Estimating Survival of Neotropical-Nearctic
Migratory Birds: Are They Dead or Just
Dispersed?
Matthew R. Marshall
R. Randy Wilson
Robert J. Cooper
Abstract—The most common method for estimating adult survival
in site specific demographic studies of Neotropical-Nearctic migratory bird populations is by measuring the return rate of marked
individuals. Return rate historically has been defined as the ratio of
resighted birds to the total number banded (i.e., with bands on) the
prior year, and has been used as a “minimum number known alive”
estimate of survival. Return rates potentially underestimate true
survival (the complement of mortality) for two reasons. First, not
every bird that returns to the study plot is actually encountered by
the field researcher, and second, not every bird that survives to the
next breeding season returns to the study plot. We use a branchingtree diagram to illustrate that the essential problem with return
rate methodology is that the fate of birds that are not resighted is
unknown. It is widely recognized that Cormack-Jolly-Seber based
analyses greatly improve “survival” estimates by incorporating the
probability of resighting a bird given that it is alive and present on
the study plot. However, these estimates will underestimate true
survival if birds disperse beyond the range of the resighting effort.
Because long-distance dispersal events are an important component of migratory bird ecology, we cannot estimate true survival
from return rate data until better information on dispersal distances and probabilities are collected. We discuss several conservation implications of underestimating survival, and suggest terminology that is potentially less confusing.
The decline of some Neotropical-Nearctic migratory
landbird populations has prompted a number of demographic studies that examine the possible factors contributing to the declines. Included among the broader issues on
which recent studies have focused are source-sink dynamics
(e.g., Donovan and others 1995a,b; Brawn and Robinson
1996; Robinson and Morse, this proceedings) and revealing
how and where populations are limited (e.g., Martin and
Finch 1995; Sherry and Holmes 1996). Similarly, most
authors in this section of these proceedings recommend that
demographic responses must be investigated if we are to
In: Bonney, Rick; Pashley, David N.; Cooper, Robert J.; Niles, Larry,
eds. 2000. Strategies for bird conservation: The Partners in Flight planning process; Proceedings of the 3rd Partners in Flight Workshop; 1995
October 1-5; Cape May, NJ. Proceedings RMRS-P-16. Ogden, UT: U.S.
Department of Agriculture, Forest Service, Rocky Mountain Research
Station.
Matthew R. Marshall and Robert J. Cooper, Warnell School of Forest
Resources, University of Georgia, Athens, Georgia 30602. R. Randy Wilson,
USGS Biological Resource Division, Patuxent Wildlife Research Center,
Mississippi Valley Research Field Station, 2524 South Frontage Road,
Vicksburg, Mississippi 39180.
USDA Forest Service Proceedings RMRS-P-16. 2000
understand the effects of management practices and other
perturbations on bird populations. To understand the demography of most populations of Neotropical-Nearctic migrants requires estimates of three parameters: Productivity
(defined as the average number of fledglings produced per
pair per year), annual survival rate of hatch year (HY) birds,
and annual survival rate of adult or after hatch year (AHY)
birds. A common approach to a demographic study is to
establish one or more study plots and estimate these three
parameters simultaneously for a particular local population. However, each of the three parameters needed for these
site-specific demographic analyses is difficult to obtain.
Data collection for productivity or fecundity (defined as
the average number of female fledglings produced per female per year) estimation is labor intensive, necessitating
finding and monitoring nests and following each nest attempt of individually marked adults through the nesting
season (Sherry and Holmes, this proceedings). Models now
exist that estimate seasonal fecundity based on the sample
of nests actually found and monitored without requiring a
marked population, provided certain life history parameters
are known (Pease and Grzybowski 1995).
The survival rate of HY birds is especially difficult to
estimate, because these birds seldom return to their natal
site to breed (Greenwood and Harvey 1982). This nataldispersal will continue to challenge attempts at estimating
annual survival for this age class until the ability to monitor
between-season movements is improved. As a result, researchers often have employed the largely untested assumption that juvenile survival is half that of adults (e.g., May
and Robinson 1985).
Adult survival rate therefore becomes an essential parameter to estimate, and has been estimated using various
approaches. The typical approach for local demographic
studies is to individually mark birds after capturing them
using mist nets. Targeted mist-netting can be used in conjunction with tape recordings and study mounts for the
capture of both males and females, while nets can be placed
at or on a nest site to capture nesting females that may not
respond to tape playbacks. Based on unique color markings,
birds are “resighted” rather than physically recaptured
during a subsequent breeding season (in contrast, general
mist-netting for all species, such as that performed in constant effort mist-netting [DeSante, this proceedings] does
not target individual birds for capture and relies upon
physical recaptures in a subsequent time period). The ratio
of resighted birds to the total number banded the prior year
can be used as a “minimum number known alive” estimate
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of survival, historically referred to as the “return rate.”
