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Schäfer RB, Jackson M, Juvigny-Khenafou N, Osakpolor SE, Posthuma L, Schneeweiss A, Spaak J, Vinebrooke R. Chemical Mixtures and Multiple Stressors: Same but Different? ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2023; 42:1915-1936. [PMID: 37036219 DOI: 10.1002/etc.5629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/01/2023] [Accepted: 04/04/2023] [Indexed: 05/19/2023]
Abstract
Ecosystems are strongly influenced by multiple anthropogenic stressors, including a wide range of chemicals and their mixtures. Studies on the effects of multiple stressors have largely focussed on nonchemical stressors, whereas studies on chemical mixtures have largely ignored other stressors. However, both research areas face similar challenges and require similar tools and methods to predict the joint effects of chemicals or nonchemical stressors, and frameworks to integrate multiple chemical and nonchemical stressors are missing. We provide an overview of the research paradigms, tools, and methods commonly used in multiple stressor and chemical mixture research and discuss potential domains of cross-fertilization and joint challenges. First, we compare the general paradigms of ecotoxicology and (applied) ecology to explain the historical divide. Subsequently, we compare methods and approaches for the identification of interactions, stressor characterization, and designing experiments. We suggest that both multiple stressor and chemical mixture research are too focused on interactions and would benefit from integration regarding null model selection. Stressor characterization is typically more costly for chemical mixtures. While for chemical mixtures comprehensive classification systems at suborganismal level have been developed, recent classification systems for multiple stressors account for environmental context. Both research areas suffer from rather simplified experimental designs that focus on only a limited number of stressors, chemicals, and treatments. We discuss concepts that can guide more realistic designs capturing spatiotemporal stressor dynamics. We suggest that process-based and data-driven models are particularly promising to tackle the challenge of prediction of effects of chemical mixtures and nonchemical stressors on (meta-)communities and (meta-)food webs. We propose a framework to integrate the assessment of effects for multiple stressors and chemical mixtures. Environ Toxicol Chem 2023;42:1915-1936. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Ralf B Schäfer
- Institute for Environmental Sciences, Rheinland-Pfälzische Technische Univerität Kaiserslautern-Landau, Landau, Germany
| | | | - Noel Juvigny-Khenafou
- Institute for Environmental Sciences, Rheinland-Pfälzische Technische Univerität Kaiserslautern-Landau, Landau, Germany
| | - Stephen E Osakpolor
- Institute for Environmental Sciences, Rheinland-Pfälzische Technische Univerität Kaiserslautern-Landau, Landau, Germany
| | - Leo Posthuma
- Centre for Sustainability, Environment and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Environmental Science, Radboud University, Nijmegen, The Netherlands
| | - Anke Schneeweiss
- Institute for Environmental Sciences, Rheinland-Pfälzische Technische Univerität Kaiserslautern-Landau, Landau, Germany
| | - Jürg Spaak
- Institute for Environmental Sciences, Rheinland-Pfälzische Technische Univerität Kaiserslautern-Landau, Landau, Germany
| | - Rolf Vinebrooke
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
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2
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Meier L, Brauns M, Grimm V, Weitere M, Frank K. MASTIFF: A mechanistic model for cross-scale analyses of the functioning of multiple stressed riverine ecosystems. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110007] [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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3
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Lu Y, DeAngelis DL, Xia J, Jiang J. Modeling the impact of invasive species litter on conditions affecting its spread and potential regime shift. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.109962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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4
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Hauenstein S, Jassoy N, Mupepele A, Carroll T, Kshatriya M, Beale CM, Dormann CF. A systematic map of demographic data from elephant populations throughout Africa: implications for poaching and population analyses. Mamm Rev 2022. [DOI: 10.1111/mam.12291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Severin Hauenstein
- Department of Biometry and Environmental System Analysis University of Freiburg 79106FreiburgGermany
- Department of Biology University of York YorkYO10 5DDUK
| | - Noémi Jassoy
- Department of Biometry and Environmental System Analysis University of Freiburg 79106FreiburgGermany
| | - Anne‐Christine Mupepele
- Department of Biometry and Environmental System Analysis University of Freiburg 79106FreiburgGermany
- Department of Nature Conservation and Landscape Ecology University of Freiburg Freiburg79106Germany
| | - Thea Carroll
- CITES Secretariat – MIKE Programme United Nations Environment Programme 30552‐00100NairobiKenya
| | - Mrigesh Kshatriya
- CITES Secretariat – MIKE Programme United Nations Environment Programme 30552‐00100NairobiKenya
| | | | - Carsten F. Dormann
- Department of Biometry and Environmental System Analysis University of Freiburg 79106FreiburgGermany
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5
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Drechsler M, Wätzold F, Grimm V. The hitchhiker's guide to generic ecological-economic modelling of land-use-based biodiversity conservation policies. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2021.109861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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6
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Schouten R, Baudrot V, Umina P, Maino J. A working guide to spatial mechanistic modelling in Julia. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
| | | | - Paul Umina
- Cesar Australia Parkville Vic Australia
- School of BioSciences The University of Melbourne Parkville Vic Australia
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7
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Bogdanowski A, Banitz T, Muhsal LK, Kost C, Frank K. McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems. PLoS Comput Biol 2022; 18:e1009777. [PMID: 35073313 PMCID: PMC8830788 DOI: 10.1371/journal.pcbi.1009777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/10/2022] [Accepted: 12/20/2021] [Indexed: 01/30/2023] Open
Abstract
Individual-based modeling is widely applied to investigate the ecological mechanisms driving microbial community dynamics. In such models, the population or community dynamics emerge from the behavior and interplay of individual entities, which are simulated according to a predefined set of rules. If the rules that govern the behavior of individuals are based on generic and mechanistically sound principles, the models are referred to as next-generation individual-based models. These models perform particularly well in recapitulating actual ecological dynamics. However, implementation of such models is time-consuming and requires proficiency in programming or in using specific software, which likely hinders a broader application of this powerful method. Here we present McComedy, a modeling tool designed to facilitate the development of next-generation individual-based models of microbial consumer-resource systems. This tool allows flexibly combining pre-implemented building blocks that represent physical and biological processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems is demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. With this article, we provide a versatile tool for developing next-generation individual-based models that can foster understanding of microbial ecology in both research and education. Microorganisms such as bacteria and fungi can be found in virtually any natural environment. To better understand the ecology of these microorganisms–which is important for several research fields including medicine, biotechnology, and conservation biology–researchers often use computer models to simulate and predict the behavior of microbial communities. Commonly, a particular technique called individual-based modeling is used to generate structurally realistic models of these communities by explicitly simulating each individual microorganism. Here we developed a tool called McComedy that helps researchers applying individual-based modeling efficiently without having to program low-level processes, thus allowing them to focus on their actual research questions. To test whether McComedy is not only convenient to use but also generates meaningful models, we used it to reproduce previously reported findings of two other research groups. Given that our results could well recapitulate and furthermore extend the original findings, we are confident that McComedy is a powerful and versatile tool that can help to address fundamental questions in microbial ecology.
