Estimation of immigration rate using integrated population models

Abstract

Summary1. The dynamics of many populations is strongly affected by immigrants. However, estimating and modelling immigration is a real challenge. In the past, several methods have been developed to estimate immigration rate but they either require strong assumptions or combine in a piecewise manner the results from separate analyses. In most methods the effects of covariates cannot be modelled formally. 2. We developed a Bayesian integrated population model which combines capture-recapture data, population counts and information on reproductive success into a single model that estimates and models immigration rate, while directly assessing the impact of environmental covariates. 3. We assessed parameter identifiability by comparing posterior distributions of immigration rates under varying priors, and illustrated the application of the model with long term demographic data of a little owl Athene noctua population from Southern Germany. We further assessed the impact of environmental covariates on immigration. 4. The resulting posterior distributions were insensitive to different prior distributions and dominated by the observed data, indicating that the immigration rate was identifiable. Average yearly immigration into the little owl population was 0AE293 (95% credible interval 0AE183-0AE418), which means that ca 0AE3 female per resident female entered the population every year. Immigration rate tended to increase with increasing abundance of voles, the main prey of little owls. 5. Synthesis and applications. The means to estimate and model immigration is an important step towards a better understanding of the dynamics of geographically open populations. The demographic estimates obtained from the developed integrated population model facilitate population diagnoses and can be used to assess population viability. The structural flexibility of the model should constitute a useful tool for wildlife managers and conservation ecologists.

Quantifying changes in abundance without counting animals: extensions to a method of fitting integrated population models

“…This raises the possibility that, for species too rare or elusive to be covered in sufficient number by a scheme such as the CBC, BBS or UKBMS, augmentation by additional P/A data much more simply gathered in quantity may profoundly increase our confidence in estimates of their population levels, with or without additional demographic data. Although not considered here, possible extensions of the approach to wider contexts include models for presenceonly data (Ward et al 2009), or relaxing the assumption of closure to estimate immigration (Abadi et al 2010). Employing repeated visits within a season and estimating separately the probabilities of a species being present, and those of its being detected (MacKenzie et al, 2006;Royle and Nichols, 2003), also appears to be a promising line of future research.…”

Integrated population models: a novel analysis framework for deeper insights into population dynamics

“…We consider the case of a geographically open population of a short-lived bird species (imagine a passerine) from which we have annual counts of breeders, capture-recapture data and data on productivity. The resulting model is described in detail in Abadi et al (2010b), where computer code to run the model as well as data are also available.…”

“…The posterior distribution can easily be approximated using simulation-based approaches such as Markov chain Monte Carlo (MCMC; Brooks 2003). The model just described has been applied to data of populations of Little Owls (Athene noctua; Abadi et al 2010b).…”

Age-specific survival of male Golden-cheeked Warblers on the Fort Hood Military Reservation, Texas

“…Another possibility is to combine the captureresight data analyzed herein with ancillary GCWA population data within an integrated population model (Besbeas et al 2002, Brooks et al 2004, Schaub and Abadi 2011. By combining the likelihoods of multiple data sets, integrated population models allow for the estimation of population parameters for which few or no explicit data are available, including immigration rates (Abadi et al 2010). It is worth noting that these two approaches can be combined within a single analysis using a Bayesian approach.…”