1
|
Puniya BL, Verma M, Damiani C, Bakr S, Dräger A. Perspectives on computational modeling of biological systems and the significance of the SysMod community. BIOINFORMATICS ADVANCES 2024; 4:vbae090. [PMID: 38948011 PMCID: PMC11213628 DOI: 10.1093/bioadv/vbae090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/12/2024] [Accepted: 06/14/2024] [Indexed: 07/02/2024]
Abstract
Motivation In recent years, applying computational modeling to systems biology has caused a substantial surge in both discovery and practical applications and a significant shift in our understanding of the complexity inherent in biological systems. Results In this perspective article, we briefly overview computational modeling in biology, highlighting recent advancements such as multi-scale modeling due to the omics revolution, single-cell technology, and integration of artificial intelligence and machine learning approaches. We also discuss the primary challenges faced: integration, standardization, model complexity, scalability, and interdisciplinary collaboration. Lastly, we highlight the contribution made by the Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI) associated with the International Society of Computational Biology (ISCB) in driving progress within this rapidly evolving field through community engagement (via both in person and virtual meetings, social media interactions), webinars, and conferences. Availability and implementation Additional information about SysMod is available at https://sysmod.info.
Collapse
Affiliation(s)
- Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Meghna Verma
- Systems Medicine, Clinical Pharmacology and Quantitative Pharmacology, R&D BioPharmaceuticals, AstraZeneca, Gaithersburg, MD 20878, United States
| | - Chiara Damiani
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan 20126, Italy
| | - Shaimaa Bakr
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA 94305-5479, United States
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen 72076, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen 72076, Germany
- Quantitative Biology Center (QBiC), Eberhard Karl University of Tübingen, Tübingen 72076, Germany
- Data Analytics and Bioinformatics, Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale) 06120, Germany
| |
Collapse
|
2
|
Nzei JM, Martínez-Médez N, Mwanzia VM, Kurauka JK, Wang QF, Li ZZ, Chen JM. Climatic niche evolution and niche conservatism of Nymphaea species in Africa, South America, and Australia. BMC PLANT BIOLOGY 2024; 24:476. [PMID: 38816799 PMCID: PMC11137912 DOI: 10.1186/s12870-024-05141-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND Interest in the evolution of climatic niches, particularly in understanding the potential adaptive responses of species under climate change, has increased both theoretically and within macroecological studies. These studies have provided valuable insights into how climatic traits of species influence their niche evolution. In this study, we aim to investigate whether niche conservatism plays a role in the species diversification of Nymphaea, a group of aquatic plants with a cosmopolitan distribution that is facing severe habitat loss. We will use climatic models and phylogenetic data for 23 species to reconstruct Nymphaea's niche evolution, measure niche overlap, and assess disparity through time while testing for evolutionary models. RESULTS There was a lot of overlap in niches both within and between groups, especially for species that can be found in many places. The breadth and peaks of the niche profile varied depending on the bioclimatic variables, which suggested that the species evolved differently to cope with changes in climate. The analysis also showed that evolutionary changes happened across the phylogeny, with weak to moderate signals. The morphological disparity index (MDI) values indicated that there were disparities within subclades over time but not between or among them. Niche reconstruction and evolution analysis revealed both convergent and divergent evolution among various variables. For example, N. immutabilis, N. atrans, N. violancea, and N. nouchali evolved towards intermediate temperatures for bio2 and bio3 (isothermity) while moving towards extreme temperatures for bio8 and bio9 (wettest and driest average quarterly temperatures). CONCLUSION Our study will improve our understanding of how changes in climatic niches are potentially driving the evolution of Nymphaea. It has significant scientific implications for the limits, assemblages, evolution, and diversification of species. This information is crucial for the ongoing efforts of conservation and management, particularly considering the inevitable effects of climate change.
Collapse
Affiliation(s)
- John M Nzei
- Aquatic Plant Research Center, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Norberto Martínez-Médez
- Departamento de Zoología, Escuela Nacional de Ciencias Biológicas del Instituto Politécnico Nacional, Ciudad de México, México
| | - Virginia M Mwanzia
- School of Agriculture Technical Studies and Natural Sciences, Lukenya University, P.O Box 90-90128, Mtito Andei, Kenya
| | - Joseph K Kurauka
- School of Agriculture and Environmental Sciences, Kenyatta University, P.O. Box 43844-00100, Nairobi, Kenya
| | - Qing-Feng Wang
- Hubei Key Laboratory of Wetland Evolution & Ecological Restoration, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China
| | - Zhi-Zhong Li
- Aquatic Plant Research Center, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China.
- Hubei Key Laboratory of Wetland Evolution & Ecological Restoration, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China.
| | - Jin-Ming Chen
- Aquatic Plant Research Center, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China.
- Hubei Key Laboratory of Wetland Evolution & Ecological Restoration, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China.
| |
Collapse
|
3
|
A density functional theory for ecology across scales. Nat Commun 2023; 14:1089. [PMID: 36841818 PMCID: PMC9968302 DOI: 10.1038/s41467-023-36628-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 02/09/2023] [Indexed: 02/27/2023] Open
Abstract
Ecology lacks a holistic approach that can model phenomena across temporal and spatial scales, largely because of the challenges in modelling systems with a large number of interacting constituents. This hampers our understanding of complex ecosystems and the impact that human interventions (e.g., deforestation, wildlife harvesting and climate change) have on them. Here we use density functional theory, a computational method for many-body problems in physics, to develop a computational framework for ecosystem modelling. Our methods accurately fit experimental and synthetic data of interacting multi-species communities across spatial scales and can project to unseen data. As the key concept we establish and validate a cost function that encodes the trade-offs between the various ecosystem components. We show how this single general modelling framework delivers predictions on par with established, but specialised, approaches for systems from predatory microbes to territorial flies to tropical tree communities. Our density functional framework thus provides a promising avenue for advancing our understanding of ecological systems.
Collapse
|
4
|
Clark AT, Mühlbauer LK, Hillebrand H, Karakoç C. Measuring stability in ecological systems without static equilibria. Ecosphere 2022. [DOI: 10.1002/ecs2.4328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
| | | | - Helmut Hillebrand
- Institute for Chemistry and Biology of Marine Environments Carl‐von‐Ossietzky University Oldenburg Wilhelmshaven Germany
- Helmholtz‐Institute for Functional Marine Biodiversity at the University of Oldenburg Oldenburg Germany
- Alfred Wegener Institute, Helmholtz‐Centre for Polar and Marine Research Bremerhaven Germany
| | - Canan Karakoç
- Department of Biology Indiana University Bloomington Indiana USA
| |
Collapse
|
5
|
De Marco A, Sicard P, Feng Z, Agathokleous E, Alonso R, Araminiene V, Augustatis A, Badea O, Beasley JC, Branquinho C, Bruckman VJ, Collalti A, David‐Schwartz R, Domingos M, Du E, Garcia Gomez H, Hashimoto S, Hoshika Y, Jakovljevic T, McNulty S, Oksanen E, Omidi Khaniabadi Y, Prescher A, Saitanis CJ, Sase H, Schmitz A, Voigt G, Watanabe M, Wood MD, Kozlov MV, Paoletti E. Strategic roadmap to assess forest vulnerability under air pollution and climate change. GLOBAL CHANGE BIOLOGY 2022; 28:5062-5085. [PMID: 35642454 PMCID: PMC9541114 DOI: 10.1111/gcb.16278] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 03/02/2022] [Accepted: 05/18/2022] [Indexed: 05/13/2023]
Abstract
Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long-term monitoring programs.