However, it is widely recognized that differences in the
ability to detect a given bird will vary with researcher effort,
resighting technique, and bird behavior, resulting in several
biases (Nichols 1986; Lebreton and others 1992; Martin and
others 1995). This is one reason why many researchers are
analyzing return rate data using extensions of the CormackJolly-Seber (CJS) approach (Cormack 1964; Jolly 1965;
Seber 1965) that incorporate the probability of resighting a
bird (Lebreton and others 1992). The objective of this approach is to arrive at the number of birds that were alive and
present on the study plot, but “missed” by the field researcher. If resighting probability is <1, that is, marked
birds that are alive and present on the study plot go undetected, return rate will underestimate the true survival rate
(Pollock and others 1990; Martin and others 1995).
Field researchers also have recognized that where a particular plot or sampling area is searched for marked birds,
the potential exists for birds to disperse from the study area.
That is, a marked bird that survives to the next breeding
season may not return to the study plot due to betweenseason breeding dispersal (Greenwood 1980). Numerous
between-season, long-distance dispersal events have been
recorded (Marshall and others, in review) and many anecdotal observations exist where a color-marked bird is
resighted a great distance from the original place of banding.
It is unknown how frequently these dispersal events occur,
and therefore to what degree this phenomenon will lead to
underestimates of true survival. Difficulties posed by
nonterritorial birds (floaters) in survival estimation have
long been recognized (Nur and others, this proceedings)
where birds disperse after initial capture, are not captured
again, and are therefore indistinguishable from dead birds.
Corrections can sometimes be made by omitting singlecaught birds from analyses (Nur and others, this proceedings). A similar situation exists where between-season breeding dispersal results in permanent emigration from the area
of a local demographic study. The degree of philopatry
exhibited can be affected by a variety of factors, including
past reproductive performance at the previous year’s breeding site (Robinson and Morse, this proceedings; Marshall
and others, in review) and thus attempts at estimating
survival become confounded with permanent dispersal.
In this paper, we illustrate that the essential problem with
return rate methodology is that the fate of birds which are
not resighted is unknown. We discuss the historical use of
the term “return rate” and scenarios in which existing
terminology is ambiguous and confusing; make suggestions
on more consistent terminology; and provide suggestions
concerning appropriate parameters to use with different
study objectives.
Surviving, Dispersing, and
Returning: The Potential Fate of a
Color-Marked Bird _______________
Illustrating the possible fates of marked birds, a branching tree diagram (fig. 1) follows a bird through two time
periods (years), branching at each point where one of two
events could occur. For example, a marked bird could survive
196
(SURVIVED) or die (DIED) during time period t to time
period t + 1. Similarly, if a bird survives and returns to the
study plot, it can be resighted (RESIGHTED) or missed
(NOT RESIGHTED). We extend the diagram originally
presented by Nur and Clobert (1986) to include the event
that a bird could return to the study plot (ON PLOT) or off
the study plot (OFF PLOT) the following year.
Examination of figure 1 reveals several important points:
(1) there are 13 possible paths or unique event-histories that
an individual bird could follow over these two time intervals;
(2) of these 13 unique paths, only four separate events are
observable by the field researcher who either resights (R) the
bird or does not resight (NR) the bird in each of the time
periods t + 1 and t + 2; (3) only one of the four observable
events, (R-R), is unique in that all of the events that make up
that event-history are known; (4) the other three observable
events (R-NR, NR-R, and NR-NR) are confounded by the
inability of the field researcher to determine whether a bird
that is not resighted is dead, alive on the plot but missed, or
alive and off the plot; and (5) seven of the 13 unique paths
result in the bird not being resighted in either time period
(NR-NR), but in only one of the seven is the bird actually
dead in both periods.
