Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics
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{"title"=>"Metamodels for transdisciplinary analysis of wildlife population dynamics", "type"=>"journal", "authors"=>[{"first_name"=>"Robert C.", "last_name"=>"Lacy", "scopus_author_id"=>"7004534220"}, {"first_name"=>"Philip S.", "last_name"=>"Miller", "scopus_author_id"=>"7404427156"}, {"first_name"=>"Philip J.", "last_name"=>"Nyhus", "scopus_author_id"=>"6701551347"}, {"first_name"=>"J. P.", "last_name"=>"Pollak", "scopus_author_id"=>"36182247500"}, {"first_name"=>"Becky E.", "last_name"=>"Raboy", "scopus_author_id"=>"6701801450"}, {"first_name"=>"Sara L.", "last_name"=>"Zeigler", "scopus_author_id"=>"36500517500"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84892515946", "sgr"=>"84892515946", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0084211", "pmid"=>"24349567", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pui"=>"372141735"}, "id"=>"9d1cc030-b26a-34c5-9ce8-ad0436882f3a", "abstract"=>"Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological – physical – human systems. We describe a “metamodel” approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples – one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics – to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation.", "link"=>"http://www.mendeley.com/research/metamodels-transdisciplinary-analysis-wildlife-population-dynamics", "reader_count"=>78, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>3, "Student > Doctoral Student"=>8, "Researcher"=>26, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Other"=>5, "Student > Master"=>12, "Student > Bachelor"=>6, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>3, "Student > Doctoral Student"=>8, "Researcher"=>26, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Other"=>5, "Student > Master"=>12, "Student > Bachelor"=>6, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Agricultural and Biological Sciences"=>39, "Computer Science"=>1, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Engineering"=>1, "Environmental Science"=>19, "Biochemistry, Genetics and Molecular Biology"=>3, "Materials Science"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>2, "Physics and Astronomy"=>1, "Social Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>19}, "Engineering"=>{"Engineering"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>39}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"Canada"=>1, "Argentina"=>1, "United States"=>2, "Brazil"=>1, "Mexico"=>1, "Italy"=>1, "Chile"=>1, "Australia"=>2, "Germany"=>1, "Spain"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1314272"], "description"=>"<p>Simulated dynamics of the two-species predator-prey metamodel showed several different patterns for independent iterations. Examples are shown for iterations that exhibited patterns where (A) both predator and prey populations initially increased but then collapsed, (B) the predator population went extinct while there were still enough prey individuals to allow for recovery, and (C) both predator and prey persisted with dynamics that suggested that there could be various, possibly unstable, equilibria. </p>", "links"=>[], "tags"=>["trajectories", "prey", "populations", "coupled", "metamodel", "subjected", "stochastic"], "article_id"=>877132, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert C. Lacy", "Philip S. Miller", "Philip J. Nyhus", "J. P. Pollak", "Becky E. Raboy", "Sara L. Zeigler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0084211.g007", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Individual_trajectories_of_predator_and_prey_populations_in_a_coupled_metamodel_subjected_to_stochastic_variation_/877132", "title"=>"Individual trajectories of predator and prey populations in a coupled metamodel subjected to stochastic variation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-13 02:44:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1314269"], "description"=>"<p>Mean prey population size through time is predicted by a single-species PVA model that assumed a fixed predator population size. Simulations were run for predator populations of 50, 60, 70, 80, and 100 individuals. The prey population was sustained at a size of N = 10000 or more if there were 80 or fewer predators.</p>", "links"=>[], "tags"=>["trajectories", "prey", "subjected"], "article_id"=>877129, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert C. Lacy", "Philip S. Miller", "Philip J. Nyhus", "J. P. Pollak", "Becky E. Raboy", "Sara L. Zeigler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0084211.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Population_trajectories_for_a_prey_species_subjected_to_different_levels_of_predation_/877129", "title"=>"Population trajectories for a prey species subjected to different levels of predation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-13 02:44:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1314267"], "description"=>"<p>Metapopulation size is projected by (A) a PVA model in <i>Vortex</i> assuming varied rates of dispersal, annual fluctuations in demographic rates, and inbreeding depression; (B) a metamodel that linked a PVA model in <i>Vortex</i> to an infectious disease model in <i>Outbreak</i>, assuming varied rates of dispersal, minimal annual fluctuations in demographic rates, and no inbreeding depression; and (C) a metamodel that linked a PVA in <i>Vortex</i> to an infectious disease model in <i>Outbreak</i>, assuming varied rates of dispersal, annual fluctuations in demographic rates, and inbreeding depression. In (A), higher rates of dispersal increase growth and stability of the metapopulation because stochastic effects in local subpopulations are dampened. When disease was introduced but stochasticity was removed, as in (B), higher rates of dispersal depress population size because of the faster spread of disease. Finally, when stochasticity, disease, and dispersal were considered in (C), higher dispersal initially reduced population size because of the faster spread of disease. In later years, disease was largely eliminated from the system, and higher rates of dispersal stabilized the population against stochastic fluctuations. During a few years in the middle of the simulation, disease and stochastic processes were equally important, and intermediate rates of dispersal led to the highest population size.