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Smith P, Guiver C, Adams B. Quantifying the per-capita contribution of all components of a migratory cycle: A modelling framework. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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2
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Payo-Payo A, Acker P, Bocedi G, Travis JMJ, Burthe SJ, Harris MP, Wanless S, Newell M, Daunt F, Reid JM. Modelling the responses of partially-migratory metapopulations to changing seasonal migration rates: from theory to data. J Anim Ecol 2022; 91:1781-1796. [PMID: 35633181 PMCID: PMC9545393 DOI: 10.1111/1365-2656.13748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/04/2022] [Indexed: 11/30/2022]
Abstract
Among‐individual and within‐individual variation in expression of seasonal migration versus residence is widespread in nature and could substantially affect the dynamics of partially migratory metapopulations inhabiting seasonally and spatially structured environments. However, such variation has rarely been explicitly incorporated into metapopulation dynamic models for partially migratory systems. We, therefore, lack general frameworks that can identify how variable seasonal movements, and associated season‐ and location‐specific vital rates, can control system persistence. We constructed a novel conceptual framework that captures full‐annual‐cycle dynamics and key dimensions of metapopulation structure for partially migratory species inhabiting seasonal environments. We conceptualize among‐individual variation in seasonal migration as two variable vital rates: seasonal movement probability and associated movement survival probability. We conceptualize three levels of within‐individual variation (i.e. plasticity), representing seasonal or annual variation in seasonal migration or lifelong fixed strategies. We formulate these concepts as a general matrix model, which is customizable for diverse life‐histories and seasonal landscapes. To illustrate how variable seasonal migration can affect metapopulation growth rate, demographic structure and vital rate elasticities, we parameterize our general models for hypothetical short‐ and longer‐lived species. Analyses illustrate that elasticities of seasonal movement probability and associated survival probability can sometimes equal or exceed those of vital rates typically understood to substantially influence metapopulation dynamics (i.e. seasonal survival probability or fecundity), that elasticities can vary non‐linearly, and that metapopulation outcomes depend on the level of within‐individual plasticity. We illustrate how our general framework can be applied to evaluate the consequences of variable and changing seasonal movement probability by parameterizing our models for a real partially migratory metapopulation of European shags Gulosus aristotelis assuming lifelong fixed strategies. Given observed conditions, metapopulation growth rate was most elastic to breeding season adult survival of the resident fraction in the dominant population. However, given doubled seasonal movement probability, variation in survival during movement would become the primary driver of metapopulation dynamics. Our general conceptual and matrix model frameworks, and illustrative analyses, thereby highlight complex ways in which structured variation in seasonal migration can influence dynamics of partially migratory metapopulations, and pave the way for diverse future theoretical and empirical advances.
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Affiliation(s)
- Ana Payo-Payo
- School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Avenue, Aberdeen, UK
| | - Paul Acker
- School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Avenue, Aberdeen, UK.,Centre for Biodiversity Dynamics, NTNU, Trondheim, Norway
| | - Greta Bocedi
- School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Avenue, Aberdeen, UK
| | - Justin M J Travis
- School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Avenue, Aberdeen, UK
| | - Sarah J Burthe
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, UK
| | - Michael P Harris
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, UK
| | - Sarah Wanless
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, UK
| | - Mark Newell
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, UK
| | - Francis Daunt
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, UK
| | - Jane M Reid
- School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Avenue, Aberdeen, UK.,Centre for Biodiversity Dynamics, NTNU, Trondheim, Norway
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Sample C, Bieri JA, Allen B, Dementieva Y, Carson A, Higgins C, Piatt S, Qiu S, Stafford S, Mattsson BJ, Semmens DJ, Diffendorfer JE, Thogmartin WE. Quantifying the Contribution of Habitats and Pathways to a Spatially Structured Population Facing Environmental Change. Am Nat 2020; 196:157-168. [PMID: 32673098 DOI: 10.1086/709009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The consequences of environmental disturbance and management are difficult to quantify for spatially structured populations because changes in one location carry through to other areas as a result of species movement. We develop a metric, G, for measuring the contribution of a habitat or pathway to network-wide population growth rate in the face of environmental change. This metric is different from other contribution metrics, as it quantifies effects of modifying vital rates for habitats and pathways in perturbation experiments. Perturbation treatments may range from small degradation or enhancement to complete habitat or pathway removal. We demonstrate the metric using a simple metapopulation example and a case study of eastern monarch butterflies. For the monarch case study, the magnitude of environmental change influences the ordering of node contribution. We find that habitats within which all individuals reside during one season are the most important to short-term network growth under complete removal scenarios, whereas the central breeding region contributes most to population growth over all but the strongest disturbances. The metric G provides for more efficient management interventions that proactively mitigate impacts of expected disturbances to spatially structured populations.
