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Lofton ME, Brentrup JA, Beck WS, Zwart JA, Bhattacharya R, Brighenti LS, Burnet SH, McCullough IM, Steele BG, Carey CC, Cottingham KL, Dietze MC, Ewing HA, Weathers KC, LaDeau SL. Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2590. [PMID: 35343013 PMCID: PMC9287081 DOI: 10.1002/eap.2590] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 08/16/2021] [Accepted: 09/16/2021] [Indexed: 06/01/2023]
Abstract
Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.
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Affiliation(s)
- Mary E. Lofton
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | - Jennifer A. Brentrup
- Department of Biological SciencesDartmouth CollegeHanoverNew HampshireUSA
- Present address:
Biology and Environmental Studies DepartmentSt. Olaf CollegeNorthfieldMinnesotaUSA
| | - Whitney S. Beck
- Department of Biology and Graduate Degree Program in EcologyColorado State UniversityFort CollinsColoradoUSA
- Present address:
U.S. Environmental Protection AgencyWashingtonDistrict of ColumbiaUSA
| | - Jacob A. Zwart
- U.S. Geological SurveyIntegrated Information Dissemination DivisionMiddletonWisconsinUSA
| | - Ruchi Bhattacharya
- Legacies of Agricultural Pollutants (LEAP)University of WaterlooWaterlooOntarioCanada
| | | | - Sarah H. Burnet
- Department of Fish and Wildlife ResourcesUniversity of IdahoMoscowIdahoUSA
| | - Ian M. McCullough
- Department of Fisheries and WildlifeMichigan State UniversityEast LansingMichiganUSA
| | | | - Cayelan C. Carey
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | | | - Michael C. Dietze
- Department of Earth and EnvironmentBoston UniversityBostonMassachusettsUSA
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Sebald J, Thrippleton T, Rammer W, Bugmann H, Seidl R. Mixing tree species at different spatial scales: The effect of alpha, beta and gamma diversity on disturbance impacts under climate change. J Appl Ecol 2021. [DOI: 10.1111/1365-2664.13912] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Julius Sebald
- Department of Forest‐ and Soil Sciences Institute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU) Vienna Vienna Austria
- Ecosystem Dynamics and Forest Management Group School of Life Sciences Technical University of Munich Freising Germany
| | - Timothy Thrippleton
- Department of Environmental Systems Science, Forest Ecology Swiss Federal Institute of Technology (ETH Zurich) Zürich Switzerland
- Forest Resources and Management Sustainable Forestry Swiss Federal Research Institute WSL Birmensdorf Switzerland
| | - Werner Rammer
- Ecosystem Dynamics and Forest Management Group School of Life Sciences Technical University of Munich Freising Germany
| | - Harald Bugmann
- Department of Environmental Systems Science, Forest Ecology Swiss Federal Institute of Technology (ETH Zurich) Zürich Switzerland
| | - Rupert Seidl
- Department of Forest‐ and Soil Sciences Institute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU) Vienna Vienna Austria
- Ecosystem Dynamics and Forest Management Group School of Life Sciences Technical University of Munich Freising Germany
- Berchtesgaden National Park Berchtesgaden Germany
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Tangang F, Juneng L, Cruz F, Chung JX, Ngai ST, Salimun E, Mohd MSF, Santisirisomboon J, Singhruck P, PhanVan T, Ngo-Duc T, Narisma G, Aldrian E, Gunawan D, Sopaheluwakan A. Multi-model projections of precipitation extremes in Southeast Asia based on CORDEX-Southeast Asia simulations. ENVIRONMENTAL RESEARCH 2020; 184:109350. [PMID: 32179268 DOI: 10.1016/j.envres.2020.109350] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 03/03/2020] [Accepted: 03/03/2020] [Indexed: 06/10/2023]
Abstract
This study examines the projected precipitation extremes for the end of 21st century (2081-2100) over Southeast Asia (SEA) using the output of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment - Southeast Asia (SEACLID/CORDEX-SEA). Eight ensemble members, representing a subset of archived CORDEX-SEA simulations at 25 km spatial resolution, were examined for emission scenarios of RCP4.5 and RCP8.5. The study utilised four different indicators of rainfall extreme, i.e. the annual/seasonal rainfall total (PRCPTOT), consecutive dry days (CDD), frequency of extremely heavy rainfall (R50mm) and annual/seasonal maximum of daily rainfall (RX1day). In general, changes in extreme indices are more pronounced and covering wider area under RCP8.5 than RCP4.5. The decrease in annual PRCPTOT is projected over most of SEA region, except for Myanmar and Northern Thailand, with magnitude as much as 20% (30%) under RCP4.5 (RCP8.5) scenario. The most significant and robust changes were noted in CDD, which is projected to increase by as much as 30% under RCP4.5 and 60% under RCP8.5, particularly over Maritime Continent (MC). The projected decrease in PRCPTOT over MC is significant and robust during June to August (JJA) and September to November (SON). During March to May (MAM) under RCP8.5, significant and robust PRCPTOT decreases are also projected over Indochina. The CDD changes during JJA and SON over MC are even higher, more robust and significant compared to the annual changes. At the same time, a wetting tendency is also projected over Indochina. The R50mm and RX1day are projected to increase, during all seasons with significant and robust signal of RX1day during JJA and SON.