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Affiliation(s)
- André Bogdanowski
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
| | - Thomas Banitz
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
| | - Linea Katharina Muhsal
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
| | - Christian Kost
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
| | - Karin Frank
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
- Osnabrück University, Institute for Environmental Systems Research, Osnabrück, Germany
- iDiv – German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, Germany
- * E-mail:
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8
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Refocusing multiple stressor research around the targets and scales of ecological impacts. Nat Ecol Evol 2021; 5:1478-1489. [PMID: 34556829 DOI: 10.1038/s41559-021-01547-4] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 08/01/2021] [Indexed: 02/07/2023]
Abstract
Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research.
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Gegear RJ, Heath KN, Ryder EF. Modeling scale up of anthropogenic impacts from individual pollinator behavior to pollination systems. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2021; 35:1519-1529. [PMID: 33993540 PMCID: PMC8518484 DOI: 10.1111/cobi.13754] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 05/04/2023]
Abstract
Understanding how anthropogenic disturbances affect plant-pollinator systems has important implications for the conservation of biodiversity and ecosystem functioning. Previous laboratory studies show that pesticides and pathogens, which have been implicated in the rapid global decline of pollinators over recent years, can impair behavioral processes needed for pollinators to adaptively exploit floral resources and effectively transfer pollen among plants. However, the potential for these sublethal stressor effects on pollinator-plant interactions at the individual level to scale up into changes to the dynamics of wild plant and pollinator populations at the system level remains unclear. We developed an empirically parameterized agent-based model of a bumblebee pollination system called SimBee to test for effects of stressor-induced decreases in the memory capacity and information processing speed of individual foragers on bee abundance (scenario 1), plant diversity (scenario 2), and bee-plant system stability (scenario 3) over 20 virtual seasons. Modeling of a simple pollination network of a bumblebee and four co-flowering bee-pollinated plant species indicated that bee decline and plant species extinction events could occur when only 25% of the forager population showed cognitive impairment. Higher percentages of impairment caused 50% bee loss in just five virtual seasons and system-wide extinction events in less than 20 virtual seasons under some conditions. Plant species extinctions occurred regardless of bee population size, indicating that stressor-induced changes to pollinator behavior alone could drive species loss from plant communities. These findings indicate that sublethal stressor effects on pollinator behavioral mechanisms, although seemingly insignificant at the level of individuals, have the cumulative potential in principle to degrade plant-pollinator species interactions at the system level. Our work highlights the importance of an agent-based modeling approach for the identification and mitigation of anthropogenic impacts on plant-pollinator systems.