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Xu Y, Si Y, Takekawa J, Liu Q, Prins HHT, Yin S, Prosser DJ, Gong P, de Boer WF. A network approach to prioritize conservation efforts for migratory birds. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2020; 34:416-426. [PMID: 31268188 PMCID: PMC7154769 DOI: 10.1111/cobi.13383] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 06/12/2019] [Accepted: 06/27/2019] [Indexed: 06/09/2023]
Abstract
Habitat loss can trigger migration network collapse by isolating migratory bird breeding grounds from nonbreeding grounds. Theoretically, habitat loss can have vastly different impacts depending on the site's importance within the migratory corridor. However, migration-network connectivity and the impacts of site loss are not completely understood. We used GPS tracking data on 4 bird species in the Asian flyways to construct migration networks and proposed a framework for assessing network connectivity for migratory species. We used a node-removal process to identify stopover sites with the highest impact on connectivity. In general, migration networks with fewer stopover sites were more vulnerable to habitat loss. Node removal in order from the highest to lowest degree of habitat loss yielded an increase of network resistance similar to random removal. In contrast, resistance increased more rapidly when removing nodes in order from the highest to lowest betweenness value (quantified by the number of shortest paths passing through the specific node). We quantified the risk of migration network collapse and identified crucial sites by first selecting sites with large contributions to network connectivity and then identifying which of those sites were likely to be removed from the network (i.e., sites with habitat loss). Among these crucial sites, 42% were not designated as protected areas. Setting priorities for site protection should account for a site's position in the migration network, rather than only site-specific characteristics. Our framework for assessing migration-network connectivity enables site prioritization for conservation of migratory species.
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Affiliation(s)
- Yanjie Xu
- Ministry of Education Key Laboratory for Earth System Modelling and Department of Earth System ScienceTsinghua University30 Shuangqing RoadBeijing100084China
- Resource Ecology GroupWageningen University and ResearchDroevendaalsesteeg 3a6708 PBWageningenthe Netherlands
| | - Yali Si
- Ministry of Education Key Laboratory for Earth System Modelling and Department of Earth System ScienceTsinghua University30 Shuangqing RoadBeijing100084China
- Resource Ecology GroupWageningen University and ResearchDroevendaalsesteeg 3a6708 PBWageningenthe Netherlands
| | - John Takekawa
- Suisun Resource Conservation District2544 Grizzly Island RoadSuisun CityCA94585U.S.A.
| | - Qiang Liu
- Network Architectures and Services GroupDelft University of TechnologyMekelweg 42600GADelftthe Netherlands
| | - Herbert H. T. Prins
- Resource Ecology GroupWageningen University and ResearchDroevendaalsesteeg 3a6708 PBWageningenthe Netherlands
| | - Shenglai Yin
- Resource Ecology GroupWageningen University and ResearchDroevendaalsesteeg 3a6708 PBWageningenthe Netherlands
| | - Diann J. Prosser
- U.S. Geological SurveyPatuxent Wildlife Research CentreLaurelMD20708U.S.A.
| | - Peng Gong
- Ministry of Education Key Laboratory for Earth System Modelling and Department of Earth System ScienceTsinghua University30 Shuangqing RoadBeijing100084China
| | - Willem F. de Boer
- Resource Ecology GroupWageningen University and ResearchDroevendaalsesteeg 3a6708 PBWageningenthe Netherlands
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Sample C, Bieri JA, Allen B, Dementieva Y, Carson A, Higgins C, Piatt S, Qiu S, Stafford S, Mattsson BJ, Semmens DJ, Thogmartin WE, Diffendorfer JE. Quantifying source and sink habitats and pathways in spatially structured populations: A generalized modelling approach. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Fraser KC, Davies KTA, Davy CM, Ford AT, Flockhart DTT, Martins EG. Tracking the Conservation Promise of Movement Ecology. Front Ecol Evol 2018. [DOI: 10.3389/fevo.2018.00150] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Reid JM, Travis JMJ, Daunt F, Burthe SJ, Wanless S, Dytham C. Population and evolutionary dynamics in spatially structured seasonally varying environments. Biol Rev Camb Philos Soc 2018; 93:1578-1603. [PMID: 29575449 PMCID: PMC6849584 DOI: 10.1111/brv.12409] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 02/17/2018] [Accepted: 02/20/2018] [Indexed: 01/12/2023]
Abstract