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Affiliation(s)
- Fredolin Tangang
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia; Regional Climate Systems Laboratory, Manila Observatory, Quezon City, Philippines.
| | - Liew Juneng
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Faye Cruz
- Regional Climate Systems Laboratory, Manila Observatory, Quezon City, Philippines
| | - Jing Xiang Chung
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia; Institute of Oceanography and Environment, Universiti Malaysia Terengganu, Terengganu, Malaysia
| | - Sheau Tieh Ngai
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Ester Salimun
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | | | - Jerasorn Santisirisomboon
- Centre of Regional Climate Change and Renewable Energy (RU-CORE), Ramkhamhaeng University, Bangkok, Thailand
| | - Patama Singhruck
- Department of Marine Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Tan PhanVan
- Department of Meteorology and Climate Change, VNU University of Science, Hanoi, Viet Nam
| | - Thanh Ngo-Duc
- Department of Space and Aeronautics, University of Science and Technology of Hanoi, Viet Nam
| | - Gemma Narisma
- Regional Climate Systems Laboratory, Manila Observatory, Quezon City, Philippines; Atmospheric Science Program, Physics Department, Ateneo de Manila University, Quezon City, Philippines
| | - Edvin Aldrian
- UPT-HB, Agency for the Assessment and Application of Technology (BPPT), Jakarta, Indonesia
| | - Dodo Gunawan
- Centre for Climate Change Information, Agency for Meteorology, Climatology and Geophysics (BMKG), Jakarta, Indonesia
| | - Ardhasena Sopaheluwakan
- Centre for Research and Development, Agency for Meteorology, Climatology and Geophysics (BMKG), Jakarta, Indonesia
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Gauthier G, Péron G, Lebreton JD, Grenier P, van Oudenhove L. Partitioning prediction uncertainty in climate-dependent population models. Proc Biol Sci 2016; 283:rspb.2016.2353. [PMID: 28003456 DOI: 10.1098/rspb.2016.2353] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 11/22/2016] [Indexed: 01/19/2023] Open
Abstract
The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate-demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose (Chen caeruslescens atlantica), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.
