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1. Sustainable strategies for harvesting predators and prey in a fluctuating environment

Author

Edwige Bellier, Bernt-Erik Sæther, Steinar Engen, Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Department of Biology [Trondheim] (IBI NTNU), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU), Norwegian University of Science and Technology (NTNU), and The Arctic University of Norway (UiT)

Subjects
0106 biological sciences, education.field_of_study, Stochastic modelling, Ecology, 010604 marine biology & hydrobiology, Ecological Modeling, [SDV]Life Sciences [q-bio], Population, Biology, 010603 evolutionary biology, 01 natural sciences, Predation, education, Stable state
Abstract

International audience; The effects of harvest and fluctuating environment on interdependent predators and prey are complex and not well-known. We define a stochastic model where the predators and prey dynamically interact. The novelty of the model holds on the fact that predators and prey dynamics are simultaneously affected by correlated environmental noises. Interacting predators and prey are harvested using a proportional threshold harvesting strategy that accounts for stochastic population dynamics. Optimal yield of prey can be obtained with identical harvesting strategies when the predators and prey responded to the environment similarly (i.e., synchrony between species) and differently (i.e., asynchrony between species). Remarkably, our study demonstrates that two different harvesting strategies, the proportional harvesting strategy for the prey and the proportional threshold harvesting strategy for the predators, are needed to optimize the annual yield of predators and prey when both species are harvested simultaneously. Our study finds optimal strategies for harvesting interacting species affected by environmental variations (i.e., correlated noises) with parameters representing the joint dynamics of predators and prey at a stable state.

Published
2020
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2. Predicting the effects of climate change on bird population dynamics

Author

Steinar Engen, Bernt-Erik Sæther, Marlène Gamelon, Vidar Grøtan, Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Peter O. Dunn, and Anders Pape Møller

Subjects
education.field_of_study, demography, stochasticity, Ecology, Population, population models, age structure, prediction, density-dependence, Geography, 13. Climate action, Effects of global warming, growth rate, [SDV.EE.BIO]Life Sciences [q-bio]/Ecology, environment/Bioclimatology, education
Abstract

International audience; Climate variation strongly influences fluctuations in size of avian populations. In this chapter, we show that it is difficult to predict how the abundance of birds will respond to climate change. A major reason for this is that most available time series of fluctuations in population size are in a statistical sense short, thus often resulting in large uncertainties in parameter estimates. We therefore argue that reliable population predictions must be based on models that capture how climate change will affect vital rates as well as including other processes (e.g. density-dependences) known to affect the population dynamics of the species in question. Our survey of examples of such forecast studies show that reliable predictions necessarily contain a high level of uncertainty. A major reason for this is that avian population dynamics are strongly influenced by environmental stochasticity, which is for most species, irrespective of their life history, the most important driver of fluctuations in population size. Credible population predictions must therefore assess the effects of such uncertainties as well as biases in population estimates.

Published
2019
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3. Demographic routes to variability and regulation in bird populations

Author

Kurt Jerstad, Bernt-Erik Sæther, Ben J. Hatchwell, Ole Wiggo Røstad, Hannu Pietiäinen, B. Rosemary Grant, Alexandre Roulin, Lars Gustafsson, Marcel E. Visser, Patrik Karell, Steinar Engen, Peter R. Grant, Jon E. Brommer, Tim Coulson, Vidar Grøtan, Henri Weimerskirch, Department of Biology (Norwegian University of Science and Technology), Norwegian University of Science and Technology [Trondheim] (NTNU), Department of Mathematical Sciences (Norwegian University of Science and Technology), Division of Biology and Centre for Population Biology, Imperial College London, Department of Ecology and Evolutionary Biology [Princeton], Princeton University, Department of Animal Ecology, Netherlands Institute of Ecology, Department of Biology, University Hill & University of Turku, Department of Animal Ecology - Evolutionary Biology Centre, Uppsala University, Department of Animal and Plant Sciences [Sheffield], University of Sheffield [Sheffield], Norwegian Polar Institute, Aurebekksveien 61, NO-4516 Mandal, Norway, Université Paris Diderot - Paris 7 (UPD7), Department of Biosciences, Monmouth University, Coastal Zone Research Team, Novia University of Applied Sciences, Faculty of Biological and Environmental Sciences [Helsinki], University of Helsinki, Department of Ecology and Evolution - Biophore, Université de Lausanne (UNIL), Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences (NMBU), Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Institut National de la Recherche Agronomique (INRA)-Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS), Biosciences, Ecology and Evolutionary Biology, Department of Biology [Trondheim] (IBI NTNU), Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU), Jerstad Viltforvaltning, Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Animal Ecology (AnE)

