Modelling demographic processes in marked ... - Wiley Online Library

Methods in Ecology and Evolution 2014, 5, 1265–1268

doi: 10.1111/2041-210X.12300

SPECIAL ISSUE: INTRODUCTION

Modelling demographic processes in marked populations: proceedings of the EURING 2013 analytical meeting Charles M. Francis1*, Richard J. Barker2 and Evan G. Cooch3 1

Canadian Wildlife Service, Environment Canada, Ottawa, ON, K1A 0H3, Canada; 2Department of Mathematics and Statistics, University of Otago, P. O. Box 56, Dunedin, New Zealand; and 3Department of Natural Resources, Cornell University, Ithaca, NY 14853 , USA

Summary 1. This article introduces a special issue of papers presented at the EURING 2013 technical conference on the analysis of data from marked individuals. 2. The EURING technical conferences were originally established to bring together ecologists and statisticians to develop methods to estimate demographic parameters such as survival, recruitment, density, population size and movement from the large accumulated datasets on marked birds, but many methods are also relevant to other taxa. 3. Papers in this issue cover a range of topics include new statistical modelling approaches, spatially explicit modelling, experimentation and hypothesis testing, integrating data from multiple sources and occupancy modelling.

Key-words: modelling, population ecology, EURING, banding, tagging, capture-recapture, occupancy, statistics

The European Ringing Association (EURING) technical conferences were established to advance the field of ecological modelling by bringing together biologists with statistically challenging problems related to the analysis of data from marked animals and statisticians with the expertise to help develop appropriate models for the analysis of those data. The initial impetus of the conferences was to develop improved methods for the analysis of data derived from the millions of birds that have been ringed or otherwise uniquely marked since the early 20th century in Europe and elsewhere in the world. Since then, these conferences have evolved to present leading edge statistical developments and applications relevant to many different types of work on marked animals, as well as other fields that benefit from mark–recapture-type methodologies such as occupancy modelling. The focus on marked animals implied in the name arose out of the need to account for imperfect detection when making inference about population dynamics. The early focus of the capture–recapture literature was on the estimation of abundance, with application primarily to fisheries and wildlife management. The appearance of key papers written by Richard Cormack (1964), George Jolly (1965) and George Seber (1965) provided the foundation for robust inference about population dynamics based on modern statistical modelling techniques.

*Correspondence author. E-mail: [email protected]

Subsequent work by Doug Robson, David Anderson, Ken Burnham, Cavell Brownie, Ken Pollock and their collaborators led to the rapid development of methods for analyses of demographic parameters, motivated in part by the need to estimate survival and harvest parameters from exploited populations including waterfowl and fish. A seminal work was the handbook Statistical Inference from Band Recovery Data (Brownie et al. 1978), which was followed by many papers making use of these methods to analyse waterfowl data to support planning of harvest management. The first EURING technical meeting in 1986 brought together statisticians working in fisheries and wildlife management, along with bird ecologists, to develop methods to analyse the very large data sets that had accumulated on repeated encounters of marked birds. Most of the standard life-table methods that had been in general use in the ornithological community were based on assumptions that have since been discredited, but the alternatives were not obvious. The 1986 EURING meeting proved a watershed moment, as it became clear that not only could some of the methods developed and applied in fisheries and wildlife be used for analysis of other types of ornithological data, but that the larger underlying statistical issues – in particular, imperfect detection – were widespread throughout ecology and required general, accessible solutions. Participants in the ensuing series of conferences have directly or indirectly been responsible for many of the major new

© 2014 Her Majesty the Queen in Right of Canada. Methods in Ecology and Evolution © 2014 British Ecological Society. Reproduced with the permission of the Minister of the Environment