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Affiliation(s)
- Robert J. Gegear
- Department of BiologyUniversity of Massachusetts DartmouthDartmouthMassachusettsUSA
| | - Kevin N. Heath
- Program in Bioinformatics and Computational BiologyWorcester Polytechnic InstituteWorcesterMassachusettsUSA
| | - Elizabeth F. Ryder
- Program in Bioinformatics and Computational BiologyWorcester Polytechnic InstituteWorcesterMassachusettsUSA
- Department of Biology and BiotechnologyWorcester Polytechnic InstituteWorcesterMassachusettsUSA
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10
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Schlaepfer DR, Bradford JB, Lauenroth WK, Shriver RK. Understanding the future of big sagebrush regeneration: challenges of projecting complex ecological processes. Ecosphere 2021. [DOI: 10.1002/ecs2.3695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Daniel R. Schlaepfer
- Southwest Biological Science Center U.S. Geological Survey Flagstaff Arizona 86001 USA
- Center for Adaptable Western Landscapes Northern Arizona University Flagstaff Arizona 86011 USA
- Yale School of the Environment Yale University New Haven Connecticut 06511 USA
| | - John B. Bradford
- Southwest Biological Science Center U.S. Geological Survey Flagstaff Arizona 86001 USA
| | - William K. Lauenroth
- Yale School of the Environment Yale University New Haven Connecticut 06511 USA
- Department of Botany University of Wyoming Laramie Wyoming 82071 USA
| | - Robert K. Shriver
- Department of Natural Resources and Environmental Science University of Nevada‐Reno Reno Nevada 89557 USA
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11
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Mammola S, Lunghi E, Bilandžija H, Cardoso P, Grimm V, Schmidt SI, Hesselberg T, Martínez A. Collecting eco-evolutionary data in the dark: Impediments to subterranean research and how to overcome them. Ecol Evol 2021; 11:5911-5926. [PMID: 34141192 PMCID: PMC8207145 DOI: 10.1002/ece3.7556] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/25/2022] Open
Abstract
Caves and other subterranean habitats fulfill the requirements of experimental model systems to address general questions in ecology and evolution. Yet, the harsh working conditions of these environments and the uniqueness of the subterranean organisms have challenged most attempts to pursuit standardized research.Two main obstacles have synergistically hampered previous attempts. First, there is a habitat impediment related to the objective difficulties of exploring subterranean habitats and our inability to access the network of fissures that represents the elective habitat for the so-called "cave species." Second, there is a biological impediment illustrated by the rarity of most subterranean species and their low physiological tolerance, often limiting sample size and complicating laboratory experiments.We explore the advantages and disadvantages of four general experimental setups (in situ, quasi in situ, ex situ, and in silico) in the light of habitat and biological impediments. We also discuss the potential of indirect approaches to research. Furthermore, using bibliometric data, we provide a quantitative overview of the model organisms that scientists have exploited in the study of subterranean life.Our over-arching goal is to promote caves as model systems where one can perform standardized scientific research. This is important not only to achieve an in-depth understanding of the functioning of subterranean ecosystems but also to fully exploit their long-discussed potential in addressing general scientific questions with implications beyond the boundaries of this discipline.
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Affiliation(s)
- Stefano Mammola
- Laboratory for Integrative Biodiversity Research (LIBRe)Finnish Museum of Natural History (LUOMUS)University of HelsinkiHelsinkiFinland
- Dark‐MEG: Molecular Ecology GroupWater Research Institute (IRSA)National Research Council (CNR)VerbaniaItaly
| | - Enrico Lunghi
- Key Laboratory of the Zoological Systematics and EvolutionInstitute of ZoologyChinese Academy of SciencesBeijingChina
- Museo di Storia Naturale dell'Università degli Studi di Firenze“La Specola”FirenzeItaly
| | - Helena Bilandžija
- Department of Molecular BiologyRudjer Boskovic InstituteZagrebCroatia
| | - Pedro Cardoso
- Laboratory for Integrative Biodiversity Research (LIBRe)Finnish Museum of Natural History (LUOMUS)University of HelsinkiHelsinkiFinland
| | - Volker Grimm
- Department of Ecological ModellingHelmholtz Centre for Environmental Research – UFZLeipzigGermany
- Plant Ecology and Nature ConservationUniversity of PotsdamPotsdamGermany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Susanne I. Schmidt
- Institute of HydrobiologyBiology Centre CASČeské BudějoviceCzech Republic
| | | | - Alejandro Martínez
- Dark‐MEG: Molecular Ecology GroupWater Research Institute (IRSA)National Research Council (CNR)VerbaniaItaly
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12
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Gallagher CA, Chudzinska M, Larsen-Gray A, Pollock CJ, Sells SN, White PJC, Berger U. From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models. Biol Rev Camb Philos Soc 2021; 96:1868-1888. [PMID: 33978325 DOI: 10.1111/brv.12729] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 01/21/2023]
Abstract
To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.
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Affiliation(s)
- Cara A Gallagher
- Department of Plant Ecology and Conservation Biology, University of Potsdam, Am Mühlenberg 3, Potsdam, 14469, Germany.,Department of Bioscience, Aarhus University, Frederiksborgvej 399, Roskilde, 4000
| | - Magda Chudzinska
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, St Andrews, KY16 9ST, U.K
| | - Angela Larsen-Gray
- Department of Integrative Biology, University of Wisconsin-Madison, 250 N. Mills St., Madison, WI, 53706, U.S.A
| | | | - Sarah N Sells
- Montana Cooperative Wildlife Research Unit, The University of Montana, 205 Natural Sciences, Missoula, MT, 59812, U.S.A
| | - Patrick J C White
- School of Applied Sciences, Edinburgh Napier University, 9 Sighthill Ct., Edinburgh, EH11 4BN, U.K
| | - Uta Berger
- Institute of Forest Growth and Computer Science, Technische Universität Dresden, Dresden, 01062, Germany
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13
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Crawford MS, Schlägel UE, May F, Wurst S, Grimm V, Jeltsch F. While shoot herbivores reduce, root herbivores increase nutrient enrichment's impact on diversity in a grassland model. Ecology 2021; 102:e03333. [PMID: 33710633 DOI: 10.1002/ecy.3333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/04/2020] [Accepted: 01/11/2021] [Indexed: 11/09/2022]
Abstract
Nutrient enrichment is widespread throughout grassland systems and expected to increase during the Anthropocene. Trophic interactions, like aboveground herbivory, have been shown to mitigate its effect on plant diversity. Belowground herbivory may also impact these habitats' response to nutrient enrichment, but its influence is much less understood, and likely to depend on factors such as the herbivores' preference for dominant species and the symmetry of belowground competition. If preferential toward the dominant, fastest growing species, root herbivores may reduce these species' relative fitness and support diversity during nutrient enrichment. However, as plant competition belowground is commonly considered to be symmetric, root herbivores may be less impactful than shoot herbivores because they do not reduce any competitive asymmetry between the dominant and subordinate plants. To better understand this system, we used an established, two-layer, grassland community model to run a full-factorially designed simulation experiment, crossing the complete removal of aboveground herbivores and belowground herbivores with nutrient enrichment. After 100 yr of simulation, we analyzed communities' diversity, competition on the individual level, as well as their resistance and recovery. The model reproduced both observed general effects of nutrient enrichment in grasslands and the short-term trends of specific experiments. We found that belowground herbivores exacerbate the negative influence of nutrient enrichment on Shannon diversity within our model grasslands, while aboveground herbivores mitigate its effect. Indeed, data on individuals' above- and belowground resource uptake reveals that root herbivory reduces resource limitation belowground. As with nutrient enrichment, this shifts competition aboveground. Since shoot competition is asymmetric, with larger, taller individuals gathering disproportionate resources compared to their smaller, shorter counterparts, this shift promotes the exclusion of the smallest species. While increasing the root herbivores' preferences toward dominant species lessens their negative impact, at best they are only mildly advantageous, and they do very little reduce the negative consequences of nutrient enrichment. Because our model's belowground competition is symmetric, we hypothesize that root herbivores may be beneficial when root competition is asymmetric. Future research into belowground herbivory should account for the nature of competition belowground to better understand the herbivores' true influence.
