EURING 2023 • Montpellier, France •
honour & plenary presentations
The EURING 2023 Analytical Meeting & Workshop is highlighted by a series of
plenary presentations, which are intended to present expert summaries of various areas
of current research interest.
Honour Speaker:
Emmanuelle Cam (Monday, 17 April - 5:30-6:30 PM)
'30 years of data collection, capture-mark-recapture (CMR) modeling
and teaching: what is seen matters, what is unseen matters as much, and sometimes we
estimate quantities that are unobservable.'
Biography
Dr. Emmanuelle Cam has been a professor at the University of Western Britanny (Brest, France)
since 2017. She is a population ecologist who combines fieldwork and statistical modeling to
address the demographic processes underlying population dynamics. She is involved in a
kittiwake (Rissa tridactyla) monitoring program based on individually marked birds (1979 to
the
present). The primary theme of her research focuses on variation in individual decisions
(breeding or not, timing of breeding, mate and site fidelity) according to ontogenetic
factors and social or environmental conditions, and on individual responses to changing
conditions.
She teaches courses on population dynamics, evolutionary ecology, applied modeling for
conservation biology, and a specific course on modeling for the conservation of marine
megafauna (International Master of Science in Marine Biological Resources). Before that
(2002-2017) she was an associate professor and then a professor at the University of
Toulouse (France).
Prior to 2017 she was a post-doctoral fellow with the biometry group at Patuxent Wildlife
Research Center (U.S.A), with the Center for Wildlife Ecology at Simon Fraser University
(Canada), and with the Ecology and Evolution group at the Mediterranean Institute for
Advanced Studies (Spain). She has MSc degree (1993) and a PhD in ecology (1997) from Pierre
and Marie Curie University in (Paris, France).
Abstract
In the 20th century, the probability of a young biologist with a basic training in
statistics successfully building the CMR models required to address ambitious hypotheses
based on ecological or evolutionary theory was small. The situation has changed thanks to
the amazing dynamism of researchers involved in the development of CMR models, pieces of
software, courses offered in universities and workshops worldwide, and the publication of
papers and textbooks. EURING Analytical meetings have played a large part in the sustained
dynamics of the community of statisticians and biologists using CMR models. As a user,
studying how to design CMR models brings you more than 'recipes' to overcome problems
inherent in imperfect detection of marked individuals. For example, any piece of software
designed to build CMR models includes tools to conduct model selection based on Information
Criteria because leading researchers have popularized this approach to assess hypotheses
about the processes that give rise to data. Kendall and Gould (2002) published an opinion
paper focused on the curriculum of wildlife students and highlighted difficulties that are
still true: 'Relating statistics to scientific methodology is a good idea in any case...
However, the more this is necessary, the fewer statistical topics can be covered. In
addition, by teaching scientific methods in a statistics course, we have found students can
misinterpret the differences between scientific hypotheses and statistical hypotheses.' The
constant innovation in CMR modeling has widened the gap between modern concepts and
techniques and the basic training of students. Teaching CMR modeling is a fantastic means of
opening the students' minds to complex ideas: 'what is seen matters, what is unseen matters,
and sometimes you estimate quantities that remain unobservable even if you use the most
efficient tools of investigation of micro- and macroscopic processes governing the state of
Nature.' The integration of occupancy and CMR models within the common framework of hidden
Markov models, combined with flexible programming languages has created an imposing
background that leads to numerous publications that even the most 'statophobic' student
can't ignore today. Long-term monitoring programs of individually marked animals have
tremendously benefited from innovation in CMR modeling: ecological or evolutionary
hypotheses that were impossible to address can now be evaluated. New results have raised new
questions and highlighted new obstacles. From the biologist's viewpoint, some innovations in
CMR modeling such as the estimation of model parameters that are impossible to measure even
with perfect detectability can be difficult to advocate in the ecology or evolution
literature due to a lack of familiarity of many biologists with the way we draw inferences
about ecological or evolutionary hypotheses using data and CMR models, and because there is
ongoing debate in biology concerning the interpretation of some models. This sheds light on
the twofold challenge that biologists must overcome and that teachers must help students
overcome: the more powerful the technology becomes (fast computers, flexible programming
languages, efficient algorithms to estimate the parameters of complex models), the deeper
our understanding of the conceptual foundations of both ecology or evolution and statistics
must be.