1266 C. M. Francis, R. J. Barker & E. G. Cooch developments in analysis methods for data from marked animals. From this inaugural meeting, regular conferences have been held every 2–4 years. These have been kept relatively small (80–150 people), with a limited number of oral presentations, to encourage active interaction among participants. The interactions between statisticians and ecologists have been central to these developments. Efforts to apply newly developed models to challenging ecological data sets often highlight opportunities and needs for further development. The proceedings from these conferences document the tremendous advances made in the field over nearly 30 years since the first conference (see http://www.euring.org/meetings/ technical_meetings/list_of_proceedings.htm for a list of conferences and their proceedings). Development and application of appropriate models to account for detection probabilities, both for mark–recovery and mark–recapture data, was soon followed by a range of other developments. These included models jointly incorporating both recaptures and recoveries, models allowing for group-level and individual covariates, models to allow for transients and other forms of heterogeneity, and models to estimate additional parameters such as recruitment and population growth rate. Some of these models have also inspired new developments outside the traditional framework of ‘marked’ individuals including occupancy models and N-mixture models. Lebreton et al. (1992) laid the conceptual groundwork for much of what has since transpired by casting the problem of modelling parameters in a consistent, and readily accessible linear modelling framework with model selection based on the use of Akaike’s information criterion (AIC). Link and Barker (2004) helped to catalyse the more recent shift towards hierarchical Bayes models as an analytic framework, recognizing that most complex biological data sets are most appropriately analysed as random or mixed effects models. In parallel with the development of improved statistical methods, new software was developed and demonstrated at the conferences, making these methods more widely available to practicing biologists. The availability of flexible, user-friendly software (in particular, programs SURGE, JOLLY, JOLLYAGE, SURVIV, and the highly flexible and powerful MARK, although many others now exist) led to an explosion of use of these models. More recently, the recognition of the power of hierarchical modelling and random effects and the advent of Markov chain Monte Carlo (MCMC) methods in a Bayesian inference framework have seen these techniques dominate in analyses, using packages such as WinBUGS, OpenBUGS, JAGS, and custom-written software.

Overview of the 2013 conference proceedings The latest of these conferences, held in Athens, Georgia, USA, from 28 April to 4 May 2013, continued the tradition of bringing together biologists and statisticians to advance the field. The papers presented, of which 31 are here included in the proceedings, covered a range of topics and included both the development of new statistical approaches and application of

established methods to specific biological data sets. The conference consisted of a mixture of plenary papers, presenting a broad overview of a topic, invited oral presentations and poster presentations. The proceedings have been published in an innovative manner, using a virtual issue to bring together articles published in two journals. The review process for all papers was run through Methods in Ecology and Evolution. A selection of papers presenting a broad overview of a topic (primarily plenary papers) or otherwise felt by the editors to be particularly innovative or of general interest were accepted in this journal. The remainder, after acceptance by one of the guest editors, were transferred to Ecology and Evolution, along with the reviews and guest editor recommendations, allowing that journal’s editor to make a quick decision. Papers were published online as soon as they were accepted, so that authors did not have to wait for completion of the proceedings to see wide distribution of their work. Following is a summary of some of the key themes represented in these proceedings, with examples of some of the papers fitting under each theme, although many papers touch on multiple themes, and readers should browse the table of contents of the issue for full details on each paper. ADVANCES IN STATISTICAL METHODS

Many papers present new developments in statistical methods, including new approaches for analysis of data from multisample batch-marking studies, either on their own (Cowen et al. 2014) or in combination with radio-telemetry data (Schwarz, Cope & Fratton 2013); new model fitting technology in an MCMC framework for addressing individual covariates (Bonner & Schofield 2014); fitting temporally varying individual covariates in hierarchical nest survival models (Converse et al. 2013); using posterior predictive checking as an aid to model selection (Chambert, Rotella & Higgs 2014); using generalized estimating equations as an alternative to Bayesian approaches to model heterogeneity in catchability (Akanda & Alpizar-Jara 2014); using hidden Markov models to expand the toolbox for mark–recapture modelling (Choquet, Bechet & Guedon 2014); approaches for integrating memory models with mark–recapture modelling (Cole et al. 2014); and methods to deal with incompletely read marks in mark-resighting studies (McClintock et al. 2014). Methods for testing model assumptions were also presented and applied (White, Cordes & Arnold 2013). SPATIALLY REFERENCED DATA