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Affiliation(s)
- Michael S Crawford
- Transformation Pathways, Potsdam Institute for Climate Impact Research (PIK), Building A65 Room 120, P.O. Box 60 12 03, Telegraphenberg, Potsdam, 14412, Germany.,Department of Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Ulrike E Schlägel
- Department of Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Felix May
- Theoretical Ecology, Institute for Biology, Freie Universität, Berlin, Germany
| | - Susanne Wurst
- Functional Biodiversity, Dahlem Centre of Plant Sciences, Institute of Biology, Freie Universität, Berlin, Germany
| | - Volker Grimm
- Department of Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.,Department of Ecological Modelling, Helmholtz-Centre for Environmental Research - UFZ, Leipzig, Germany.,Biodiversity Economics, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Florian Jeltsch
- Department of Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.,Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany
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14
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Kumar P, Eriksen RL, Simko I, Mou B. Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce ( Lactuca spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning. Front Genet 2021; 12:634554. [PMID: 33679897 PMCID: PMC7935093 DOI: 10.3389/fgene.2021.634554] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 01/29/2021] [Indexed: 11/23/2022] Open
Abstract
Deep understanding of genetic architecture of water-stress tolerance is critical for efficient and optimal development of water-stress tolerant cultivars, which is the most economical and environmentally sound approach to maintain lettuce production with limited irrigation. Lettuce (Lactuca sativa L.) production in areas with limited precipitation relies heavily on the use of ground water for irrigation. Lettuce plants are highly susceptible to water-stress, which also affects their nutrient uptake efficiency. Water stressed plants show reduced growth, lower biomass, and early bolting and flowering resulting in bitter flavors. Traditional phenotyping methods to evaluate water-stress are labor intensive, time-consuming and prone to errors. High throughput phenotyping platforms using kinetic chlorophyll fluorescence and hyperspectral imaging can effectively attain physiological traits related to photosynthesis and secondary metabolites that can enhance breeding efficiency for water-stress tolerance. Kinetic chlorophyll fluorescence and hyperspectral imaging along with traditional horticultural traits identified genomic loci affected by water-stress. Supervised machine learning models were evaluated for their accuracy to distinguish water-stressed plants and to identify the most important water-stress related parameters in lettuce. Random Forest (RF) had classification accuracy of 89.7% using kinetic chlorophyll fluorescence parameters and Neural Network (NN) had classification accuracy of 89.8% using hyperspectral imaging derived vegetation indices. The top ten chlorophyll fluorescence parameters and vegetation indices selected by sequential forward selection by RF and NN were genetically mapped using a L. sativa × L. serriola interspecific recombinant inbred line (RIL) population. A total of 25 quantitative trait loci (QTL) segregating for water-stress related horticultural traits, 26 QTL for the chlorophyll fluorescence traits and 34 QTL for spectral vegetation indices (VI) were identified. The percent phenotypic variation (PV) explained by the horticultural QTL ranged from 6.41 to 19.5%, PV explained by chlorophyll fluorescence QTL ranged from 6.93 to 13.26% while the PV explained by the VI QTL ranged from 7.2 to 17.19%. Eight QTL clusters harboring co-localized QTL for horticultural traits, chlorophyll fluorescence parameters and VI were identified on six lettuce chromosomes. Molecular markers linked to the mapped QTL clusters can be targeted for marker-assisted selection to develop water-stress tolerant lettuce.