The associated probability of each event occurring can be
assigned to each branch (fig. 1), and a particular event
history can be described by its respective cumulative probability. Each observable event, therefore, is the sum of each
of the cumulative probabilities that are described by that
observable event (table 1). Therefore, we can see that to
actually model the true survival rate of the species in question
from return rate data, two additional pieces of information are
required for which we do not yet have good estimates. The
first is the probability of a color-marked bird that was
present on the study plot in year t, surviving to year t + 1 but
not returning to the study plot. This is a recognized (e.g.,
Lebreton and others 1992; Robinson and others 1995a) but
perhaps underappreciated phenomenon in studying migratory passerine bird demography and is likely due to the
difficulty in obtaining such estimates. The second and
equally difficult piece of information to obtain is the probability that a marked bird that returned to an area off plot
in year t + 1, survives and returns to settle back on the study
plot in year t + 2.
Existing and Suggested
Terminology ____________________
Although the probability of resighting a bird if it is alive
and present on the study area is largely a function of
researcher effort, the resighting probability resulting from
even the most rigorous of efforts still cannot be assumed to
be 1, and will likely vary across species, sexes, habitats, and
years (Martin and others 1995). Many capture-recapture
software programs exist for modeling “survival” rates and
should be used in all cases so that estimates are not biased
when capture probability is <1. If sufficient data exist, a
fairly complex model with separate parameters for sex, age,
habitat, or other independent variables can be supported
(e.g., Clobert and others 1988; Lebreton and others 1992).
However, an implicit assumption of these models is that
the resulting “survival” estimate includes the complement of
USDA Forest Service Proceedings RMRS-P-16. 2000
Figure 1—Branching-tree diagram illustrating the potential fate of a marked bird over three time periods. Birds are marked
in year t and then survive to year t + 1 with probability S, emigrate from the study plot with probability 1-E and then, if alive and
present on the study plot, are resighted with probability P. The branching continues for a second year, (t + 2), generating 13 unique
event-histories a marked bird could follow. A marked bird that is off-plot in year t + 1 returns to the study plot in the year t + 2 with
probability I. A field researcher either resights (R) or does not resight (NR) the bird in t + 1 and t + 2, respectively (the observable
events, last column).
Table 1—Summary of each observable event (marked in year t and resighted [R] or not resighted [NR] in t + 1 and t + 2), also expressed
as a capture history (1 = resighted, 0 = not resighted), and the number (No.) of unique event-histories that are expressed by
each observable event/capture history. The probability associated with a particular capture history is the sum of the cumulative
probabilities of the individual event histories (fig. 1) expressed by that capture history.
Observable event
t
t+1
t+2
t
Capture history
t+1
t+2
No.
Cumulative probabilities
Marked
R
R
1
1
1
1
S1(1-E1)(P1)(S2)(1-E2)(P2)
Marked
NR
R
1
0
1
2
S1(1-E1)(1-P1)(S2)(1-E2)(P2) + S1(E1)(S2)(I2)(P2)
Marked
R
NR
1
1
0
3
S1(1-E1)(P1)(S2)(1-E2)(1-P2)+S1(1-E1)(P1)(S2)(E2) + S1
(1-E1)(P1)(1-S2)
Marked
NR
NR
1
0
0
7
S1(1-E1)(1-P1)(S2)(1-E2)(1-P2) + … + S1(E1)(S2)(1-I2) +
S1(E1)(1-S2) + (1-S1)
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mortality and permanent emigration (Lebreton and others
1992). Recognizing that dispersal is an inherent aspect of the
ecology of these birds, even “survival” estimates that take
into account resighting probability will underestimate true
survival if birds disperse beyond the range of the resighting
effort. While it is true that “CJS models yield unbiased
estimates of survival,” it is potentially confusing with regards to site-specific resighting studies, because the estimates are still underestimates of true survival (the complement of actual mortality) if permanent emigration occurs.
Even though these estimates are preferable to simple return
rates, we cannot estimate true survival from local resighting
studies until we can estimate the probability of dispersing
“off-plot” and also the probability of settling “back on the
plot” in a subsequent year (fig. 1).
We suggest alternate terminology to be used in local
demographic studies that should accomplish two tasks.
First, the term itself will imply what is actually measured,
and second, will modify existing terminology only slightly
for clarification.
•
•
•
Apparent Return Rate—Ratio of marked birds that are
resighted on the study plot in year t + 1 to the number
banded (i.e., the number that had bands) in year t.
Resighting Probability—Probability of resighting a
marked bird given that it is alive and present on the
study plot.
Return Rate—Probability model-based estimate of the
number of birds that return to the study plot in year t + 1
to the number banded in year t.
