references, abstracts and comments. Where there is no
abstract, an abstract has been written, where abstracts are too long
they have been abridged. Abstracts in languages other than English have
been translated into English. The comment is personal, it points out
errors and possible follow-ups, it is begun: CP:
A few lines commenting on fitness, state
and patch are quoted from Mangel and Clark 1988,
Bednekoff, P. A. & A. I.
Houston (1994): Dynamic models of mass-dependent
predation, risk-sensitive foraging, and premigratory fattening in
birds. Ecology 75: 1131-1140.
The effect of mass-dependent
risk-sensitive foraging choices of small birds in two ecological
contexts was examined using stochastic dynamic models. In the first
context, nonmigratory foraging, predation risk dependent on the bird's
body mass leads to risk-averse behavior in situations where
risk-neutral behavior would otherwise be expected and generally reduces
the amount of foraging and the level of reserves carried by foragers.
In the second context, premigratory foraging, mass-dependent predation
can lead to risk proneness during the buildup of reserves for
migration. Risk proneness is favored if birds can reduce mass and hence
predation after migration, thereby giving them an advantage to rapid
departure. Other advantages of early departure can also lead to
premigratory risk proneness but require more specific ecological
assumptions. The literature on risk sensitivity in amount of food
reward suggests a connection between migration and risk proneness on
negative energy budgets but more tests are needed before any
conclusions can be drawn.
Caraco, T. & S. L. Lima (1987): Survival, energy budgets, and foraging risk. In Commons, M. L., Kacelnik, A. & S. J. Shettleworth (eds.): Quantitative
analyses of behavior, Vol. VI, Foraging; pp. 1 - 21. Lawrence Erlbaum, London.
Clark, C. W. & R. W. Butler (1999): Fitness components of avian
migration: a dynamic model of Western Sandpiper migration. Evol. Ecol. Res. 1: 443 - 457.
Farmer, A. H. & A. H. Parent (1997): Effects of the landscape on
shorebird movements at spring migration stopovers. Condor 99: 698 - 707.
We monitored the inter-wetland movements
of 115 radio-tagged Pectoral Sandpipers (Calidris melanotos)
at three migration stopovers in the Great Plains of North America
during April and May from 1992 to 1995. While resident at a stopover,
individuals were very localized in their movements. Over 40 % of the
birds made no inter-wetland movements, and over 90 % of individuals
moved less than 10 km from their original release site. Characteristics
of wetlands where birds were released did not affect bird movements.
However, the structure of the surrounding landscape explained up to 46
% of variation in individual bird movements. As the distance between
wetlands decreased, and the proportion of the landscape composed of
wetlands increased, individual birds moved between wetlands more
frequently and moved longer distances from their release site. These
movement patterns indicate that a more connected landscape allows
shorebirds to exploit more feeding sites with reduced searchingcosts; a
result consistent with foraging theory. We estimate a degree of
landscape connectivity at which a wetland complex functions as a single
large wetland as measured by sandpiper feeding patterns. Our data
provide support for the idea that complexes of small, closely spaced
wetlands can be important migration stopovers and may have significant
Farmer, A. H. & J.A. Wiens
(1998): Optimal migration schedules depend on the landscape
and the physical environment: a dunamic programming view. J. Av. Biol.
29: 405 - 415.
We developed a dynamic state variable
model of individual migrating shorebirds for use in testing hypotheses
about spring migration strategies of the Pectoral Sandpiper Calidris
melanotos. We conducted model sensitivity analyses to
determine how predicted migration schedules might vary with respect to
the landscape and the physical environment.
In landscapes with closely spaced, high-quality stopovers,
female Pectoral Sandpipers can vary widely in their migration schedules
and still arrive on the breeding grounds early enough and with
sufficient energy reserves to achieve maximum reproductive success. Such a population might appear quite variable,
and show no stopover patterns, even if all individuals were making
optimal decisions. Latitudinal gradients in temperature
and photoperiod differentially affect a bird's energy budget as it
moves northward in the spring. Stopovers at more northerly locations
are associated with higher metabolic rates, lower food abundance in
early spring, and longer days for feeding. The optimal migration
schedule in these conditions can be quite different from that in a
homogeneous environment, and patterns observed in the field can be
misinterpreted if the environmental gradients are not considered.