Collapse
Affiliation(s)
| | | | - Zhaozhong Feng
- Key Laboratory of Agro‐Meteorology of Jiangsu Province, School of Applied MeteorologyNanjing University of Information Science & TechnologyNanjingChina
| | - Evgenios Agathokleous
- Key Laboratory of Agro‐Meteorology of Jiangsu Province, School of Applied MeteorologyNanjing University of Information Science & TechnologyNanjingChina
| | - Rocio Alonso
- Ecotoxicology of Air Pollution, CIEMATMadridSpain
| | - Valda Araminiene
- Lithuanian Research Centre for Agriculture and ForestryKaunasLithuania
| | - Algirdas Augustatis
- Faculty of Forest Sciences and EcologyVytautas Magnus UniversityKaunasLithuania
| | - Ovidiu Badea
- “Marin Drăcea” National Institute for Research and Development in ForestryVoluntariRomania
- Faculty of Silviculture and Forest Engineering“Transilvania” UniversityBraşovRomania
| | - James C. Beasley
- Savannah River Ecology Laboratory and Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAikenSouth CarolinaUSA
| | - Cristina Branquinho
- Centre for Ecology, Evolution and Environmental Changes, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Viktor J. Bruckman
- Commission for Interdisciplinary Ecological StudiesAustrian Academy of SciencesViennaAustria
| | | | | | - Marisa Domingos
- Instituto de BotanicaNucleo de Pesquisa em EcologiaSao PauloBrazil
| | - Enzai Du
- Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
| | | | - Shoji Hashimoto
- Department of Forest SoilsForestry and Forest Products Research InstituteTsukubaJapan
| | | | | | | | - Elina Oksanen
- Department of Environmental and Biological SciencesUniversity of Eastern FinlandJoensuuFinland
| | - Yusef Omidi Khaniabadi
- Department of Environmental Health EngineeringIndustrial Medial and Health, Petroleum Industry Health Organization (PIHO)AhvazIran
| | | | - Costas J. Saitanis
- Lab of Ecology and Environmental ScienceAgricultural University of AthensAthensGreece
| | - Hiroyuki Sase
- Ecological Impact Research DepartmentAsia Center for Air Pollution Research (ACAP)NiigataJapan
| | - Andreas Schmitz
- State Agency for Nature, Environment and Consumer Protection of North Rhine‐WestphaliaRecklinghausenGermany
| | | | - Makoto Watanabe
- Institute of AgricultureTokyo University of Agriculture and Technology (TUAT)FuchuJapan
| | - Michael D. Wood
- School of Science, Engineering and EnvironmentUniversity of SalfordSalfordUK
| | | | - Elena Paoletti
- Department of Forest SoilsForestry and Forest Products Research InstituteTsukubaJapan
| |
Collapse
|
6
|
Lewis ASL, Rollinson CR, Allyn AJ, Ashander J, Brodie S, Brookson CB, Collins E, Dietze MC, Gallinat AS, Juvigny‐Khenafou N, Koren G, McGlinn DJ, Moustahfid H, Peters JA, Record NR, Robbins CJ, Tonkin J, Wardle GM. The power of forecasts to advance ecological theory. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13955] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | - Jaime Ashander
- U.S. Geological Survey, Eastern Ecological Science Center, Patuxent Research Refuge Laurel MD USA
| | - Stephanie Brodie
- Institute of Marine Science University of California Santa Cruz Monterey CA USA
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration Monterey CA USA
| | - Cole B. Brookson
- Department of Biological Sciences University of Alberta Edmonton AB Canada
| | - Elyssa Collins
- Center for Geospatial Analytics North Carolina State University Raleigh NC USA
| | - Michael C. Dietze
- Department of Earth & Environment Boston University Boston MA United States
| | | | - Noel Juvigny‐Khenafou
- iES—Institute for Environmental Sciences University of Koblenz‐Landau Landau i. d. Pfalz Germany
| | - Gerbrand Koren
- Copernicus Institute of Sustainable Development Utrecht University Utrecht The Netherlands
| | | | | | | | | | - Caleb J. Robbins
- Department of Biology, Center for Reservoir and Aquatic Systems Research Baylor University Waco TX USA
| | - Jonathan Tonkin
- School of Biological Sciences University of Canterbury Christchurch New Zealand
- Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems New Zealand
- Bioprotection Aotearoa, Centre of Research Excellence New Zealand
| | - Glenda M. Wardle
- School of Life and Environmental Sciences University of Sydney Sydney New South Wales Australia
| |
Collapse
|
7
|
De Marco A, Garcia-Gomez H, Collalti A, Khaniabadi YO, Feng Z, Proietti C, Sicard P, Vitale M, Anav A, Paoletti E. Ozone modelling and mapping for risk assessment: An overview of different approaches for human and ecosystems health. ENVIRONMENTAL RESEARCH 2022; 211:113048. [PMID: 35257686 DOI: 10.1016/j.envres.2022.113048] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/07/2021] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
Tropospheric ozone (O3) is one of the most concernedair pollutants dueto its widespread impacts on land vegetated ecosystems and human health. Ozone is also the third greenhouse gas for radiative forcing. Consequently, it should be carefully and continuously monitored to estimate its potential adverse impacts especially inthose regions where concentrations are high. Continuous large-scale O3 concentrations measurement is crucial but may be unfeasible because of economic and practical limitations; therefore, quantifying the real impact of O3over large areas is currently an open challenge. Thus, one of the final objectives of O3 modelling is to reproduce maps of continuous concentrations (both spatially and temporally) and risk assessment for human and ecosystem health. We here reviewedthe most relevant approaches used for O3 modelling and mapping starting from the simplest geo-statistical approaches andincreasing in complexity up to simulations embedded into the global/regional circulation models and pro and cons of each mode are highlighted. The analysis showed that a simpler approach (mostly statistical models) is suitable for mappingO3concentrationsat the local scale, where enough O3concentration data are available. The associated error in mapping can be reduced by using more complex methodologies, based on co-variables. The models available at the regional or global level are used depending on the needed resolution and the domain where they are applied to. Increasing the resolution corresponds to an increase in the prediction but only up to a certain limit. However, with any approach, the ensemble models should be preferred.
Collapse
Affiliation(s)
| | | | - Alessio Collalti
- Forest Modelling Lab., ISAFOM-CNR, Via Madonna Alta, Perugia, Italy
| | - Yusef Omidi Khaniabadi
- Department of Environmental Health Engineering, Industrial Medial and Health, Petroleum Industry Health Organization (PIHO), Ahvaz, Iran
| | - Zhaozhong Feng
- Key Laboratory of Agro-meteorology of Jiangsu Province, School of Applied Meteorology,Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | | | | | - Marcello Vitale
- Sapienza University of Rome, Piazzale Aldo Moro, Rome, Italy
| | | | - Elena Paoletti
- IRET-CNR, Via Madonna Del Piano, Sesto Fiorentino, Florence, Italy
| |
Collapse
|
8
|
Kuiper JJ, Kooi BW, Peterson GD, Mooij WM. Bridging Theories for Ecosystem Stability Through Structural Sensitivity Analysis of Ecological Models in Equilibrium. Acta Biotheor 2022; 70:18. [PMID: 35737146 PMCID: PMC9225980 DOI: 10.1007/s10441-022-09441-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 05/27/2022] [Indexed: 11/24/2022]
Abstract
Ecologists are challenged by the need to bridge and synthesize different approaches and theories to obtain a coherent understanding of ecosystems in a changing world. Both food web theory and regime shift theory shine light on mechanisms that confer stability to ecosystems, but from different angles. Empirical food web models are developed to analyze how equilibria in real multi-trophic ecosystems are shaped by species interactions, and often include linear functional response terms for simple estimation of interaction strengths from observations. Models of regime shifts focus on qualitative changes of equilibrium points in a slowly changing environment, and typically include non-linear functional response terms. Currently, it is unclear how the stability of an empirical food web model, expressed as the rate of system recovery after a small perturbation, relates to the vulnerability of the ecosystem to collapse. Here, we conduct structural sensitivity analyses of classical consumer-resource models in equilibrium along an environmental gradient. Specifically, we change non-proportional interaction terms into proportional ones, while maintaining the equilibrium biomass densities and material flux rates, to analyze how alternative model formulations shape the stability properties of the equilibria. The results reveal no consistent relationship between the stability of the original models and the proportionalized versions, even though they describe the same biomass values and material flows. We use these findings to critically discuss whether stability analysis of observed equilibria by empirical food web models can provide insight into regime shift dynamics, and highlight the challenge of bridging alternative modelling approaches in ecology and beyond.
Collapse
Affiliation(s)
- Jan J Kuiper
- Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, SE 10691, Stockholm, Sweden.
- Department of Aquatic Ecology, Netherlands Institute of Ecology, P.O. Box 50, 6700 AB, Wageningen, The Netherlands.
| | - Bob W Kooi
- Faculty of Science, VU University, de Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - Garry D Peterson
- Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, SE 10691, Stockholm, Sweden
| | - Wolf M Mooij
- Department of Aquatic Ecology, Netherlands Institute of Ecology, P.O. Box 50, 6700 AB, Wageningen, The Netherlands
- Aquatic Ecology and Water Quality Management Group, Department of Environmental Sciences, Wageningen University, P.O. Box 47, 6700 AA, Wageningen, The Netherlands
| |
Collapse
|
9
|
Capello M, Rault J, Deneubourg JL, Dagorn L. Schooling in habitats with aggregative sites: the case of tropical tuna and floating objects. J Theor Biol 2022; 547:111163. [DOI: 10.1016/j.jtbi.2022.111163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/07/2022] [Accepted: 05/11/2022] [Indexed: 10/18/2022]
|
10
|
Gutierrez AP, Ponti L, Neteler M, Suckling DM, Cure JR. Invasive potential of tropical fruit flies in temperate regions under climate change. Commun Biol 2021; 4:1141. [PMID: 34593969 PMCID: PMC8484444 DOI: 10.1038/s42003-021-02599-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 08/24/2021] [Indexed: 02/08/2023] Open
Abstract
Tropical fruit flies are considered among the most economically important invasive species detected in temperate areas of the United States and the European Union. Detections often trigger quarantine and eradication programs that are conducted without a holistic understanding of the threat posed. Weather-driven physiologically-based demographic models are used to estimate the geographic range, relative abundance, and threat posed by four tropical tephritid fruit flies (Mediterranean fruit fly, melon fly, oriental fruit fly, and Mexican fruit fly) in North and Central America, and the European-Mediterranean region under extant and climate change weather (RCP8.5 and A1B scenarios). Most temperate areas under tropical fruit fly propagule pressure have not been suitable for establishment, but suitability is predicted to increase in some areas with climate change. To meet this ongoing challenge, investments are needed to collect sound biological data to develop mechanistic models to predict the geographic range and relative abundance of these and other invasive species, and to put eradication policies on a scientific basis.
Collapse
Affiliation(s)
- Andrew Paul Gutierrez
- Center for the Analysis of Sustainable Agricultural Systems (www.casasglobal.org), Kensington, CA, USA.
- Division of Ecosystem Science, College of Natural Resources, University of California, Berkeley, CA, USA.
| | - Luigi Ponti
- Center for the Analysis of Sustainable Agricultural Systems (www.casasglobal.org), Kensington, CA, USA.
- Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo economico sostenibile (ENEA), Centro Ricerche Casaccia, Roma, Italy.
| | | | - David Maxwell Suckling
- The New Zealand Institute for Plant and Food Research Ltd., Christchurch, New Zealand
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
| | - José Ricardo Cure
- Center for the Analysis of Sustainable Agricultural Systems (www.casasglobal.org), Kensington, CA, USA
- Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Bogotá, Colombia
| |
Collapse
|
11
|
Brown S, Stillman RA. Evidence‐based conservation in a changing world: lessons from waterbird individual‐based models. Ecosphere 2021. [DOI: 10.1002/ecs2.3632] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Sally Brown
- Department of Life and Environmental Sciences Faculty of Science and Technology Bournemouth University Fern Barrow Poole DorsetBH12 5BBUK
| | - Richard A. Stillman
- Department of Life and Environmental Sciences Faculty of Science and Technology Bournemouth University Fern Barrow Poole DorsetBH12 5BBUK
| |
Collapse
|
12
|
Maclean IM, Klinges DH. Microclimc: A mechanistic model of above, below and within-canopy microclimate. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2021.109567] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
13
|
Vanlandeghem V, Drapeau P, Prima M, St‐Laurent M, Fortin D. Management‐mediated predation rate in the caribou–moose–wolf system: spatial configuration of logging activities matters. Ecosphere 2021. [DOI: 10.1002/ecs2.3550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
| | - Pierre Drapeau
- Département des Sciences Biologique Université du Québec à Montréal Montreal QuebecH3C 3P8Canada
| | | | - Martin‐Hugues St‐Laurent
- Département de Biologie, Chimie et Géographie Université du Québec à Rimouski Rimouski QuebecG5L 3A1Canada
| | - Daniel Fortin
- Département de Biologie Université Laval Quebec QuebecG1V 0A6Canada
| |
Collapse
|
14
|
Reflections of two systems ecologists on modelling coupled human and natural (socio-ecological, socio-environmental) systems. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2020.109403] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
15
|
Paiva F, Brennecke D, Pansch C, Briski E. Consistency of aquatic enclosed experiments: The importance of scale and ecological complexity. DIVERS DISTRIB 2020. [DOI: 10.1111/ddi.13213] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- Filipa Paiva
- GEOMAR Helmholtz‐Zentrum für Ozeanforschung Kiel Kiel Germany
- MARE – Marine and Environmental Sciences Centre Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação (ARDITI) Edifício Madeira Tecnopolo Funchal Madeira Portugal
| | - Dennis Brennecke
- Institute for Terrestrial and Aquatic Wildlife Research University of Veterinary Medicine Hannover, Foundation Büsum Germany
- Department of Biology Marine Biological Research Centre University of Southern Denmark Kerteminde Denmark
- Leibniz Institute for Science and Mathematics Education Kiel Germany
| | - Christian Pansch
- Department of Environmental & Marine Biology Åbo Akademi University Turku Finland
| | | |
Collapse
|
16
|
Wilcox KR, Komatsu KJ, Avolio ML. Improving collaborations between empiricists and modelers to advance grassland community dynamics in ecosystem models. THE NEW PHYTOLOGIST 2020; 228:1467-1471. [PMID: 33460147 DOI: 10.1111/nph.16900] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Affiliation(s)
- Kevin R Wilcox
- Department of Ecosystem Science and Management, University of Wyoming, Laramie, WY, 82071, USA
| | | | - Meghan L Avolio
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA
| |
Collapse
|
17
|
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] [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.
Collapse
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
| |
Collapse
|
18
|
Ortega JCG, Figueiredo BRS, Graça WJ, Agostinho AA, Bini LM. Negative effect of turbidity on prey capture for both visual and non‐visual aquatic predators. J Anim Ecol 2020; 89:2427-2439. [DOI: 10.1111/1365-2656.13329] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 07/10/2020] [Indexed: 12/27/2022]
Affiliation(s)
- Jean C. G. Ortega
- Programa de Pós‐Graduação em Ecologia e Evolução Universidade Federal de Goiás Goiânia Brazil
| | - Bruno R. S. Figueiredo
- Departamento de Ecologia e Zoologia Universidade Federal de Santa Catarina Florianópolis Brazil
| | - Weferson J. Graça
- Núcleo de Pesquisas em Limnologia, Ictiologia e Aquicultura Universidade Estadual de Maringá Maringá Brazil
- Programa de Pós‐Graduação em Ecologia de Ambientes Aquáticos Continentais Universidade Estadual de Maringá Maringá Brazil
| | - Angelo A. Agostinho
- Núcleo de Pesquisas em Limnologia, Ictiologia e Aquicultura Universidade Estadual de Maringá Maringá Brazil
- Programa de Pós‐Graduação em Ecologia de Ambientes Aquáticos Continentais Universidade Estadual de Maringá Maringá Brazil
| | - Luis M. Bini
- Departamento de Ecologia Universidade Federal de Goiás Goiânia Brazil
| |
Collapse
|
19
|
A guide to ecosystem models and their environmental applications. Nat Ecol Evol 2020; 4:1459-1471. [PMID: 32929239 DOI: 10.1038/s41559-020-01298-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 08/04/2020] [Indexed: 12/12/2022]
Abstract
Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.
Collapse
|
20
|
Damgaard C. Measurement Uncertainty in Ecological and Environmental Models. Trends Ecol Evol 2020; 35:871-873. [PMID: 32727661 DOI: 10.1016/j.tree.2020.07.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/29/2020] [Accepted: 07/07/2020] [Indexed: 10/23/2022]
Abstract
In many applied cases of ecological and environmental modeling there is sizeable variation in the independent variables as a result of measurement and sampling errors. This uncertainty may lead to biased predictions. It is possible to avoid this problem by increased sampling and by modeling the errors using hierarchical modeling.
Collapse
|
21
|
Bodner K, Fortin M, Molnár PK. Making predictive modelling ART: accurate, reliable, and transparent. Ecosphere 2020. [DOI: 10.1002/ecs2.3160] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Korryn Bodner
- Department of Ecology & Evolutionary Biology University of Toronto Toronto Ontario Canada
- Laboratory of Quantitative Global Change Ecology Department of Biological Sciences University of Toronto Scarborough Toronto Ontario Canada
| | - Marie‐Josée Fortin
- Department of Ecology & Evolutionary Biology University of Toronto Toronto Ontario Canada
| | - Péter K. Molnár
- Department of Ecology & Evolutionary Biology University of Toronto Toronto Ontario Canada
- Laboratory of Quantitative Global Change Ecology Department of Biological Sciences University of Toronto Scarborough Toronto Ontario Canada
| |
Collapse
|
22
|
Beckman NG, Aslan CE, Rogers HS, Kogan O, Bronstein JL, Bullock JM, Hartig F, HilleRisLambers J, Zhou Y, Zurell D, Brodie JF, Bruna EM, Cantrell RS, Decker RR, Efiom E, Fricke EC, Gurski K, Hastings A, Johnson JS, Loiselle BA, Miriti MN, Neubert MG, Pejchar L, Poulsen JR, Pufal G, Razafindratsima OH, Sandor ME, Shea K, Schreiber S, Schupp EW, Snell RS, Strickland C, Zambrano J. Advancing an interdisciplinary framework to study seed dispersal ecology. AOB PLANTS 2020; 12:plz048. [PMID: 32346468 PMCID: PMC7179845 DOI: 10.1093/aobpla/plz048] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 07/26/2019] [Indexed: 05/23/2023]
Abstract
Although dispersal is generally viewed as a crucial determinant for the fitness of any organism, our understanding of its role in the persistence and spread of plant populations remains incomplete. Generalizing and predicting dispersal processes are challenging due to context dependence of seed dispersal, environmental heterogeneity and interdependent processes occurring over multiple spatial and temporal scales. Current population models often use simple phenomenological descriptions of dispersal processes, limiting their ability to examine the role of population persistence and spread, especially under global change. To move seed dispersal ecology forward, we need to evaluate the impact of any single seed dispersal event within the full spatial and temporal context of a plant's life history and environmental variability that ultimately influences a population's ability to persist and spread. In this perspective, we provide guidance on integrating empirical and theoretical approaches that account for the context dependency of seed dispersal to improve our ability to generalize and predict the consequences of dispersal, and its anthropogenic alteration, across systems. We synthesize suitable theoretical frameworks for this work and discuss concepts, approaches and available data from diverse subdisciplines to help operationalize concepts, highlight recent breakthroughs across research areas and discuss ongoing challenges and open questions. We address knowledge gaps in the movement ecology of seeds and the integration of dispersal and demography that could benefit from such a synthesis. With an interdisciplinary perspective, we will be able to better understand how global change will impact seed dispersal processes, and potential cascading effects on plant population persistence, spread and biodiversity.