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Affiliation(s)
- Gilles Gauthier
- Département de Biologie and Centre d'Études Nordiques, Université Laval, 1045 avenue de la Médecine, Québec, Quebec, Canada G1V 0A6
| | - Guillaume Péron
- Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA 22630, USA.,UMR CNRS 5558 - LBBE 'Biométrie et Biologie Évolutive' UCB Lyon 1, Bât. Grégor Mendel, 43 bd du 11 novembre 1918, 69622 Villeurbanne Cedex, France
| | - Jean-Dominique Lebreton
- UMR 5175, Centre d'écologie fonctionnelle et évolutive, CNRS, 1919 Route de Mende, 34293 Montpellier Cedex 5, France
| | - Patrick Grenier
- Groupe Scénarios et services climatiques, Ouranos, 550 rue Sherbrooke Ouest, Montréal, Quebec, Canada H3A 1B9
| | - Louise van Oudenhove
- Département de Biologie and Centre d'Études Nordiques, Université Laval, 1045 avenue de la Médecine, Québec, Quebec, Canada G1V 0A6.,UMR 1355, INRA, Institut Sophia Agrobiotech, 400 Route des Chappes, 06903 Sophia Antipolis, France
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An efficient protocol for the global sensitivity analysis of stochastic ecological models. Ecosphere 2016. [DOI: 10.1002/ecs2.1238] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
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Gårdmark A, Lindegren M, Neuenfeldt S, Blenckner T, Heikinheimo O, Müller-Karulis B, Niiranen S, Tomczak MT, Aro E, Wikström A, Möllmann C. Biological ensemble modeling to evaluate potential futures of living marine resources. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2013; 23:742-54. [PMID: 23865226 DOI: 10.1890/12-0267.1] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Natural resource management requires approaches to understand and handle sources of uncertainty in future responses of complex systems to human activities. Here we present one such approach, the "biological ensemble modeling approach," using the Eastern Baltic cod (Gadus morhua callarias) as an example. The core of the approach is to expose an ensemble of models with different ecological assumptions to climate forcing, using multiple realizations of each climate scenario. We simulated the long-term response of cod to future fishing and climate change in seven ecological models ranging from single-species to food web models. These models were analyzed using the "biological ensemble modeling approach" by which we (1) identified a key ecological mechanism explaining the differences in simulated cod responses between models, (2) disentangled the uncertainty caused by differences in ecological model assumptions from the statistical uncertainty of future climate, and (3) identified results common for the whole model ensemble. Species interactions greatly influenced the simulated response of cod to fishing and climate, as well as the degree to which the statistical uncertainty of climate trajectories carried through to uncertainty of cod responses. Models ignoring the feedback from prey on cod showed large interannual fluctuations in cod dynamics and were more sensitive to the underlying uncertainty of climate forcing than models accounting for such stabilizing predator-prey feedbacks. Yet in all models, intense fishing prevented recovery, and climate change further decreased the cod population. Our study demonstrates how the biological ensemble modeling approach makes it possible to evaluate the relative importance of different sources of uncertainty in future species responses, as well as to seek scientific conclusions and sustainable management solutions robust to uncertainty of food web processes in the face of climate change.
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Affiliation(s)
- Anna Gårdmark
- Swedish University of Agricultural Sciences, Department of Aquatic Resources, Institute of Coastal Research, Skolgatan 6, SE-742 42 Oregrund, Sweden.
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Hernández-Matías A, Real J, Moleón M, Palma L, Sánchez-Zapata JA, Pradel R, Carrete M, Gil-Sánchez JM, Beja P, Balbontín J, Vincent-Martin N, Ravayrol A, Benítez JR, Arroyo B, Fernández C, Ferreiro E, García J. From local monitoring to a broad-scale viability assessment: a case study for the Bonelli's Eagle in western Europe. ECOL MONOGR 2013. [DOI: 10.1890/12-1248.1] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Cuddington K, Fortin MJ, Gerber LR, Hastings A, Liebhold A, O'Connor M, Ray C. Process-based models are required to manage ecological systems in a changing world. Ecosphere 2013. [DOI: 10.1890/es12-00178.1] [Citation(s) in RCA: 145] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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9
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Cropper WP, Holm JA, Miller CJ. An inverse analysis of a matrix population model using a genetic algorithm. ECOL INFORM 2012. [DOI: 10.1016/j.ecoinf.2011.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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10
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Melbourne-Thomas J, Johnson C, Fulton E. Characterizing sensitivity and uncertainty in a multiscale model of a complex coral reef system. Ecol Modell 2011. [DOI: 10.1016/j.ecolmodel.2011.07.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Liu S, Sheppard A, Kriticos D, Cook D. Incorporating uncertainty and social values in managing invasive alien species: a deliberative multi-criteria evaluation approach. Biol Invasions 2011. [DOI: 10.1007/s10530-011-0045-4] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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12
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Keenan TF, Grote R, Sabaté S. Overlooking the canopy: The importance of canopy structure in scaling isoprenoid emissions from the leaf to the landscape. Ecol Modell 2011. [DOI: 10.1016/j.ecolmodel.2010.11.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Di Vittorio AV, Anderson RS, White JD, Miller NL, Running SW. Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses. Ecol Modell 2010. [DOI: 10.1016/j.ecolmodel.2010.05.013] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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