Increasingly imperative objectives in ecology are to understand and forecast population dynamic and evolutionary responses to seasonal environmental variation and change. Such population and evolutionary dynamics result from immediate and lagged responses of all key life‐history traits, and resulting demographic rates that affect population growth rate, to seasonal environmental conditions and population density. However, existing population dynamic and eco‐evolutionary theory and models have not yet fully encompassed within‐individual and among‐individual variation, covariation, structure and heterogeneity, and ongoing evolution, in a critical life‐history trait that allows individuals to respond to seasonal environmental conditions: seasonal migration. Meanwhile, empirical studies aided by new animal‐tracking technologies are increasingly demonstrating substantial within‐population variation in the occurrence and form of migration versus year‐round residence, generating diverse forms of ‘partial migration’ spanning diverse species, habitats and spatial scales. Such partially migratory systems form a continuum between the extreme scenarios of full migration and full year‐round residence, and are commonplace in nature. Here, we first review basic scenarios of partial migration and associated models designed to identify conditions that facilitate the maintenance of migratory polymorphism. We highlight that such models have been fundamental to the development of partial migration theory, but are spatially and demographically simplistic compared to the rich bodies of population dynamic theory and models that consider spatially structured populations with dispersal but no migration, or consider populations experiencing strong seasonality and full obligate migration. Second, to provide an overarching conceptual framework for spatio‐temporal population dynamics, we define a ‘partially migratory meta‐population’ system as a spatially structured set of locations that can be occupied by different sets of resident and migrant individuals in different seasons, and where locations that can support reproduction can also be linked by dispersal. We outline key forms of within‐individual and among‐individual variation and structure in migration that could arise within such systems and interact with variation in individual survival, reproduction and dispersal to create complex population dynamics and evolutionary responses across locations, seasons, years and generations. Third, we review approaches by which population dynamic and eco‐evolutionary models could be developed to test hypotheses regarding the dynamics and persistence of partially migratory meta‐populations given diverse forms of seasonal environmental variation and change, and to forecast system‐specific dynamics. To demonstrate one such approach, we use an evolutionary individual‐based model to illustrate that multiple forms of partial migration can readily co‐exist in a simple spatially structured landscape. Finally, we summarise recent empirical studies that demonstrate key components of demographic structure in partial migration, and demonstrate diverse associations with reproduction and survival. We thereby identify key theoretical and empirical knowledge gaps that remain, and consider multiple complementary approaches by which these gaps can be filled in order to elucidate population dynamic and eco‐evolutionary responses to spatio‐temporal seasonal environmental variation and change.
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Affiliation(s)
- Jane M Reid
- School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Avenue, Aberdeen, AB24 2TZ, U.K
| | - Justin M J Travis
- School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Avenue, Aberdeen, AB24 2TZ, U.K
| | - Francis Daunt
- Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, U.K
| | - Sarah J Burthe
- Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, U.K
| | - Sarah Wanless
- Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, U.K
| | - Calvin Dytham
- Department of Biology, University of York, Heslington, York, YO10 5DD, U.K
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8
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Sample C, Fryxell JM, Bieri JA, Federico P, Earl JE, Wiederholt R, Mattsson BJ, Flockhart DTT, Nicol S, Diffendorfer JE, Thogmartin WE, Erickson RA, Norris DR. A general modeling framework for describing spatially structured population dynamics. Ecol Evol 2017; 8:493-508. [PMID: 29321888 PMCID: PMC5756893 DOI: 10.1002/ece3.3685] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 09/27/2017] [Accepted: 11/08/2017] [Indexed: 11/06/2022] Open
Abstract
Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles.
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Affiliation(s)
| | - John M Fryxell
- Department of Integrative Biology University of Guelph Guelph ON Canada
| | - Joanna A Bieri
- Department of Mathematics University of Redlands Redlands CA USA
| | - Paula Federico
- Department of Mathematics, Computer Science and Physics Capital University Columbus OH USA
| | - Julia E Earl
- School of Biological Sciences Louisiana Tech University Ruston LA USA
| | | | - Brady J Mattsson
- Institute of Silviculture University of Natural Resources and Life Sciences Vienna Austria.,Present address: Institute of Wildlife Biology & Game Management University of Natural Resources & Life Sciences (BOKU) Vienna Austria
| | | | - Sam Nicol
- CSIRO Land and Water, EcoSciences Precinct Dutton Park Qld Australia
| | - Jay E Diffendorfer
- U.S. Geological Survey, Geosciences and Environmental Change Science Center Denver CO USA
| | - Wayne E Thogmartin
- U.S. Geological Survey Upper Midwest Environmental Sciences Center La Crosse WI USA
| | - Richard A Erickson
- U.S. Geological Survey Upper Midwest Environmental Sciences Center La Crosse WI USA
| | - D Ryan Norris
- Department of Integrative Biology University of Guelph Guelph ON Canada
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