Collapse
Affiliation(s)
- Noelle G Beckman
- Department of Biology & Ecology Center, Utah State University, Logan, UT, USA
| | - Clare E Aslan
- Landscape Conservation Initiative, Northern Arizona University, Flagstaff, AZ, USA
| | - Haldre S Rogers
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | - Oleg Kogan
- Physics Department, California Polytechnic State University, San Luis Obispo, CA, USA
| | - Judith L Bronstein
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - James M Bullock
- Centre for Ecology and Hydrology, Benson Lane, Wallingford, UK
| | - Florian Hartig
- Theoretical Ecology, University of Regensburg, Regensburg, Germany
| | | | - Ying Zhou
- Department of Mathematics, Lafayette College, Easton, PA, USA
| | - Damaris Zurell
- Swiss Federal Research Institute WSL, Dept. Land Change Science, Birmensdorf, Switzerland
- Humboldt-University Berlin, Geography Dept., Berlin, Germany
| | - Jedediah F Brodie
- Division of Biological Sciences and Wildlife Biology Program, University of Montana, Missoula, MT, USA
| | - Emilio M Bruna
- Department of Wildlife Ecology & Conservation & Center for Latin American Studies, University of Florida, Gainesville, FL, USA
| | | | - Robin R Decker
- Department of Environmental Science and Policy, University of California, Davis, CA, USA
| | - Edu Efiom
- REDD+ Unit, Cross River State Forestry Commission, Calabar, Nigeria
- Biology Department, Lund University, Lund, Sweden
| | - Evan C Fricke
- National Socio-Environmental Synthesis Center, University of Maryland, Annapolis, MD, USA
| | - Katherine Gurski
- Department of Mathematics, Howard University, Washington, DC, USA
| | - Alan Hastings
- Department of Environmental Science and Policy, University of California, Davis, CA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Jeremy S Johnson
- School of Forestry, Northern Arizona University, Flagstaff, AZ, USA
| | - Bette A Loiselle
- Center for Latin American Studies and Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | - Maria N Miriti
- Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, OH, USA
| | - Michael G Neubert
- Department of Biology, Woods Hole Oceanographic Institution, Woods Hole, MA, USA
| | - Liba Pejchar
- Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA
| | - John R Poulsen
- Nicholas School of the Environment, Duke University, Durham, NC, USA
| | - Gesine Pufal
- Natur Conservation and Landscape Ecology, University of Freiburg Freiburg, Germany
| | | | - Manette E Sandor
- Landscape Conservation Initiative, Northern Arizona University, Flagstaff, AZ, USA
| | - Katriona Shea
- Department of Biology, The Pennsylvania State University, University Park, PA, USA
| | - Sebastian Schreiber
- Department of Evolution and Ecology and Center for Population Biology, University of California, Davis, CA, USA
| | - Eugene W Schupp
- Department of Wildland Resources & Ecology Center, Utah State University, Logan, UT, USA
| | - Rebecca S Snell
- Department of Environmental and Plant Biology, Ohio University, Athens, OH, USA
| | | | - Jenny Zambrano
- Department of Biology, University of Maryland, College Park, MD, USA
| |
Collapse
|
23
|
Maclean IMD. Predicting future climate at high spatial and temporal resolution. GLOBAL CHANGE BIOLOGY 2020; 26:1003-1011. [PMID: 31638296 PMCID: PMC7027457 DOI: 10.1111/gcb.14876] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/14/2019] [Accepted: 10/16/2019] [Indexed: 05/22/2023]
Abstract
Most studies on the biological effects of future climatic changes rely on seasonally aggregated, coarse-resolution data. Such data mask spatial and temporal variability in microclimate driven by terrain, wind and vegetation, and ultimately bear little resemblance to the conditions that organisms experience in the wild. Here, I present the methods for providing fine-grained, hourly and daily estimates of current and future temperature and soil moisture over decadal timescales. Observed climate data and spatially coherent probabilistic projections of daily future weather were disaggregated to hourly and used to drive empirically calibrated physical models of thermal and hydrological microclimates. Mesoclimatic effects (cold-air drainage, coastal exposure and elevation) were determined from the coarse-resolution climate surfaces using thin-plate spline models with coastal exposure and elevation as predictors. Differences between micro and mesoclimate temperatures were determined from terrain, vegetation and ground properties using energy balance equations. Soil moisture was computed in a thin upper layer and an underlying deeper layer, and the exchange of water between these layers was calculated using the van Genuchten equation. Code for processing the data and running the models is provided as a series of R packages. The methods were applied to the Lizard Peninsula, United Kingdom, to provide hourly estimates of temperature (100 m grid resolution over entire area, 1 m for a selected area) for the periods 1983-2017 and 2041-2049. Results indicated that there is a fine-resolution variability in climatic changes, driven primarily by interactions between landscape features and decadal trends in weather conditions. High-temporal resolution extremes in conditions under future climate change were predicted to be considerably less novel than the extremes estimated using seasonally aggregated variables. The study highlights the need to more accurately estimate the future climatic conditions experienced by organisms and equips biologists with the means to do so.
Collapse
|
24
|
Streib L, Kattwinkel M, Heer H, Ruzika S, Schäfer RB. How does habitat connectivity influence the colonization success of a hemimetabolous aquatic insect? - A modeling approach. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2019.108909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
25
|
Evans LC, Sibly RM, Thorbek P, Sims I, Oliver TH, Walters RJ. Quantifying the effectiveness of agri-environment schemes for a grassland butterfly using individual-based models. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.108798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
26
|
Johnston ASA, Boyd RJ, Watson JW, Paul A, Evans LC, Gardner EL, Boult VL. Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach. Proc Biol Sci 2019; 286:20191916. [PMID: 31615360 DOI: 10.1098/rspb.2019.1916] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Animal populations will mediate the response of global biodiversity to environmental changes. Population models are thus important tools for both understanding and predicting animal responses to uncertain future conditions. Most approaches, however, are correlative and ignore the individual-level mechanisms that give rise to population dynamics. Here, we assess several existing population modelling approaches and find limitations to both 'correlative' and 'mechanistic' models. We advocate the need for a standardized mechanistic approach for linking individual mechanisms (physiology, behaviour, and evolution) to population dynamics in spatially explicit landscapes. Such an approach is potentially more flexible and informative than current population models. Key to realizing this goal, however, is overcoming current data limitations, the development and testing of eco-evolutionary theory to represent interactions between individual mechanisms, and standardized multi-dimensional environmental change scenarios which incorporate multiple stressors. Such progress is essential in supporting environmental decisions in uncertain future conditions.
Collapse
Affiliation(s)
- A S A Johnston
- School of Biological Sciences, University of Reading, Reading RG6 6AH, UK
| | - R J Boyd
- School of Archaeology, Geography and Environmental Science, University of Reading, Reading RG6 6AX, UK
| | - J W Watson
- School of Biological Sciences, University of Reading, Reading RG6 6AH, UK
| | - A Paul
- School of Archaeology, Geography and Environmental Science, University of Reading, Reading RG6 6AX, UK
| | - L C Evans
- School of Biological Sciences, University of Reading, Reading RG6 6AH, UK
| | - E L Gardner
- School of Biological Sciences, University of Reading, Reading RG6 6AH, UK
| | - V L Boult
- School of Biological Sciences, University of Reading, Reading RG6 6AH, UK.,Department of Meteorology, University of Reading, Reading RG6 6AX, UK
| |
Collapse
|
27
|
Irvine LM, Palacios DM, Lagerquist BA, Mate BR. Scales of Blue and Fin Whale Feeding Behavior off California, USA, With Implications for Prey Patchiness. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00338] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
|
28
|
Chong J, Amourda C, Saunders TE. Temporal development of Drosophila embryos is highly robust across a wide temperature range. J R Soc Interface 2019; 15:rsif.2018.0304. [PMID: 29997261 PMCID: PMC6073635 DOI: 10.1098/rsif.2018.0304] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 06/18/2018] [Indexed: 11/12/2022] Open
Abstract
Development is a process precisely coordinated in both space and time. Spatial precision has been quantified in a number of developmental systems, and such data have contributed significantly to our understanding of, for example, morphogen gradient interpretation. However, comparatively little quantitative analysis has been performed on timing and temporal coordination during development. Here, we use Drosophila to explore the temporal robustness of embryonic development within physiologically normal temperatures. We find that development is temporally very precise across a wide range of temperatures in the three Drosophila species investigated. However, we find temperature dependence in the timing of developmental events. A simple model incorporating history dependence can explain the developmental temporal trajectories. Interestingly, history dependence is temperature-specific, with either effective negative or positive feedback at different temperatures. We also find that embryos are surprisingly robust to shifting temperatures during embryogenesis. We further identify differences between tropical and temperate species, potentially due to different mechanisms regulating temporal development that depend on the local environment. Our data show that Drosophila embryonic development is temporally robust across a wide range of temperatures. This robustness shows interesting species-specific differences that are suggestive of different sensitivity to temperature fluctuations between Drosophila species.
Collapse
Affiliation(s)
- Jeronica Chong
- Mechanobiology Institute, National University of Singapore, Singapore, Republic of Singapore
| | - Christopher Amourda
- Mechanobiology Institute, National University of Singapore, Singapore, Republic of Singapore
| | - Timothy E Saunders
- Mechanobiology Institute, National University of Singapore, Singapore, Republic of Singapore .,Department of Biological Sciences, National University of Singapore, Singapore, Republic of Singapore.,Institute of Molecular and Cell Biology, A*Star, Proteos, Singapore, Republic of Singapore
| |
Collapse
|
29
|
Bio-Based Production Systems: Why Environmental Assessment Needs to Include Supporting Systems. SUSTAINABILITY 2019. [DOI: 10.3390/su11174678] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The transition to a bio-based economy is expected to deliver substantial environmental and economic benefits. However, bio-based production systems still come with significant environmental challenges, and there is a need for assessment methods that are adapted for the specific characteristics of these systems. In this review, we investigated how the environmental aspects of bio-based production systems differ from those of non-renewable systems, what requirements these differences impose when assessing their sustainability, and to what extent mainstream assessment methods fulfil these requirements. One unique characteristic of bio-based production is the need to maintain the regenerative capacity of the system. The necessary conditions for maintaining regenerative capacity are often provided through direct or indirect interactions between the production system and surrounding “supporting” systems. Thus, in the environmental assessment, impact categories affected in both the primary production system and the supporting systems need to be included, and impact models tailored to the specific context of the study should be used. Development in this direction requires efforts to broaden the system boundaries of conventional environmental assessments, to increase the level of spatial and temporal differentiation, and to improve our understanding of how local uniqueness and temporal dynamics affect the performance of the investigated system.