What historically has been referred to as Return Rate is
now referred to as Apparent Return Rate to imply that this
measure reflects only the birds that the field researcher
actually encounters. Return Rate should now be the result of
a model-based estimate that adjusts the Apparent Return
Rate by the probability of resighting the marked birds. Here
again, we are referring to the rate at which marked birds
return to the study plot. This term is equivalent to a “CJS
estimates of survival,” but more clearly implies that we are
not estimating true survival, the complement of mortality.
To use the term “survival” in the context of local, resighting
studies, we must take into account dispersal of marked birds
out of and into the study plot (fig. 1).
Implications for Demographic
Analyses ______________________
True Survival Estimates
Questions involving sex-specific survival, the costs of
reproduction, and other aspects of life-history theory require
that true survival be estimated. Once the means for separating emigration from mortality are incorporated into
sampling designs that measure return rate as a response
variable, modeling true survival can be accomplished. Similar model structures already exist for estimating true survival from other sampling designs. These estimates require
that a portion of the birds that emigrate from the study plots
be captured, resighted, or recovered elsewhere (e.g., Lebreton
and North 1993 and references therein). For example, models
combining the use of capture-recapture and band recovery
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data (Burnham 1993) seem promising as more birds are
banded nationwide. Multi-state capture-recapture models
(Nichols and others 1993), in which capture-recapture/
resighting data are collected at a number of isolated breeding sites, also allow estimation of true survival as well as
movement rates (e.g., Spendelow and others 1995). The
MAPS program (DeSante and others 1995), which uses a
constant effort mist-netting approach, is also a means by
which true survival estimates can be collected by combining
data from a number of trapping locations.
Treatment or Management Effects
If the objective of the study is to compare demographic
rates among different treatments, management options,
habitats, or other factors, then return rate (modified by
capture probability) is a meaningful parameter, because it
reflects the multiple ways in which the population may be
affected by the factor in question. Hypotheses regarding the
effects of a particular treatment still can be tested with
meaningful results, as long as it is recognized that the
response variable includes both mortality and movements
from the study area. One potential problem still exists
regarding birds that may temporarily (i.e., for one breeding
season) disperse from the study area (fig. 1) in response to a
short-term treatment effect. These movements are important to detect, but currently are indistinguishable from birds
that were simply “missed” during that year (table 1).
Source-Sink Dynamics
Prompt management decisions often are needed without
a complete knowledge of the system, and it may seem that
underestimates of true survival are “better” because management recommendations would be more conservative.
However, underestimates of survival may result in the
misidentification of potential source populations (or at least
stable populations) based on the measured reproductive
output. Fecundity and/or juvenile survival would have to be
greater than what is truly needed to balance an underestimate of adult survival. The problem is twofold, especially
when considering that population models are typically female only (May and Robinson 1985) and the potential to
disperse from the study is greater for females (Marshall and
others, in review). First, birds that are alive, but have
dispersed from the study plot, are considered dead in the
“survival” component of the equation. Second, these birds
are not only alive but breeding elsewhere, and their contributions to reproductive success are not included in the
fecundity component. Thus we need to either incorporate
dispersal from the study plot into the “survival’ component,
or to add their reproductive contributions to the “fecundity”
component. Misidentifying a source population that is sustaining one or several sink populations could have serious
conservation implications (Pulliam 1988). Pragmatic conservation recommendations with respect to these sources of
uncertainty are discussed in Robinson and Morse (this
proceedings) and Sherry and Holmes (this proceedings).
This cautionary note reemphasizes a longstanding recommendation to obtain better information on dispersal probabilities and distances dispersed across species, sex, and age
USDA Forest Service Proceedings RMRS-P-16. 2000
classes for a better understanding of the population dynamics and conservation needs of long-distance migrant landbirds.
Eventually, some of these problems may be solved when
radiotelemetry technology allows investigators to closely
follow small birds over great distances and long periods of
time. Until then, we hope that some of the recommendations
made here will help clarify our thinking on this subject.
University of Memphis, University of Georgia, and at the
workshop on ecology of migratory birds held at the University of Southern Mississippi. The manuscript was greatly
improved by reviews from M. Baltz, J. Brawn, M. Conroy,
C. Francis, T. Martin, C. Moore, F. R. Moore, J. Nichols,
L. Powell and T. Sherry. J. Nichols and M. Conroy, in
particular, clarified our thinking on this subject.
Acknowledgments ______________
This paper originated from a term project conducted in
RJC’s population ecology class and was enhanced by discussions with fellow graduate students and colleagues at the
USDA Forest Service Proceedings RMRS-P-16. 2000
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