Third Joint APERU/RSS Highland Local Group Meeting:
Statistical Methods in Population Ecology
 
Speakers:
Ottar N. Bjornstad (Penn State University, USA):
The dynamics of spatially-extended populations: spatial synchrony and
spatial correlation functions
 
Len Thomas (University of St Andrews):
Use of sequential Monte-Carlo methods to fit and compare models for the
dynamics of wild animal populations.
 
Venue: Zoology Lecture Theatre, Zoology Building, Aberdeen University
(see http://www.abdn.ac.uk/central/vcampus/ for directions)
Time: 4 - 6pm on Tuesday, 16 November
 
Timetable
 
4.00 Ottar Bjornstad
4.50 tea / coffee
5.05 Len Thomas
 
Ottar N. Bjornstad ( Penn State University, USA):
The dynamics of spatially-extended populations: spatial synchrony and
spatial correlation functions
 
Spatiotemporal dynamics can be understood by considering how local
interactions affect patterns of spatial correlation (and
cross-correlation), and how these in turn affect local dynamics. This
feedback holds the key to understand (i) spatial synchrony, (ii) local
dynamics and (iii) regional persistence. In particular, the local
dynamics of consumer-resource systems are often unstable. Because of the
inherent local instability, mobility introduce spatially transient
associations. Persistence is achieved though spatial interactions. In
this talk I focus on spatial correlation functions and how these relate
to pattern and process. I subsequently outline theoretical predictions
about correlation functions and cross-correlation functions. The
correlation functions can be estimated from data. I discuss theory and
data with respect to a range of case-studies.
 
 
 
Len Thomas (University of St Andrews), John Harwood, Stephen T. Buckland
and Ken B. Newman:
Use of sequential Monte-Carlo methods to fit and compare models for the
dynamics of wild animal populations.
 
In a previous talk to this group, Steve Buckland showed how state-space
models are a convenient and flexible framework for specifying stochastic
models for the dynamics of wild animal populations and for the data
available about these populations. We briefly review this work in the
context of a spatially explicit model for the population of British grey
seals, for which the available data include estimates of numbers of pups
born each year in each breeding colony.
 
Specifying these models is relatively easy, but fitting them is not. A
number of techniques are available including Markov chain Monte-Carlo,
Kalman filtering and sequential Monte-Carlo particle filtering (also
called sequential importance sampling). We give an intuitive
introduction to sequential Monte-Carlo methods and illustrate their
application to the seal model. We also show how these methods can be
used in a model selection problem, where the goal is to determine
whether culls of seals around salmon farms could be causing the recent
levelling-off in seal counts in some areas.
 
References
 
· Thomas, L., S.T. Buckland, K.B. Newman & J. Harwood. In press.
A unified framework for modelling wildlife population dynamics.
Australian and New Zealand Journal of Statistics.
· Newman, K.B., S.T. Buckland, S.T. Lindley, L. Thomas & C
Fernández. In press. Hidden process models for animal population
dynamics. Ecological Applications.
· Thomas, L. & J. Harwood. 2004. Possible impacts on the
British grey seal population of deliberate killing related to salmon
farming. SCOS briefing paper 04/7.
http://smub.st-and.ac.uk/CurrentResearch.htm/scos.htm
· Buckland, S.T., K.B. Newman, L. Thomas & N.B. Koesters. 2004.
State-space models for the dynamics of wild animal populations.
Ecological modelling 171: 157-175.
 

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 Claus-D. Mayer | http://www.bioss.ac.uk
 Biomathematics & Statistics Scotland | email: [log in to unmask]
 Rowett Research Institute | Telephone: +44 (0) 1224 716652
 Aberdeen AB21 9SB, Scotland, UK. | Fax: +44 (0) 1224 716608
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