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Simmonds EG, Adjei KP, Cretois B, Dickel L, González-Gil R, Laverick JH, Mandeville CP, Mandeville EG, Ovaskainen O, Sicacha-Parada J, Skarstein ES, O'Hara B. Recommendations for quantitative uncertainty consideration in ecology and evolution. Trends Ecol Evol 2024; 39:328-337. [PMID: 38030538 DOI: 10.1016/j.tree.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 09/13/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers - a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation - which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation through the use of hierarchical models.
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Affiliation(s)
- Emily G Simmonds
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Institute for Biology, Norwegian University of Science and Technology, Trondheim 7491, Norway; Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, UK.
| | - Kwaku P Adjei
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim 7034, Norway
| | - Benjamin Cretois
- Norwegian Institute for Nature Research, Torgarden, Trondheim, Trøndelag 7485, Norway
| | - Lisa Dickel
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Institute for Biology, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Ricardo González-Gil
- Observatorio Marino de Asturias (OMA), Departamento de Biología de Organismos y Sistemas, University of Oviedo, 33071 Oviedo, Spain; GOAL, Colonia Castaño Sur, Casa 1901, Calle Paseo Virgilio Zelaya Rubí, Tegucigalpa, Honduras, CA, USA
| | - Jack H Laverick
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XH, UK
| | - Caitlin P Mandeville
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Natural History, Norwegian University of Science and Technology, Trondheim, Trøndelag 7491, Norway
| | | | - Otso Ovaskainen
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki 00014, Finland; Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä 40014, Finland
| | - Jorge Sicacha-Parada
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim 7034, Norway
| | - Emma S Skarstein
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim 7034, Norway
| | - Bob O'Hara
- The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim 7034, Norway
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Rai S, Jain S, Rallapalli S, Magner J, Singh AP, Goonetilleke A. Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed. WATER RESEARCH 2024; 249:120998. [PMID: 38096723 DOI: 10.1016/j.watres.2023.120998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/13/2023] [Accepted: 12/07/2023] [Indexed: 01/03/2024]
Abstract
Rising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009-2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.
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Affiliation(s)
- Saumitra Rai
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Shruti Jain
- Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India
| | - Srinivas Rallapalli
- Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India; Department of Bioproducts and Biosystems Engineering, University of Minnesota, Twin Cities, USA.
| | - Joe Magner
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, Twin Cities, USA
| | - Ajit Pratap Singh
- Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India
| | - Ashantha Goonetilleke
- School of Civil and Environmental Engineering, Faculty of Engineering, Queensland University of Technology, Brisbane, Australia
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Urso L, Sy MM, Gonze MA, Hartmann P, Steiner M. Quantification of Conceptual Model Uncertainty in the Modeling of Wet Deposited Atmospheric Pollutants. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2022; 42:757-769. [PMID: 34528280 DOI: 10.1111/risa.13807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/21/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
Conceptual model uncertainty and parameter uncertainty are dominant contributors to the total uncertainty of a radioecological model output. In the present study the focus is on conceptual model uncertainty, which is often not acknowledged. Conceptual model uncertainty is assessed by subtracting from the total uncertainty of the model output the propagated parameter uncertainty, obtained by means of Bayesian inference analysis. The conceptual model uncertainty is quantified for two process-based models, which describe the interception of wet deposited pollutants under equilibrium and kinetic conditions, respectively. The natural variability due the chemical valence of the elements considered is accounted for in both models. Quantitative evidence has been obtained that the conceptual model uncertainty can contribute to the total uncertainty budget of the models for interception of wet deposited pollutants at least as much as, if not more than, parameter uncertainty.
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Affiliation(s)
- Laura Urso
- Federal Office for Radiation Protection (BfS), Oberschleissheim, Germany
| | | | - Marc-André Gonze
- Institute of Radiation Protection and Nuclear Safety (IRSN), St-Paul-lez-Durance Cedex, France
| | - Philipp Hartmann
- Federal Office for Radiation Protection (BfS), Oberschleissheim, Germany
| | - Martin Steiner
- Federal Office for Radiation Protection (BfS), Oberschleissheim, Germany
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Murray NJ, Kennedy EV, Álvarez-Romero JG, Lyons MB. Data Freshness in Ecology and Conservation. Trends Ecol Evol 2021; 36:485-487. [PMID: 33863603 DOI: 10.1016/j.tree.2021.03.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 03/16/2021] [Accepted: 03/16/2021] [Indexed: 11/30/2022]
Abstract
Evolving capabilities in environmental data collection, sharing, and processing, are enabling unprecedented use of data from a wide range of sources. Yet data freshness, an important quality dimension associated with the age of data, is a poorly reported aspect of data quality that can lead to additional uncertainty in research findings.
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Affiliation(s)
- Nicholas J Murray
- College of Science and Engineering, James Cook University, Townsville, Queensland 4811, Australia.
| | - Emma V Kennedy
- Remote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, 4072, Australia; Australian Institute of Marine Science, Townsville, Queensland 4810, Australia
| | - Jorge G Álvarez-Romero
- Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland 4811, Australia
| | - Mitchell B Lyons
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, New South Wales, Australia
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Novak M, Stouffer DB. Systematic bias in studies of consumer functional responses. Ecol Lett 2021; 24:580-593. [PMID: 33381898 DOI: 10.1111/ele.13660] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 11/09/2020] [Accepted: 11/18/2020] [Indexed: 12/31/2022]
Abstract
Functional responses are a cornerstone to our understanding of consumer-resource interactions, so how to best describe them using models has been actively debated. Here we focus on the consumer dependence of functional responses to evidence systematic bias in the statistical comparison of functional-response models and the estimation of their parameters. Both forms of bias are universal to nonlinear models (irrespective of consumer dependence) and are rooted in a lack of sufficient replication. Using a large compilation of published datasets, we show that - due to the prevalence of low sample size studies - neither the overall frequency by which alternative models achieve top rank nor the frequency distribution of parameter point estimates should be treated as providing insight into the general form or central tendency of consumer interference. We call for renewed clarity in the varied purposes that motivate the study of functional responses, purposes that can compete with each other in dictating the design, analysis and interpretation of functional-response experiments.
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Affiliation(s)
- Mark Novak
- Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA
| | - Daniel B Stouffer
- Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, 8140, New Zealand
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