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Affiliation(s)
- Pawan Kumar
- Crop Improvement and Protection Research Unit, USDA-ARS, Salinas, CA, United States
| | - Renee L Eriksen
- Forage Seed and Cereal Research Unit, USDA-ARS, Corvallis, OR, United States
| | - Ivan Simko
- Crop Improvement and Protection Research Unit, USDA-ARS, Salinas, CA, United States
| | - Beiquan Mou
- Crop Improvement and Protection Research Unit, USDA-ARS, Salinas, CA, United States
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15
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Brown C, Rounsevell M. How can social–ecological system models simulate the emergence of social–ecological crises? PEOPLE AND NATURE 2020. [DOI: 10.1002/pan3.10167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Affiliation(s)
- Calum Brown
- Institute of Meteorology and Climate Research Atmospheric Environmental Research (IMK‐IFU) Department of Geo‐Ecology (IFGG) Karlsruhe Institute of Technology Garmisch‐Partenkirchen Germany
| | - Mark Rounsevell
- Institute of Meteorology and Climate Research Atmospheric Environmental Research (IMK‐IFU) Department of Geo‐Ecology (IFGG) Karlsruhe Institute of Technology Garmisch‐Partenkirchen Germany
- School of Geosciences University of Edinburgh Edinburgh UK
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16
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Milles A, Dammhahn M, Grimm V. Intraspecific trait variation in personality‐related movement behavior promotes coexistence. OIKOS 2020. [DOI: 10.1111/oik.07431] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Alexander Milles
- Plant Ecology and Nature Conservation, Univ. of Potsdam Am Mühlenberg 3 DE‐14476 Potsdam Germany
| | - Melanie Dammhahn
- Animal Ecology, Univ. of Potsdam, Potsdam, Germany, and: Berlin‐Brandenburg Inst. of Advanced Biodiversity Research (BBIB) Berlin Germany
| | - Volker Grimm
- Plant Ecology and Nature Conservation, Univ. of Potsdam Am Mühlenberg 3 DE‐14476 Potsdam Germany
- Dept of Ecological Modelling, Helmholtz Centre for Environmental Research‐UFZ Leipzig Germany
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17
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Bathmann J, Peters R, Naumov D, Fischer T, Berger U, Walther M. The MANgrove–GroundwAter feedback model (MANGA) – Describing belowground competition based on first principles. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.108973] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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18
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Exploring resilience with agent-based models: State of the art, knowledge gaps and recommendations for coping with multidimensionality. ECOLOGICAL COMPLEXITY 2019. [DOI: 10.1016/j.ecocom.2018.06.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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19
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Fulton EA, Blanchard JL, Melbourne-Thomas J, Plagányi ÉE, Tulloch VJD. Where the Ecological Gaps Remain, a Modelers' Perspective. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00424] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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20
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Salecker J, Sciaini M, Meyer KM, Wiegand K. The
nlrx r
package: A next‐generation framework for reproducible NetLogo model analyses. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13286] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Jan Salecker
- Department of Ecosystem Modelling University of Göttingen Göttingen Germany
| | - Marco Sciaini
- Department of Ecosystem Modelling University of Göttingen Göttingen Germany
| | - Katrin M. Meyer
- Department of Ecosystem Modelling University of Göttingen Göttingen Germany
| | - Kerstin Wiegand
- Department of Ecosystem Modelling University of Göttingen Göttingen Germany
- Centre of Biodiversity and Sustainable Land Use (CBL) University of Goettingen Göttingen Germany
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21
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Zakharova L, Meyer K, Seifan M. Trait-based modelling in ecology: A review of two decades of research. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.05.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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22
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Briscoe NJ, Elith J, Salguero-Gómez R, Lahoz-Monfort JJ, Camac JS, Giljohann KM, Holden MH, Hradsky BA, Kearney MR, McMahon SM, Phillips BL, Regan TJ, Rhodes JR, Vesk PA, Wintle BA, Yen JDL, Guillera-Arroita G. Forecasting species range dynamics with process-explicit models: matching methods to applications. Ecol Lett 2019; 22:1940-1956. [PMID: 31359571 DOI: 10.1111/ele.13348] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 01/14/2019] [Accepted: 06/20/2019] [Indexed: 01/14/2023]
Abstract
Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.
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Affiliation(s)
- Natalie J Briscoe
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
| | - Jane Elith
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
| | - Roberto Salguero-Gómez
- Department of Zoology, University of Oxford, Oxford, UK.,School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia.,Max Planck Institute for Demographic Research, Rostock, Germany
| | | | - James S Camac
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
| | | | - Matthew H Holden
- School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Bronwyn A Hradsky
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
| | - Michael R Kearney
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
| | - Sean M McMahon
- Forest Global Earth Observatory, Smithsonian Environmental Research Center, Edgewater, MD, USA
| | - Ben L Phillips
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
| | - Tracey J Regan
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia.,The Arthur Rylah Institute for Environmental Research, Department of Environment, Land, Water and Planning, Heidelberg, Vic., Australia
| | - Jonathan R Rhodes
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Qld, Australia
| | - Peter A Vesk
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
| | - Brendan A Wintle
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
| | - Jian D L Yen
- School of BioSciences, University of Melbourne, Melbourne, Vic., Australia
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23
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Bouchet PJ, Peterson AT, Zurell D, Dormann CF, Schoeman D, Ross RE, Snelgrove P, Sequeira AMM, Whittingham MJ, Wang L, Rapacciuolo G, Oppel S, Mellin C, Lauria V, Krishnakumar PK, Jones AR, Heinänen S, Heikkinen RK, Gregr EJ, Fielding AH, Caley MJ, Barbosa AM, Bamford AJ, Lozano-Montes H, Parnell S, Wenger S, Yates KL. Better Model Transfers Require Knowledge of Mechanisms. Trends Ecol Evol 2019; 34:489-490. [PMID: 31054858 DOI: 10.1016/j.tree.2019.04.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 04/02/2019] [Accepted: 04/03/2019] [Indexed: 10/26/2022]
Affiliation(s)
- Phil J Bouchet
- Centre for Research into Ecological & Environmental Modelling, School of Mathematics and Statistics, University of St Andrews, St Andrews, UK.