The landscape and the physical environment shape migration schedules
and influence one's ability to interpret patterns observed at
stopovers. Modeling these factors may lead to new insights about
migration adaptations in heterogeneous environments.
Farmer, A. H. & J.A. Wiens (1999): Models and reality: time-energy trade-offs in Pectoral Sandpiper (Calidris melanotos). Ecology 80: 2566 - 2580.
We used a combination of modeling and field studies to determine the spring migration strategy of Pectoral Sandpipers (Calidris melanotos). We developed a dynamic programming model to predict patterns that should be detected along the migratory route if Pectoral Sandpipers use a
strategy of early arrival at the breeding grounds (time minimization) or arrival at the breeding grounds with excess energy reserves (energy maximization). The predictions were then compared to data collected at stopover sites in the mid-continent of North America and at the breeding grounds in Alaska over a 5-yr period (1992-1996).
During spring migration to their
Arctic breeding-grounds, Pectoral Sandpipers stop periodically to feed.
The length-of-stay of such stopovers, for both time minimizers and
energy maximizers, was predicted to vary inversely with date and body
fat, and to vary directly with invertebrate abundance. We observed
that: (1) length-of-stay was negatively correlated with capture date in
Missouri and Nebraska, but not in Texas; (2) length-of-stay was not
correlated with body fat at any site; and (3) length-of-stay was
positively related to invertebrate abundance at the Nebraska and
Missouri sites. As the population moves northwards in the spring, three
regional patterns are diagnostic of migration strategy. Length-of-stay
is predicted to be bimodal (energy maximizer) or constant (time
minimizer) with respect to latitude, but neither pattern was observed.
The migration window, or period of time
during which spring migrants occur, was predicted to decrease with
increasing latitude for time minimizers, a pattern that was seen for
both males and females. Body fat was predicted to increase with
latitude for energy maximizers, a pattern that was seen for females but
not for males.
The evidence suggests that males
and females differ in their spring migration strategies. Both sexes
attempt to arrive in the Arctic as early as possible after ice breakup
in the spring. Additionally, females gain significantly higher fat
loads than males (up to 60 % body fat for females) during migration,
and these energy reserves may later enhance female reproductive
success. However, females gained large fat loads only during 1993 and
1995, which had above normal spring precipitation along the migration
route. We believe that the correlation between female body fat and
precipitation reflects an abundance of high-quality stopover habitat
during wet springs. This
view is supported by model sensitivity analyses showing that the
spacing and quality of stopover habitat can strongly influence observed
migration patterns. Our results suggest the need to focus additional
research on the landscape-level features of the flyway through which
Houston, A. I., Clark, C. W.,
McNamara, J. M. & M. Mangel (1988): Dynamic
models in behavioural and evolutionary ecology. Nature 332: 29 - 34.
(Conclusions) The technique for analysing
behaviour we present here has the following advantages: (i) It takes
account of the state of the animal and how that state changes according
to the animal's action and the environment; (ii)it provides a common
currency for assessing behavioural choices in terms of overall fitness,
which can be used to analyse trade-offs between different actions;
(iii) it includes constraints on state variables and behaviour. Models
based on this technique often include a higher degree of biological
realism and lead to predictions and insights not provided by simpler
methods. Stochastic dynamic programming provides a method for finding
the optimal behaviour (or behaviours)within the dynamic framework. As a
computational technique, stochastic dynamic programming has certain
limitations, perhaps the most serious of which is the 'curse of
dimensionality' in which computational needs grow vastly as the number
of state variables increases. Hence, there is an upper limit to the
degree of biological complexity that the method can realistically
We have applied this dynamic approach to many other
situations, such as diel vertical migration of aquatic organisms,
optimal choice of prey items, diving behaviour of water birds, growth
and migration of salmon, sex change in slugs, web locations of spiders,
and food hoarding by small birds.
In our main examples, there are no interactions between
animals. We have mentioned that under some circumstances evolutionarily
stable dynamic strategies can be modelled in a relatively simple way.
In general, the development of such models is likely to encounter
conceptual and computational difficulties. Nevertheless, we believe
that this is a biologically important issue that deserves further
theoretical and empirical research, and that the technique of
stochastic dynamic programming will be a powerful tool in this
Houston, A. I. & J. M.
