ASSESSING THE RELATIVE IMPORTANCE OF DIFFERENT SOURCES OF MORTALITY FROM RECOVERIES OF MARKED ANIMALS

Overall mortality rates often are based upon a variety of mortality sources such as predation, disease, and accidents, and each of these sources may influence population dynamics differently. To better understand population dynamics or to derive effective con- servation plans, it is thus crucial to know the frequency of specific mortality causes as well as their variation over time. However, although the mortality cause of retrieved marked animals is often known, this information cannot be used directly to estimate the frequency of a mortality cause. By calculating the ratio of the number of animals reported dead from a specific cause to the total number of retrieved animals, one does not consider the fact that the probability of finding a dead individual depends on the cause of its death. Although frequently used, such ad hoc estimates can be heavily biased. Here we present a new way of estimating the frequency of a mortality cause from ring-recovery (band-recovery) data without bias. We consider the states ''alive,'' ''dead because of mortality cause A,'' and ''dead due to all other causes'' and estimate within a multistate capture-recapture framework the transition probabilities as well as the state-specific resighting probabilities. Among the transition probabilities are the overall survival probability and the proportion of animals dying because of A. From these, the probability that an animal dies during a year due to the specific cause of interest (cause A kill rate) can easily be calculated. We illustrate this model using data from White Storks Ciconia ciconia ringed in Switzerland to estimate the proportion of storks that died due to power line collision. Average unbiased estimates of this proportion were 0.37 6 0.08 (mean 6 1 SE) for juveniles, about 25% lower than ad hoc estimates, and 0.35 6 0.09 for adults. The annual survival rate of juveniles was 0.33 6 0.05 and of adults, 0.83 6 0.02. Power line mortality is thus important for White Storks, with about one in four juveniles and one in 17 adults dying each year because of power line collision. We discuss advantages and disadvantages of the new model and how the results could be used to explore the link between a specific mortality cause and population dynamics.

[1]  Dennis M. Heisey,et al.  Evaluation of survival and cause-specific mortality rates using telemetry data , 1985 .

[2]  Roger Pradel,et al.  Competing events, mixtures of information and multistratum recapture models , 1999 .

[3]  A. N. Arnason,et al.  Parameter estimates from mark-recapture experiments on two populations subject to migration and death , 1972, Researches on Population Ecology.

[4]  B. Morgan,et al.  A model with first-year variation for ring-recovery data , 1989 .

[5]  F. Bairlein,et al.  Der Bestand des WeißstorchsCiconia ciconia in Südwestdeutschland: eine Analyse der Bestandsentwicklung , 2005, Journal für Ornithologie.

[6]  G. Zink.  Populationsdynamik des Weissen Storchs, Ciconia ciconia, in Mitteleuropa , 1967 .

[7]  M. Massot,et al.  Diagnosing the environmental causes of the decline in Grey Partridge Perdix perdix survival in France , 2001 .

[8]  P. Ferreras,et al.  Rates and causes of mortality in a fragmented population of Iberian lynx Felis pardina Temminck, 1824 , 1992 .

[9]  B. Sæther,et al.  AVIAN LIFE HISTORY VARIATION AND CONTRIBUTION OF DEMOGRAPHIC TRAITS TO THE POPULATION GROWTH RATE , 2000 .

[10]  D. O. Logofet.  Matrix Population Models: Construction, Analysis, and Interpretation , 2002 .

[11]  Byron J. T. Morgan,et al.  Detecting parameter redundancy , 1997 .

[12]  C. M. Lessells,et al.  The Evolution of Life Histories , 1994 .

[13]  K. Burnham,et al.  Program MARK: survival estimation from populations of marked animals , 1999 .

[14]  Byron J. T. Morgan,et al.  Solving problems in parameter redundancy using computer algebra , 2002 .

[15]  Kenneth H. Pollock,et al.  Estimating Transition Probabilities for Stage‐Based Population Projection Matrices Using Capture‐Recapture Data , 1992 .

[16]  David R. Anderson,et al.  Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies , 1992 .

[17]  Olivier Gimenez,et al.  Parameter Redundancy in Multistate Capture‐Recapture Models , 2003 .

[18]  David R. Anderson,et al.  Tests of Compensatory vs. Additive Hypotheses of Mortality in Mallards , 1984 .

[19]  G. Seber.  The estimation of animal abundance and related parameters , 1974 .

[20]  David R. Anderson,et al.  Model Selection and Inference: A Practical Information-Theoretic Approach , 2001 .

[21]  E. A. Catchpole,et al.  On the Near‐Singularity of Models for Animal Recovery Data , 2001, Biometrics.

[22]  Vladimir Grosbois,et al.  MODELING DISPERSAL WITH CAPTURE–RECAPTURE DATA: DISENTANGLING DECISIONS OF LEAVING AND SETTLEMENT , 2003 .

[23]  M. Conroy,et al.  Analysis and Management of Animal Populations , 2002 .

[24]  I. Newton,et al.  Trends in the numbers and mortality patterns of sparrowhawks ( Accipiter nisus ) and kestrels ( Falco tinnunculus ) in Britain, as revealed by carcass analyses , 1999 .

[25]  H. Caswell.  PROSPECTIVE AND RETROSPECTIVE PERTURBATION ANALYSES: THEIR ROLES IN CONSERVATION BIOLOGY , 2000 .

[26]  Hal Caswell,et al.  Demography of the endangered North Atlantic right whale , 2001, Nature.

[27]  G. P. Kirkwood,et al.  Estimation of survival in marked populations with possibly dependent sighting probabilities , 1981 .

[28]  Roger Pradel,et al.  A Proposal for a Goodness‐of‐Fit Test to the Arnason‐Schwarz Multisite Capture‐Recapture Model , 2003, Biometrics.

[29]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[30]  A. N. Arnason,et al.  The estimation of population size, migration rates and survival in a stratified population , 1973, Researches on Population Ecology.