Todd Arnold (Friday, 21 April)
'Density dependence, individual heterogeneity, and demographic compensation:
new approaches to age-old questions.'
Biography
Dr. Todd Arnold is a Morse Distinguished Teaching Professor in the Department of Fisheries, Wildlife and Conservation Biology
at the University of Minnesota, USA. He received his Ph.D. in Zoology from Western University in Ontario, Canada, where he
conducted dissertation research on the adaptive significance of clutch size in American coots. Current research interests
focus on developing better methods of estimating population size of secretive wildlife,
estimation of population vital rates such as nest success and annual survival, and development of integrated population
models to better guide conservation activities.
Abstract
Understanding the impacts of human harvest on population dynamics was a key motivation behind the development of modern
tag-recovery models, and the general framework can be expanded to address any source of natural or anthropogenic mortality
of interest. Mortality sources are considered additive if they lead to a net reduction in annual survival that cannot be
ameliorated over the course of the annual cycle, or fully compensatory if the reduction is completely ameliorated through
increases in natural survival. Compensation can also occur if harvest mortality falls disproportionately upon frail
individuals that were less likely to survive anyway. Additive and compensatory mortality are reference points along a
continuum, and methods to estimate partial compensation are sorely needed. I review previous analytical approaches and
propose an alternative method that estimates annual survival as a function of harvest mortality. If harvest or other
anthropogenic mortality sources are seasonally restricted, then models that allow annual mortality to be partitioned
into seasonal hazard rates (rather than annual survival probabilities) further facilitates assessment of compensatory
mechanisms, as do integrated population models (IPMs) that allow compensation to occur via other vital rates (e.g.,
fecundity or immigration). IPMs also facilitate direct assessment of density-dependent relationships that might promote
demographic compensation. However,
identifying demographic compensation is complicated by temporal and individual variation in vital rates, and I hope to
inspire audience members to help address many of the remaining important questions.
Marlène Gamelon (Thursday, 20 April)
'Biotic interactions matter: how intra- and interspecific competition shape vital rates and population dynamics.'
Biography
Dr. Marlène Gamelon is a researcher at the Centre National de la Recherche Scientifique (CNRS) in the Biometry and
Evolutionary Biology Lab (LBBE) in Lyon, France and at the Centre for Biodiversity Dynamics at the University of Science
and Technology (NTNU) in Trondheim, Norway. She is a population ecologist interested in understanding how free-ranging
animal populations respond to environmental changes, including abiotic (climate conditions), anthropogenic (harvest), and
biotic (intra- and interspecific interactions) factors. She uses modelling approaches to study how these factors shape phenotypic traits, demographic
rates and population growth rate. Her research primarily relies on individual long-term monitoring of natural populations
of birds and mammals, with implications in conservation and management.
Abstract
Understanding components of intra- and interspecific competition is a major goal in ecological studies. Classical
approaches for the analyses of density dependence typically consider equal responses and contribution of all individuals
to density dependence. However, in age-structured populations, individuals of different ages may differ in their responses
to changes in population size and how they contribute to density dependence affecting the growth rate of the whole population.
Based on the long-term individual monitoring of sympatric competing species at several locations across Europe, we analyzed
capture-mark-recapture data to assess how vital rates (e.g. survival, reproduction) and population growth rate of the focal
species are affected by intra- and interspecific competition. We provide evidence for age-dependent
responses and effects of intra- and interspecific competition. Accounting for both age structure and biotic interactions
greatly improve predictions of annual variations in population size.