Bird marking was originally developed to study movement patterns, but models to estimate movement parameters have received much less attention than those to estimate demographic parameters. However, in this issue, several papers examine methods to correct for heterogeneity in ring recovery probabilities, leading to better models for understanding migratory connectivity (Cohen et al. 2014; Korner-Nievergelt, Liechti & Thorup 2014; Thorup et al. 2014). Methods are also

© 2014 Her Majesty the Queen in Right of Canada. Methods in Ecology and Evolution © 2014 British Ecological Society. Methods in Ecology and Evolution, 5, 1265–1268

Demographic processes in marked populations presented for using multistate models to test for geographic variation in demographic parameters of birds (Jansen et al. 2014) and fish (Yackulic et al. 2014). Two different methods are presented for using spatially explicit mark–recapture to separate true survival and emigration (Ergon & Gardner 2014; Schaub & Royle 2014) – without additional data, these two parameters are confounded. EXPERIMENTATION AND HYPOTHESIS TESTING

Hypothesis testing is the foundation of the modern scientific method yet relatively few ecological studies of population parameters make use of formal experiments or designed comparisons to test hypotheses. Exceptions presented here include use of mark–recapture models to test for sexual selection (Senar et al. 2014); use of dynamic N-mixture models in a before-after-control-impact (BACI) design to test for cavity limitation in flying squirrels (Priol et al. 2014); and use of hidden Markov models to test how individual heterogeneity in survival probabilities affects harvest dynamics of black brant (Lindberg, Sedinger & Lebreton 2013). INTEGRATING DATA FROM DIFFERENT SOURCES

Integration of data from different sources to improve inference is a common theme in many different contexts. Topics include combining radio-telemetry and dead recovery data to reduce bias and increase precision in demographic estimates (Buderman et al. 2014); combining genetic markers from hair samples with occupancy data to improve population estimates (Chandler & Clark 2014); and combining automated detection of infrared images with visual analysis of photographs using double-sampling to account for incomplete detection, false positives, and species misidentification (Conn et al. 2014). Integrated population modelling, using data from multiple sampling methods to estimate different parameters on the same population, is explored in several papers (Alisauskas et al. 2013; Besbeas & Morgan 2014; Davis et al. 2014; Robinson, Morrison & Baillie 2014; Zipkin et al. 2014). With data collection technology advancing at a rapid rate, we anticipate that integration of data from a variety of sources will remain a dominant theme at future conferences. OCCUPANCY MODELS

While occupancy models do not involve marked individuals, the statistical concepts are very similar to those of mark–recapture, and the data can be relevant to understanding population dynamics at large spatial scales where individual marking may be impractical. Papers presented include an overview of new developments and challenges in occupancy modelling (Bailey, MacKenzie & Nichols 2014); an application of occupancy models to study dynamic changes in species ranges in relation to climate change (Bled, Nichols & Altwegg 2013); and methods for grouping species in community-level studies (Pacifici et al. 2014).

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Future directions One of the most important principles of sound statistical inference is the need to properly account for uncertainty, not only in parameters of interest, but also in ‘nuisance’ parameters such as detection probabilities. The EURING conferences have both witnessed and driven much of our understanding of how this should be done, through the clear articulation of probability models that account for sampling processes and that describe the processes underlying population dynamics. But as the technology evolves, some of the old challenges remain and others become more difficult. For example, model selection and evaluating model fit, which are key for valid statistical inference, become more challenging with the widespread use of potentially more realistic, but also more complex hierarchical models. There is also a need to continue to consider closely issues related to experimental design and data selection. Many sophisticated statistical models have been developed to address the limitations of increasingly complex ecological data. New technological developments are likely to result in ever-increasing volumes of data at finer scales and over larger areas. However, the availability of ‘big’ and ‘crowd-sourced’ data offers both opportunities and traps. More data are not necessarily better; what is needed is more of the right kind of data. It is important to continue to emphasize data quality, either through sound experimental design or through careful data selection. Fundamental statistical principles of sampling and drivers of sound statistical inference remain just as important as the use of appropriate models.

Acknowledgements We would like to thank Bob O’Hara and Samantha Ponton for their efforts in guiding all of the manuscripts through the publication process. We would also like to thank Mike Conroy and the local committee for their support in organizing the conference.

Data accessibility This manuscript does not use data.

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