1
|
Fronhofer EA, Corenblit D, Deshpande JN, Govaert L, Huneman P, Viard F, Jarne P, Puijalon S. Eco-evolution from deep time to contemporary dynamics: The role of timescales and rate modulators. Ecol Lett 2023; 26 Suppl 1:S91-S108. [PMID: 37840024 DOI: 10.1111/ele.14222] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 10/17/2023]
Abstract
Eco-evolutionary dynamics, or eco-evolution for short, are often thought to involve rapid demography (ecology) and equally rapid heritable phenotypic changes (evolution) leading to novel, emergent system behaviours. We argue that this focus on contemporary dynamics is too narrow: Eco-evolution should be extended, first, beyond pure demography to include all environmental dimensions and, second, to include slow eco-evolution which unfolds over thousands or millions of years. This extension allows us to conceptualise biological systems as occupying a two-dimensional time space along axes that capture the speed of ecology and evolution. Using Hutchinson's analogy: Time is the 'theatre' in which ecology and evolution are two interacting 'players'. Eco-evolutionary systems are therefore dynamic: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can change the speed of ecology and evolution, and hence impact eco-evolution. Environmental change may synchronise the speed of ecology and evolution via these rate modulators, increasing the occurrence of eco-evolution and emergent system behaviours. This represents substantial challenges for prediction, especially in the context of global change. Our perspective attempts to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from today to deep time.
Collapse
Affiliation(s)
| | - Dov Corenblit
- GEOLAB, Université Clermont Auvergne, CNRS, Clermont-Ferrand, France
- Laboratoire écologie fonctionnelle et environnement, Université Paul Sabatier, CNRS, INPT, UPS, Toulouse, France
| | | | - Lynn Govaert
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Philippe Huneman
- Institut d'Histoire et de Philosophie des Sciences et des Techniques (CNRS/Université Paris I Sorbonne), Paris, France
| | - Frédérique Viard
- ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France
| | - Philippe Jarne
- CEFE, UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - IRD - EPHE, Montpellier Cedex 5, France
| | - Sara Puijalon
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, Villeurbanne, France
| |
Collapse
|
2
|
Sun GQ, Li L, Li J, Liu C, Wu YP, Gao S, Wang Z, Feng GL. Impacts of climate change on vegetation pattern: Mathematical modeling and data analysis. Phys Life Rev 2022; 43:239-270. [PMID: 36343569 DOI: 10.1016/j.plrev.2022.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 11/27/2022]
Abstract
Climate change has become increasingly severe, threatening ecosystem stability and, in particular, biodiversity. As a typical indicator of ecosystem evolution, vegetation growth is inevitably affected by climate change, and therefore has a great potential to provide valuable information for addressing such ecosystem problems. However, the impacts of climate change on vegetation growth, especially the spatial and temporal distribution of vegetation, are still lacking of comprehensive exposition. To this end, this review systematically reveals the influences of climate change on vegetation dynamics in both time and space by dynamical modeling the interactions of meteorological elements and vegetation growth. Moreover, we characterize the long-term evolution trend of vegetation growth under climate change in some typical regions based on data analysis. This work is expected to lay a necessary foundation for systematically revealing the coupling effect of climate change on the ecosystem.
Collapse
Affiliation(s)
- Gui-Quan Sun
- Department of Mathematics, North University of China, Taiyuan, 030051, China; Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
| | - Li Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Jing Li
- School of Applied Mathematics, Shanxi University of Finance and Economics, Taiyuan, 030006, China
| | - Chen Liu
- Center for Ecology and Environmental Sciences, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yong-Ping Wu
- College of Physics Science and Technology, Yangzhou University, Yangzhou, 225002, China
| | - Shupeng Gao
- School of Mechanical Engineering and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xian, 710072, China
| | - Zhen Wang
- School of Mechanical Engineering and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xian, 710072, China.
| | - Guo-Lin Feng
- College of Physics Science and Technology, Yangzhou University, Yangzhou, 225002, China; Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, 100081, China.
| |
Collapse
|
3
|
Blair J, Weiser MD, de Beurs K, Kaspari M, Siler C, Marshall KE. Embracing imperfection: Machine-assisted invertebrate classification in real-world datasets. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
4
|
Midway SR, Sievert NA, Lynch AJ, Whittier JB, Pope KL. Asking nicely: Best practices for requesting data. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
5
|
Rajtmajer SM, Errington TM, Hillary FG. Science Forum: How failure to falsify in high-volume science contributes to the replication crisis. eLife 2022; 11:78830. [PMID: 35939392 PMCID: PMC9398444 DOI: 10.7554/elife.78830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/28/2022] [Indexed: 11/29/2022] Open
Abstract
The number of scientific papers published every year continues to increase, but scientific knowledge is not progressing at the same rate. Here we argue that a greater emphasis on falsification – the direct testing of strong hypotheses – would lead to faster progress by allowing well-specified hypotheses to be eliminated. We describe an example from neuroscience where there has been little work to directly test two prominent but incompatible hypotheses related to traumatic brain injury. Based on this example, we discuss how building strong hypotheses and then setting out to falsify them can bring greater precision to the clinical neurosciences, and argue that this approach could be beneficial to all areas of science.
Collapse
Affiliation(s)
- Sarah M Rajtmajer
- College of Information Sciences and Technology, Pennsylvania State University, University Park, United States
| | | | - Frank G Hillary
- Department of Psychology, Pennsylvania State University, University Park, United States
| |
Collapse
|
6
|
Homage to Reptiles and Amphibians as Model Systems: One Ecologist's View. J HERPETOL 2022. [DOI: 10.1670/21-020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
7
|
Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T. Perspectives in machine learning for wildlife conservation. Nat Commun 2022; 13:792. [PMID: 35140206 PMCID: PMC8828720 DOI: 10.1038/s41467-022-27980-y] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 12/08/2021] [Indexed: 11/08/2022] Open
Abstract
Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.