McNamara (1988): A framework for the functional
analysis of behaviour. Behav. and Brain Sciences 11: 117 - 154.
Houston, A. I., McNamara, J. M.
& J. Hutchinson (1993):
General results concerning the trade-offs between gaining energy and
avoiding predation. Phil. Trans. R. Sc. Lond. B341: 375-397.
animals can choose from a range of feeding options, often those options
with a higher energetic gain carry a higher risk of predation.
This paper analyses the optimal trade-off between food and predation.
We are primarily interested in how an animal's decision and its state
change over time. Our models are very general. They can be applied to
growth decisions, such as choice of habitat, in which case we might
consider how the state variable size changes over an animal's lifetime.
Equally, our models are applicable to short-term foraging decisions,
such as vigilance level, in which case we might consider how energy
reserves vary over a day. We concentrate on two cases: (i) the animal
must reach a fixed state, its fitness depending on when this is
attained; (ii) the animal must survive to a fixed time, its fitness
depending on its final state.
In case (i) minimization of
mortality per unit increase of state is optimal under certain baseline
conditions. In case (ii) behaviour is constant over time under baseline
conditions (the 'Risk-spreading Theorem'). We analyse how these
patterns are modified by complicating factors, e.g. time penalties,
premature termination of the food supply, stochasticity in food supply
or in metabolic expenditure, and state-dependence in the ability to
obtain food, in metabolic expenditure and in predation risk. From this
analysis we obtain a variety of possible explanations for why an animal
should reduce its intake rate over time (i.e. show satiation). We show
how earlier work can be viewed as special cases of our results.
Houston, A. I. & J. M.
Models of adaptive behaviour: an approach based on state. Cambridge Un.
M. & C. W. Clark
(1986): Towards a unified foraging theory. Ecology 332: 29 -
Mangel, M. & C. W. Clark
(1988): Dynamic modeling in behavioral ecology. Princeton Un.
(selection as a paradigm): If 'foraging theory,' understood in the
usual modern context, has a clear starting-point, it can probably be
traced to two papers which appeared together in teh American
Naturalist in 1966 (MacArthur and Pianka 1966, and Emlen
1966) on the optimal use of a patchy environment. In these papers the
concern is with behavior that in some sense optimizes a rate of energy
return to the foraging individual.
We will think of 'patch selection' in a much broader context - although
certainly the search for food is a crucial aspect of animal behavior -
in which 'patches' correspond to activity choices. These activity
choices are characterized by rewards to the individual (e.g. food
obtained or eggs laid), costs to the individual (e.g. time spent,
metabolic costs), and risks to tghe individual (e.g. the possibility of
predation). The problem of 'optimal patch use' thus reduces to the
following question: What is the best way to trade off the rewards with
the costs and the risks?(...)
In this section we discuss the patch selection problem using the
simplest possible state variable model. We use a single state variable
X(t) to characterize the state of the forager at time t. At this point,
the exact nature and units of X(t) do not need to be specified, but it
may be helpful to think of X(t) as the forager's energy reserves at
The principle of evolution by natural selection suggests that the
appropriate optimization criterion for behavioral models is lifetime
fitness. In this book we have not provided a 'grand' or 'unifying'
definition of fitness, but instead have shown how the definition of
fitness arises in a natural way in each instance, as a consequence of
the basic biology of the problem. Typically, we have adopted a
definition of lifetime fitness in terms of survival and
McNamara, J. M. & A. I.
Houston (1982): Short-term behaviour and life-time
fitness. In: McFarland, D. J. (ed.): Functional ontogeny, pp. 60 - 87.
McNamara, J. M. & A. I.
Houston (1986): The common currency for behavioral
decisions. Am. Naturalist 127: 358-378.
The study of optimal life histories
involves the maximization of lifetime fitness but usually ignores the
details of behavioral sequences. In contrast, the study of optimal
behavioral sequences usually looks at the details of a sequence in
isolation and not as a part of the whole life history of the animal.
The currency that is maximized is assumed to be related to lifetime
fitness, but this relationship is rarely explored. In this paper we
develop a common currency for behavioral decisions that is directly
related to lifetime fitness. The common currency makes it possible to
compare the benefits of qualitatively different behaviours. We show that many different costs can be used to
explain a given behavioral sequence. Most of these costs have no
biological interpretation. From these we single out a particular cost,
the canonical cost, which measures the reduction in fitness that
results from choosing a suboptimal action.