Collapse
|
30
|
Yu R, Ruddell BL, Kang M, Kim J, Childers D. Anticipating global terrestrial ecosystem state change using FLUXNET. GLOBAL CHANGE BIOLOGY 2019; 25:2352-2367. [PMID: 30793451 DOI: 10.1111/gcb.14602] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 01/17/2019] [Indexed: 06/09/2023]
Abstract
Ecosystems can be characterized as complex systems that traverse a variety of functional and structural states in response to changing bioclimatic forcings. A central challenge of global change biology is the robust empirical description of these states and state transitions. An ecosystem's functional state can be empirically described using Process Networks (PN) that use timeseries observations to determine the strength of process-level functional couplings between ecosystem components. A globally extensive source of in-situ observations of terrestrial ecosystem dynamics is the FLUXNET eddy-covariance network that provides standardized observations of micrometeorology and carbon, water, and energy flux dynamics. We employ the LaThuile FLUXNET synthesis dataset to delineate each month's functional state for 204 sites, yielding the LaThuile PN version 1.0 database that describes the strength of an ecosystem's functional couplings from air temperature and precipitation to carbon fluxes during each site-month. Then we calculate the elasticity of these couplings to seasonal scale forcings: air temperature, precipitation, solar radiation, and phenophase. Finally, we train artificial neural networks to extrapolate these elasticities from 204 sites to the globe, yielding maps of the estimated functional elasticity of every terrestrial ecosystem's functional states to changing seasonal bioclimatic forcings. These maps provide theoretically novel resource that can be used to anticipate ecological state transitions in response to climate change and to validate process-based models of ecological change. These elasticity maps show that each ecosystem can be expected to respond uniquely to changing forcings. Tropical forests, hot deserts, savannas, and high elevations are most elastic to climate change, and elasticity of ecosystems to seasonal air temperature is on average an order of magnitude higher than elasticity to other bioclimatic forcings. We also observed a reasonable amount of moderate relationships between functional elasticity and structural state change across different ecosystems.
Collapse
Affiliation(s)
- Rong Yu
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska
| | - Benjamin L Ruddell
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona
| | - Minseok Kang
- National Center for AgroMeteorology, Seoul, South Korea
| | - Joon Kim
- National Center for AgroMeteorology, Seoul, South Korea
| | - Dan Childers
- School of Sustainability, Arizona State University, Tempe, Arizona
| |
Collapse
|
31
|
Bennett AE, Preedy K, Golubski A, Umbanhowar J, Borrett SR, Byrne L, Apostol K, Bever JD, Biederman L, Classen AT, Cuddington K, Graaff M, Garrett KA, Gross L, Hastings A, Hoeksema JD, Hrynkiv V, Karst J, Kummel M, Lee CT, Liang C, Liao W, Mack K, Miller L, Ownley B, Rojas C, Simms EL, Walsh VK, Warren M, Zhu J. Beyond the black box: promoting mathematical collaborations for elucidating interactions in soil ecology. Ecosphere 2019. [DOI: 10.1002/ecs2.2799] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Alison E. Bennett
- Department of Evolution, Ecology, and Organismal Biology The Ohio State University Columbus Ohio 43210 USA
| | - Katharine Preedy
- Biomathematics and Statistics Scotland The James Hutton Institute Invergowrie Dundee DD2 5DA UK
| | - Antonio Golubski
- Ecology, Evolution, and Organismal Biology Kennesaw State University Kennesaw Georgia 30144 USA
| | - James Umbanhowar
- Department of Biology University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599‐3280 USA
| | - Stuart R. Borrett
- Department of Biology and Marine Biology University of North Carolina‐Wilmington Wilmington North Carolina 28403‐5915 USA
| | - Loren Byrne
- Roger Williams University One Old Ferry Road Bristol Rhode Island 02809 USA
| | - Kent Apostol
- Environmental Review 925N. Fairgrounds Road Goldendale Washington 98620 USA
| | - James D. Bever
- Department of Ecology & Evolutionary Biology University of Kansas Lawrence Kansas 66045 USA
| | | | - Aimée T. Classen
- The Rubenstein School of Environment and Natural Resources University of Vermont Burlington Vermont 05405 USA
| | | | | | - Karen A. Garrett
- Institute for Sustainable Food Systems and Plant Pathology Department University of Florida Gainesville Florida 32611 USA
| | - Lou Gross
- National Institute for Mathematical and Biological Synthesis University of Tennessee Knoxville Tennessee 37996‐1610 USA
| | - Alan Hastings
- Environmental Science and Policy University of California Davis Davis California 95616 USA
| | - Jason D. Hoeksema
- Department of Biology University of Mississippi University Mississippi 38677‐1848 USA
| | | | - Justine Karst
- Renewable Resources University of Alberta Edmonton Alberta T6G 2E3 Canada
| | - Miro Kummel
- Colorado College Colorado Springs Colorado 80903 USA
| | - Charlotte T. Lee
- Department of Biology Duke University Durham North Carolina 27708 USA
| | - Chao Liang
- Key Laboratory of Forest Ecology and Management Institute of Applied Ecology Chinese Academy of Sciences Shenyang 110016 China
| | - Wei Liao
- University of Wisconsin Madison Wisconsin 53706 USA
| | - Keenan Mack
- Department of Biology Illinois College Jacksonville Illinois 62650 USA
| | - Laura Miller
- University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599‐3280 USA
| | - Bonnie Ownley
- The University of Tennessee Institute of Agriculture Knoxville Tennessee 37996 USA
| | - Claudia Rojas
- Institute of Agronomic Sciences University of O'Higgins Rancagua Chile
| | - Ellen L. Simms
- Department of Integrative Biology University of California, Berkeley Berkeley California 94720‐3140 USA
| | - Vonda K. Walsh
- Virginia Military Institute Lexington Virginia 24450‐0304 USA
| | - Matthew Warren
- Northern Research Station United States Department of Agriculture Forest Service Durham New Hampshire 03824 USA
| | - Jun Zhu
- University of Wisconsin Madison Wisconsin 53706‐1598 USA
| |
Collapse
|
32
|
Carey CC, Ward NK, Farrell KJ, Lofton ME, Krinos AI, McClure RP, Subratie KC, Figueiredo RJ, Doubek JP, Hanson PC, Papadopoulos P, Arzberger P. Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward. Ecosphere 2019. [DOI: 10.1002/ecs2.2753] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Cayelan C. Carey
- Department of Biological Sciences Virginia Tech Blacksburg Virginia USA
| | - Nicole K. Ward
- Department of Biological Sciences Virginia Tech Blacksburg Virginia USA
| | | | - Mary E. Lofton
- Department of Biological Sciences Virginia Tech Blacksburg Virginia USA
| | - Arianna I. Krinos
- Department of Biological Sciences Virginia Tech Blacksburg Virginia USA
| | - Ryan P. McClure
- Department of Biological Sciences Virginia Tech Blacksburg Virginia USA
| | | | - Renato J. Figueiredo
- Electrical and Computer Engineering University of Florida Gainesville Florida USA
| | | | - Paul C. Hanson
- Center for Limnology University of Wisconsin‐Madison Madison Wisconsin USA
| | - Philip Papadopoulos
- San Diego Supercomputer Center University of California‐San Diego La Jolla California USA
| | - Peter Arzberger
- Pacific Rim Applications and Grid Middleware Assembly (PRAGMA) University of California‐San Diego La Jolla California USA
| |
Collapse
|
33
|
Wang HH, Teel PD, Grant WE, Soltero F, Urdaz J, Ramírez AEP, Miller RJ, Pérez de León AA. Simulation tools for assessment of tick suppression treatments of Rhipicephalus (Boophilus) microplus on non-lactating dairy cattle in Puerto Rico. Parasit Vectors 2019; 12:185. [PMID: 31029149 PMCID: PMC6487003 DOI: 10.1186/s13071-019-3443-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 04/12/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The southern cattle fever tick (SCFT), Rhipicephalus (Boophilus) microplus, remains endemic in Puerto Rico. Systematic treatment programmes greatly reduced and even eradicated temporarily this tick from the island. However, a systemic treatment programme that includes integrated management practices for livestock against SCFT remains to be established in the island. We describe a spatially-explicit, individual-based model that simulates climate-livestock-SCFT-landscape interactions. This model was developed as an investigative tool to aid in a research project on integrated management of the SCFT that took place in Puerto Rico between 2014 and 2017. We used the model to assess the efficacy of tick suppression and probability of tick elimination when applying safer acaricides at 3-week intervals to different proportions of a herd of non-lactating dairy cattle. RESULTS Probabilities of eliminating host-seeking larvae from the simulated system decreased from ≈ 1 to ≈ 0 as the percentage of cattle treated decreased from 65 to 45, with elimination probabilities ≈ 1 at higher treatment percentages and ≈ 0 at lower treatment percentages. For treatment percentages between 65% and 45%, a more rapid decline in elimination probabilities was predicted by the version of the model that produced higher densities of host-seeking larvae. Number of weeks after the first acaricide application to elimination of host-seeking larvae was variable among replicate simulations within treatment percentages, with within-treatment variation increasing markedly at treatment percentages ≤ 65. Number of weeks after first application to elimination generally varied between 30 and 40 weeks for those treatment percentages with elimination probabilities ≈ 1. CONCLUSIONS Explicit simulation of the spatial and temporal dynamics of off-host (host-seeking) larvae in response to control methods should be an essential element of research that involves the evaluation of integrated SCFT management programmes. This approach could provide the basis to evaluate novel control technologies and to develop protocols for their cost-effective use with other treatment methods.
