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Murphy KJ, Ciuti S, Kane A. An introduction to agent-based models as an accessible surrogate to field-based research and teaching. Ecol Evol 2020; 10:12482-12498. [PMID: 33250988 PMCID: PMC7679541 DOI: 10.1002/ece3.6848] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/17/2020] [Accepted: 08/31/2020] [Indexed: 01/09/2023] Open
Abstract
There are many barriers to fieldwork including cost, time, and physical ability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork in the natural sciences. Travel restrictions, concerns about our carbon footprint, and the global lockdown have extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially among graduate students and early-career researchers. In this paper, we discuss agent-based modeling as an open-source, accessible, and inclusive resource to substitute for lost fieldwork during COVID-19 and for future scenarios of travel restrictions such as climate change and economic downturn. We describe the benefits of Agent-Based models as a teaching and training resource for students across education levels. We discuss how and why educators and research scientists can implement them with examples from the literature on how agent-based models can be applied broadly across life science research. We aim to amplify awareness and adoption of this technique to broaden the diversity and size of the agent-based modeling community in ecology and evolutionary research. Finally, we discuss the challenges facing agent-based modeling and discuss how quantitative ecology can work in tandem with traditional field ecology to improve both methods.
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Affiliation(s)
- Kilian J. Murphy
- School of Biology and Environmental Science and the Earth InstituteUniversity College DublinDublinIreland
| | - Simone Ciuti
- School of Biology and Environmental Science and the Earth InstituteUniversity College DublinDublinIreland
| | - Adam Kane
- School of Biology and Environmental Science and the Earth InstituteUniversity College DublinDublinIreland
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Garnier R, Grenfell BT, Nisbet AJ, Matthews JB, Graham AL. Integrating immune mechanisms to model nematode worm burden: an example in sheep. Parasitology 2016; 143:894-904. [PMID: 26283186 DOI: 10.1017/S0031182015000992] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Gastrointestinal nematodes represent important sources of economic losses in farmed ruminants, and the increasing frequency of anthelmintic resistance requires an increased ability to explore alternative strategies. Theoretical approaches at the crossroads of immunology and epidemiology are valuable tools in that context. In the case of Teladorsagia circumcincta in sheep, the immunological mechanisms important for resistance are increasingly well-characterized. However, despite the existence of a wide range of theoretical models, there is no framework integrating the characteristic features of this immune response into a tractable phenomenological model. Here, we propose to bridge that gap by developing a flexible modelling framework that allows for variability in nematode larval intake which can be used to track the variations in worm burdens. We parameterize this model using data from trickle infection of sheep and show that using simple immunological assumptions, our model can capture the dynamics of both adult worm burdens and nematode fecal egg counts. In addition, our analysis reveals interesting dose-dependent effects on the immune response. Finally, we discuss potential developments of this model and highlight how an improved cross-talk between empiricists and theoreticians would facilitate important advances in the study of infectious diseases.
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Gimenez O, Buckland ST, Morgan BJT, Bez N, Bertrand S, Choquet R, Dray S, Etienne MP, Fewster R, Gosselin F, Mérigot B, Monestiez P, Morales JM, Mortier F, Munoz F, Ovaskainen O, Pavoine S, Pradel R, Schurr FM, Thomas L, Thuiller W, Trenkel V, de Valpine P, Rexstad E. Statistical ecology comes of age. Biol Lett 2015; 10:20140698. [PMID: 25540151 PMCID: PMC4298184 DOI: 10.1098/rsbl.2014.0698] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.