Collapse
Affiliation(s)
- Devis Tuia
- School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Benjamin Kellenberger
- School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sara Beery
- Department of Computing and Mathematical Sciences, California Institute of Technology (Caltech), Pasadena, CA, USA
| | - Blair R Costelloe
- Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Silvia Zuffi
- Institute for Applied Mathematics and Information Technologies, IMATI-CNR, Pavia, Italy
| | - Benjamin Risse
- Computer Science Department, University of Münster, Münster, Germany
| | - Alexander Mathis
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mackenzie W Mathis
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Tilo Burghardt
- Computer Science Department, University of Bristol, Bristol, UK
| | - Roland Kays
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA
- North Carolina Museum of Natural Sciences, Raleigh, NC, USA
| | - Holger Klinck
- Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA
| | - Martin Wikelski
- Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
| | - Iain D Couzin
- Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Grant van Horn
- Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA
| | - Margaret C Crofoot
- Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Charles V Stewart
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Tanya Berger-Wolf
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA
- Departments of Computer Science and Engineering; Electrical and Computer Engineering; Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, USA
| |
Collapse
|
8
|
Ellison AM, Barker Plotkin AA, Patel MV, Record S. Broadening the ecological mindset. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02347. [PMID: 34181285 DOI: 10.1002/eap.2347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 12/10/2020] [Accepted: 02/03/2021] [Indexed: 06/13/2023]
Abstract
Over the past three decades, the Harvard Forest Summer Research Program in Ecology (HF-SRPE) has been at the forefront of expanding the ecological tent for minoritized or otherwise marginalized students. By broadening the definition of ecology to include fields such as data science, software engineering, and remote sensing, we attract a broader range of students, including those who may not prioritize field experiences or who may feel unsafe working in rural or urban field sites. We also work towards a more resilient society in which minoritized or marginalized students can work safely, in part by building teams of students and mentors. Teams collaborate on projects that require a diversity of approaches and create opportunities for students and mentors alike to support one another and share leadership. Finally, HF-SRPE promotes an expanded view of what it means to become an ecologist. We value and support diverse career paths for ecologists to work in all parts of society, to diversify the face of ecology, and to bring different perspectives together to ensure innovations in environmental problem solving for our planet.
Collapse
Affiliation(s)
- Aaron M Ellison
- Harvard Forest, Harvard University, Petersham, Massachusetts, 01366, USA
- Sound Solutions for Sustainable Science, Boston, Massachusetts, 02135, USA
| | | | - Manisha V Patel
- Harvard Forest, Harvard University, Petersham, Massachusetts, 01366, USA
- Sound Solutions for Sustainable Science, Boston, Massachusetts, 02135, USA
| | - Sydne Record
- Harvard Forest, Harvard University, Petersham, Massachusetts, 01366, USA
- Department of Biology, Bryn Mawr College, Bryn Mawr, Pennsylvania, 19010, USA
| |
Collapse
|
9
|
Shoemaker LG, Walter JA, Gherardi LA, DeSiervo MH, Wisnoski NI. Writing mathematical ecology: A guide for authors and readers. Ecosphere 2021. [DOI: 10.1002/ecs2.3701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
| | - Jonathan A. Walter
- Department of Environmental Sciences University of Virginia Charlottesville Virginia 22904 USA
| | | | | | | |
Collapse
|
10
|
Lawing AM, McCoy M, Reinke BA, Sarkar SK, Smith FA, Wright D. A Framework for Investigating Rules of Life by Establishing Zones of Influence. Integr Comp Biol 2021; 61:2095-2108. [PMID: 34297089 DOI: 10.1093/icb/icab169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/26/2021] [Accepted: 07/20/2021] [Indexed: 12/18/2022] Open
Abstract
The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (Smocovitis, 1992) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (Leroi, 2014) island biogeography, and (Gropp, 2016) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.
Collapse
Affiliation(s)
| | - Michael McCoy
- Department of Biology, East Carolina University, NC, USA
| | - Beth A Reinke
- Department of Biology, Northeastern Illinois University, IL, USA
| | | | - Felisa A Smith
- Department of Biology, University of New Mexico, NM, USA
| | - Derek Wright
- Department of Physics, Colorado School of Mines, CO, USA
| |
Collapse
|
11
|
Lürig MD, Donoughe S, Svensson EI, Porto A, Tsuboi M. Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.642774] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.