Subjects
0106 biological sciences, Male, DYNAMICS, DENSITY-DEPENDENCE, Time Factors, Population Dynamics, General Physics and Astronomy, 01 natural sciences, education.field_of_study, Multidisciplinary, CLIMATE-CHANGE, Ecology, Population size, Biodiversity, Variation (linguistics), Density dependence, international, 1181 Ecology, evolutionary biology, [SDE]Environmental Sciences, SURVIVAL, Female, DARWINS FINCHES, Science, Population, Antarctic Regions, Biology, 010603 evolutionary biology, General Biochemistry, Genetics and Molecular Biology, Article, Birds, Species Specificity, Population growth, Carrying capacity, Animals, education, Population Growth, Ecosystem, TEMPORAL VARIATION, LIFETIME FITNESS, Ekologi, Population Density, Stochastic Processes, Models, Statistical, 010604 marine biology & hydrobiology, Small population size, General Chemistry, Interspecific competition, Strigiformes, Genetics, Population, BREEDING BIRD, BARN OWL, PATTERNS, ta1181
Abstract

There is large interspecific variation in the magnitude of population fluctuations, even among closely related species. The factors generating this variation are not well understood, primarily because of the challenges of separating the relative impact of variation in population size from fluctuations in the environment. Here, we show using demographic data from 13 bird populations that magnitudes of fluctuations in population size are mainly driven by stochastic fluctuations in the environment. Regulation towards an equilibrium population size occurs through density-dependent mortality. At small population sizes, population dynamics are primarily driven by environment-driven variation in recruitment, whereas close to the carrying capacity K, variation in population growth is more strongly influenced by density-dependent mortality of both juveniles and adults. Our results provide evidence for the hypothesis proposed by Lack that population fluctuations in birds arise from temporal variation in the difference between density-independent recruitment and density-dependent mortality during the non-breeding season., Why does population variability differ? Sæther et al. show that the magnitude of temporal variation of avian population fluctuations is mainly determined by environmental fluctuations affecting recruitment, whereas regulation of mean population size occurs through density-dependent mortality.

Published
2016
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4. Distance decay of similarity, effects of environmental noise and ecological heterogeneity among species in the spatio-temporal dynamics of a dispersal-limited community

Author

Ivar Herfindal, Anders Gravbrøt Finstad, Edwige Bellier, Vidar Grøtan, Ann Kristin Schartau, Steinar Engen, Norwegian Institute for Nature Research (NINA), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Department of Mathematical Sciences (Norwegian University of Science and Technology), Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU), Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), and Centre for Conservation Biology

Subjects
Distance decay, Ecology, [SDV]Life Sciences [q-bio], Community structure, 15. Life on land, Biology, 6. Clean water, Overdispersion, Abundance (ecology), Poisson sampling, Biological dispersal, Species richness, Relative species abundance, Ecology, Evolution, Behavior and Systematics, ComputingMilieux_MISCELLANEOUS
Abstract

The joint spatial and temporal fluctuations in community structure may be due to dispersal, variation in environmental conditions, ecological heterogeneity among species and demographic stochasticity. These factors are not mutually exclusive, and their relative contribution towards shaping species abundance distributions and in causing species fluctuations have been hard to disentangle. To better understand community dynamics when the exchange of individuals between localities is very low, we studied the dynamics of the freshwater zooplankton communities in 17 lakes located in independent catchment areas, sampled at end of summer from 2002 to 2008 in Norway. We analysed the joint spatial and temporal fluctuations in the community structure by fitting the two-dimensional Poisson lognormal model under a two-stage sampling scheme. We partitioned the variance of the distribution of log abundance for a random species at a random time and location into components of demographic stochasticity, ecological heterogeneity among species, and independent environmental noise components for the different species. Non-neutral mechanisms such as ecological heterogeneity among species (20%) and spatiotemporal variation in the environment (75%) explained the majority of the variance in log abundances. Overdispersion relative to Poisson sampling and demographic stochasticity had a small contribution to the variance (5%). Among a set of environmental variables, lake acidity was the environmental variable that was most strongly related to decay of community similarity in space and time.

Published
2013
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5. How life history influences population dynamics in fluctuating environments