</p>", "links"=>[], "tags"=>["influenced"], "article_id"=>877127, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert C. Lacy", "Philip S. Miller", "Philip J. Nyhus", "J. P. Pollak", "Becky E. Raboy", "Sara L. Zeigler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0084211.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metapopulation_dynamics_influenced_by_dispersal_/877127", "title"=>"Metapopulation dynamics influenced by dispersal.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-13 02:44:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1314268"], "description"=>"<p>Mean predator population size through time is predicted by a single-species PVA model that assumed a fixed prey population size. Simulations were run for prey populations of 5000, 6000, 7000, 8000, 10000, and 15000 individuals. Approximately 6000 prey was sufficient to sustain growth of the predator population from its initial N = 50 to more than 100.</p>", "links"=>[], "tags"=>["trajectories", "prey"], "article_id"=>877128, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert C. Lacy", "Philip S. Miller", "Philip J. Nyhus", "J. P. Pollak", "Becky E. Raboy", "Sara L. Zeigler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0084211.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Population_trajectories_for_a_predator_species_at_different_levels_of_prey_availability_/877128", "title"=>"Population trajectories for a predator species at different levels of prey availability.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-13 02:44:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1314266"], "description"=>"<p>(A) Nested data representing a species in a metamodel. Global state variables (GSvar), population state variables (PSvar), and individual state variables (ISvar) are descriptors of the overall system, each population, and each individual, respectively. (B) Flow of control among component models. Curved arrows represent access to and modification of data. Block arrows represent control passed among models. (C) A two-species metamodel, with one modifier and one translator model acting on one species and two modifier models acting on the second species. Control alternates between the species, as illustrated by solid block arrows. Each system, modifier, and translator model has access to change any property of its populations and individuals as well as any shared global state variables.</p>", "links"=>[], "tags"=>["implemented"], "article_id"=>877126, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert C. Lacy", "Philip S. Miller", "Philip J. Nyhus", "J. P. Pollak", "Becky E. Raboy", "Sara L. Zeigler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0084211.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_data_structure_and_program_flow_implemented_by_MetaModel_Manager_/877126", "title"=>"Examples of data structure and program flow implemented by <i>MetaModel</i><i>Manager</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-13 02:44:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1314270"], "description"=>"<p>Mean population densities (N/K) for a predator and a prey species are predicted by a two-species metamodel, which assumed that the density of one species would impact the other species. In (A), externally driven sources of stochasticity (e.g., environmental variation, catastrophes) and inbreeding did not impact either population, and we found that the predator population grew rapidly, causing collapse of the prey population followed by collapse of the predator population. In (B), externally driven stochasticity and inbreeding depression could impact each population. For this scenario, the average trajectory shows that the predator population grew, followed by a decline in prey, causing subsequent decline in the predator, eventually resulting in a possibly stable state in which a reduced prey population sustained a reduced predator population.</p>", "links"=>[], "tags"=>["predator-prey", "coupled", "metamodels", "did", "stochastic"], "article_id"=>877130, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert C. Lacy", "Philip S. Miller", "Philip J. Nyhus", "J. P. Pollak", "Becky E. Raboy", "Sara L. Zeigler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0084211.g006", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_predator_prey_dynamics_in_coupled_metamodels_that_A_did_not_include_and_B_did_include_stochastic_variation_/877130", "title"=>"Mean predator-prey dynamics in coupled metamodels that (A) did not include and (B) did include stochastic variation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-13 02:44:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1314265"], "description"=>"<p>A PVA program acts as the system model (solid outline) to simulate individual survival and reproduction based on individual and population state variables (shown in italics) passed from other models. Modifier models (dashed outlines) simulate habitat dynamics, individual movements, and individual transitions in disease status. A central facilitator program passes state variables between the system and modifier models at appropriate time steps. The ultimate results are measures of population dynamics and extinction risk for a species impacted by habitat change and disease.</p>", "links"=>[], "tags"=>["integrates"], "article_id"=>877125, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert C. Lacy", "Philip S. Miller", "Philip J. Nyhus", "J. P. Pollak", "Becky E. Raboy", "Sara L. Zeigler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0084211.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metamodel_that_integrates_demography_landscape_change_dispersal_and_disease_status_/877125", "title"=>"Metamodel that integrates demography, landscape change, dispersal, and disease status.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-13 02:44:19"}

PMC Usage Stats | Further Information

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Relative Metric

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