Collapse
|
12
|
Gallinat AS, Pearse WD. Phylogenetic generalized linear mixed modeling presents novel opportunities for eco‐evolutionary synthesis. OIKOS 2021. [DOI: 10.1111/oik.08048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Amanda S. Gallinat
- Dept of Biology and Ecology Center, Utah State Univ. Logan UT USA
- Dept of Geography, Univ. of Wisconsin‐Milwaukee Milwaukee WI USA
| | - William D. Pearse
- Dept of Biology and Ecology Center, Utah State Univ. Logan UT USA
- Dept of Life Sciences, Imperial College London Silwood Park Campus Ascot Berkshire UK
| |
Collapse
|
13
|
Nazir S, Kaleem M. Advances in image acquisition and processing technologies transforming animal ecological studies. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101212] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
14
|
Capinha C, Ceia-Hasse A, Kramer AM, Meijer C. Deep learning for supervised classification of temporal data in ecology. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
15
|
Emery NC, Bledsoe EK, Hasley AO, Eaton CD. Cultivating inclusive instructional and research environments in ecology and evolutionary science. Ecol Evol 2021; 11:1480-1491. [PMID: 33613983 PMCID: PMC7882980 DOI: 10.1002/ece3.7062] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 10/19/2020] [Accepted: 10/29/2020] [Indexed: 12/30/2022] Open
Abstract
As we strive to lift up a diversity of voices in science, it is important for ecologists, evolutionary scientists, and educators to foster inclusive environments in their research and teaching. Academics in science often lack exposure to research on best practices in diversity, equity, and inclusion and may not know where to start to make scientific environments more welcoming and inclusive. We propose that by approaching research and teaching with empathy, flexibility, and a growth mind-set, scientists can be more supportive and inclusive of their colleagues and students. This paper provides guidance, explores strategies, and directs scientists to resources to better cultivate an inclusive environment in three common settings: the classroom, the research laboratory, and the field. As ecologists and evolutionary scientists, we have an opportunity to adapt our teaching and research practices in order to foster an inclusive educational ecosystem for students and colleagues alike.
Collapse
Affiliation(s)
- Nathan C. Emery
- Department of Plant BiologyMichigan State UniversityEast LansingMIUSA
| | | | | | | |
Collapse
|
16
|
Leeming ER, Louca P, Gibson R, Menni C, Spector TD, Le Roy CI. The complexities of the diet-microbiome relationship: advances and perspectives. Genome Med 2021; 13:10. [PMID: 33472701 PMCID: PMC7819159 DOI: 10.1186/s13073-020-00813-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 11/25/2020] [Indexed: 02/07/2023] Open
Abstract
Personalised dietary modulation of the gut microbiota may be key to disease management. Current investigations provide a broad understanding of the impact of diet on the composition and activity of the gut microbiota, yet detailed knowledge in applying diet as an actionable tool remains limited. Further to the relative novelty of the field, approaches are yet to be standardised and extremely heterogeneous research outcomes have ensued. This may be related to confounders associated with complexities in capturing an accurate representation of both diet and the gut microbiota. This review discusses the intricacies and current methodologies of diet-microbial relations, the implications and limitations of these investigative approaches, and future considerations that may assist in accelerating applications. New investigations should consider improved collection of dietary data, further characterisation of mechanistic interactions, and an increased focus on -omic technologies such as metabolomics to describe the bacterial and metabolic activity of food degradation, together with its crosstalk with the host. Furthermore, clinical evidence with health outcomes is required before therapeutic dietary strategies for microbial amelioration can be made. The potential to reach detailed understanding of diet-microbiota relations may depend on re-evaluation, progression, and unification of research methodologies, which consider the complexities of these interactions.
Collapse
Affiliation(s)
- Emily R Leeming
- The Department of Twin Research, St Thomas' Hospital, King's College London, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
| | - Panayiotis Louca
- The Department of Twin Research, St Thomas' Hospital, King's College London, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
| | - Rachel Gibson
- Department of Nutritional Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London, SE1 9NH, UK
| | - Cristina Menni
- The Department of Twin Research, St Thomas' Hospital, King's College London, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
| | - Tim D Spector
- The Department of Twin Research, St Thomas' Hospital, King's College London, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Caroline I Le Roy
- The Department of Twin Research, St Thomas' Hospital, King's College London, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK.
| |
Collapse
|
17
|
Long-term trends in herring growth primarily linked to temperature by gradient boosting regression trees. ECOL INFORM 2020. [DOI: 10.1016/j.ecoinf.2020.101154] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
18
|
Effects of sample size and network depth on a deep learning approach to species distribution modeling. ECOL INFORM 2020. [DOI: 10.1016/j.ecoinf.2020.101137] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
19
|
Varghese J, Crawford SS. A cultural framework for Indigenous, Local, and Science knowledge systems in ecology and natural resource management. ECOL MONOGR 2020. [DOI: 10.1002/ecm.1431] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Jeji Varghese
- Department of Sociology and Anthropology University of Guelph Guelph OntarioN1G 2W1Canada
| | - Stephen S. Crawford
- Department of Integrative Biology University of Guelph Guelph OntarioN1G 2W1Canada
| |
Collapse
|
20
|
Application of machine learning algorithms to identify cryptic reproductive habitats using diverse information sources. Oecologia 2020; 194:283-298. [PMID: 33006076 DOI: 10.1007/s00442-020-04753-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 09/08/2020] [Indexed: 10/23/2022]
Abstract
Information on ecological systems often comes from diverse sources with varied levels of complexity, bias, and uncertainty. Accordingly, analytical techniques continue to evolve that address these challenges to reveal the characteristics of ecological systems and inform conservation actions. We applied multiple statistical learning algorithms (i.e., machine learning) with a range of information sources including fish tracking data, environmental data, and visual surveys to identify potential spawning aggregation sites for a marine fish species, permit (Trachinotus falcatus), in the Florida Keys. Recognizing the potential complementarity and some level of uncertainty in each information source, we applied supervised (classic and conditional random forests; RF) and unsupervised (fuzzy k-means; FKM) algorithms. The two RF models had similar predictive performance, but generated different predictor variable importance structures and spawning site predictions. Unsupervised clustering using FKM identified unique site groupings that were similar to the likely spawning sites identified with RF. The conservation of aggregate spawning fish species depends heavily on the protection of key spawning sites; many of these potential sites were identified here for permit in the Florida Keys, which consisted of relatively deep-water natural and artificial reefs with high mean permit residency periods. The application of multiple machine learning algorithms enabled the integration of diverse information sources to develop models of an ecological system. Faced with increasingly complex and diverse data sources, ecologists, and conservation practitioners should find increasing value in machine learning algorithms, which we discuss here and provide resources to increase accessibility.