Collapse
Affiliation(s)
- Hsiao-Hsuan Wang
- Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX, 77843, USA.
| | - Pete D Teel
- Department of Entomology, Texas A&M AgriLife Research, College Station, TX, 77843, USA
| | - William E Grant
- Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Fred Soltero
- United States Department of Agriculture-Animal and Plant Health Inspection Service, Veterinary Services, 654 Munoz Rivera Ave. Plaza Bldg. Suite 700, San Juan, 00918, Puerto Rico
| | - José Urdaz
- United States Department of Agriculture-Animal and Plant Health Inspection Service, Veterinary Services, 2150 Centre Ave. Bldg. B, MS-3E13, Ft. Collins, CO, 80526, USA
| | - Alejandro E Pérez Ramírez
- Veterinary Services and Animal Health, Puerto Rico Department of Agriculture, P.O. Box 10163, San Juan, 00908-1163, Puerto Rico
| | - Robert J Miller
- Cattle Fever Tick Research Laboratory, United States Department of Agriculture-Agricultural Research Service, Edinburg, TX, 78541, USA
| | - Adalberto A Pérez de León
- Knipling-Bushland U.S. Livestock Insects Research Laboratory, and Veterinary Pest Genomics Center, United States Department of Agriculture-Agricultural Research Service, Kerrville, TX, 78028, USA
| |
Collapse
|
34
|
Yen JDL, Tonkin Z, Lyon J, Koster W, Kitchingman A, Stamation K, Vesk PA. Integrating Multiple Data Types to Connect Ecological Theory and Data Among Levels. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
35
|
Reimer JR, Caswell H, Derocher AE, Lewis MA. Ringed seal demography in a changing climate. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2019; 29:e01855. [PMID: 30672632 DOI: 10.1002/eap.1855] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 10/09/2018] [Accepted: 11/13/2018] [Indexed: 06/09/2023]
Abstract
Climate change is affecting species' distributions and abundances worldwide. Baseline population estimates, against which future observations may be compared, are necessary if we are to detect ecological change. Arctic sea ice ecosystems are changing rapidly and we lack baseline population estimates for many ice-associated species. Provided we can detect them, changes in Arctic marine ecosystems may be signaled by changes in indicator species such as ringed seals (Pusa hispida). Ringed seal monitoring has provided estimates of survival and fertility rates, but these have not been used for population-level inference. Using matrix population models, we synthesized existing demographic parameters to obtain estimates of historical ringed seal population growth and structure in Amundsen Gulf and Prince Albert Sound, Canada. We then formalized existing hypotheses about the effects of emerging environmental stressors (i.e., earlier spring ice breakup and reduced snow depth) on ringed seal pup survival. Coupling the demographic model to ice and snow forecasts available from the Coupled Model Intercomparison Project resulted in projections of ringed seal population size and structure up to the year 2100. These projections showed median declines in population size ranging from 50% to 99%. Corresponding to these projected declines were substantial changes in population structure, with increasing proportions of ringed seal pups and adults and declining proportions of juveniles. We explored if currently collected, harvest-based data could be used to detect the projected changes in population stage structure. Our model suggests that at a present sample size of 100 seals per year, the projected changes in stage structure would only be reliably detected by mid-century, even for the most extreme climate models. This modeling process revealed inconsistencies in existing estimates of ringed seal demographic rates. Mathematical population models such as these can contribute both to understanding past population trends as well as predicting future ones, both of which are necessary if we are to detect and interpret future observations.
Collapse
Affiliation(s)
- Jody R Reimer
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta , T6G 2E9, Canada
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1, Canada
| | - Hal Caswell
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, 1090, The Netherlands
| | - Andrew E Derocher
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta , T6G 2E9, Canada
| | - Mark A Lewis
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta , T6G 2E9, Canada
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1, Canada
| |
Collapse
|
36
|
Rammer W, Seidl R. Harnessing Deep Learning in Ecology: An Example Predicting Bark Beetle Outbreaks. FRONTIERS IN PLANT SCIENCE 2019; 10:1327. [PMID: 31719829 PMCID: PMC6827389 DOI: 10.3389/fpls.2019.01327] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 09/24/2019] [Indexed: 05/02/2023]
Abstract
Addressing current global challenges such as biodiversity loss, global change, and increasing demands for ecosystem services requires improved ecological prediction. Recent increases in data availability, process understanding, and computing power are fostering quantitative approaches in ecology. However, flexible methodological frameworks are needed to utilize these developments towards improved ecological prediction. Deep learning is a rapidly evolving branch of machine learning, yet has received only little attention in ecology to date. It refers to the training of deep neural networks (DNNs), i.e. artificial neural networks consisting of many layers and a large number of neurons. We here provide a reproducible example (including code and data) of designing, training, and applying DNNs for ecological prediction. Using bark beetle outbreaks in conifer-dominated forests as an example, we show that DNNs are well able to predict both short-term infestation risk at the local scale and long-term outbreak dynamics at the landscape level. We furthermore highlight that DNNs have better overall performance than more conventional approaches to predicting bark beetle outbreak dynamics. We conclude that DNNs have high potential to form the backbone of a comprehensive disturbance forecasting system. More broadly, we argue for an increased utilization of the predictive power of DNNs for a wide range of ecological problems.
Collapse
|
37
|
Hindle BJ, Rees M, Sheppard AW, Quintana‐Ascencio PF, Menges ES, Childs DZ. Exploring population responses to environmental change when there is never enough data: a factor analytic approach. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13085] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Bethan J. Hindle
- Department of Animal and Plant SciencesUniversity of Sheffield Sheffield UK
| | - Mark Rees
- Department of Animal and Plant SciencesUniversity of Sheffield Sheffield UK
| | - Andy W. Sheppard
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Canberra ACT Australia
| | | | | | - Dylan Z. Childs
- Department of Animal and Plant SciencesUniversity of Sheffield Sheffield UK
| |
Collapse
|
38
|
Yates KL, Bouchet PJ, Caley MJ, Mengersen K, Randin CF, Parnell S, Fielding AH, Bamford AJ, Ban S, Barbosa AM, Dormann CF, Elith J, Embling CB, Ervin GN, Fisher R, Gould S, Graf RF, Gregr EJ, Halpin PN, Heikkinen RK, Heinänen S, Jones AR, Krishnakumar PK, Lauria V, Lozano-Montes H, Mannocci L, Mellin C, Mesgaran MB, Moreno-Amat E, Mormede S, Novaczek E, Oppel S, Ortuño Crespo G, Peterson AT, Rapacciuolo G, Roberts JJ, Ross RE, Scales KL, Schoeman D, Snelgrove P, Sundblad G, Thuiller W, Torres LG, Verbruggen H, Wang L, Wenger S, Whittingham MJ, Zharikov Y, Zurell D, Sequeira AM. Outstanding Challenges in the Transferability of Ecological Models. Trends Ecol Evol 2018; 33:790-802. [DOI: 10.1016/j.tree.2018.08.001] [Citation(s) in RCA: 277] [Impact Index Per Article: 46.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 08/03/2018] [Accepted: 08/03/2018] [Indexed: 11/30/2022]
|
39
|
Longo M, Knox RG, Levine NM, Alves LF, Bonal D, Camargo PB, Fitzjarrald DR, Hayek MN, Restrepo-Coupe N, Saleska SR, da Silva R, Stark SC, Tapajós RP, Wiedemann KT, Zhang K, Wofsy SC, Moorcroft PR. Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts. THE NEW PHYTOLOGIST 2018; 219:914-931. [PMID: 29786858 DOI: 10.1111/nph.15185] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 03/20/2018] [Indexed: 05/12/2023]
Abstract
The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2-7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2 ) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.
Collapse
Affiliation(s)
- Marcos Longo
- Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
| | - Ryan G Knox
- Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Naomi M Levine
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Luciana F Alves
- Center for Tropical Research, Institute of the Environment and Sustainability, UCLA, Los Angeles, CA, 90095, USA
| | | | - Plinio B Camargo
- Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, SP, 13416-000, Brazil
| | | | - Matthew N Hayek
- Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Natalia Restrepo-Coupe
- Climate Change Cluster, University of Technology Sydney, Sydney, NSW, 2007, Australia
- University of Arizona, Tucson, AZ, 85721, USA
| | | | - Rodrigo da Silva
- Universidade Federal do Oeste do Pará, Santarém, PA, 68040-255, USA
| | - Scott C Stark
- Michigan State University, East Lansing, MI, 48824, USA
| | | | - Kenia T Wiedemann
- Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Ke Zhang
- Hohai University, Nanjing, Jiangsu, 210029, China
| | - Steven C Wofsy
- Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Paul R Moorcroft
- Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA
| |
Collapse
|
40
|
Fell M, Barber J, Lichstein JW, Ogle K. Multidimensional trait space informed by a mechanistic model of tree growth and carbon allocation. Ecosphere 2018. [DOI: 10.1002/ecs2.2060] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Michael Fell
- School of Life Sciences Arizona State University P.O. Box 874501 Tempe Arizona 85287 USA
- School of Informatics, Computing, and Cyber Systems Northern Arizona University P.O. Box 5693 Flagstaff Arizona 86011 USA
| | - Jarrett Barber
- School of Informatics, Computing, and Cyber Systems Northern Arizona University P.O. Box 5693 Flagstaff Arizona 86011 USA
| | - Jeremy W. Lichstein
- Department of Biology University of Florida P.O. Box 118525 Gainesville Florida 32611 USA
| | - Kiona Ogle
- School of Informatics, Computing, and Cyber Systems Northern Arizona University P.O. Box 5693 Flagstaff Arizona 86011 USA
- Center for Ecosystem Science and Society Northern Arizona University P.O. Box 5620 Flagstaff Arizona 86011 USA
- Department of Biological Sciences Northern Arizona University P.O. Box 5640 Flagstaff Arizona 86011 USA
| |
Collapse
|
41
|
Fisher RA, Koven CD, Anderegg WRL, Christoffersen BO, Dietze MC, Farrior CE, Holm JA, Hurtt GC, Knox RG, Lawrence PJ, Lichstein JW, Longo M, Matheny AM, Medvigy D, Muller-Landau HC, Powell TL, Serbin SP, Sato H, Shuman JK, Smith B, Trugman AT, Viskari T, Verbeeck H, Weng E, Xu C, Xu X, Zhang T, Moorcroft PR. Vegetation demographics in Earth System Models: A review of progress and priorities. GLOBAL CHANGE BIOLOGY 2018; 24:35-54. [PMID: 28921829 DOI: 10.1111/gcb.13910] [Citation(s) in RCA: 183] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 08/12/2017] [Accepted: 08/17/2017] [Indexed: 05/24/2023]
Abstract
Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.
