Th Complex Population Dynamics (CPD) working group has been running
since January 1996. We have had 5 meetings, and a full-time postdoc.
The goals of the group are twofold: to try to synthesize what we know
about population cycles, looking for broad patterns across
populations, and to bring two complementary techniques -- time series
analysis and mechanistic population modeling -- to bear on hypotheses
about the causes of cycles in particular populations. We have made
some progress, although the some aspects of the problem have turned
out to be harder than we anticipated!
At our initial meeting we developed a preliminary synthesis of the
causes of cycles in population models, and the relationships that the
models predict between time series statistics such as period and
life-history characteristics such as generation time.
We then turned to the question of how to quantitatively assess
competing hypotheses of the mechanisms that underly cycles. We
developed an approach comprising: (1) embody the hypotheses as
mathematical models; (2) use modern statistical techniques to estimate
parameters by fitting the models to time-series data; (3) generate
synthetic time series from the models; and (4) use time series
statistics to compare the synthetic time series to the actual time
series. We demonstrated this approach with a classic, and
well-understood, lab population, Nicholson's blowflies (Kendall et
al. in revision). We are now in the process of applying the approach to
several populations of forest defoliating insects (pine looper, larch
budmoth, and winter moth) and Scandinavian voles (Turchin and Ellner
in review). We are having some difficulty with the forest insects,
for many models make very similar predictions about the dynamics.
In the process of doing this work we have developed a number of
methodological tools for fitting models to time series data (Ellner et
al. 1997, Wood in prep). Some of these tools are quite sophisticated
and require close attention to technical detail.
More recently we have turned to the search for broad patterns in
population cycles. In collaboration with John Prendergast, whose
growing Global Population Dynamics Database already has over 700
population time series of suitable length, we have examined
latitudinal patterns in population cycles (Kendall and Prendergast in
prep) and are beginning to test the simple ideas from our synthesis of
We have also been analyzing data from two modern laboratory
populations that allow us to study critical issues in population
dynamics. The first is a spatially structured mite predator-prey
system by Arne Janssen, with complete censuses and locations of adults
every two days. This is the most detailed spatio-temporal population
dynamic data available, and allows us to explore how spatially
structured populations might be analyzed. We have analyzed the
empirical patterns of colonization (McCauley et al. in prep) and from
this will test a variety of hypotheses about dispersal and the ways in
which spatial structure promotes persistence.
The second study is a set of blowfly populations. These are similar
to Nicholson's experiments, with two important exceptions: there are
replicate populations, and there are complete life history
measurements of the flies before and after the the experiments. This
allows us to examine the between-replicate variation in the dynamics
and independently parameterize models to examine the dynamical effects
of life history evolution.
We have found our working group's size (8-10) and the length &
frequency of meetings (2-4 wks, twice a year) to be ideal for
productive interactions. The postdoc keeps things moving between
meetings, and brief, intense one-on-one collaborations between the
postdoc and one of the other working group members (either at NCEAS or
the other individual's home institution) can really get projects
The computer support and facilities are great!
PUBLICATIONS and MANUSCRIPTS
Ellner, S. P., B. E. Kendall, S. N. Wood, E. McCauley, and C. J.
Briggs. 1997. Inferring mechanism from time-series data: delay
differential equations. Physica D 110: 182-194.
Kendall, B. E., C. J. Briggs, W. W. Murdoch, P. Turchin, S. P. Ellner,
E. McCauley, R. M. Nisbet, and S. N. Wood. In revision.
Understanding complex population dynamics: a synthetic approach.
Kendall, B. E., and J. Prendergast. In preparation. Latitudinal
patterns in population cycles. To be submitted to Nature.
McCauley, E., B. Kendall, A. Janssen, P. Hosseini, C. Briggs,
S. Ellner, W. Murdoch, R. Nisbet, P. Turchin, and S. Wood. In
preparation. Inferring colonization process from population dynamics
in spatially-structured predator-prey systems. To be submitted to
Turchin, P., and S. P. Ellner. In review. Living on the
chaos: population dynamics of Fennoscandian voles. Proceedings of the
Royal Society of London.
Wood, S. N. In review. Extreme sensitivity to ecological
structure in biocontrol models. Submitted to Nature.
Wood, S. N. In preparation. Using trajectory-matching to
population dynamic models.
Wood, S. N. In preparation. Multiple smoothing parameter
Nov 1996. Inferring causes of population cycles by combining
mechanistic models and time-series analysis. NCEAS symposium on
Synthesis in Ecology: Applications, Opportunities, and Challenges,
Santa Barbara, CA (Bruce Kendall).
Dec 1996. Hierarchies of information from time series to models:
much data do we need? British Ecological Society annual meeting
Feb 1997. Estimating zooplankton vital rates by semi-mechanistic
model fitting. Americal Society of Limnology and Oceanography annual
meeting (Simon Wood).
Aug 1997. Inferring causes of population cycles by combining
mechanistic models and time-series analysis. Ecological Society of
America annual meeting, Albuquerque, NM (Bruce Kendall).
Arne Janssen (University of Amsterdam)
John Prendergast (Silwood Park)
Jens Roland (University of Alberta)
Robert Smith (University of Leicester)
UCSB grad students