Collapse
|
21
|
Laubmeier AN, Cazelles B, Cuddington K, Erickson KD, Fortin MJ, Ogle K, Wikle CK, Zhu K, Zipkin EF. Ecological Dynamics: Integrating Empirical, Statistical, and Analytical Methods. Trends Ecol Evol 2020; 35:1090-1099. [PMID: 32933777 DOI: 10.1016/j.tree.2020.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 08/12/2020] [Accepted: 08/17/2020] [Indexed: 10/23/2022]
Abstract
Understanding ecological processes and predicting long-term dynamics are ongoing challenges in ecology. To address these challenges, we suggest an approach combining mathematical analyses and Bayesian hierarchical statistical modeling with diverse data sources. Novel mathematical analysis of ecological dynamics permits a process-based understanding of conditions under which systems approach equilibrium, experience large oscillations, or persist in transient states. This understanding is improved by combining ecological models with empirical observations from a variety of sources. Bayesian hierarchical models explicitly couple process-based models and data, yielding probabilistic quantification of model parameters, system characteristics, and associated uncertainties. We outline relevant tools from dynamical analysis and hierarchical modeling and argue for their integration, demonstrating the value of this synthetic approach through a simple predator-prey example.
Collapse
Affiliation(s)
- Amanda N Laubmeier
- Department of Mathematics & Statistics, Texas Tech University, Lubbock, TX, USA.
| | - Bernard Cazelles
- Eco-Evolutionary Mathematics, CNRS UMR 8197, Ecole Normale Supérieure, Paris, France
| | - Kim Cuddington
- Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
| | - Kelley D Erickson
- Center for Conservation and Sustainable Development, Missouri Botanical Garden, St. Louis, MO, USA
| | - Marie-Josée Fortin
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Kiona Ogle
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
| | | | - Kai Zhu
- Department of Environmental Studies, University of California, Santa Cruz, CA, USA
| | - Elise F Zipkin
- Department of Integrative Biology, Program in Ecology, Evolution, and Behavior, Michigan State University, East Lansing, MI, USA
| |
Collapse
|
22
|
|
23
|
A comparison of supervised machine learning algorithms for mosquito identification from backscattered optical signals. ECOL INFORM 2020. [DOI: 10.1016/j.ecoinf.2020.101090] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
24
|
Rhinehart TA, Chronister LM, Devlin T, Kitzes J. Acoustic localization of terrestrial wildlife: Current practices and future opportunities. Ecol Evol 2020; 10:6794-6818. [PMID: 32724552 PMCID: PMC7381569 DOI: 10.1002/ece3.6216] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/02/2020] [Accepted: 03/04/2020] [Indexed: 01/17/2023] Open
Abstract
Autonomous acoustic recorders are an increasingly popular method for low-disturbance, large-scale monitoring of sound-producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time-synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time-synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor-intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open-source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low-disturbance, large-scale collection of animal position data.
Collapse
Affiliation(s)
- Tessa A. Rhinehart
- Department of Biological SciencesUniversity of PittsburghPittsburghPAUSA
| | | | - Trieste Devlin
- Department of Biological SciencesUniversity of PittsburghPittsburghPAUSA
| | - Justin Kitzes
- Department of Biological SciencesUniversity of PittsburghPittsburghPAUSA
| |
Collapse
|
25
|
Han BA, O'Regan SM, Paul Schmidt J, Drake JM. Integrating data mining and transmission theory in the ecology of infectious diseases. Ecol Lett 2020; 23:1178-1188. [PMID: 32441459 PMCID: PMC7384120 DOI: 10.1111/ele.13520] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 01/21/2020] [Accepted: 03/27/2020] [Indexed: 01/07/2023]
Abstract
Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent-borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining-modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.
Collapse
Affiliation(s)
- Barbara A Han
- Cary Institute of Ecosystem Studies, Box AB Millbrook, NY, 12571, USA
| | - Suzanne M O'Regan
- Department of Mathematics and Statistics, North Carolina A&T State University, 1601 E. Market St., Greensboro, NC, 27411, USA
| | - John Paul Schmidt
- Odum School of Ecology, University of Georgia, 140 E. Green St., Athens, GA, 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, 203 D.W. Brooks Drive, Athens, GA, 30602, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, 140 E. Green St., Athens, GA, 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, 203 D.W. Brooks Drive, Athens, GA, 30602, USA
| |
Collapse
|
26
|
Mattei F, Scardi M. Embedding ecological knowledge into artificial neural network training: A marine phytoplankton primary production model case study. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.108985] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
27
|
Reichert P. Towards a comprehensive uncertainty assessment in environmental research and decision support. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2020; 81:1588-1596. [PMID: 32644952 DOI: 10.2166/wst.2020.032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.
Collapse
Affiliation(s)
- Peter Reichert
- Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland E-mail:
| |
Collapse
|
28
|
Schuwirth N, Borgwardt F, Domisch S, Friedrichs M, Kattwinkel M, Kneis D, Kuemmerlen M, Langhans SD, Martínez-López J, Vermeiren P. How to make ecological models useful for environmental management. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.108784] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
29
|
Recknagel F, Staiano A. Editorial: Analysis and synthesis of ecological data by machine learning. ECOL INFORM 2019. [DOI: 10.1016/j.ecoinf.2019.05.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
30
|
Soranno PA, Wagner T, Collins SM, Lapierre JF, Lottig NR, Oliver SK. Spatial and temporal variation of ecosystem properties at macroscales. Ecol Lett 2019; 22:1587-1598. [PMID: 31347258 DOI: 10.1111/ele.13346] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 06/03/2019] [Accepted: 06/26/2019] [Indexed: 01/16/2023]
Abstract
Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation - spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.