| | | | - Damaris Zurell
- Swiss Federal Research Institute WSL, Dept. Landscape Dynamics, Zuercherstrasse 111, CH-8903 Birmensdorf, Switzerland; Humboldt-Universität zu Berlin, Geography Dept., Unter den Linden 6, D-10099 Berlin, Germany
| | - Carsten F Dormann
- Biometry & Environmental System Analysis, University of Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany
| | - David Schoeman
- School of Science & Engineering, The University of the Sunshine Coast, Maroochydore, QLD 4558, Australia; Centre for African Conservation Ecology, Department of Zoology, Nelson Mandela University, Port Elizabeth, South Africa
| | - Rebecca E Ross
- School of Biological and Marine Sciences, Plymouth University, Drake Circus, Plymouth, PL4 8AA, UK; Institute for Marine Research, Nordnesgaten 50, 5005 Bergen, Norway
| | - Paul Snelgrove
- Department of Ocean Sciences and Department of Biology, Memorial University of Newfoundland, St. John's, NL A1C 5S7, Canada
| | - Ana M M Sequeira
- School of Biological Sciences, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia; IOMRC and The University of Western Australia Oceans Institute, University of Western Australia, Crawley, WA 6009, Australia
| | - Mark J Whittingham
- Biology, School of Natural and Environmental Sciences, Newcastle University, Newcastle-Upon-Tyne, NE1 7RU, UK
| | - Lifei Wang
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON M5S 3B2, Canada; Gulf of Maine Research Institute, Portland, ME 04101, USA
| | | | - Steffen Oppel
- RSPB Centre for Conservation Science, Royal Society for the Protection of Birds, The David Attenborough Building, Pembroke Street, Cambridge CB2 3QZ, UK
| | - Camille Mellin
- The Environment Institute and School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia; Australian Institute of Marine Science, PMB No 3, Townsville 4810, QLD, Australia
| | - Valentina Lauria
- Istituto per l'Ambiente Marino Costiero, IAMC-CNR, Mazara del Vallo, Trapani, Italy
| | - Periyadan K Krishnakumar
- Center for Environment and Water, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Alice R Jones
- The Environment Institute and School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia
| | - Stefan Heinänen
- DHI, Ecology and Environment Department, Agern Allé 5, DK-2970 Hørsholm, Denmark; Novia University of Applied Sciences, Raseborgsvägen 9, 10600 Ekenäs, Finland
| | - Risto K Heikkinen
- Finnish Environment Institute, Biodiversity Centre, PO Box 140, FIN- 00251 Helsinki, Finland
| | - Edward J Gregr
- Institute for Resources, Environment, and Sustainability, University of British Columbia, AERL Building, 2202 Main Mall, Vancouver, BC, Canada; SciTec h Environmental Consulting, 2136 Napier Street, Vancouver, BC V5L 2N9, Canada
| | | | - M Julian Caley
- ARC Centre for Excellence in Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD, Australia; School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - A Márcia Barbosa
- Centro de Investigação em Ciências Geo-Espaciais, Faculdade de Ciências, Universidade do Porto, Observatório Astronómico Prof. Manuel de Barros, Alameda do Monte da Virgem, 4430-146 Vila Nova de Gaia, Portugal
| | - Andrew J Bamford
- Wildfowl &Wetlands Trust, Slimbridge, Gloucestershire, GL2 7BT, UK
| | - Hector Lozano-Montes
- CSIRO Oceans and Atmosphere, Indian Ocean Marine Research Centre, The University of Western Australia, Crawley, WA 6009, Australia
| | - Stephen Parnell
- School of Environment and Life Sciences, University of Salford, Manchester, UK
| | - Seth Wenger
- Odum School of Ecology, University of Georgia, Athens, GA 30601, USA
| | - Katherine L Yates
- School of Environment and Life Sciences, University of Salford, Manchester, UK
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24
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Jeltsch F, Grimm V, Reeg J, Schlägel UE. Give chance a chance: from coexistence to coviability in biodiversity theory. Ecosphere 2019. [DOI: 10.1002/ecs2.2700] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Florian Jeltsch
- Department of Plant Ecology and Nature Conservation University of Potsdam Am Mühlenberg 3 Potsdam‐Golm DE‐14476 Germany
- Berlin‐Brandenburg Institute of Advanced Biodiversity Research (BBIB) Berlin DE‐14195 Germany
| | - Volker Grimm
- Department of Plant Ecology and Nature Conservation University of Potsdam Am Mühlenberg 3 Potsdam‐Golm DE‐14476 Germany
- Department of Ecological Modelling Helmholtz Centre for Environmental Research‐UFZ Permoserstraße 15 Leipzig 04318 Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Deutscher Platz 5e Leipzig 04103 Germany
| | - Jette Reeg
- Department of Plant Ecology and Nature Conservation University of Potsdam Am Mühlenberg 3 Potsdam‐Golm DE‐14476 Germany
| | - Ulrike E. Schlägel
- Department of Plant Ecology and Nature Conservation University of Potsdam Am Mühlenberg 3 Potsdam‐Golm DE‐14476 Germany
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25
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Klimenko AI, Matushkin YG, Kolchanov NA, Lashin SA. Spatial heterogeneity promotes antagonistic evolutionary scenarios in microbial community explained by ecological stratification: a simulation study. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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26
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Ma P, Han XH, Lin Y, Moore J, Guo YX, Yue M. Exploring the relative importance of biotic and abiotic factors that alter the self-thinning rule: Insights from individual-based modelling and machine-learning. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.01.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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27
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Radchuk V, Kramer-Schadt S, Grimm V. Transferability of Mechanistic Ecological Models Is About Emergence. Trends Ecol Evol 2019; 34:487-488. [PMID: 30795841 DOI: 10.1016/j.tree.2019.01.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 01/18/2019] [Accepted: 01/23/2019] [Indexed: 11/25/2022]
Affiliation(s)
- Viktoriia Radchuk
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Straße 17, Berlin, Germany.
