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2. Connecting the data landscape of long-term ecological studies: The SPI-Birds data hub

Author

Culina, Antica, Adriaensen, Frank, Bailey, Liam D., Burgess, Malcolm D., Charmantier, Anne, Cole, Ella F., Eeva, Tapio, Matthysen, Erik, Nater, Chloé R., Sheldon, Ben C., Sæther, Bernt Erik, Vriend, Stefan J.G., Zajkova, Zuzana, Adamík, Peter, Aplin, Lucy M., Angulo, Elena, Artemyev, Alexandr, Barba, Emilio, Barišić, Sanja, Belda, Eduardo, Bilgin, Cemal Can, Bleu, Josefa, Both, Christiaan, Bouwhuis, Sandra, Branston, Claire J., Broggi, Juli, Burke, Terry, Bushuev, Andrey, Camacho, Carlos, Campobello, Daniela, Canal, David, Cantarero, Alejandro, Caro, Samuel P., Cauchoix, Maxime, Chaine, Alexis, Cichoń, Mariusz, Ćiković, Davor, Cusimano, Camillo A., Deimel, Caroline, Dhondt, André A., Dingemanse, Niels J., Doligez, Blandine, Dominoni, Davide M., Doutrelant, Claire, Drobniak, Szymon M., Dubiec, Anna, Eens, Marcel, Einar Erikstad, Kjell, Espín, Silvia, Farine, Damien R., Figuerola, Jordi, Kavak Gülbeyaz, Pınar, Grégoire, Arnaud, Hartley, Ian R., Hau, Michaela, Hegyi, Gergely, Hille, Sabine, Hinde, Camilla A., Holtmann, Benedikt, Ilyina, Tatyana, Isaksson, Caroline, Iserbyt, Arne, Ivankina, Elena, Kania, Wojciech, Kempenaers, Bart, Kerimov, Anvar, Komdeur, Jan, Korsten, Peter, Král, Miroslav, Krist, Miloš, Lambrechts, Marcel, Lara, Carlos E., Leivits, Agu, Liker, András, Lodjak, Jaanis, Mägi, Marko, Mainwaring, Mark C., Mänd, Raivo, Massa, Bruno, Massemin, Sylvie, Martínez-Padilla, Jesús, Mazgajski, Tomasz D., Mennerat, Adèle, Moreno, Juan, Mouchet, Alexia, Nakagawa, Shinichi, Nilsson, Jan Åke, Nilsson, Johan F., Cláudia Norte, Ana, van Oers, Kees, Orell, Markku, Potti, Jaime, Quinn, John L., Réale, Denis, Kristin Reiertsen, Tone, Rosivall, Balázs, Russell, Andrew F., Rytkönen, Seppo, Sánchez-Virosta, Pablo, Santos, Eduardo S.A., Schroeder, Julia, Senar, Juan Carlos, Seress, Gábor, Slagsvold, Tore, Szulkin, Marta, Teplitsky, Céline, Tilgar, Vallo, Tolstoguzov, Andrey, Török, János, Valcu, Mihai, Vatka, Emma, Verhulst, Simon, Watson, Hannah, Yuta, Teru, Zamora-Marín, José M., Visser, Marcel E., Culina, Antica, Adriaensen, Frank, Bailey, Liam D., Burgess, Malcolm D., Charmantier, Anne, Cole, Ella F., Eeva, Tapio, Matthysen, Erik, Nater, Chloé R., Sheldon, Ben C., Sæther, Bernt Erik, Vriend, Stefan J.G., Zajkova, Zuzana, Adamík, Peter, Aplin, Lucy M., Angulo, Elena, Artemyev, Alexandr, Barba, Emilio, Barišić, Sanja, Belda, Eduardo, Bilgin, Cemal Can, Bleu, Josefa, Both, Christiaan, Bouwhuis, Sandra, Branston, Claire J., Broggi, Juli, Burke, Terry, Bushuev, Andrey, Camacho, Carlos, Campobello, Daniela, Canal, David, Cantarero, Alejandro, Caro, Samuel P., Cauchoix, Maxime, Chaine, Alexis, Cichoń, Mariusz, Ćiković, Davor, Cusimano, Camillo A., Deimel, Caroline, Dhondt, André A., Dingemanse, Niels J., Doligez, Blandine, Dominoni, Davide M., Doutrelant, Claire, Drobniak, Szymon M., Dubiec, Anna, Eens, Marcel, Einar Erikstad, Kjell, Espín, Silvia, Farine, Damien R., Figuerola, Jordi, Kavak Gülbeyaz, Pınar, Grégoire, Arnaud, Hartley, Ian R., Hau, Michaela, Hegyi, Gergely, Hille, Sabine, Hinde, Camilla A., Holtmann, Benedikt, Ilyina, Tatyana, Isaksson, Caroline, Iserbyt, Arne, Ivankina, Elena, Kania, Wojciech, Kempenaers, Bart, Kerimov, Anvar, Komdeur, Jan, Korsten, Peter, Král, Miroslav, Krist, Miloš, Lambrechts, Marcel, Lara, Carlos E., Leivits, Agu, Liker, András, Lodjak, Jaanis, Mägi, Marko, Mainwaring, Mark C., Mänd, Raivo, Massa, Bruno, Massemin, Sylvie, Martínez-Padilla, Jesús, Mazgajski, Tomasz D., Mennerat, Adèle, Moreno, Juan, Mouchet, Alexia, Nakagawa, Shinichi, Nilsson, Jan Åke, Nilsson, Johan F., Cláudia Norte, Ana, van Oers, Kees, Orell, Markku, Potti, Jaime, Quinn, John L., Réale, Denis, Kristin Reiertsen, Tone, Rosivall, Balázs, Russell, Andrew F., Rytkönen, Seppo, Sánchez-Virosta, Pablo, Santos, Eduardo S.A., Schroeder, Julia, Senar, Juan Carlos, Seress, Gábor, Slagsvold, Tore, Szulkin, Marta, Teplitsky, Céline, Tilgar, Vallo, Tolstoguzov, Andrey, Török, János, Valcu, Mihai, Vatka, Emma, Verhulst, Simon, Watson, Hannah, Yuta, Teru, Zamora-Marín, José M., and Visser, Marcel E.

