  • Binomial data (present/absent, dead/alive, ...) and count data.
  • The concept of "sampling error" and uncertainty about estimates.
  • The use of R statistical software for basic analysis.

Then we'll introduce Bayesian methods of dealing with uncertainty.

  • Using "probability" to quantify uncertainty.
  • Combining prior information with the results of our study using Bayes Rule.
  • Bayesian estimates and credible intervals for binomial, count, and continuous data.
  • Using random samples to describe a probability distribution, and an overview of methods used by Bayesian software.
  • Using the output of a Bayesian analysis as the basis for decision making.

Next we move on to Information Theoretic (IT) methods developed in the last few decades, which are widespread in ecology: the software packages PRESENCE, DISTANCE, 'secr' and MARK all make use of IT methods.

  • The concept of "likelihood", and maximum likelihood estimation of parameters.
  • Ecological models: turning hypotheses into mathematical equations which predict the observations we should get.
  • Using AIC (Akaike's Information Criterion) to compare models and to choose the most useful.

A lot of information is available on designing experiments (in particular on power and sample sizes), but in wildlife biology we rarely have the chance to experiment. Designing good observational studies is more complicated.

  • Refining the research question: point estimate, relationship or trend.
  • Sampling: avoiding bias and pseudoreplication; sampling strategies; temporal and spatial sampling.
  • Pilot studies and simulations to refine study design.
  • Data management

Once all this background has been covered, we will get on to specific wildlife variables:

  1. occupancy from "presence/absence" data,
  2. density from spatially explicit mark-recapture (SECR) data,
  3. survival and other demographic parameters from mark-recapture data.

For each of these, we will run simple analyses in R for simulated data and for a real data set, using both frequentist and Bayesian approaches. We talk about study design requirements, and review some of the more advanced analyses available.

The final day will be devoted to topics requested by participants, eg, review of key basic topics, more on advanced analyses, or discussion of participants' own projects.

What language will be used?

The Boot Camp is conducted in English. The aim is to provide participants with a starting point to follow up on their own the specific kinds of study design and data analysis needed for their own research. The resources for further study and the software manuals are all in English. So it's important to become familiar with the English terminology.

Sometimes we do pause and explain specific concepts in the local language if necessary.

Who should attend?

The workshop is aimed at science graduates who are involved in field-work in conservation or wildlife management, or who use the results of such field work. No previous knowledge of statistics is needed, ie, we'll assume you've forgotten the stats you learnt at university!

Participants should have a background in field biology, as that's where our examples come from.

We will assume familiarity with the use of computers - and in particular spreadsheets - and we'll ask you to bring a notebook computer to the course.

When and where?

The workshop covers ten days, with a break of one or two days, so 11 or 12 days in all.

See the BCSS home page for dates and venues of upcoming Boot Camps.


 

Page updated 18 Dec 2015 by Mike Meredith