Collapse
Affiliation(s)
- Patricia A Soranno
- Department of Fisheries and Wildlife, Michigan St. University, 480 Wilson Rd, East Lansing, MI, 48824, USA
| | - Tyler Wagner
- U.S. Geological Survey, Pennsylvania Cooperative Fish & Wildlife Research Unit, Pennsylvania State University, 402 Forest Resources Building, University Park, PA, 16802, USA
| | - Sarah M Collins
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY, 82071, USA
| | - Jean-Francois Lapierre
- Department of Biological Science, University of Montreal, Montreal, Quebec, Canada, H3C 3J7
| | - Noah R Lottig
- Trout Lake Research Station, Univ. of Wisconsin, 3110 Trout Lake Station Drive, Boulder Junction, WI, 54512, USA
| | - Samantha K Oliver
- Upper Midwest Water Science Center, U.S. Geological Survey, 8505 Research Way, Middleton, WI, 53562, USA
| |
Collapse
|
31
|
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
|
32
|
Barnard DM, Germino MJ, Pilliod DS, Arkle RS, Applestein C, Davidson BE, Fisk MR. Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology. Restor Ecol 2019. [DOI: 10.1111/rec.12938] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- David M. Barnard
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center 970 S. Lusk Street, Boise ID 83706 U.S.A
| | - Matthew J. Germino
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center 970 S. Lusk Street, Boise ID 83706 U.S.A
| | - David S. Pilliod
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center 970 S. Lusk Street, Boise ID 83706 U.S.A
| | - Robert S. Arkle
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center 970 S. Lusk Street, Boise ID 83706 U.S.A
| | - Cara Applestein
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center 970 S. Lusk Street, Boise ID 83706 U.S.A
| | - Bill E. Davidson
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center 970 S. Lusk Street, Boise ID 83706 U.S.A
| | - Matthew R. Fisk
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center 970 S. Lusk Street, Boise ID 83706 U.S.A
| |
Collapse
|
33
|
König C, Weigelt P, Schrader J, Taylor A, Kattge J, Kreft H. Biodiversity data integration-the significance of data resolution and domain. PLoS Biol 2019; 17:e3000183. [PMID: 30883539 PMCID: PMC6445469 DOI: 10.1371/journal.pbio.3000183] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 04/02/2019] [Indexed: 11/19/2022] Open
Abstract
Recent years have seen an explosion in the availability of biodiversity data describing the distribution, function, and evolutionary history of life on earth. Integrating these heterogeneous data remains a challenge due to large variations in observational scales, collection purposes, and terminologies. Here, we conceptualize widely used biodiversity data types according to their domain (what aspect of biodiversity is described?) and informational resolution (how specific is the description?). Applying this framework to major data providers in biodiversity research reveals a strong focus on the disaggregated end of the data spectrum, whereas aggregated data types remain largely underutilized. We discuss the implications of this imbalance for the scope and representativeness of current macroecological research and highlight the synergies arising from a tighter integration of biodiversity data across domains and resolutions. We lay out effective strategies for data collection, mobilization, imputation, and sharing and summarize existing frameworks for scalable and integrative biodiversity research. Finally, we use two case studies to demonstrate how the explicit consideration of data domain and resolution helps to identify biases and gaps in global data sets and achieve unprecedented taxonomic and geographical data coverage in macroecological analyses. This Essay highlights data resolution as central property of biodiversity data that affects the precision and representativeness of macroecological inferences. It also discusses ways to maximize synergies among data types and showcases the potential of cross-resolution, cross-domain data integration.
Collapse
Affiliation(s)
- Christian König
- Biodiversity, Macroecology & Biogeography, University of Goettingen, Goettingen, Germany
- * E-mail:
| | - Patrick Weigelt
- Biodiversity, Macroecology & Biogeography, University of Goettingen, Goettingen, Germany
| | - Julian Schrader
- Biodiversity, Macroecology & Biogeography, University of Goettingen, Goettingen, Germany
| | - Amanda Taylor
- Biodiversity, Macroecology & Biogeography, University of Goettingen, Goettingen, Germany
| | - Jens Kattge
- Research Group Functional Biogeography, Max Planck Institute for Biogeochemistry, Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Holger Kreft
- Biodiversity, Macroecology & Biogeography, University of Goettingen, Goettingen, Germany
- Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, Germany
| |
Collapse
|
34
|
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
|
35
|
Osores SJA, Ruz GA, Opitz T, Lardies MA. Discovering divergence in the thermal physiology of intertidal crabs along latitudinal gradients using an integrated approach with machine learning. J Therm Biol 2018; 78:140-150. [PMID: 30509630 DOI: 10.1016/j.jtherbio.2018.09.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 09/14/2018] [Accepted: 09/21/2018] [Indexed: 12/11/2022]
Abstract
In intertidal marine crustaceans, phenotypic variation in physiological and life-history traits is pervasive along latitudinal clines. However, organisms have complex phenotypes, and their traits do not vary independently but rather interact differentially between them, effect that is caused by genetic and/or environmental forces. We evaluated the geographic variation in phenotypic integration of three marine crab species that inhabit different vertical thermal microhabitats of the intertidal zone. We studied seven populations of each species along a latitudinal gradient that spans more than 3000 km of the Chilean coast. Specifically we measured nine physiological traits that are highly related to thermal physiology. Of the nine traits, we selected four that contributed significantly to the observed geographical variation among populations; this variation was then evaluated using mixed linear models and an integrative approach employing machine learning. The results indicate that patterns of physiological variation depend on species vertical microhabitat, which may be subject to chronic or acute environmental variation. The species that inhabit the high- intertidal sites (i.e., exposed to chronic variation) better tolerated thermal stress compared with populations that inhabit the lower intertidal. While those in the low-intertidal only face conditions of acute thermal variation, using to a greater extent the plasticity to face these events. Our main results reflect that (1) species that inhabit the high-intertidal maintain a greater integration between their physiological traits and present lower plasticity than those that inhabit the low-intertidal. (2) Inverse relationship that exists between phenotypic plasticity and phenotypic integration of the physiological traits identified, which could help optimize energy resources. In general, the study of multiple physiological traits provides a more accurate picture of how the thermal traits of organisms vary along temperature gradients especially when exposed to conditions close to tolerance limits.
