[Swarm-Modelling] Two new papers

[Steve Railsback]

There are two new noteworthy papers that used individual-based 
ecological models.

Amano et al. is interesting as an example of developing "theory" for 
IBMs: contrasting alternative models of a key individual adaptive 
behavior by testing how well each, when implemented in an IBM, 
reproduces a variety of observations.

Goss-Custard et al. is interesting because they apply an IBM (which has 
been well-tested in previous publications) to a real-world management 
problem and test its predictions. They also provide an example of how 
such a mechanistically rich model allowed them to find a novel and 
efficient solution for mitigating the predicted impact.

The citations & abstracts are:

Amano, T., K. Ushiyama, S. Moriguchi, G. Fujita, and H. Higuchi. 2006. 
Decision-making in group foragers with incomplete information: test of 
individual-based model in geese. Ecological Monographs 76:601-616.

One important challenge of spatial ecology is to generate models linking 
individual behavior to population-level phenomena. Although animals 
often face great uncertainty regarding foraging patch quality, earlier 
models explaining the aggregation of animals have rarely specified how 
stable outcomes are achieved through individual decisions, especially 
under realistic assumptions for incompletely informed foragers. We 
developed a new foraging model that assumed a realistic decision-making 
rule for incompletely informed group foragers,and we tested its 
performance against existing models with different assumptions by 
comparing how well they reproduce the patterns observed in foraging 
White-fronted Geese (Anser albifrons ). The assumptions in each of the 
four compared models were:(1)incompletely informed foraging with 
benefits of group foraging,which uses the expected gain rates for making 
decisions on diet choice, patch departure, and flock joining; 
(2)incompletely informed foraging without benefits of group 
foraging,which uses the expected gain rates to determine the timing of 
patch departure but selects a new patch randomly; (3)completely informed 
foraging without benefits of group foraging, which simply selects the 
most profitable patches; and (4)completely informed foraging with 
benefits of group foraging,which selects the most profitable patches, 
considering benefits from the presence of conspecifics. The model that 
assumed incompletely informed foragers with bene .ts of group foraging 
was best in agreement with the observed patterns in all of the five 
spatial distribution and fat deposition parameters. The models that 
assumed no benefits of group foraging could not reproduce the observed 
seasonal variation in flock sizes, whereas the models with completely 
informed foragers overestimated the flock size as well as usage by the 
geese of alternative food and the fields near the roost. These results 
supported the idea that the geese can be assumed to use the expected 
gain rates for decision-making on diet choice, patch departure, and 
flock joining. Further, the incompletely informed foragers showed 
greater disparity in foraging performance among individuals. We discuss 
the necessity of assuming, when appropriate, that foragers have 
incomplete information on patch quality in models explaining spatial 
distribution and foraging success. We also make some references to the 
applicability of the presented model in other studies.

Goss-Custard, J., N. H. K. Burton, N. A. Clark, P. N. Ferns, S. 
McGrorty, C. J. Reading, M. M. Rehfisch, R. A. Stillman, I. Townend, A. 
D. West, and D. H. Worrall. 2006. Test of a behavior-based 
individual-based model: response of shorebird mortality to habitat loss. 
Ecological Applications 16:2215-2222.

In behavior-based individual-based models (IBMs),demographic functions 
are emergent properties of the model and are not built into the model 
structure itself,as is the case with the more widely used 
demography-based IBMs. Our behavior-based IBM represents the physiology 
and behavioral decision making of individual animals and, from that, 
predicts how many survive the winter nonbreeding season,an important 
component of fitness. This paper provides the first test of such a model 
by predicting the change in winter mortality of a charadriid shorebird 
following removal of intertidal feeding habitat,the main effect of which 
was to increase bird density. After adjusting one calibration parameter 
to the level required to replicate the observed mortality rate before 
habitat loss,the model predicted that mortality would increase by 3.65 %
which compares well with the observed increase of 3.17 %. The 
implication that mortality was density-dependent was confirmed by 
predicting mortality over a range of bird densities. Further simulations 
showed that the density dependence was due to an increase in both 
interference and depletion competition as bird density increased. Other 
simulations suggested that an additional area of mud flat, equivalent to 
only 10 % of the area that had been lost, would be needed by way of 
mitigation to return mortality to its original level. Being situated at 
a high shore level with the flow of water in and out impeded by inlet 
pipes, the mitigating mud flat would be accessible to birds when all mud 
flats in the estuary were covered at high tide, thus providing the birds 
with extra feeding time and not just a small replacement mud flat.
Apart from providing the first, and confidence-raising, test of a 
behavior-based IBM, the results suggest (1) that the chosen calibration 
procedure was effective; (2) that where no new fieldwork is required, 
and despite being parameter rich, a behavior-based IBM can be 
parameterized quickly (few weeks), and thus cheaply, because so many of 
the parameter values can be obtained from the literature and are 
embedded in the model; and (3) that behavior- based IBMs can be used to 
explore system behavior (e.g., the role of depletion competition and 
interference competition in density-dependent mortality).


Steve Railsback