Abstract

The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long‐term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad‐scale global issues (e.g. climate change). To solve data integration issues and enable a new scale of ecological and evolutionary research based on long‐term studies of birds, we have created the SPI‐Birds Network and Database (www.spibirds.org)—a large‐scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI‐Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting. SPI‐Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community‐derived data and meta‐data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta‐data language). The encouraging community involvement stems from SPI‐Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI‐Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community‐speci

Published
2021

5. A 50-year series of mark-recapture data of large-sized brown trout (Salmo trutta) from Lake Mjøsa, Norway

Author

Moe, S. Jannicke, Nater, Chloé R., Rustadbakken, Atle, Vøllestad, L. Asbjørn, Lund, Espen, Qvenild, Tore, Hegge, Ola, and Aass, Per

Abstract

Individual-based mark-recapture data from animal population provide a wealth of opportunities for studies, such as individual variation in vital rates ( e.g . survival and reproduction) the links between vital rates and population dynamics. However, maintaining the collection of individual-based data over long time periods comes with large logistic efforts and costs, and studies spanning over decades are therefore rare. Salmonid fishes are of high ecological, cultural, and economical value, but many native wild populations remain in decline. Conservation concerns are particularly great for migratory salmonids as local adaptations and long life spans make them very vulnerable to environmental changes and habitat modifications, e.g., due to hydroelectric power production. This paper describes a unique long-term mark-recapture data set from a land-locked population of large-sized, piscivorous brown trout ( Salmo trutta ) in Norway: the Hunder trout, named after the main water fall (Hunderfossen) in its spawning river. During the period 1966 to 2017, nearly 15,000 Hunder trout have been captured and individually marked during their spawning migration from Lake Mjøsa to the river Gubrandsdalslågen. Fish were first captured and marked while passing a fish ladder within the hydroelectric dam at the Hunderfossen waterfall, and more than 4,000 were later recaptured there alive and/or reported as dead elsewhere. In combination with related life-history and environmental data, these data can be used to gain insights into a variety of questions regarding management and conservation of migratory salmonid populations. In this data paper, we describe (1) a database containing observations on captures and related life-history data obtained from scales (the SUSTAIN trout database), and (2) a publicly available dataset extracted from this database for analysis of survival (the SUSTAIN trout survival dataset).

Published
2019
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6. Density feedbacks mediate effects of environmental change on population dynamics of a semidesert rodent

Author

Childs, Dylan, Childs, D ( Dylan ), Nater, Chloé R; https://orcid.org/0000-0002-7975-0108, van Benthem, Koen J, Canale, Cindy I, Schradin, Carsten; https://orcid.org/0000-0002-2706-2960, Ozgul, Arpat; https://orcid.org/0000-0001-7477-2642, Childs, Dylan, Childs, D ( Dylan ), Nater, Chloé R; https://orcid.org/0000-0002-7975-0108, van Benthem, Koen J, Canale, Cindy I, Schradin, Carsten; https://orcid.org/0000-0002-2706-2960, and Ozgul, Arpat; https://orcid.org/0000-0001-7477-2642