Author

Malcolm A. C. Nicoll, Andrew R. Jenkins, Carl G. Jones, Ken Norris, Steinar Engen, Arpat Ozgul, Romain Altwegg, Tim Coulson, Henri Weimerskirch, F. Stephen Dobson, Christophe Barbraud, Peter H. Becker, Vidar Grøtan, Daniel T. Blumstein, Marco Festa-Bianchet, Bernt-Erik Sæther, Madan K. Oli, Kenneth B. Armitage, Jean-Michel Gaillard, Centre for Conservation Biology, Norwegian University of Science and Technology [Trondheim] (NTNU), Division of Biology and Centre for Population Biology, Imperial College London, Department of Mathematical Sciences (Norwegian University of Science and Technology), Kirstenbosch Research Centre, South African National Biodiversity Institute, Department of Ecology and Evolutionary Biology, University of Kansas [Lawrence] (KU), Centre d'études biologiques de Chizé (CEBC), Centre National de la Recherche Scientifique (CNRS), Institute of Avian Research, University of California [Los Angeles] (UCLA), University of California-University of California, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Paul-Valéry - Montpellier 3 (UM3), Department of Zoology, Auburn University (AU), Département de Biologie, Université de Sherbrooke [Sherbrooke], Biodémographie évolutive, Département écologie évolutive [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), DST/NRF Centre of Excellence, University of Cape Town-Percy FitzPatrick Institute of African Ornithology, Mauritian Wildlife Foundation, Durrell Wildlife Conservation Trust, Centre for Agri-Environment Research (CAER), University of Reading (UOR), Centre for Agro-Environmental Research, Department of Wildlife Ecology and Conservation, University of Florida [Gainesville], Institute of Evolutionary Biology and Environmental Studies, University of Zürich [Zürich] (UZH), Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU), Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Département de biologie [Sherbrooke] (UdeS), Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), University of Florida [Gainesville] (UF), Universität Zürich [Zürich] = University of Zurich (UZH), Centre d'Études Biologiques de Chizé (CEBC), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), University of California (UC)-University of California (UC), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and University of Zurich

Subjects
0106 biological sciences, life history, Time Factors, Ecology (disciplines), Population, Population Dynamics, stochastic demography, Biology, Environment, demographic stochasticit, 010603 evolutionary biology, 01 natural sciences, Models, Biological, Birds, 10127 Institute of Evolutionary Biology and Environmental Studies, 03 medical and health sciences, Species Specificity, Population growth, Animals, Life history, education, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Mammals, 0303 health sciences, education.field_of_study, Stochastic Processes, reproductive value, Generation time, Ecology, Reproduction, Age Factors, Contrast (statistics), 15. Life on land, 1105 Ecology, Evolution, Behavior and Systematics, Variation (linguistics), [SDE]Environmental Sciences, 570 Life sciences, biology, 590 Animals (Zoology), Reproductive value, environmental stochasticity
Abstract

International audience; A major question in ecology is how age-specific variation in demographic parameters influences population dynamics. Based on long-term studies of growing populations of birds and mammals, we analyze population dynamics by using fluctuations in the total reproductive value of the population. This enables us to account for random fluctuations in age distribution. The influence of demographic and environmental stochasticity on the population dynamics of a species decreased with generation time. Variation in age-specific contributions to total reproductive value and to stochastic components of population dynamics was correlated with the position of the species along the slow-fast continuum of life-history variation. Younger age classes relative to the generation time accounted for larger contributions to the total reproductive value and to demographic stochasticity in "slow" than in "fast" species, in which many age classes contributed more equally. In contrast, fluctuations in population growth rate attributable to stochastic environmental variation involved a larger proportion of all age classes independent of life history. Thus, changes in population growth rates can be surprisingly well explained by basic species-specific life-history characteristics.

Published
2013
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6. Extinction in relation to demographic and environmental stochasticity in age-structured models

Author

Bernt-Erik Sæther, Steinar Engen, Russell Lande, Henri Weimerskirch, and Bonnet, Delphine

Subjects
Statistics and Probability, Stochastic growth rate, Leslie matrix, Population, Population Dynamics, Population process, Biology, Models, Biological, General Biochemistry, Genetics and Molecular Biology, Birds, Indian Ocean Islands, Statistics, Animals, Computer Simulation, education, education.field_of_study, Stochastic Processes, Age-structure, Extinction, General Immunology and Microbiology, Stochastic population models, Applied Mathematics, Age Factors, General Medicine, Variance (accounting), Covariance, Fecundity, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, [SDE.MCG] Environmental Sciences/Global Changes, Demographic stochasticity, Modeling and Simulation, Female, [SDE.ES] Environmental Sciences/Environmental and Society, Vital rates, General Agricultural and Biological Sciences, Environmental stochasticity
Abstract

The demographic variance of an age-structured population is defined. This parameter is further split into components generated by demographic stochasticity in each vital rate. The applicability of these parameters are investigated by checking how an age-structured population process can be approximated by a diffusion with only three parameters. These are the deterministic growth rate computed from the expected projection matrix and the environmental and demographic variances. We also consider age-structured populations where the fecundity at any stage is either zero or one, and there is neither environmental stochasticity nor dependence between individual fecundity and survival. In this case the demographic variance is uniquely determined by the vital rates defining the projection matrix. The demographic variance for a long-lived bird species, the wandering albatross in the southwestern part of the Indian Ocean, is estimated. We also compute estimates of the age-specific contributions to the total demographic variance from survival, fecundity and the covariance between survival and fecundity.

Published
2005

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