Collapse
Affiliation(s)
| | - Gonzalo A Ruz
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile; Center of Applied Ecology and Sustainability (CAPES-UC), Santiago, Chile
| | - Tania Opitz
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Marco A Lardies
- Facultad de Artes Liberales, Universidad Adolfo Ibáñez, Santiago, Chile.
| |
Collapse
|
36
|
Cheruvelil KS, Soranno PA. Data-Intensive Ecological Research Is Catalyzed by Open Science and Team Science. Bioscience 2018. [DOI: 10.1093/biosci/biy097] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Kendra Spence Cheruvelil
- Professor in Lyman Briggs College and the Department of Fisheries and Wildlife
- Conceptualization and writing of this article
| | - Patricia A Soranno
- Professor in the Department of Fisheries and Wildlife, at Michigan State University, in East Lansing
- Conceptualization and writing of this article
| |
Collapse
|
37
|
Peters DPC, Burruss ND, Rodriguez LL, McVey DS, Elias EH, Pelzel-McCluskey AM, Derner JD, Schrader TS, Yao J, Pauszek SJ, Lombard J, Archer SR, Bestelmeyer BT, Browning DM, Brungard CW, Hatfield JL, Hanan NP, Herrick JE, Okin GS, Sala OE, Savoy H, Vivoni ER. An Integrated View of Complex Landscapes: A Big Data-Model Integration Approach to Transdisciplinary Science. Bioscience 2018. [DOI: 10.1093/biosci/biy069] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Affiliation(s)
- Debra P C Peters
- US Department of Agriculture, Agricultural Research Service, Jornada Experimental Range Unit and the Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - N Dylan Burruss
- New Mexico State University, Jornada Experimental Range Unit, and Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - Luis L Rodriguez
- US Department of Agriculture, Agricultural Research Service, Plum Island Animal Disease Center, in Orient Point, New York
| | - D Scott McVey
- US Department of Agriculture, Agricultural Research Service, Center for Grain and Animal Health Research, Arthropod-Borne Animal Diseases Research Unit, in Manhattan, Kansas
| | - Emile H Elias
- US Department of Agriculture, Agricultural Research Service, Jornada Experimental Range Unit and the Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - Angela M Pelzel-McCluskey
- US Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, in Fort Collins, Colorado
| | - Justin D Derner
- US Department of Agriculture, Agricultural Research Service, Rangeland Resources and Systems Research Unit, in Cheyenne, Wyoming
| | - T Scott Schrader
- US Department of Agriculture, Agricultural Research Service, Jornada Experimental Range Unit and the Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - Jin Yao
- US Department of Agriculture, Agricultural Research Service, Jornada Experimental Range Unit and the Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - Steven J Pauszek
- US Department of Agriculture, Agricultural Research Service, Plum Island Animal Disease Center, in Orient Point, New York
| | - Jason Lombard
- US Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, in Fort Collins, Colorado
| | - Steven R Archer
- School of Natural Resources and the Environment at the University of Arizona, in Tucson
| | - Brandon T Bestelmeyer
- US Department of Agriculture, Agricultural Research Service, Jornada Experimental Range Unit and the Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - Dawn M Browning
- US Department of Agriculture, Agricultural Research Service, Jornada Experimental Range Unit and the Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - Colby W Brungard
- Department of Plant and Environmental Sciences, Jornada Basin Long Term Ecological Research Program, New Mexico State University, in Las Cruces
| | - Jerry L Hatfield
- US Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and the Environment, in Ames, Iowa
| | - Niall P Hanan
- Department of Plant and Environmental Sciences, Jornada Basin Long Term Ecological Research Program, New Mexico State University, in Las Cruces
| | - Jeffrey E Herrick
- US Department of Agriculture, Agricultural Research Service, Jornada Experimental Range Unit and the Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - Gregory S Okin
- Department of Geography at the University of California, Los Angeles
| | - Osvaldo E Sala
- School of Life Sciences at Arizona State University, in Tempe
| | - Heather Savoy
- New Mexico State University, Jornada Experimental Range Unit, and Jornada Basin Long Term Ecological Research Program, in Las Cruces, New Mexico
| | - Enrique R Vivoni
- School of Earth and Space Exploration and the School of Sustainable Engineering and the Built Environment at Arizona State University, in Tempe
| |
Collapse
|
38
|
Farley SS, Dawson A, Goring SJ, Williams JW. Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions. Bioscience 2018. [DOI: 10.1093/biosci/biy068] [Citation(s) in RCA: 104] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Affiliation(s)
- Scott S Farley
- MSc in Geography at the University of Wisconsin-Madison and specializes in geovisualization, scientific data services, and cloud computing
| | - Andria Dawson
- Mathematical and statistical ecologist at Mount Royal University interested in developing and applying statistical methods to ecological data to infer ecosystem change
| | - Simon J Goring
- (http://goring.org) Data scientist and paleoecologist at the University of Wisconsin-Madison serving as the IT lead for the Neotoma Paleoecology Database and on the EarthCube (http://earthcube.org) Leadership Council
| | - John W Williams
- Paleoecologist, biogeographer, and earth-system scientist at the University of Wisconsin-Madison studying the responses of species and communities to past and present environmental change
| |
Collapse
|
39
|
La Salle J, Williams KJ, Moritz C. Biodiversity analysis in the digital era. Philos Trans R Soc Lond B Biol Sci 2017; 371:rstb.2015.0337. [PMID: 27481789 PMCID: PMC4971189 DOI: 10.1098/rstb.2015.0337] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2016] [Indexed: 11/16/2022] Open
Abstract
This paper explores what the virtual biodiversity e-infrastructure will look like as it takes advantage of advances in ‘Big Data’ biodiversity informatics and e-research infrastructure, which allow integration of various taxon-level data types (genome, morphology, distribution and species interactions) within a phylogenetic and environmental framework. By overcoming the data scaling problem in ecology, this integrative framework will provide richer information and fast learning to enable a deeper understanding of biodiversity evolution and dynamics in a rapidly changing world. The Atlas of Living Australia is used as one example of the advantages of progressing towards this future. Living in this future will require the adoption of new ways of integrating scientific knowledge into societal decision making. This article is part of the themed issue ‘From DNA barcodes to biomes’.
