Acknowledgements We thank the team at Woodchester Park for conducting the field work and badger sampling. References Anderson R. The population dynamics of microparasites and their invertebrate hosts. B Biol. Impact of bovine tuberculosis on a population of brushtail possums Trichosurus vulpecula Kerr in the Orongorongo Valley, New Zealand. Variation in extinction risk among birds: chance or evolutionary predisposition?
Biometrics , 58 , — General methods for monitoring convergence of iterative simulations. Mating system of the Eurasian badger, Meles meles , in a high density population. Harvesting can increase severity of wildlife disease epidemics. Positive and negative effects of widespread badger culling on tuberculosis in cattle.
Nature , , — Diagnostic accuracy and optimal use of three tests for tuberculosis in live badgers. Temporal variation in survival of mammals: a case of environmental canalization? Ecology , 84 , — Population dynamics of large herbivores: variable recruitment with constant adult survival. Trends Ecol. Temporal variation in fitness components and population dynamics of large herbivores. Hygiene , 79 , — Fish Aquat.
Diseases shared between wildlife and livestock: a European perspective. Direction of association between bite wounds and Mycobacterium bovis infection in badgers: implications for transmission.
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Living fast and dying of infection: host life history drives interspecific variation in infection and disease risk. Academic Press, Waltham, MA. The puzzles of population cycles and outbreaks of small mammals solved? Bioscience , 54 , — The impact of disease on the survival and population growth rate of the Tasmanian devil. Ecology , 83 , — Compensatory effects of recruitment and survival when amphibian populations are perturbed by disease. Oikos , 97 , — Natl Acad. USA , 95 , — HarperCollins, London.
How life history influences population dynamics in fluctuating environments. Biometrics , 52 , — Bayesian measures of model complexity and fit.
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Initially, the meetings concentrated on parameter estimation, especially of survival and abundance. More recently methods to integrate different datasets and analysis of data involving transition between states, where these states are unknown, or identified with error, have become more and more common in the talks and posters.
The importance of accessible software to allow users to apply the latest methods has always been key though.
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These include:. There are a lot more topics covered by the 17 articles in this Special Feature. The articles published in Ecology and Evolution are all Open Access and those published in Methods in Ecology and Evolution have been made freely available for the whole of The methods developed in and around the Euring Analytical meetings continue to have relevance today far beyond the bird-marking community. We hope that this Special Feature will help bring more attention to them. Some individuals in this second sample will have been marked during the initial visit and are now known as recaptures.
Other animals captured during the second visit, will not have been captured during the first visit to the study area. These unmarked animals are usually given a tag or band during the second visit and then are released. Population size can be estimated from as few as two visits to the study area. Commonly, more than two visits are made, particularly if estimates of survival or movement are desired. Regardless of the total number of visits, the researcher simply records the date of each capture of each individual.
The "capture histories" generated are analyzed mathematically to estimate population size, survival, or movement. A biologist wants to estimate the size of a population of turtles in a lake. She captures 10 turtles on her first visit to the lake, and marks their backs with paint. A week later she returns to the lake and captures 15 turtles.
Five of these 15 turtles have paint on their backs, indicating that they are recaptured animals.
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The problem is to estimate N. The Lincoln—Petersen method  also known as the Petersen—Lincoln index  or Lincoln index can be used to estimate population size if only two visits are made to the study area. This method assumes that the study population is "closed" [ citation needed ]. In other words, the two visits to the study area are close enough in time so that no individuals die, are born, or move into or out of the study area between visits. The model also assumes that no marks fall off animals between visits to the field site by the researcher, and that the researcher correctly records all marks.
It is assumed  that all individuals have the same probability of being captured in the second sample, regardless of whether they were previously captured in the first sample with only two samples, this assumption cannot be tested directly. For example, if half of the marked individuals were recaptured, it would be assumed that half of the total population was included in the second sample. The Lincoln—Peterson estimator is asymptotically unbiased as sample size approaches infinity, but is biased at small sample sizes.
Note that the answer provided by this equation must be truncated not rounded. Thus, the Chapman method estimates 28 turtles in the lake. The above form is more convenient for mathematical purposes.
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A derivation is found here: Talk:Mark and recapture Statistical treatment. The capture probability refers to the probability of a detecting an individual animal or person of interest,  and has been used in both ecology and epidemiology for detecting animal or human diseases,  respectively. The capture probability is often defined as a two-variable model, in which f is defined as the fraction of a finite resource devoted to detecting the animal or person of interest from a high risk sector of an animal or human population, and q is the frequency of time that the problem e.
Then the capture probability P was defined as:. Importantly, the formula can be re-written as a linear equation in terms of f :. Because this is a linear function, it follows that for certain versions of q for which the slope of this line the first term multiplied by f is positive, all of the detection resource should be devoted to the high-risk population f should be set to 1 to maximize the capture probability , whereas for other value of q , for which the slope of the line is negative, all of the detection should be devoted to the low-risk population f should be set to 0.
We can solve the above equation for the values of q for which the slope will be positive to determine the values for which f should be set to 1 to maximize the capture probability:. This is an example of linear optimization. The literature on the analysis of capture-recapture studies has blossomed since the early s [ citation needed ].