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Affiliation(s)
- Olivier Gimenez
- CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919 Route de Mende, 34293 Montpellier Cedex 5, France
| | - Stephen T Buckland
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews KY16 9LZ, UK
| | - Byron J T Morgan
- School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7NF, UK
| | | | | | - Rémi Choquet
- CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919 Route de Mende, 34293 Montpellier Cedex 5, France
| | - Stéphane Dray
- Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de 18 Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France
| | | | - Rachel Fewster
- Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Frédéric Gosselin
- Irstea, UR EFNO, Centre de Nogent-sur-Vernisson, 45290 Nogent-sur-Vernisson, France
| | | | | | - Juan M Morales
- Laboratorio Ecotono, CRUB, INIBIOMA-CONICET, Bariloche, Argentina
| | | | - François Munoz
- UM2, UMR AMAP, Bd de la Lironde, TA A-51/PS2, 34398 Montpellier Cedex 5, France
| | - Otso Ovaskainen
- Department of Biosciences, University of Helsinki, Helsinki, Finland
| | - Sandrine Pavoine
- UMR 7204 CNRS UPMC, Centre for Ecology and Conservation Sciences, Muséum National d'Histoire Naturelle, 55-61 rue Buffon, 75005 Paris, France Mathematical Ecology Research Group, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Roger Pradel
- CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919 Route de Mende, 34293 Montpellier Cedex 5, France
| | - Frank M Schurr
- Institute of Landscape and Plant Ecology, University of Hohenheim, 70593 Stuttgart, Germany
| | - Len Thomas
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews KY16 9LZ, UK
| | - Wilfried Thuiller
- Laboratoire d'Ecologie Alpine, UMR CNRS 5553, Université Joseph Fourier, Grenoble I, BP 53, 38041 Grenoble Cedex 9, France
| | - Verena Trenkel
- Ifremer, Rue de l'île d'Yeu, BP 21105, 44311 Nantes Cedex 3, France
| | - Perry de Valpine
- Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA
| | - Eric Rexstad
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews KY16 9LZ, UK
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Barraquand F, Ezard THG, Jørgensen PS, Zimmerman N, Chamberlain S, Salguero-Gómez R, Curran TJ, Poisot T. Lack of quantitative training among early-career ecologists: a survey of the problem and potential solutions. PeerJ 2014; 2:e285. [PMID: 24688862 PMCID: PMC3961151 DOI: 10.7717/peerj.285] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2013] [Accepted: 01/31/2014] [Indexed: 11/29/2022] Open
Abstract
Proficiency in mathematics and statistics is essential to modern ecological science, yet few studies have assessed the level of quantitative training received by ecologists. To do so, we conducted an online survey. The 937 respondents were mostly early-career scientists who studied biology as undergraduates. We found a clear self-perceived lack of quantitative training: 75% were not satisfied with their understanding of mathematical models; 75% felt that the level of mathematics was “too low” in their ecology classes; 90% wanted more mathematics classes for ecologists; and 95% more statistics classes. Respondents thought that 30% of classes in ecology-related degrees should be focused on quantitative disciplines, which is likely higher than for most existing programs. The main suggestion to improve quantitative training was to relate theoretical and statistical modeling to applied ecological problems. Improving quantitative training will require dedicated, quantitative classes for ecology-related degrees that contain good mathematical and statistical practice.
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Affiliation(s)
- Frédéric Barraquand
- Department of Arctic and Marine Biology, University of Tromsø , Tromsø , Norway
| | - Thomas H G Ezard
- Centre for Biological Sciences, University of Southampton , Southampton , United Kingdom
| | - Peter S Jørgensen
- Center for Macroecology, Evolution and Climate, University of Copenhagen , Copenhagen , Denmark
| | | | | | - Roberto Salguero-Gómez
- Max Planck Institute for Demographic Research, Evolutionary Biodemography Laboratory , Rostock , Germany ; School of Biological Sciences, Centre for Biodiversity and Conservation Science, University of Queensland , Brisbane , Australia
| | - Timothy J Curran
- Department of Ecology, Lincoln University , Canterbury , New Zealand
| | - Timothée Poisot
- Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski , Rimouski (QC) , Canada ; Québec Centre for Biodiversity Sciences, McGill University , Canada
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