Collapse
Affiliation(s)
- Rosie A Fisher
- National Center for Atmospheric Research, Boulder, CO, USA
| | | | | | | | - Michael C Dietze
- Department of Earth and Environment, Boston University, Boston, MA, USA
| | - Caroline E Farrior
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | | | - George C Hurtt
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
| | - Ryan G Knox
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | | | - Marcos Longo
- Embrapa Agricultural Informatics, Campinas, Brazil
| | - Ashley M Matheny
- Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA
| | - David Medvigy
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | | | | | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Hisashi Sato
- Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
| | | | - Benjamin Smith
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
| | - Anna T Trugman
- Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
| | - Toni Viskari
- Smithsonian Tropical Research Institute, Panamá, Panamá
| | - Hans Verbeeck
- Department of Applied Ecology and Environmental Biology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium
| | - Ensheng Weng
- Center for Climate Systems Research, Columbia University, New York, NY, USA
| | - Chonggang Xu
- Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Xiangtao Xu
- Department of Geosciences, Princeton University, Princeton, NJ, USA
| | - Tao Zhang
- Department of Biology, University of Florida, Gainesville, FL, USA
| | - Paul R Moorcroft
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| |
Collapse
|
42
|
Getz WM, Marshall CR, Carlson CJ, Giuggioli L, Ryan SJ, Romañach SS, Boettiger C, Chamberlain SD, Larsen L, D'Odorico P, O'Sullivan D. Making ecological models adequate. Ecol Lett 2017; 21:153-166. [PMID: 29280332 DOI: 10.1111/ele.12893] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 11/07/2017] [Accepted: 11/12/2017] [Indexed: 12/22/2022]
Abstract
Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.
Collapse
Affiliation(s)
- Wayne M Getz
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA.,Schools of Mathematical Sciences and Life Sciences, University of KwaZulu, Natal, South Africa
| | - Charles R Marshall
- Museum of Paleontology and Department Integrative Biology, University of California, Berkeley, CA, 94720, USA
| | - Colin J Carlson
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Luca Giuggioli
- Bristol Centre for Complexity Sciences, Department of Engineering Mathematics, and School of Biological Sciences, University of Bristol, Bristol, UK
| | - Sadie J Ryan
- Department of Geography, and Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32601, USA.,Schools of Mathematical Sciences and Life Sciences, University of KwaZulu, Natal, South Africa
| | - Stephanie S Romañach
- Wetland and Aquatic Research Center, U.S. Geological Survey, Fort Lauderdale, FL, 33314, USA
| | - Carl Boettiger
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Samuel D Chamberlain
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Laurel Larsen
- Department of Geography, University of California, Berkeley, CA, 94720, USA
| | - Paolo D'Odorico
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - David O'Sullivan
- Department of Geography, University of California, Berkeley, CA, 94720, USA
| |
Collapse
|
43
|
Tomlinson S, Webber BL, Bradshaw SD, Dixon KW, Renton M. Incorporating biophysical ecology into high‐resolution restoration targets: insect pollinator habitat suitability models. Restor Ecol 2017. [DOI: 10.1111/rec.12561] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Sean Tomlinson
- School of Biological Sciences The University of Western Australia 35 Stirling Highway, Crawley WA 6009 Australia
- Kings Park Botanic Gardens Fraser Avenue, Kings Park, Perth WA 6005 Australia
| | - Bruce Lloyd Webber
- School of Biological Sciences The University of Western Australia 35 Stirling Highway, Crawley WA 6009 Australia
- CSIRO Land and Water 147 Underwood Avenue, Floreat WA 6016 Australia
| | - Sidney Don Bradshaw
- School of Biological Sciences The University of Western Australia 35 Stirling Highway, Crawley WA 6009 Australia
| | - Kingsley Wayne Dixon
- Department of Environment and Agriculture Curtin University Kent Street, Bentley WA 6102 Australia
| | - Michael Renton
- School of Biological Sciences The University of Western Australia 35 Stirling Highway, Crawley WA 6009 Australia
- School of Agriculture and Environment The University of Western Australia 35 Stirling Highway, Crawley WA 6009 Australia
| |
Collapse
|
44
|
Curi LM, Peltzer PM, Martinuzzi C, Attademo MA, Seib S, Simoniello MF, Lajmanovich RC. Altered development, oxidative stress and DNA damage in Leptodactylus chaquensis (Anura: Leptodactylidae) larvae exposed to poultry litter. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2017; 143:62-71. [PMID: 28505481 DOI: 10.1016/j.ecoenv.2017.05.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 05/02/2017] [Accepted: 05/08/2017] [Indexed: 06/07/2023]
Abstract
Poultry litter (PL), which is usually used as organic fertilizer, is a source of nutrients, metals, veterinary pharmaceuticals and bacterial pathogens, which, through runoff, may end up in the nearest aquatic ecosystems. In this study, Leptodactylus chaquensis at different development stages (eggs, larval stages 28 and 31 here referred to as stages I, II and III respectively) were exposed to PL test sediments as follows: 6.25% (T1), 12.5% (T2); 25% (T3); 50% (T4); 75% (T5); 100% PL (T6) and to dechlorinated water as control. Larval survival, development endpoints (growth rate -GR-, development rate -DR-, abnormalities), antioxidant enzyme activities (Catalase -CAT- and Glutathione-S-Transferase -GST-), and genotoxic effect (DNA damage index by the Comet assay) were analyzed at different times. In stage I, no egg eclosion was observed in treatments T3-T6, and 50% of embryo mortality was recorded after 24h of exposure to T2. In stages II and III, mortality in treatments T3-T6 reached 100% between 24 and 48h. In the three development stages evaluated, the DR and GR were higher in controls than in PL treatments (T1, T2), except for those T1-treated larvae of stage II. Larvae of stage I showed five types of morphological abnormalities, being diamond body shape and lateral displacement of the intestine the most prevalent in T1, whereas larvae of stages II and III presented lower prevalence of abnormalities. In stage I, CAT activity was similar to that of control (p>0.05), whereas it was higher in T1- and T2- treated larvae of stages II and III than controls (p<0.05). In stages I and III, GST activity was similar to that of controls (p>0.05), whereas it was inhibited in T1-treated larvae of stage II (p<0.05). T1- and T2-treated larvae of stages II and III caused higher DNA damage respect to controls (p<0.05), varying from medium to severe damage (comet types II, III and IV). These results showed that PL treatments altered development and growth and induced oxidative stress and DNA damage, resulting ecotoxic for L. chaquensis larvae.
Collapse
Affiliation(s)
- L M Curi
- Laboratorio de Ecotoxicología, Facultad de Bioquímica y Ciencias Biológicas (FBCB), Universidad Nacional del Litoral (UNL), Santa Fe, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
| | - P M Peltzer
- Laboratorio de Ecotoxicología, Facultad de Bioquímica y Ciencias Biológicas (FBCB), Universidad Nacional del Litoral (UNL), Santa Fe, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
| | - C Martinuzzi
- Laboratorio de Ecotoxicología, Facultad de Bioquímica y Ciencias Biológicas (FBCB), Universidad Nacional del Litoral (UNL), Santa Fe, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - M A Attademo
- Laboratorio de Ecotoxicología, Facultad de Bioquímica y Ciencias Biológicas (FBCB), Universidad Nacional del Litoral (UNL), Santa Fe, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - S Seib
- Laboratorio de Ecotoxicología, Facultad de Bioquímica y Ciencias Biológicas (FBCB), Universidad Nacional del Litoral (UNL), Santa Fe, Argentina
| | - M F Simoniello
- Cátedra de Toxicología, Farmacología y Bioquímica Legal. Facultad de Bioquímica y Ciencias Biológicas (FBCB), Universidad Nacional del Litoral (UNL), Santa Fe, Argentina
| | - R C Lajmanovich
- Laboratorio de Ecotoxicología, Facultad de Bioquímica y Ciencias Biológicas (FBCB), Universidad Nacional del Litoral (UNL), Santa Fe, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| |
Collapse
|
45
|
Dorning J, Harris S. Dominance, gender, and season influence food patch use in a group-living, solitary foraging canid. Behav Ecol 2017. [DOI: 10.1093/beheco/arx092] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
|
46
|
Cook ASCP, Robinson RA. Towards a framework for quantifying the population-level consequences of anthropogenic pressures on the environment: The case of seabirds and windfarms. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2017; 190:113-121. [PMID: 28040587 DOI: 10.1016/j.jenvman.2016.12.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 12/07/2016] [Accepted: 12/12/2016] [Indexed: 06/06/2023]
Affiliation(s)
- Aonghais S C P Cook
- British Trust for Ornithology, The Nunnery, Thetford, Norfolk, IP25 2PU, UK.
| | - Robert A Robinson
- British Trust for Ornithology, The Nunnery, Thetford, Norfolk, IP25 2PU, UK
| |
Collapse
|
47
|
Meir P, Shenkin A, Disney M, Rowland L, Malhi Y, Herold M, da Costa ACL. Plant Structure-Function Relationships and Woody Tissue Respiration: Upscaling to Forests from Laser-Derived Measurements. ADVANCES IN PHOTOSYNTHESIS AND RESPIRATION 2017. [DOI: 10.1007/978-3-319-68703-2_5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
|
48
|
Maclean IMD, Suggitt AJ, Wilson RJ, Duffy JP, Bennie JJ. Fine-scale climate change: modelling spatial variation in biologically meaningful rates of warming. GLOBAL CHANGE BIOLOGY 2017; 23:256-268. [PMID: 27151406 DOI: 10.1111/gcb.13343] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 04/19/2016] [Accepted: 04/28/2016] [Indexed: 05/06/2023]
Abstract
The existence of fine-grain climate heterogeneity has prompted suggestions that species may be able to survive future climate change in pockets of suitable microclimate, termed 'microrefugia'. However, evidence for microrefugia is hindered by lack of understanding of how rates of warming vary across a landscape. Here, we present a model that is applied to provide fine-grained, multidecadal estimates of temperature change based on the underlying physical processes that influence microclimate. Weather station and remotely derived environmental data were used to construct physical variables that capture the effects of terrain, sea surface temperatures, altitude and surface albedo on local temperatures, which were then calibrated statistically to derive gridded estimates of temperature. We apply the model to the Lizard Peninsula, United Kingdom, to provide accurate (mean error = 1.21 °C; RMS error = 1.63 °C) hourly estimates of temperature at a resolution of 100 m for the period 1977-2014. We show that rates of warming vary across a landscape primarily due to long-term trends in weather conditions. Total warming varied from 0.87 to 1.16 °C, with the slowest rates of warming evident on north-east-facing slopes. This variation contributed to substantial spatial heterogeneity in trends in bioclimatic variables: for example, the change in the length of the frost-free season varied from +11 to -54 days and the increase in annual growing degree-days from 51 to 267 °C days. Spatial variation in warming was caused primarily by a decrease in daytime cloud cover with a resulting increase in received solar radiation, and secondarily by a decrease in the strength of westerly winds, which has amplified the effects on temperature of solar radiation on west-facing slopes. We emphasize the importance of multidecadal trends in weather conditions in determining spatial variation in rates of warming, suggesting that locations experiencing least warming may not remain consistent under future climate change.