Collapse
Affiliation(s)
- John La Salle
- Atlas of Living Australia, CSIRO National Research Collections Australia, GPO Box 1700, Canberra ACT 2601, Australia
| | - Kristen J Williams
- Land and Water, Commonwealth Scientific and Industrial Research Organisation (CSIRO), GPO Box 1600, Canberra ACT 2601, Australia
| | - Craig Moritz
- Centre for Biodiversity Analysis and Research School of Biology, The Australian National University, Acton ACT 2601, Australia
| |
Collapse
|
40
|
VanderWaal K, Morrison RB, Neuhauser C, Vilalta C, Perez AM. Translating Big Data into Smart Data for Veterinary Epidemiology. Front Vet Sci 2017; 4:110. [PMID: 28770216 PMCID: PMC5511962 DOI: 10.3389/fvets.2017.00110] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 06/22/2017] [Indexed: 01/29/2023] Open
Abstract
The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing “big” data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having “big data” to create “smart data,” with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.
Collapse
Affiliation(s)
- Kimberly VanderWaal
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| | - Robert B Morrison
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| | - Claudia Neuhauser
- Informatics Institute, University of Minnesota, Minneapolis, MN, United States
| | - Carles Vilalta
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| | - Andres M Perez
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| |
Collapse
|
41
|
Csavina J, Roberti JA, Taylor JR, Loescher HW. Traceable measurements and calibration: a primer on uncertainty analysis. Ecosphere 2017. [DOI: 10.1002/ecs2.1683] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Janae Csavina
- Battelle–National Ecological Observatory Network (NEON) 1685 38th Street Boulder Colorado 80301 USA
| | - Joshua A. Roberti
- Battelle–National Ecological Observatory Network (NEON) 1685 38th Street Boulder Colorado 80301 USA
| | - Jeffrey R. Taylor
- Institute of Technology Campus Nova Scotia Community College 5685 Leeds Street Halifax Nova Scotia B3K 2T3 Canada
| | - Henry W. Loescher
- Battelle–National Ecological Observatory Network (NEON) 1685 38th Street Boulder Colorado 80301 USA
- Institute of Alpine and Arctic Research (INSTAAR) University of Colorado Boulder Colorado 80301 USA
| |
Collapse
|
42
|
Rosenheim JA, Gratton C. Ecoinformatics (Big Data) for Agricultural Entomology: Pitfalls, Progress, and Promise. ANNUAL REVIEW OF ENTOMOLOGY 2017; 62:399-417. [PMID: 27912246 DOI: 10.1146/annurev-ento-031616-035444] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Ecoinformatics, as defined in this review, is the use of preexisting data sets to address questions in ecology. We provide the first review of ecoinformatics methods in agricultural entomology. Ecoinformatics methods have been used to address the full range of questions studied by agricultural entomologists, enabled by the special opportunities associated with data sets, nearly all of which have been observational, that are larger and more diverse and that embrace larger spatial and temporal scales than most experimental studies do. We argue that ecoinformatics research methods and traditional, experimental research methods have strengths and weaknesses that are largely complementary. We address the important interpretational challenges associated with observational data sets, highlight common pitfalls, and propose some best practices for researchers using these methods. Ecoinformatics methods hold great promise as a vehicle for capitalizing on the explosion of data emanating from farmers, researchers, and the public, as novel sampling and sensing techniques are developed and digital data sharing becomes more widespread.
Collapse
Affiliation(s)
- Jay A Rosenheim
- Department of Entomology and Nematology, University of California, Davis, California 95616;
- Center for Population Biology, University of California, Davis, California 95616
| | - Claudio Gratton
- Department of Entomology, University of Wisconsin, Madison, Wisconsin 53706
| |
Collapse
|
43
|
|
44
|
|
45
|
Elliott KC, Cheruvelil KS, Montgomery GM, Soranno PA. Conceptions of Good Science in Our Data-Rich World. Bioscience 2016; 66:880-889. [PMID: 29599533 PMCID: PMC5862324 DOI: 10.1093/biosci/biw115] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin's research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms.