Alison Johnston (Monday, 17 April)
'People and birds: The challenges and power of citizen science data in ornithology.'
Biography
Dr Alison Johnston is a Reader in Statistics, based at the Centre for Research in Ecological
and Environmental Modelling at the University of St Andrews, UK. She is an ecological
statistician with broad interests in how we can use creative analytical methods to learn more
about the natural world, to understand the drivers of ecological changes, and to prioritise
conservation action. Most of her research has focussed on birds, with particular emphasis on
citizen/community science data, however she has also worked with a broad range of other data
including mark-recapture data, nest data, tagging data, aerial survey data, and acoustic data.
She uses a combination of both statistical and machine learning methods.
Abstract
Humans have a close relationship with birds, which has led to a long history of citizen/community science data contributing to ornithological advances. However, the last
couple of decades have seen a rapid expansion in observational data collected by volunteers in ornithology, driven by the growth of simpler and more flexible projects that
allow a broad spectrum of participation. The resulting datasets have huge power to expand our knowledge of bird distributions, migration routes, and population trends.
However, to extract reliable ecological knowledge, we need to address the numerous challenges that arise from these data and that can bias the ecological inference. It
also requires the development and application of robust analytical approaches to account for these sources of variation during data analysis. However, this work requires
us to understand people and how they relate to and record birds. Overall, there is a huge potential to use these large, messy, observational datasets to fill gaps in our
ornithological knowledge and strengthen ecological inference using CS data. To achieve these goals, we need to develop robust analytical approaches, built on the
foundation of the relationship between people and birds.
Aline Magdalena Lee (Wednesday, 18 April)
'The magic of mark-recapture.'
Biography
Dr. Aline Magdalena Lee is an associate professor and researcher at the Center for
Biodiversity Dynamics at the Norwegian
University of Science and Technology. Previously, she has been based at the University of California, Berkeley, and the University
of Aberdeen, Scotland. Her main research interest is understanding stochastic population and extinction dynamics within ecological
communities. She has worked with a wide range of species and systems, ranging from marine fish and daphnia to birds and ungulates.
Combining theoretical modeling with empirical data analysis is a central theme in her research. She has a particular fondness for
methods that can extract additional information from limited data, and for studies that attempt to uncover general
ecological processes while also teaching us about specific systems and species. She regularly uses mark-recapture models
and integrated population models in her work, alongside other methods.
Abstract
Methods for analyzing mark-recapture data have developed rapidly in recent decades and the amount and type of information that
can be extracted from such data is constantly increasing. At times, it can seem almost like magic. How can users that don't
necessarily understand all the details of the underlying math and coding be sure their analyses are in fact doing what they think
they are? How do we know whether we can trust our results? In this presentation I will use examples from my work to discuss a few
of the magical things that methods such as integrated population modeling
and multievent modeling can achieve, while demonstrating some ways that we can test whether our results are in fact telling
us what we think they are.
Matthieu Paquet (Tuesday, 18 April)
'Estimating immigration and density dependence using Integrated Population Models.'
Biography
Dr. Matthieu Paquet is interested in behavioral ecology and population dynamics and aims to better understand and
predict population fluctuations by integrating
information on interactions within (e.g. parental effects and social behaviour) as well as among species (e.g. competition
and predation), notably using Integrated Population Models.
Abstract
By combining several data sources that contain information on demographic parameters and population size, Integrated Population Models (IPMs)
have allowed us to i) jointly estimate population size and demographic parameters, and therefore density dependent effects on
the demographic parameters, ii) estimate parameters for which explicit data is hard to collect (e.g. immigration), and iii) estimate
the contribution of demographic parameters (and to some extent population size and other covariates) to variation in population growth,
in a unified statistical framework. I will briefly review recent developments, and
pinpoint some challenges, in the use of IPMs for estimating immigration and (intra and interspecies) density dependence, and their
potential importance as drivers of population dynamics.