| | - Stephanie Kramer-Schadt
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Straße 17, Berlin, Germany; Department of Ecology, Technische Universität Berlin, Rothenburgstrasse 12, 12165 Berlin, Germany
| | - Volker Grimm
- Department of Ecological Modelling, Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, Leipzig, Germany; Institute for Biochemistry and Biology, University of Potsdam, Maulbeerallee 2, Potsdam, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, Leipzig, Germany
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28
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Lorscheid I, Berger U, Grimm V, Meyer M. From cases to general principles: A call for theory development through agent-based modeling. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2018.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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29
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Alexandridis N, Bacher C, Desroy N, Jean F. Individual-based simulation of the spatial and temporal dynamics of macroinvertebrate functional groups provides insights into benthic community assembly mechanisms. PeerJ 2018; 6:e5038. [PMID: 29938137 PMCID: PMC6011875 DOI: 10.7717/peerj.5038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 05/31/2018] [Indexed: 11/20/2022] Open
Abstract
The complexity and scales of the processes that shape communities of marine benthic macroinvertebrates has limited our understanding of their assembly mechanisms and the potential to make projections of their spatial and temporal dynamics. Individual-based models can shed light on community assembly mechanisms, by allowing observed spatiotemporal patterns to emerge from first principles about the modeled organisms. Previous work in the Rance estuary (Brittany, France) revealed the principal functional components of its benthic macroinvertebrate communities and derived a set of functional relationships between them. These elements were combined here for the development of a dynamic and spatially explicit model that operates at two spatial scales. At the fine scale, modeling each individual’s life cycle allowed the representation of recruitment, inter- and intra-group competition, biogenic habitat modification and predation mortality. Larval dispersal and environmental filtering due to the tidal characteristics of the Rance estuary were represented at the coarse scale. The two scales were dynamically linked and the model was parameterized on the basis of theoretical expectations and expert knowledge. The model was able to reproduce some patterns of α- and β-diversity that were observed in the Rance estuary in 1995. Model analysis demonstrated the role of local and regional processes, particularly early post-settlement mortality and spatially restricted dispersal, in shaping marine benthos. It also indicated biogenic habitat modification as a promising area for future research. The combination of this mechanism with different substrate types, along with the representation of physical disturbances and more trophic categories, could increase the model’s realism. The precise parameterization and validation of the model is expected to extend its scope from the exploration of community assembly mechanisms to the formulation of predictions about the responses of community structure and functioning to environmental change.
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Affiliation(s)
| | - Cédric Bacher
- DYNECO-LEBCO, IFREMER, Centre de Bretagne, Plouzané, France
| | - Nicolas Desroy
- Laboratoire Environnement et Ressources de Bretagne Nord, IFREMER, Station CRESCO, Dinard, France
| | - Fred Jean
- LEMAR, Institut Universitaire Européen de la Mer, Université de Brest, UBO, CNRS, IRD, Plouzané, France
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30
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Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches. REMOTE SENSING 2018. [DOI: 10.3390/rs10050731] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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31
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Nabe-Nielsen J, van Beest FM, Grimm V, Sibly RM, Teilmann J, Thompson PM. Predicting the impacts of anthropogenic disturbances on marine populations. Conserv Lett 2018. [DOI: 10.1111/conl.12563] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Jacob Nabe-Nielsen
- Department of Bioscience; Aarhus University; Frederiksborgvej 399 DK-4000 Roskilde Denmark
| | - Floris M van Beest
- Department of Bioscience; Aarhus University; Frederiksborgvej 399 DK-4000 Roskilde Denmark
| | - Volker Grimm
- Helmholtz Centre for Environmental Research - UFZ; Department of Ecological Modelling; Permoserstraße 15 04318 Leipzig Germany
| | - Richard M Sibly
- School of Biological Sciences, University of Reading, Harborne Building; University of Reading; Whiteknights Reading Berkshire, RG6 6AS United Kingdom
| | - Jonas Teilmann
- Department of Bioscience; Aarhus University; Frederiksborgvej 399 DK-4000 Roskilde Denmark
| | - Paul M Thompson
- Lighthouse Field Station, Institute of Biological and Environmental Sciences; University of Aberdeen; Cromarty IV11 8YL United Kingdom
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32
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Ceia-Hasse A, Navarro LM, Borda-de-Água L, Pereira HM. Population persistence in landscapes fragmented by roads: Disentangling isolation, mortality, and the effect of dispersal. Ecol Modell 2018. [DOI: 10.1016/j.ecolmodel.2018.01.021] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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33
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Hansen WD, Braziunas KH, Rammer W, Seidl R, Turner MG. It takes a few to tango: changing climate and fire regimes can cause regeneration failure of two subalpine conifers. Ecology 2018; 99:966-977. [DOI: 10.1002/ecy.2181] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 01/22/2018] [Accepted: 01/25/2018] [Indexed: 01/10/2023]
Affiliation(s)
- Winslow D. Hansen
- Department of Integrative Biology University of Wisconsin‐Madison Madison Wisconsin 53706 USA
| | - Kristin H. Braziunas
- Department of Integrative Biology University of Wisconsin‐Madison Madison Wisconsin 53706 USA
| | - Werner Rammer
