BCSS has run
nearly 40 Boot Camps since 2009, located in 11 different countries and
involving over 600 participants. You can see details on our
Past Workshops page.
We target field
researchers and decision-makers who know the biology, giving
them a huge advantage over those with qualifications in statistics. They are not mathematicians, so we
take a practical approach to problems with lots of activities and
What's different about wildlife?
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
➜ Bayesian approach.
- We deal directly with complex ecological systems with many
interacting factors; we can't do experiments
- We collect binary data (present/absent, dead/alive,...)
and count data, and have small samples; normality assumptions
➜ binomial and Poisson
- 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
➜ specific designs and
software (SPSS can't do it).
We focus on terrestrial vertebrates. Some of the techniques
discussed can be adapted for invertebrates or plants, or for aquatic
animals, but we have no expertise in those fields. Almost all our
examples are for populations of terrestrial vertebrates.
the approach to learning?
Learners need to develop their own intuitive understanding of new
concepts through exploration and experimentation, rather than
absorbing explanations. So there are plenty of practical activities
and a minimum of lecturing.
Throughout the workshop, we encourage participants to ask
questions and discuss ideas with others, and we provide many
opportunities for review and reinforcement.
Above all we believe that learning should be a positive
experience - it should be fun!
What do we do during the Boot Camp?
roll dice to simulate hearing frog calls and explore binary
data, and we count orangutan in 1x1 km plots to understand
the Poisson distribution. We explore these distributions in
on Day 1 we introduce R statistical software, and we use
it throughout the workshop.
is screened for a disease, and we introduce Bayes Rule to
make sense of the results - what's the probability of having
the disease if you test positive? We then apply this to the
orangutan counts to calculate an estimate and credible
interval for the total number of orangutan.
Age is a continuous variable, and we use the ages of
those in the room to talk about two-number summaries and
alternative measures of centre and spread.
Applying probability to continuous variables means using
probability density functions, and we use spinners to
explore this idea. We also see how a normal distribution can
result from adding up many small random effects.
simulate trapping squirrels in the forest by drawing chips
from a bag, and use Bayesian software to get a posterior
distribution for the mean weight of squirrels in the forest.
We see the typical output from Bayesian software and explore
the effect of using different prior distributions. Then we
apply these ideas to look at the difference in the size of
crabs between areas with no fishing and areas with fishing.
our focus is Bayesian methods, we devote a day to maximum likelihood estimation
(MLE) methods for
estimating parameter values. This is widely used in wildlife data analysis,
often as a preliminary way to explore the data before carrying out a Bayesian
experiment with statistical models, and see how Akaike's
Information Criterion (AIC) can help to select the best
model for making predictions.
generate binary data by throwing socks into boxes, noting
our success and seeing if that is affected by the distance
from the box or which hand we use. We develop a logistic
regression model and see how maximum likelihood methods work
in a spreadsheet. Then in R we try a range of models
and use AIC to compare them.
comparison of MLE with Bayesian analysis
for monitoring beluga whale populations shows how Bayesian
results can be used for decision making.
simulate surveys of marmosets to see (1) if the population
is declining and (2) if marmosets need big trees. This leads
to a discussion of survey design were we ask each
participant to put up a research question for discussion.
discussion includes sampling designs and the use of
simulations in R to answer questions about sample size and
begin the exploration of occupancy concepts by going to a
lawn and searching for ants. (We do have an indoor
alternative in case of no lawns or wet weather.) We continue
with the analysis in R of real data sets used in the seminal
work on occupancy estimation. We use both MLE and Bayesian
explicit capture-recapture (SECR) is introduced with
simulations of geckos moving through habitat dotted with
pit-fall traps. Although not all geckos are captured, the
analysis usually gives a good estimate of the total number.
Again this is followed up with MLE and Bayesian analysis of
classic data sets and a discussion of survey design for SECR.
Long-term mark-recapture allows survival to be
estimated. Although multi-year data sets are rare in the
region, we show what can be done with them. We begin with an
experiment simulating rat captures and also work with real
data sets in R.
The final day
is 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
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 two one-day breaks, so 12 days in all.
See the BCSS home page for dates and
venues of upcoming Boot Camps. If you want to help to organise a
Boot Camp in your own country, please