Collapse
Affiliation(s)
- Ilya M D Maclean
- Environment and Sustainability Institute, University of Exeter, Cornwall Campus, Penryn, TR10 9FE, UK
| | - Andrew J Suggitt
- Environment and Sustainability Institute, University of Exeter, Cornwall Campus, Penryn, TR10 9FE, UK
| | - Robert J Wilson
- College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4PS, UK
| | - James P Duffy
- Environment and Sustainability Institute, University of Exeter, Cornwall Campus, Penryn, TR10 9FE, UK
| | - Jonathan J Bennie
- Environment and Sustainability Institute, University of Exeter, Cornwall Campus, Penryn, TR10 9FE, UK
| |
Collapse
|
49
|
Tredennick AT, Hooten MB, Adler PB. Do we need demographic data to forecast plant population dynamics? Methods Ecol Evol 2016. [DOI: 10.1111/2041-210x.12686] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Andrew T. Tredennick
- Department of Wildland Resources and the Ecology Center Utah State University 5230 Old Main Hill Logan UT 84322 USA
| | - Mevin B. Hooten
- U.S. Geological Survey Colorado Cooperative Fish and Wildlife Research Unit Fort Collins CO 80523 USA
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins CO 80523 USA
- Department of Statistics Colorado State University Fort Collins CO 80523 USA
| | - Peter B. Adler
- Department of Wildland Resources and the Ecology Center Utah State University 5230 Old Main Hill Logan UT 84322 USA
| |
Collapse
|
50
|
Hardisty AR, Bacall F, Beard N, Balcázar-Vargas MP, Balech B, Barcza Z, Bourlat SJ, De Giovanni R, de Jong Y, De Leo F, Dobor L, Donvito G, Fellows D, Guerra AF, Ferreira N, Fetyukova Y, Fosso B, Giddy J, Goble C, Güntsch A, Haines R, Ernst VH, Hettling H, Hidy D, Horváth F, Ittzés D, Ittzés P, Jones A, Kottmann R, Kulawik R, Leidenberger S, Lyytikäinen-Saarenmaa P, Mathew C, Morrison N, Nenadic A, de la Hidalga AN, Obst M, Oostermeijer G, Paymal E, Pesole G, Pinto S, Poigné A, Fernandez FQ, Santamaria M, Saarenmaa H, Sipos G, Sylla KH, Tähtinen M, Vicario S, Vos RA, Williams AR, Yilmaz P. BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology. BMC Ecol 2016; 16:49. [PMID: 27765035 PMCID: PMC5073428 DOI: 10.1186/s12898-016-0103-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 10/13/2016] [Indexed: 02/08/2023] Open
Abstract
Background Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as “Web services”) and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust “in silico” science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. Results BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible ‘virtual laboratory’, free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Conclusions Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research. Electronic supplementary material The online version of this article (doi:10.1186/s12898-016-0103-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Alex R Hardisty
- School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, CF24 3AA, UK.
| | - Finn Bacall
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Niall Beard
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Maria-Paula Balcázar-Vargas
- Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, PO Box 94248, 1090, Amsterdam, The Netherlands
| | - Bachir Balech
- Institute of Biomembranes and Bioenergetics (IBBE), National Research Council (CNR), via Amendola 165/A, 70126, Bari, Italy
| | - Zoltán Barcza
- Department of Meteorology, Eötvös Loránd University, Pázmány sétány 1/A, Budapest, 1117, Hungary
| | - Sarah J Bourlat
- Department of Marine Sciences, University of Gothenburg, Box 463, 405 30, Gothenburg, Sweden
| | - Renato De Giovanni
- Centro de Referência em Informação Ambiental, Avenida Dr. Romeu Tórtima, 388, Campinas, SP, 13084-791, Brazil
| | - Yde de Jong
- Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, PO Box 94248, 1090, Amsterdam, The Netherlands.,SIB Labs, Joensuu Science Park, University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland
| | - Francesca De Leo
- Institute of Biomembranes and Bioenergetics (IBBE), National Research Council (CNR), via Amendola 165/A, 70126, Bari, Italy
| | - Laura Dobor
- Department of Meteorology, Eötvös Loránd University, Pázmány sétány 1/A, Budapest, 1117, Hungary
| | - Giacinto Donvito
- Institute of Nuclear Physics (INFN), Via E. Orabona 4, 70125, Bari, Italy
| | - Donal Fellows
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Antonio Fernandez Guerra
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359, Bremen, Germany.,Jacobs University Bremen GmbH, Campus Ring 1, 28359, Bremen, Germany
| | - Nuno Ferreira
- Stichting EGI (EGI.eu), Science Park 140, 1098, Amsterdam, The Netherlands
| | - Yuliya Fetyukova
- SIB Labs, Joensuu Science Park, University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland
| | - Bruno Fosso
- Institute of Biomembranes and Bioenergetics (IBBE), National Research Council (CNR), via Amendola 165/A, 70126, Bari, Italy
| | - Jonathan Giddy
- School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, CF24 3AA, UK
| | - Carole Goble
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Anton Güntsch
- Botanic Garden and Botanical Museum Berlin, Freie Universität Berlin, Königin-Luise-Strasse 6-8, 14195, Berlin, Germany
| | - Robert Haines
- IT Services, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Vera Hernández Ernst
- Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
| | - Hannes Hettling
- Naturalis Biodiversity Center, Postbus 9517, 2300, Leiden, The Netherlands
| | - Dóra Hidy
- MTA-SZIE Plant Ecology Research Group, Szent István University, Páter K. u.1., Gödöllő, 2103, Hungary
| | - Ferenc Horváth
- Institute of Ecology and Botany, Centre for Ecological Research, Hungarian Academy of Sciences, Alkotmány u. 2-4., Vácrátót, 2163, Hungary
| | - Dóra Ittzés
- Institute of Ecology and Botany, Centre for Ecological Research, Hungarian Academy of Sciences, Alkotmány u. 2-4., Vácrátót, 2163, Hungary
| | - Péter Ittzés
- Institute of Ecology and Botany, Centre for Ecological Research, Hungarian Academy of Sciences, Alkotmány u. 2-4., Vácrátót, 2163, Hungary
| | - Andrew Jones
- School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, CF24 3AA, UK
| | - Renzo Kottmann
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359, Bremen, Germany
| | - Robert Kulawik
- Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
| | - Sonja Leidenberger
- Swedish Species Information Centre/ArtDatabanken, Swedish University of Agricultural Sciences, Bäcklösavägen 10, 750 07, Uppsala, Sweden
| | | | - Cherian Mathew
- Botanic Garden and Botanical Museum Berlin, Freie Universität Berlin, Königin-Luise-Strasse 6-8, 14195, Berlin, Germany
| | - Norman Morrison
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Aleksandra Nenadic
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Abraham Nieva de la Hidalga
- School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, CF24 3AA, UK
| | - Matthias Obst
- Department of Marine Sciences, University of Gothenburg, Box 463, 405 30, Gothenburg, Sweden
| | - Gerard Oostermeijer
- Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, PO Box 94248, 1090, Amsterdam, The Netherlands
| | - Elisabeth Paymal
- Fondation pour la Recherche sur la Biodiversité (FRB), 195, rue Saint-Jacques, 75005, Paris, France
| | - Graziano Pesole
- Institute of Biomembranes and Bioenergetics (IBBE), National Research Council (CNR), via Amendola 165/A, 70126, Bari, Italy.,Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari "A. Moro", via Orabona, 1514, 70126, Bari, Italy
| | - Salvatore Pinto
- Stichting EGI (EGI.eu), Science Park 140, 1098, Amsterdam, The Netherlands
| | - Axel Poigné
- Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
| | - Francisco Quevedo Fernandez
- School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, CF24 3AA, UK
| | - Monica Santamaria
- Institute of Biomembranes and Bioenergetics (IBBE), National Research Council (CNR), via Amendola 165/A, 70126, Bari, Italy
| | - Hannu Saarenmaa
- SIB Labs, Joensuu Science Park, University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland
| | - Gergely Sipos
- Stichting EGI (EGI.eu), Science Park 140, 1098, Amsterdam, The Netherlands
| | - Karl-Heinz Sylla
- Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
| | - Marko Tähtinen
- Finnish Museum of Natural History, University of Helsinki, P.O. Box 17, 00014, Helsinki, Finland
| | - Saverio Vicario
- Institute of Biomedical Technology (ITB), National Research Council (CNR), via Amendola 122/D, 70126, Bari, Italy
| | - Rutger Aldo Vos
- Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, PO Box 94248, 1090, Amsterdam, The Netherlands.,Naturalis Biodiversity Center, Postbus 9517, 2300, Leiden, The Netherlands
| | - Alan R Williams
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Pelin Yilmaz
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359, Bremen, Germany
| |
Collapse
|