- Department of Forest and Soil Sciences Institute of Silviculture University of Natural Resources and Life Sciences (BOKU) Vienna Austria
| | - Rupert Seidl
- Department of Forest and Soil Sciences Institute of Silviculture University of Natural Resources and Life Sciences (BOKU) Vienna Austria
| | - Monica G. Turner
- Department of Integrative Biology University of Wisconsin‐Madison Madison Wisconsin 53706 USA
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34
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Liukkonen L, Ayllón D, Kunnasranta M, Niemi M, Nabe-Nielsen J, Grimm V, Nyman AM. Modelling movements of Saimaa ringed seals using an individual-based approach. Ecol Modell 2018. [DOI: 10.1016/j.ecolmodel.2017.12.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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35
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Zhang J, Dennis TE, Landers TJ, Bell E, Perry GL. Linking individual-based and statistical inferential models in movement ecology: A case study with black petrels ( Procellaria parkinsoni ). Ecol Modell 2017. [DOI: 10.1016/j.ecolmodel.2017.07.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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36
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Rieb JT, Chaplin-Kramer R, Daily GC, Armsworth PR, Böhning-Gaese K, Bonn A, Cumming GS, Eigenbrod F, Grimm V, Jackson BM, Marques A, Pattanayak SK, Pereira HM, Peterson GD, Ricketts TH, Robinson BE, Schröter M, Schulte LA, Seppelt R, Turner MG, Bennett EM. When, Where, and How Nature Matters for Ecosystem Services: Challenges for the Next Generation of Ecosystem Service Models. Bioscience 2017. [DOI: 10.1093/biosci/bix075] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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37
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A General Approach to Model Movement in (Highly) Fragmented Patch Networks. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2017. [DOI: 10.1007/s13253-017-0298-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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38
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Forbes VE, Salice CJ, Birnir B, Bruins RJF, Calow P, Ducrot V, Galic N, Garber K, Harvey BC, Jager H, Kanarek A, Pastorok R, Railsback SF, Rebarber R, Thorbek P. A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2017; 36:845-859. [PMID: 28370293 PMCID: PMC6147012 DOI: 10.1002/etc.3720] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 11/14/2016] [Accepted: 12/20/2016] [Indexed: 05/29/2023]
Abstract
Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxicological endpoints to chemical impacts on populations and communities and the ecosystem services that they provide. This framework builds on considerable advances in mechanistic effects models designed to span multiple levels of biological organization and account for various types of biological interactions and feedbacks. For illustration, the authors introduce 2 case studies that employ well-developed and validated mechanistic effects models: the inSTREAM individual-based model for fish populations and the AQUATOX ecosystem model. They also show how dynamic energy budget theory can provide a common currency for interpreting organism-level toxicity. They suggest that a framework based on mechanistic models that predict impacts on ecosystem services resulting from chemical exposure, combined with economic valuation, can provide a useful approach for informing environmental management. The authors highlight the potential benefits of using this framework as well as the challenges that will need to be addressed in future work. Environ Toxicol Chem 2017;36:845-859. © 2017 SETAC.
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Affiliation(s)
- Valery E Forbes
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota, USA
| | - Chris J Salice
- Environmental Science and Studies Program and Department of Biological Sciences, Towson University, Towson, Maryland, USA
| | - Bjorn Birnir
- Center for Complex and Nonlinear Science and Department of Mathematics, University of California Santa Barbara, Santa Barbara, California, USA
| | - Randy J F Bruins
- Systems Exposure Division, National Exposure Research Laboratory, US Environmental Protection Agency, Cincinnati, Ohio
| | - Peter Calow
- Humphrey School of Public Affairs, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Nika Galic
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota, USA
| | - Kristina Garber
- Environmental Fate and Effects Division, Office of Pesticide Programs, US Environmental Protection Agency, Washington, DC
| | - Bret C Harvey
- Pacific Southwest Research Station, US Department of Agriculture Forest Service, Arcata, California
| | - Henriette Jager
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Andrew Kanarek
- Environmental Fate and Effects Division, Office of Pesticide Programs, US Environmental Protection Agency, Washington, DC
| | - Robert Pastorok
- Ecology Group, Integral Consulting, Woodinville, Washington, USA
| | | | - Richard Rebarber
- Department of Mathematics, University of Nebraska, Lincoln, Nebraska, USA
| | - Pernille Thorbek
- Environmental Safety, Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom
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39
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The role of Dynamic Energy Budget theory in predictive modeling of stressor impacts on ecological systems. Phys Life Rev 2017; 20:43-45. [DOI: 10.1016/j.plrev.2017.01.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 01/11/2017] [Indexed: 11/19/2022]
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40
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Grimm V, Ayllón D, Railsback SF. Next-Generation Individual-Based Models Integrate Biodiversity and Ecosystems: Yes We Can, and Yes We Must. Ecosystems 2016. [DOI: 10.1007/s10021-016-0071-2] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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41
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Grimm V, Berger U. Next-generation ecological modelling: A special issue dedicated to Donald DeAngelis on the occasion of his 70th birthday. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2015.12.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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