Abstract

Population dynamics are the result of an interplay between extrinsic and intrinsic environmental drivers. Predicting the effects of environmental change on wildlife populations therefore requires a thorough understanding of the mechanisms through which different environmental drivers interact to generate changes in population size and structure. In this study, we disentangled the roles of temperature, food availability and population density in shaping short‐ and long‐term population dynamics of the African striped mouse, a small rodent inhabiting a semidesert with high intra‐ and interannual variation in environmental conditions. We parameterized a female‐only stage‐structured matrix population model with vital rates depending on temperature, food availability and population density, using monthly mark–recapture data from 1609 mice trapped over 9 years (2005–2014). We then applied perturbation analyses to determine relative strengths and demographic pathways of these drivers in affecting population dynamics. Furthermore, we used stochastic population projections to gain insights into how three different climate change scenarios might affect size, structure and persistence of this population. We identified food availability, acting through reproduction, as the main driver of changes in both short‐ and long‐term population dynamics. This mechanism was mediated by strong density feedbacks, which stabilized the population after high peaks and allowed it to recover from detrimental crashes. Density dependence thus buffered the population against environmental change, and even adverse climate change scenarios were predicted to have little effect on population persistence (extinction risk over 100 years <5%) despite leading to overall lower abundances. Explicitly linking environment–demography relationships to population dynamics allowed us to accurately capture past population dynamics. It further enabled establishing the roles and relative importances of extrinsic and i

Published
2018

7. Long-term mark-recapture and growth data for large-sized migratory brown trout (Salmo trutta) from Lake Mjøsa, Norway.

Author

Moe, S. Jannicke, Nater, Chloé R., Rustadbakken, Atle, Vøllestad, L. Asbjørn, Lund, Espen, Qvenild, Tore, Hegge, Ola, and Aass, Per

Subjects
BROWN trout, AQUATIC ecology, MIGRATORY fishes, SCLEROCHRONOLOGY, FRESHWATER ecology
Abstract

Long-term data from marked animals provide a wealth of opportunities for studies with high relevance to both basic ecological understanding and successful management in a changing world. The key strength of such data is that they allow us to quantify individual variation in vital rates (e.g. survival, growth, reproduction) and then link it mechanistically to dynamics at the population level. However, maintaining the collection of individual-based data over long time periods comes with large logistic efforts and costs and studies spanning over decades are therefore rare. This is the case particularly for migratory aquatic species, many of which are in decline despite their high ecological, cultural and economical value. [ABSTRACT FROM AUTHOR]

Published
2020
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9. Density feedbacks mediate effects of environmental change on population dynamics of a semidesert rodent

Author

Cindy I. Canale, Chloé R. Nater, Arpat Ozgul, Koen J. van Benthem, Carsten Schradin, Department of Evolutionary Biology and Environmental Studies, University of Zurich, Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences [Oslo], Faculty of Mathematics and Natural Sciences [Oslo], University of Oslo (UiO)-University of Oslo (UiO)-Faculty of Mathematics and Natural Sciences [Oslo], University of Oslo (UiO)-University of Oslo (UiO), School of Animal, Plant & Environmental Sciences, University of the Witwatersrand [Johannesburg] (WITS), Département Ecologie, Physiologie et Ethologie (DEPE-IPHC), Institut Pluridisciplinaire Hubert Curien (IPHC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Childs, Dylan, and Nater, Chloé R

Subjects
0106 biological sciences, Environmental change, Evolution, Climate Change, Population, Population Dynamics, Climate change, Rodentia, Biology, 010603 evolutionary biology, 01 natural sciences, Population density, 10127 Institute of Evolutionary Biology and Environmental Studies, Mice, Behavior and Systematics, Animals, education, Ecology, Evolution, Behavior and Systematics, ComputingMilieux_MISCELLANEOUS, Demography, 2. Zero hunger, Population Density, education.field_of_study, Ecology, 010604 marine biology & hydrobiology, Population size, 15. Life on land, Density dependence, 1105 Ecology, Evolution, Behavior and Systematics, Population model, 13. Climate action, [SDE]Environmental Sciences, 570 Life sciences, biology, 590 Animals (Zoology), Animal Science and Zoology, Female, Vital rates, 1103 Animal Science and Zoology
Abstract

1. Population dynamics are the result of an interplay between extrinsic and intrinsic environmental drivers. Predicting the effects of environmental change on wildlife populations therefore requires a thorough understanding of the mechanisms through which different environmental drivers interact to generate changes in population size and structure. 2. In this study, we disentangled the roles of temperature, food availability and population density in shaping short‐ and long‐term population dynamics of the African striped mouse, a small rodent inhabiting a semidesert with high intra‐ and interannual variation in environmental conditions. 3. We parameterized a female‐only stage‐structured matrix population model with vital rates depending on temperature, food availability and population density, using monthly mark–recapture data from 1609 mice trapped over 9 years (2005–2014). We then applied perturbation analyses to determine relative strengths and demographic pathways of these drivers in affecting population dynamics. Furthermore, we used stochastic population projections to gain insights into how three different climate change scenarios might affect size, structure and persistence of this population. 4. We identified food availability, acting through reproduction, as the main driver of changes in both short‐ and long‐term population dynamics. This mechanism was mediated by strong density feedbacks, which stabilized the population after high peaks and allowed it to recover from detrimental crashes. Density dependence thus buffered the population against environmental change, and even adverse climate change scenarios were predicted to have little effect on population persistence (extinction risk over 100 years

Published
2018

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