Collapse
Affiliation(s)
- Kevin C Elliott
- Kevin C. Elliott is an associate professor in Lyman Briggs College, the Department of Fisheries and Wildlife, and the Department of Philosophy; Kendra S. Cheruvelil is an associate professor in Lyman Briggs College and the Department of Fisheries and Wildlife; Georgina M. Montgomery is an associate professor in Lyman Briggs College and the Department of History; and Patricia A. Soranno is a professor in the Department of Fisheries and Wildlife at Michigan State University, in East Lansing. All authors contributed equally to the conceptualization of the paper and the supporting research. KCE organized the collaboration and initiated the writing process. All authors contributed text, reviewed manuscript drafts, and approved the final version
| | - Kendra S Cheruvelil
- Kevin C. Elliott is an associate professor in Lyman Briggs College, the Department of Fisheries and Wildlife, and the Department of Philosophy; Kendra S. Cheruvelil is an associate professor in Lyman Briggs College and the Department of Fisheries and Wildlife; Georgina M. Montgomery is an associate professor in Lyman Briggs College and the Department of History; and Patricia A. Soranno is a professor in the Department of Fisheries and Wildlife at Michigan State University, in East Lansing. All authors contributed equally to the conceptualization of the paper and the supporting research. KCE organized the collaboration and initiated the writing process. All authors contributed text, reviewed manuscript drafts, and approved the final version
| | - Georgina M Montgomery
- Kevin C. Elliott is an associate professor in Lyman Briggs College, the Department of Fisheries and Wildlife, and the Department of Philosophy; Kendra S. Cheruvelil is an associate professor in Lyman Briggs College and the Department of Fisheries and Wildlife; Georgina M. Montgomery is an associate professor in Lyman Briggs College and the Department of History; and Patricia A. Soranno is a professor in the Department of Fisheries and Wildlife at Michigan State University, in East Lansing. All authors contributed equally to the conceptualization of the paper and the supporting research. KCE organized the collaboration and initiated the writing process. All authors contributed text, reviewed manuscript drafts, and approved the final version
| | - Patricia A Soranno
- Kevin C. Elliott is an associate professor in Lyman Briggs College, the Department of Fisheries and Wildlife, and the Department of Philosophy; Kendra S. Cheruvelil is an associate professor in Lyman Briggs College and the Department of Fisheries and Wildlife; Georgina M. Montgomery is an associate professor in Lyman Briggs College and the Department of History; and Patricia A. Soranno is a professor in the Department of Fisheries and Wildlife at Michigan State University, in East Lansing. All authors contributed equally to the conceptualization of the paper and the supporting research. KCE organized the collaboration and initiated the writing process. All authors contributed text, reviewed manuscript drafts, and approved the final version
| |
Collapse
|
46
|
Moran MS, Heilman P, Peters DPC, Holifield Collins C. Agroecosystem research with big data and a modified scientific method using machine learning concepts. Ecosphere 2016. [DOI: 10.1002/ecs2.1493] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- M. Susan Moran
- USDA ARS Southwest Watershed Research Center Tucson Arizona 85719 USA
| | - Philip Heilman
- USDA ARS Southwest Watershed Research Center Tucson Arizona 85719 USA
| | - Debra P. C. Peters
- USDA ARS Jornada Experimental Range and the Jornada Basin Long Term Ecological Research Program New Mexico State University Las Cruces New Mexico 88003 USA
| | | |
Collapse
|
47
|
Brown GR, Matthews IM. A review of extensive variation in the design of pitfall traps and a proposal for a standard pitfall trap design for monitoring ground-active arthropod biodiversity. Ecol Evol 2016; 6:3953-64. [PMID: 27247760 PMCID: PMC4867678 DOI: 10.1002/ece3.2176] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 03/29/2016] [Accepted: 04/13/2016] [Indexed: 11/07/2022] Open
Abstract
To understand change in global biodiversity patterns requires large-scale, long-term monitoring. The ability to draw meaningful comparison across studies is severely hampered by extensive variation in the design of the sampling equipment and how it is used. Here, we present a meta-analysis and description highlighting this variation in a common, widely used entomological survey technique. We report a decline in the completeness of methodological reporting over a 20-year period, while there has been no clear reduction in the methodological variation between researchers using pitfall traps for arthropod sampling. There is a growing need for improved comparability between studies to facilitate the generation of large-scale, long-term biodiversity datasets. However, our results show that, counterproductive to this goal, over the last 20 years there has little progress in reducing the methodological variation. We propose a standardized pitfall trap design for the study of ground-active arthropods. In addition, we provide a table to promote a more standardized reporting of the key methodological variables. Widespread adoption of more standardized methods and reporting would facilitate more nuanced analysis of biodiversity change.
Collapse
Affiliation(s)
- Grant R Brown
- Centre for Biological Diversity University of St Andrews Sir Harold Mitchell Building St Andrews KY16 9TH UK
| | - Iain M Matthews
- Centre for Biological Diversity University of St Andrews Sir Harold Mitchell Building St Andrews KY16 9TH UK
| |
Collapse
|
48
|
Pfeiffer DU, Stevens KB. Spatial and temporal epidemiological analysis in the Big Data era. Prev Vet Med 2015; 122:213-20. [PMID: 26092722 PMCID: PMC7114113 DOI: 10.1016/j.prevetmed.2015.05.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Revised: 05/27/2015] [Accepted: 05/31/2015] [Indexed: 10/27/2022]
Abstract
Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.
Collapse
Affiliation(s)
- Dirk U Pfeiffer
- Veterinary Epidemiology, Economics & Public Health Group, Department of Production & Population Health, Royal Veterinary College, London, UK.
| | - Kim B Stevens
- Veterinary Epidemiology, Economics & Public Health Group, Department of Production & Population Health, Royal Veterinary College, London, UK
| |
Collapse
|