Our general framework is illustrated by the example of a small bird in
winter. We quantify the value of energy in terms of fitness and show
how this value depends on energy reserves and time of day. As a result
of this dependence, optimal foraging decisions depend on energy
reserves and time of day, as does the optimal trade-off between
foraging and looking around for predators.
McNamara, J. M. & A. I.
Houston (1987): A general framework for
understanding the effects of variability and interruptions on foraging
behavior. Acta Biotheoretica 36: 3-22.
McNamara, J. M., Merad, S.
& A. I. Houston (1991): Risk-sensitive
foraging for a reproducing animal. Anim. Behav. 41: 787 - 792.
A model in which a foraging animal can
reproduce if its energy reserves reach a critical level is presented.
The animal has the choice of two foraging options which have the same
mean net gain but differ in their variance. The policy that maximizes
expected lifetime reproductive success was found. The pattern of
risk-sensitive behaviour predicted by this model was compared with that
predicted by a similar model in which no reproduction occurs and the
optimality criterion is to minimize the probability of death. The two
models give different predictions. This serves to emphasize that there
is no single model of risk-sensitive foraging. Risk-sensitivity results
from a non-linear relationship between fitness and energy reserves.
This relationship provides a single principle that is common to all
models of risk-sensitive foraging. Not surprisingly, the form of the
relationship will depend on the biology of the animal under
McNamara, J. M. & A. I.
Houston (1992): Risk-sensitive foraging-a review of
the theory. Bull. Math. Biol. 54: 355-378.
K. M. & J. C.
Wingfield (1995): Spring and autumn migration in
Arctic shorebirds: same distance, different strategies. Amer. Zool. 35:
222 - 233.
The Arctic is an extremely
inhospitable region for most of the year, but during the summer months
it bursts with life. A major proportion of avian species nesting in the
Arctic are shorebirds. They migrate thousands of kilometers from their
wintering grounds to take advantage of abundant food resources each
summer and display a variety of migratory strategies. In an attempt to
classify this variation, not only between spring and autumn migration,
but within a migration, we present four categories. These relate to the
distance a species generally flies between stopovers: short distance
bout, intermediate distance bout, long distance bout, and combinations.
We then explore further differences between spring and autumn
migration. Spring migrants experience poor weather and decreased food
availability as they fly north. Many cope with huge flocks, whichserve
as protection from predators, but may also reduce foraging efficiency
and increase aggression. In contrast, autumn migrants generally
encounter favorable weather and ample food. Flock sizes are usually
smaller, thus foraging efficiency is higher and aggression
lower than during spring migration. Physiologically, spring migrants
are preparing for breeding and reproductive hormones are secreted. In
the Western Sandpiper (Calidris mauri),
luteinizing hormone levels are higher for spring than autumn migrants.
Late spring migrants have higher testosterone levels than either early
spring migrants or autumn migrants. Corticosterone levels are also
higher in spring vs. autumn migrants. Although spring and autumn
migrants travel similar distances, their strategies differ behaviorally
Real, L. A & T. Caraco (1986): Risk and foraging in stochastic environments. Ann. Rev. of Ecology and Systematics. 17: 371-390.
Stephens, D. W. & J. R. Krebs (1986): Foraging Theory. Princeton Un. Press.
To "Studies of migrating Dunlin Calidris alpina in the Sound area, S. Sweden: Introduction"
To "Risk-prone or risk-averse? Dunlin Calidris alpina migrating with and without moult-gaps in the Baltic area"
To "Phenology and biometry of Dunlin Calidris alpina migrating by way of the Sound area, S. Sweden"
To "Migrating Dunlin Calidris alpina in the Baltic area: the moult issue"
To "Wintering and spring staging Dunlin Calidris alpina in the south Baltic area"
To "Migratory progress of juvenile and adult Dunlin Calidris alpina from two perspectives: the
Baltic and the Waddensea"
To "Bill-length distributions in Dunlin Calidris alpina"
To the bill length account
About "adult buff" coverts
To the Meissner scale
Last addition (20 entries) 9.3.06.